IO Tools (Text, CSV, HDF5, …)¶

IO Tools (Text, CSV, HDF5, …)¶

The pandas I/O API is a set of top level reader functions accessed
like
pandas.read_csv()
that generally return a pandas object. The corresponding writer
functions are object methods that are accessed like
DataFrame.to_csv().
Below is a table containing available readers and writers.

Format Type	Data Description	Reader	Writer
text	CSV	read_csv	to_csv
text	JSON	read_json	to_json
text	HTML	read_html	to_html
text	Local clipboard	read_clipboard	to_clipboard
binary	MS Excel	read_excel	to_excel
binary	HDF5 Format	read_hdf	to_hdf
binary	Feather Format	read_feather	to_feather
binary	Parquet Format	read_parquet	to_parquet
binary	Msgpack	read_msgpack	to_msgpack
binary	Stata	read_stata	to_stata
binary	SAS	read_sas
binary	Python Pickle Format	read_pickle	to_pickle
SQL	SQL	read_sql	to_sql
SQL	Google Big Query	read_gbq	to_gbq

Here
is an informal performance comparison for some of these IO methods.

Note

For examples that use the StringIO class, make sure you import it
according to your Python version, i.e. from StringIO import StringIO
for Python 2 and from io import StringIO for Python 3.

CSV & Text files¶

The two workhorse functions for reading text files (a.k.a. flat files)
are
read_csv()
and
read_table().
They both use the same parsing code to intelligently convert tabular
data into a DataFrame object. See the
cookbook
for some advanced strategies.

Parsing options¶

The functions
read_csv()
and
read_table()
accept the following common arguments:

Basic¶

filepath_or_buffer : various

Either a path to a file (a
str,
pathlib.Path,
or py._path.local.LocalPath), URL (including http, ftp, and S3
locations), or any object with a read() method (such as an open file
or
StringIO).

sep : str, defaults to ',' for read_csv(), \t for read_table()

Delimiter to use. If sep is None, the C engine cannot automatically
detect the separator, but the Python parsing engine can, meaning the
latter will be used and automatically detect the separator by Python’s
builtin sniffer tool,
csv.Sniffer.
In addition, separators longer than 1 character and different from
'\s+' will be interpreted as regular expressions and will also force
the use of the Python parsing engine. Note that regex delimiters are
prone to ignoring quoted data. Regex example: '\\r\\t'.

delimiter : str, default None

Alternative argument name for sep.

delim_whitespace : boolean, default False

Specifies whether or not whitespace (e.g. ' ' or '\t') will be used
as the delimiter. Equivalent to setting sep='\s+'. If this option is
set to True, nothing should be passed in for the delimiter
parameter.

New in version 0.18.1: support for
the Python parser.

Column and Index Locations and Names¶

header : int or list of ints, default 'infer'

Row number(s) to use as the column names, and the start of the data.
Default behavior is to infer the column names: if no names are passed
the behavior is identical to header=0 and column names are inferred
from the first line of the file, if column names are passed explicitly
then the behavior is identical to header=None. Explicitly pass
header=0 to be able to replace existing names.

The header can be a list of ints that specify row locations for a
multi-index on the columns e.g. [0,1,3]. Intervening rows that are not
specified will be skipped (e.g. 2 in this example is skipped). Note that
this parameter ignores commented lines and empty lines if
skip_blank_lines=True, so header=0 denotes the first line of data
rather than the first line of the file.

names : array-like, default None

List of column names to use. If file contains no header row, then you
should explicitly pass header=None. Duplicates in this list will cause
a UserWarning to be issued.

index_col : int or sequence or False, default None

Column to use as the row labels of the DataFrame. If a sequence is
given, a MultiIndex is used. If you have a malformed file with
delimiters at the end of each line, you might consider index_col=False
to force pandas to not use the first column as the index (row names).

usecols : list-like or callable, default None

Return a subset of the columns. If list-like, all elements must either
be positional (i.e. integer indices into the document columns) or
strings that correspond to column names provided either by the user in
names or inferred from the document header row(s). For example, a valid
list-like usecols parameter would be [0, 1, 2] or
['foo', 'bar', 'baz'].

Element order is ignored, so usecols=[0, 1] is the same as [1, 0].
To instantiate a DataFrame from data with element order preserved use
pd.read_csv(data, usecols=['foo', 'bar'])[['foo', 'bar']] for columns
in ['foo', 'bar'] order or
pd.read_csv(data, usecols=['foo', 'bar'])[['bar', 'foo']] for
['bar', 'foo'] order.

If callable, the callable function will be evaluated against the column
names, returning names where the callable function evaluates to True:

In[1]: data = 'col1,col2,col3\na,b,1\na,b,2\nc,d,3'

In[2]: pd.read_csv(StringIO(data))
Out[2]: 
  col1 col2  col3
0    a    b     1
1    a    b     2
2    c    d     3

In[3]: pd.read_csv(StringIO(data), usecols=lambda x: x.upper() in ['COL1', 'COL3'])
Out[3]: 
  col1  col3
0    a     1
1    a     2
2    c     3

Using this parameter results in much faster parsing time and lower
memory usage.

squeeze : boolean, default False

If the parsed data only contains one column then return a Series.

prefix : str, default None

Prefix to add to column numbers when no header, e.g. ‘X’ for X0, X1, …

mangle_dupe_cols : boolean, default True

Duplicate columns will be specified as ‘X’, ‘X.1’…’X.N’, rather than
‘X’…’X’. Passing in False will cause data to be overwritten if there
are duplicate names in the columns.

General Parsing Configuration¶

dtype : Type name or dict of column -> type, default None

Data type for data or columns. E.g. {'a': np.float64, 'b': np.int32}
(unsupported with engine='python'). Use str or object together with
suitable na_values settings to preserve and not interpret dtype.

New in version 0.20.0: support for
the Python parser.

engine : {'c', 'python'}

Parser engine to use. The C engine is faster while the Python engine is
currently more feature-complete.

converters : dict, default None

Dict of functions for converting values in certain columns. Keys can
either be integers or column labels.

true_values : list, default None

Values to consider as True.

false_values : list, default None

Values to consider as False.

skipinitialspace : boolean, default False

Skip spaces after delimiter.

skiprows : list-like or integer, default None

Line numbers to skip (0-indexed) or number of lines to skip (int) at the
start of the file.

If callable, the callable function will be evaluated against the row
indices, returning True if the row should be skipped and False
otherwise:

In[4]: data = 'col1,col2,col3\na,b,1\na,b,2\nc,d,3'

In[5]: pd.read_csv(StringIO(data))
Out[5]: 
  col1 col2  col3
0    a    b     1
1    a    b     2
2    c    d     3

In[6]: pd.read_csv(StringIO(data), skiprows=lambda x: x % 2 != 0)
Out[6]: 
  col1 col2  col3
0    a    b     2

skipfooter : int, default 0

Number of lines at bottom of file to skip (unsupported with engine=’c’).

nrows : int, default None

Number of rows of file to read. Useful for reading pieces of large
files.

low_memory : boolean, default True

Internally process the file in chunks, resulting in lower memory use
while parsing, but possibly mixed type inference. To ensure no mixed
types either set False, or specify the type with the dtype
parameter. Note that the entire file is read into a single DataFrame
regardless, use the chunksize or iterator parameter to return the
data in chunks. (Only valid with C parser)

memory_map : boolean, default False

If a filepath is provided for filepath_or_buffer, map the file object
directly onto memory and access the data directly from there. Using this
option can improve performance because there is no longer any I/O
overhead.

NA and Missing Data Handling¶

na_values : scalar, str, list-like, or dict, default None

Additional strings to recognize as NA/NaN. If dict passed, specific
per-column NA values. See
na values const
below for a list of the values interpreted as NaN by default.

keep_default_na : boolean, default True

Whether or not to include the default NaN values when parsing the data.
Depending on whether na_values is passed in, the behavior is as
follows:

If keep_default_na is True, and na_values are specified,

na_values is appended to the default NaN values used for parsing.
If keep_default_na is True, and na_values are not specified,

only the default NaN values are used for parsing.
If keep_default_na is False, and na_values are specified, only

the NaN values specified na_values are used for parsing.
If keep_default_na is False, and na_values are not specified,

no strings will be parsed as NaN.

Note that if na_filter is passed in as False, the keep_default_na
and na_values parameters will be ignored.

na_filter : boolean, default True

Detect missing value markers (empty strings and the value of
na_values). In data without any NAs, passing na_filter=False can
improve the performance of reading a large file.

verbose : boolean, default False

Indicate number of NA values placed in non-numeric columns.

skip_blank_lines : boolean, default True

If True, skip over blank lines rather than interpreting as NaN values.

Datetime Handling¶

parse_dates : boolean or list of ints or names or list of lists or dict, default False.

If True -> try parsing the index.
If [1, 2, 3] -> try parsing columns 1, 2, 3 each as a separate

date column.
If [[1, 3]] -> combine columns 1 and 3 and parse as a single

date column.
If {'foo': [1, 3]} -> parse columns 1, 3 as date and call

result ‘foo’. A fast-path exists for iso8601-formatted dates.

infer_datetime_format : boolean, default False

If True and parse_dates is enabled for a column, attempt to infer the
datetime format to speed up the processing.

keep_date_col : boolean, default False

If True and parse_dates specifies combining multiple columns then
keep the original columns.

date_parser : function, default None

Function to use for converting a sequence of string columns to an array
of datetime instances. The default uses dateutil.parser.parser to do
the conversion. Pandas will try to call date_parser in three different
ways, advancing to the next if an exception occurs: 1) Pass one or more
arrays (as defined by parse_dates) as arguments; 2) concatenate
(row-wise) the string values from the columns defined by parse_dates
into a single array and pass that; and 3) call date_parser once for
each row using one or more strings (corresponding to the columns defined
by parse_dates) as arguments.

dayfirst : boolean, default False

DD/MM format dates, international and European format.

Iteration¶

iterator : boolean, default False

Return TextFileReader object for iteration or getting chunks with
get_chunk().

chunksize : int, default None

Return TextFileReader object for iteration. See
iterating and chunking
below.

Quoting, Compression, and File Format¶

compression : {'infer', 'gzip', 'bz2', 'zip', 'xz', None}, default 'infer'

For on-the-fly decompression of on-disk data. If ‘infer’, then use gzip,
bz2, zip, or xz if filepath_or_buffer is a string ending in ‘.gz’,
‘.bz2’, ‘.zip’, or ‘.xz’, respectively, and no decompression otherwise.
If using ‘zip’, the ZIP file must contain only one data file to be read
in. Set to None for no decompression.

New in version 0.18.1: support for
‘zip’ and ‘xz’ compression.

thousands : str, default None

Thousands separator.

decimal : str, default '.'

Character to recognize as decimal point. E.g. use ',' for European
data.

float_precision : string, default None

Specifies which converter the C engine should use for floating-point
values. The options are None for the ordinary converter, high for
the high-precision converter, and round_trip for the round-trip
converter.

lineterminator : str (length 1), default None

Character to break file into lines. Only valid with C parser.

quotechar : str (length 1)

The character used to denote the start and end of a quoted item. Quoted
items can include the delimiter and it will be ignored.

quoting : int or csv.QUOTE_* instance, default 0

Control field quoting behavior per csv.QUOTE_* constants. Use one of
QUOTE_MINIMAL (0), QUOTE_ALL (1), QUOTE_NONNUMERIC (2) or
QUOTE_NONE (3).

doublequote : boolean, default True

When quotechar is specified and quoting is not QUOTE_NONE,
indicate whether or not to interpret two consecutive quotechar
elements inside a field as a single quotechar element.

escapechar : str (length 1), default None

One-character string used to escape delimiter when quoting is
QUOTE_NONE.

comment : str, default None

Indicates remainder of line should not be parsed. If found at the
beginning of a line, the line will be ignored altogether. This parameter
must be a single character. Like empty lines (as long as
skip_blank_lines=True), fully commented lines are ignored by the
parameter header but not by skiprows. For example, if comment='#',
parsing ‘#empty\na,b,c\n1,2,3’ with header=0 will result in ‘a,b,c’
being treated as the header.

encoding : str, default None

Encoding to use for UTF when reading/writing (e.g. 'utf-8').
List of Python standard encodings.

dialect : str or csv.Dialect instance, default None

If provided, this parameter will override values (default or not) for
the following parameters: delimiter, doublequote, escapechar,
skipinitialspace, quotechar, and quoting. If it is necessary to override
values, a ParserWarning will be issued. See
csv.Dialect
documentation for more details.

tupleize_cols : boolean, default False

Deprecated since version 0.21.0.

This argument will be removed and will always convert to MultiIndex

Leave a list of tuples on columns as is (default is to convert to a
MultiIndex on the columns).

Error Handling¶

error_bad_lines : boolean, default True

Lines with too many fields (e.g. a csv line with too many commas) will
by default cause an exception to be raised, and no DataFrame will be
returned. If False, then these “bad lines” will dropped from the
DataFrame that is returned. See
bad lines
below.

warn_bad_lines : boolean, default True

If error_bad_lines is False, and warn_bad_lines is True, a
warning for each “bad line” will be output.

Specifying column data types¶

You can indicate the data type for the whole DataFrame or individual
columns:

In[7]: data = 'a,b,c\n1,2,3\n4,5,6\n7,8,9'

In[8]: print(data)
a,b,c
1,2,3
4,5,6
7,8,9

In[9]: df = pd.read_csv(StringIO(data), dtype=object)

In[10]: df
Out[10]: 
   a  b  c
0  1  2  3
1  4  5  6
2  7  8  9

In[11]: df['a'][0]
Out[11]: '1'

In[12]: df = pd.read_csv(StringIO(data), dtype={'b': object, 'c': np.float64})

In[13]: df.dtypes
Out[13]: 
a      int64
b     object
c    float64
dtype: object

Fortunately, pandas offers more than one way to ensure that your
column(s) contain only one dtype. If you’re unfamiliar with these
concepts, you can see
here
to learn more about dtypes, and
here
to learn more about object conversion in pandas.

For instance, you can use the converters argument of
read_csv():

In[14]: data = "col_1\n1\n2\n'A'\n4.22"

In[15]: df = pd.read_csv(StringIO(data), converters={'col_1': str})

In[16]: df
Out[16]: 
  col_1
0     1
1     2
2   'A'
3  4.22

In[17]: df['col_1'].apply(type).value_counts()
Out[17]: 
<class 'str'>    4
Name: col_1, dtype: int64

Or you can use the
to_numeric()
function to coerce the dtypes after reading in the data,

In[18]: df2 = pd.read_csv(StringIO(data))

In[19]: df2['col_1'] = pd.to_numeric(df2['col_1'], errors='coerce')

In[20]: df2
Out[20]: 
   col_1
0   1.00
1   2.00
2    NaN
3   4.22

In[21]: df2['col_1'].apply(type).value_counts()
Out[21]: 
<class 'float'>    4
Name: col_1, dtype: int64

which will convert all valid parsing to floats, leaving the invalid
parsing as NaN.

Ultimately, how you deal with reading in columns containing mixed dtypes
depends on your specific needs. In the case above, if you wanted to
NaN out the data anomalies, then
to_numeric()
is probably your best option. However, if you wanted for all the data to
be coerced, no matter the type, then using the converters argument of
read_csv()
would certainly be worth trying.

New in version 0.20.0: support
for the Python parser.

The dtype option is supported by the ‘python’ engine.

Note

In some cases, reading in abnormal data with columns containing mixed
dtypes will result in an inconsistent dataset. If you rely on pandas to
infer the dtypes of your columns, the parsing engine will go and infer
the dtypes for different chunks of the data, rather than the whole
dataset at once. Consequently, you can end up with column(s) with mixed
dtypes. For example,

In[22]: df = pd.DataFrame({'col_1': list(range(500000)) + ['a', 'b'] + list(range(500000))})

In[23]: df.to_csv('foo.csv')

In[24]: mixed_df = pd.read_csv('foo.csv')

In[25]: mixed_df['col_1'].apply(type).value_counts()
Out[25]: 
<class 'int'>    737858
<class 'str'>    262144
Name: col_1, dtype: int64

In[26]: mixed_df['col_1'].dtype
Out[26]: dtype('O')

will result with mixed_df containing an int dtype for certain chunks
of the column, and str for others due to the mixed dtypes from the
data that was read in. It is important to note that the overall column
will be marked with a dtype of object, which is used for columns
with mixed dtypes.

Specifying Categorical dtype¶

New in version 0.19.0.

Categorical columns can be parsed directly by specifying
dtype='category' or dtype=CategoricalDtype(categories, ordered).

In[27]: data = 'col1,col2,col3\na,b,1\na,b,2\nc,d,3'

In[28]: pd.read_csv(StringIO(data))
Out[28]: 
  col1 col2  col3
0    a    b     1
1    a    b     2
2    c    d     3

In[29]: pd.read_csv(StringIO(data)).dtypes
Out[29]: 
col1    object
col2    object
col3     int64
dtype: object

In[30]: pd.read_csv(StringIO(data), dtype='category').dtypes
Out[30]: 
col1    category
col2    category
col3    category
dtype: object

Individual columns can be parsed as a Categorical using a dict
specification:

In[31]: pd.read_csv(StringIO(data), dtype={'col1': 'category'}).dtypes
Out[31]: 
col1    category
col2      object
col3       int64
dtype: object

New in version 0.21.0.

Specifying dtype='cateogry' will result in an unordered Categorical
whose categories are the unique values observed in the data. For more
control on the categories and order, create a
CategoricalDtype
ahead of time, and pass that for that column’s dtype.

In[32]: from pandas.api.types import CategoricalDtype

In[33]: dtype = CategoricalDtype(['d', 'c', 'b', 'a'], ordered=True)

In[34]: pd.read_csv(StringIO(data), dtype={'col1': dtype}).dtypes
Out[34]: 
col1    category
col2      object
col3       int64
dtype: object

When using dtype=CategoricalDtype, “unexpected” values outside of
dtype.categories are treated as missing values.

In[35]: dtype = CategoricalDtype(['a', 'b', 'd'])  # No 'c'

In[36]: pd.read_csv(StringIO(data), dtype={'col1': dtype}).col1
Out[36]: 
0      a
1      a
2    NaN
Name: col1, dtype: category
Categories (3, object): [a, b, d]

This matches the behavior of Categorical.set_categories().

Note

With dtype='category', the resulting categories will always be parsed
as strings (object dtype). If the categories are numeric they can be
converted using the
to_numeric()
function, or as appropriate, another converter such as
to_datetime().

When dtype is a CategoricalDtype with homogenous categories ( all
numeric, all datetimes, etc.), the conversion is done automatically.

In[37]: df = pd.read_csv(StringIO(data), dtype='category')

In[38]: df.dtypes
Out[38]: 
col1    category
col2    category
col3    category
dtype: object

In[39]: df['col3']
Out[39]: 
0    1
1    2
2    3
Name: col3, dtype: category
Categories (3, object): [1, 2, 3]

In[40]: df['col3'].cat.categories = pd.to_numeric(df['col3'].cat.categories)

In[41]: df['col3']
Out[41]: 
0    1
1    2
2    3
Name: col3, dtype: category
Categories (3, int64): [1, 2, 3]

Naming and Using Columns¶

Handling column names¶

A file may or may not have a header row. pandas assumes the first row
should be used as the column names:

In[42]: data = 'a,b,c\n1,2,3\n4,5,6\n7,8,9'

In[43]: print(data)
a,b,c
1,2,3
4,5,6
7,8,9

In[44]: pd.read_csv(StringIO(data))
Out[44]: 
   a  b  c
0  1  2  3
1  4  5  6
2  7  8  9

By specifying the names argument in conjunction with header you can
indicate other names to use and whether or not to throw away the header
row (if any):

In[45]: print(data)
a,b,c
1,2,3
4,5,6
7,8,9

In[46]: pd.read_csv(StringIO(data), names=['foo', 'bar', 'baz'], header=0)
Out[46]: 
   foo  bar  baz
0    1    2    3
1    4    5    6
2    7    8    9

In[47]: pd.read_csv(StringIO(data), names=['foo', 'bar', 'baz'], header=None)
Out[47]: 
  foo bar baz
0   a   b   c
1   1   2   3
2   4   5   6
3   7   8   9

If the header is in a row other than the first, pass the row number to
header. This will skip the preceding rows:

In[48]: data = 'skip this skip it\na,b,c\n1,2,3\n4,5,6\n7,8,9'

In[49]: pd.read_csv(StringIO(data), header=1)
Out[49]: 
   a  b  c
0  1  2  3
1  4  5  6
2  7  8  9

Note

Default behavior is to infer the column names: if no names are passed
the behavior is identical to header=0 and column names are inferred
from the first nonblank line of the file, if column names are passed
explicitly then the behavior is identical to header=None.

Duplicate names parsing¶

If the file or header contains duplicate names, pandas will by default
distinguish between them so as to prevent overwriting data:

In[50]: data = 'a,b,a\n0,1,2\n3,4,5'

In[51]: pd.read_csv(StringIO(data))
Out[51]: 
   a  b  a.1
0  0  1    2
1  3  4    5

There is no more duplicate data because mangle_dupe_cols=True by
default, which modifies a series of duplicate columns ‘X’, …, ‘X’ to
become ‘X’, ‘X.1’, …, ‘X.N’. If mangle_dupe_cols=False, duplicate data
can arise:

In[2]: data = 'a,b,a\n0,1,2\n3,4,5'
In[3]: pd.read_csv(StringIO(data), mangle_dupe_cols=False)
Out[3]:
   a  b  a
0  2  1  2
1  5  4  5

To prevent users from encountering this problem with duplicate data, a
ValueError exception is raised if mangle_dupe_cols != True:

In[2]: data = 'a,b,a\n0,1,2\n3,4,5'
In[3]: pd.read_csv(StringIO(data), mangle_dupe_cols=False)
...
ValueError: Setting mangle_dupe_cols=False is not supported yet

Filtering columns (`usecols`)¶

The usecols argument allows you to select any subset of the columns in
a file, either using the column names, position numbers or a callable:

New in version 0.20.0: support for
callable usecols arguments

In[52]: data = 'a,b,c,d\n1,2,3,foo\n4,5,6,bar\n7,8,9,baz'

In[53]: pd.read_csv(StringIO(data))
Out[53]: 
   a  b  c    d
0  1  2  3  foo
1  4  5  6  bar
2  7  8  9  baz

In[54]: pd.read_csv(StringIO(data), usecols=['b', 'd'])
Out[54]: 
   b    d
0  2  foo
1  5  bar
2  8  baz

In[55]: pd.read_csv(StringIO(data), usecols=[0, 2, 3])
Out[55]: 
   a  c    d
0  1  3  foo
1  4  6  bar
2  7  9  baz

In[56]: pd.read_csv(StringIO(data), usecols=lambda x: x.upper() in ['A', 'C'])
Out[56]: 
   a  c
0  1  3
1  4  6
2  7  9

The usecols argument can also be used to specify which columns not to
use in the final result:

In[57]: pd.read_csv(StringIO(data), usecols=lambda x: x not in ['a', 'c'])
Out[57]: 
   b    d
0  2  foo
1  5  bar
2  8  baz

In this case, the callable is specifying that we exclude the “a” and “c”
columns from the output.

Comments and Empty Lines¶

Ignoring line comments and empty lines¶

If the comment parameter is specified, then completely commented lines
will be ignored. By default, completely blank lines will be ignored as
well.

In[58]: data = '\na,b,c\n  \n# commented line\n1,2,3\n\n4,5,6'

In[59]: print(data)

a,b,c

# commented line
1,2,3

4,5,6

In[60]: pd.read_csv(StringIO(data), comment='#')
Out[60]: 
   a  b  c
0  1  2  3
1  4  5  6

If skip_blank_lines=False, then read_csv will not ignore blank
lines:

In[61]: data = 'a,b,c\n\n1,2,3\n\n\n4,5,6'

In[62]: pd.read_csv(StringIO(data), skip_blank_lines=False)
Out[62]: 
     a    b    c
0  NaN  NaN  NaN
1  1.0  2.0  3.0
2  NaN  NaN  NaN
3  NaN  NaN  NaN
4  4.0  5.0  6.0

Warning

The presence of ignored lines might create ambiguities involving line
numbers; the parameter header uses row numbers (ignoring
commented/empty lines), while skiprows uses line numbers (including
commented/empty lines):

In[63]: data = '#comment\na,b,c\nA,B,C\n1,2,3'

In[64]: pd.read_csv(StringIO(data), comment='#', header=1)
Out[64]: 
   A  B  C
0  1  2  3

In[65]: data = 'A,B,C\n#comment\na,b,c\n1,2,3'

In[66]: pd.read_csv(StringIO(data), comment='#', skiprows=2)
Out[66]: 
   a  b  c
0  1  2  3

If both header and skiprows are specified, header will be relative
to the end of skiprows. For example:

In[67]: data = '# empty\n# second empty line\n# third empty' \

In[67]: 'line\nX,Y,Z\n1,2,3\nA,B,C\n1,2.,4.\n5.,NaN,10.0'

In[68]: print(data)
# empty
# second empty line
# third emptyline
X,Y,Z
1,2,3
A,B,C
1,2.,4.
5.,NaN,10.0

In[69]: pd.read_csv(StringIO(data), comment='#', skiprows=4, header=1)
Out[69]: 
     A    B     C
0  1.0  2.0   4.0
1  5.0  NaN  10.0

Comments¶

Sometimes comments or meta data may be included in a file:

In[70]: print(open('tmp.csv').read())
ID,level,category
Patient1,123000,x # really unpleasant
Patient2,23000,y # wouldn't take his medicine
Patient3,1234018,z # awesome

By default, the parser includes the comments in the output:

In[71]: df = pd.read_csv('tmp.csv')

In[72]: df
Out[72]: 
         ID    level                        category
0  Patient1   123000           x # really unpleasant
1  Patient2    23000  y # wouldn't take his medicine
2  Patient3  1234018                     z # awesome

We can suppress the comments using the comment keyword:

In[73]: df = pd.read_csv('tmp.csv', comment='#')

In[74]: df
Out[74]: 
         ID    level category
0  Patient1   123000       x 
1  Patient2    23000       y 
2  Patient3  1234018       z

Dealing with Unicode Data¶

The encoding argument should be used for encoded unicode data, which
will result in byte strings being decoded to unicode in the result:

In[75]: data = b'word,length\nTr\xc3\xa4umen,7\nGr\xc3\xbc\xc3\x9fe,5'.decode('utf8').encode('latin-1')

In[76]: df = pd.read_csv(BytesIO(data), encoding='latin-1')

In[77]: df
Out[77]: 
      word  length
0  Träumen       7
1    Grüße       5

In[78]: df['word'][1]
Out[78]: 'Grüße'

Some formats which encode all characters as multiple bytes, like UTF-16,
won’t parse correctly at all without specifying the encoding.
Full list of Python standard encodings.

Index columns and trailing delimiters¶

If a file has one more column of data than the number of column names,
the first column will be used as the DataFrame’s row names:

In[79]: data = 'a,b,c\n4,apple,bat,5.7\n8,orange,cow,10'

In[80]: pd.read_csv(StringIO(data))
Out[80]: 
        a    b     c
4   apple  bat   5.7
8  orange  cow  10.0

In[81]: data = 'index,a,b,c\n4,apple,bat,5.7\n8,orange,cow,10'

In[82]: pd.read_csv(StringIO(data), index_col=0)
Out[82]: 
            a    b     c
index                   
4       apple  bat   5.7
8      orange  cow  10.0

Ordinarily, you can achieve this behavior using the index_col option.

There are some exception cases when a file has been prepared with
delimiters at the end of each data line, confusing the parser. To
explicitly disable the index column inference and discard the last
column, pass index_col=False:

In[83]: data = 'a,b,c\n4,apple,bat,\n8,orange,cow,'

In[84]: print(data)
a,b,c
4,apple,bat,
8,orange,cow,

In[85]: pd.read_csv(StringIO(data))
Out[85]: 
        a    b   c
4   apple  bat NaN
8  orange  cow NaN

In[86]: pd.read_csv(StringIO(data), index_col=False)
Out[86]: 
   a       b    c
0  4   apple  bat
1  8  orange  cow

If a subset of data is being parsed using the usecols option, the
index_col specification is based on that subset, not the original
data.

In[87]: data = 'a,b,c\n4,apple,bat,\n8,orange,cow,'

In[88]: print(data)
a,b,c
4,apple,bat,
8,orange,cow,

In[89]: pd.read_csv(StringIO(data), usecols=['b', 'c'])
Out[89]: 
     b   c
4  bat NaN
8  cow NaN

In[90]: pd.read_csv(StringIO(data), usecols=['b', 'c'], index_col=0)
Out[90]: 
     b   c
4  bat NaN
8  cow NaN

Date Handling¶

Specifying Date Columns¶

To better facilitate working with datetime data,
read_csv()
and
read_table()
use the keyword arguments parse_dates and date_parser to allow users
to specify a variety of columns and date/time formats to turn the input
text data into datetime objects.

The simplest case is to just pass in parse_dates=True:

# Use a column as an index, and parse it as dates.
In[91]: df = pd.read_csv('foo.csv', index_col=0, parse_dates=True)

In[92]: df
Out[92]: 
            A  B  C
date               
2009-01-01  a  1  2
2009-01-02  b  3  4
2009-01-03  c  4  5

# These are Python datetime objects
In[93]: df.index
Out[93]: DatetimeIndex(['2009-01-01', '2009-01-02', '2009-01-03'], dtype='datetime64[ns]', name='date', freq=None)

It is often the case that we may want to store date and time data
separately, or store various date fields separately. the parse_dates
keyword can be used to specify a combination of columns to parse the
dates and/or times from.

You can specify a list of column lists to parse_dates, the resulting
date columns will be prepended to the output (so as to not affect the
existing column order) and the new column names will be the
concatenation of the component column names:

In[94]: print(open('tmp.csv').read())
KORD,19990127, 19:00:00, 18:56:00, 0.8100
KORD,19990127, 20:00:00, 19:56:00, 0.0100
KORD,19990127, 21:00:00, 20:56:00, -0.5900
KORD,19990127, 21:00:00, 21:18:00, -0.9900
KORD,19990127, 22:00:00, 21:56:00, -0.5900
KORD,19990127, 23:00:00, 22:56:00, -0.5900

In[95]: df = pd.read_csv('tmp.csv', header=None, parse_dates=[[1, 2], [1, 3]])

In[96]: df
Out[96]: 
                  1_2                 1_3     0     4
0 1999-01-27 19:00:00 1999-01-27 18:56:00  KORD  0.81
1 1999-01-27 20:00:00 1999-01-27 19:56:00  KORD  0.01
2 1999-01-27 21:00:00 1999-01-27 20:56:00  KORD -0.59
3 1999-01-27 21:00:00 1999-01-27 21:18:00  KORD -0.99
4 1999-01-27 22:00:00 1999-01-27 21:56:00  KORD -0.59
5 1999-01-27 23:00:00 1999-01-27 22:56:00  KORD -0.59

By default the parser removes the component date columns, but you can
choose to retain them via the keep_date_col keyword:

In[97]: df = pd.read_csv('tmp.csv', header=None, parse_dates=[[1, 2], [1, 3]],
   ....:                  keep_date_col=True)
   ....: 

In[98]: df
Out[98]: 
                  1_2                 1_3     0         1          2          3     4
0 1999-01-27 19:00:00 1999-01-27 18:56:00  KORD  19990127   19:00:00   18:56:00  0.81
1 1999-01-27 20:00:00 1999-01-27 19:56:00  KORD  19990127   20:00:00   19:56:00  0.01
2 1999-01-27 21:00:00 1999-01-27 20:56:00  KORD  19990127   21:00:00   20:56:00 -0.59
3 1999-01-27 21:00:00 1999-01-27 21:18:00  KORD  19990127   21:00:00   21:18:00 -0.99
4 1999-01-27 22:00:00 1999-01-27 21:56:00  KORD  19990127   22:00:00   21:56:00 -0.59
5 1999-01-27 23:00:00 1999-01-27 22:56:00  KORD  19990127   23:00:00   22:56:00 -0.59

Note that if you wish to combine multiple columns into a single date
column, a nested list must be used. In other words, parse_dates=
indicates that the second and third columns should each be parsed as
separate date columns while parse_dates=[[1, 2]] means the two columns
should be parsed into a single column.

You can also use a dict to specify custom name columns:

In[99]: date_spec = {'nominal': [1, 2], 'actual': [1, 3]}

In[100]: df = pd.read_csv('tmp.csv', header=None, parse_dates=date_spec)

In[101]: df
Out[101]: 
              nominal              actual     0     4
0 1999-01-27 19:00:00 1999-01-27 18:56:00  KORD  0.81
1 1999-01-27 20:00:00 1999-01-27 19:56:00  KORD  0.01
2 1999-01-27 21:00:00 1999-01-27 20:56:00  KORD -0.59
3 1999-01-27 21:00:00 1999-01-27 21:18:00  KORD -0.99
4 1999-01-27 22:00:00 1999-01-27 21:56:00  KORD -0.59
5 1999-01-27 23:00:00 1999-01-27 22:56:00  KORD -0.59

It is important to remember that if multiple text columns are to be
parsed into a single date column, then a new column is prepended to the
data. The index_col specification is based off of this new set of
columns rather than the original data columns:

In[102]: date_spec = {'nominal': [1, 2], 'actual': [1, 3]}

In[103]: df = pd.read_csv('tmp.csv', header=None, parse_dates=date_spec,
   .....:                  index_col=0)  # index is the nominal column
   .....: 

In[104]: df
Out[104]: 
                                 actual     0     4
nominal                                            
1999-01-27 19:00:00 1999-01-27 18:56:00  KORD  0.81
1999-01-27 20:00:00 1999-01-27 19:56:00  KORD  0.01
1999-01-27 21:00:00 1999-01-27 20:56:00  KORD -0.59
1999-01-27 21:00:00 1999-01-27 21:18:00  KORD -0.99
1999-01-27 22:00:00 1999-01-27 21:56:00  KORD -0.59
1999-01-27 23:00:00 1999-01-27 22:56:00  KORD -0.59

Note

If a column or index contains an unparseable date, the entire column or
index will be returned unaltered as an object data type. For
non-standard datetime parsing, use
to_datetime()
after pd.read_csv.

Note

read_csv has a fast_path for parsing datetime strings in iso8601
format, e.g “2000-01-01T00:01:02+00:00” and similar variations. If you
can arrange for your data to store datetimes in this format, load times
will be significantly faster, ~20x has been observed.

Note

When passing a dict as the parse_dates argument, the order of the
columns prepended is not guaranteed, because dict objects do not impose
an ordering on their keys. On Python 2.7+ you may use
collections.OrderedDict instead of a regular dict if this matters to
you. Because of this, when using a dict for ‘parse_dates’ in
conjunction with the index_col argument, it’s best to specify
index_col as a column label rather then as an index on the resulting
frame.

Date Parsing Functions¶

Finally, the parser allows you to specify a custom date_parser
function to take full advantage of the flexibility of the date parsing
API:

In[105]: import pandas.io.date_converters as conv

In[106]: df = pd.read_csv('tmp.csv', header=None, parse_dates=date_spec,
   .....:                  date_parser=conv.parse_date_time)
   .....: 

In[107]: df
Out[107]: 
              nominal              actual     0     4
0 1999-01-27 19:00:00 1999-01-27 18:56:00  KORD  0.81
1 1999-01-27 20:00:00 1999-01-27 19:56:00  KORD  0.01
2 1999-01-27 21:00:00 1999-01-27 20:56:00  KORD -0.59
3 1999-01-27 21:00:00 1999-01-27 21:18:00  KORD -0.99
4 1999-01-27 22:00:00 1999-01-27 21:56:00  KORD -0.59
5 1999-01-27 23:00:00 1999-01-27 22:56:00  KORD -0.59

Pandas will try to call the date_parser function in three different
ways. If an exception is raised, the next one is tried:

date_parser is first called with one or more arrays as arguments,

as defined using parse_dates (e.g.,
date_parser(['2013', '2013'], ['1', '2'])).
If #1 fails, date_parser is called with all the columns

concatenated row-wise into a single array (e.g.,
date_parser(['2013 1', '2013 2'])).
If #2 fails, date_parser is called once for every row with one or

more string arguments from the columns indicated with parse_dates
(e.g., date_parser('2013', '1') for the first row,
date_parser('2013', '2') for the second, etc.).

Note that performance-wise, you should try these methods of parsing
dates in order:

Try to infer the format using infer_datetime_format=True (see

section below).
If you know the format, use pd.to_datetime():

date_parser=lambda x: pd.to_datetime(x, format=...).
If you have a really non-standard format, use a custom date_parser

function. For optimal performance, this should be vectorized, i.e.,
it should accept arrays as arguments.

You can explore the date parsing functionality in
date_converters.py
and add your own. We would love to turn this module into a community
supported set of date/time parsers. To get you started,
date_converters.py contains functions to parse dual date and time
columns, year/month/day columns, and year/month/day/hour/minute/second
columns. It also contains a generic_parser function so you can curry
it with a function that deals with a single date rather than the entire
array.

Inferring Datetime Format¶

If you have parse_dates enabled for some or all of your columns, and
your datetime strings are all formatted the same way, you may get a
large speed up by setting infer_datetime_format=True. If set, pandas
will attempt to guess the format of your datetime strings, and then use
a faster means of parsing the strings. 5-10x parsing speeds have been
observed. pandas will fallback to the usual parsing if either the format
cannot be guessed or the format that was guessed cannot properly parse
the entire column of strings. So in general, infer_datetime_format
should not have any negative consequences if enabled.

Here are some examples of datetime strings that can be guessed (All
representing December 30th, 2011 at 00:00:00):

“20111230”
“2011/12/30”
“20111230 00:00:00”
“12/30/2011 00:00:00”
“30/Dec/2011 00:00:00”
“30/December/2011 00:00:00”

Note that infer_datetime_format is sensitive to dayfirst. With
dayfirst=True, it will guess “01/12/2011” to be December 1st. With
dayfirst=False (default) it will guess “01/12/2011” to be January
12th.

# Try to infer the format for the index column
In[108]: df = pd.read_csv('foo.csv', index_col=0, parse_dates=True,
   .....:                  infer_datetime_format=True)
   .....: 

In[109]: df
Out[109]: 
            A  B  C
date               
2009-01-01  a  1  2
2009-01-02  b  3  4
2009-01-03  c  4  5

International Date Formats¶

While US date formats tend to be MM/DD/YYYY, many international formats
use DD/MM/YYYY instead. For convenience, a dayfirst keyword is
provided:

In[110]: print(open('tmp.csv').read())
date,value,cat
1/6/2000,5,a
2/6/2000,10,b
3/6/2000,15,c

In[111]: pd.read_csv('tmp.csv', parse_dates=[0])
Out[111]: 
        date  value cat
0 2000-01-06      5   a
1 2000-02-06     10   b
2 2000-03-06     15   c

In[112]: pd.read_csv('tmp.csv', dayfirst=True, parse_dates=[0])
Out[112]: 
        date  value cat
0 2000-06-01      5   a
1 2000-06-02     10   b
2 2000-06-03     15   c

Specifying method for floating-point conversion¶

The parameter float_precision can be specified in order to use a
specific floating-point converter during parsing with the C engine. The
options are the ordinary converter, the high-precision converter, and
the round-trip converter (which is guaranteed to round-trip values after
writing to a file). For example:

In[113]: val = '0.3066101993807095471566981359501369297504425048828125'

In[114]: data = 'a,b,c\n1,2,{0}'.format(val)

In[115]: abs(pd.read_csv(StringIO(data), engine='c', float_precision=None)['c'][0] - float(val))
Out[115]: 1.1102230246251565e-16

In[116]: abs(pd.read_csv(StringIO(data), engine='c', float_precision='high')['c'][0] - float(val))
Out[116]: 5.5511151231257827e-17

In[117]: abs(pd.read_csv(StringIO(data), engine='c', float_precision='round_trip')['c'][0] - float(val))
Out[117]: 0.0

Thousand Separators¶

For large numbers that have been written with a thousands separator, you
can set the thousands keyword to a string of length 1 so that integers
will be parsed correctly:

By default, numbers with a thousands separator will be parsed as
strings:

In[118]: print(open('tmp.csv').read())
ID|level|category
Patient1|123,000|x
Patient2|23,000|y
Patient3|1,234,018|z

In[119]: df = pd.read_csv('tmp.csv', sep='|')

In[120]: df
Out[120]: 
         ID      level category
0  Patient1    123,000        x
1  Patient2     23,000        y
2  Patient3  1,234,018        z

In[121]: df.level.dtype
Out[121]: dtype('O')

The thousands keyword allows integers to be parsed correctly:

In[122]: print(open('tmp.csv').read())
ID|level|category
Patient1|123,000|x
Patient2|23,000|y
Patient3|1,234,018|z

In[123]: df = pd.read_csv('tmp.csv', sep='|', thousands=',')

In[124]: df
Out[124]: 
         ID    level category
0  Patient1   123000        x
1  Patient2    23000        y
2  Patient3  1234018        z

In[125]: df.level.dtype
Out[125]: dtype('int64')

NA Values¶

To control which values are parsed as missing values (which are
signified by NaN), specify a string in na_values. If you specify a
list of strings, then all values in it are considered to be missing
values. If you specify a number (a float, like 5.0 or an integer
like 5), the corresponding equivalent values will also imply a missing
value (in this case effectively [5.0, 5] are recognized as NaN).

To completely override the default values that are recognized as
missing, specify keep_default_na=False.

The default NaN recognized values are
['-1.#IND', '1.#QNAN', '1.#IND', '-1.#QNAN', '#N/A N/A', '#N/A', 'N/A', 'n/a', 'NA', '#NA', 'NULL', 'null', 'NaN', '-NaN', 'nan', '-nan', ''].

Let us consider some examples:

read_csv(path, na_values=[5])

In the example above 5 and 5.0 will be recognized as NaN, in
addition to the defaults. A string will first be interpreted as a
numerical 5, then as a NaN.

read_csv(path, keep_default_na=False, na_values=[""])

Above, only an empty field will be recognized as NaN.

read_csv(path, keep_default_na=False, na_values=["NA", "0"])

Above, both NA and 0 as strings are NaN.

read_csv(path, na_values=["Nope"])

The default values, in addition to the string "Nope" are recognized as
NaN.

Infinity¶

inf like values will be parsed as np.inf (positive infinity), and
-inf as -np.inf (negative infinity). These will ignore the case of
the value, meaning Inf, will also be parsed as np.inf.

Returning Series¶

Using the squeeze keyword, the parser will return output with a single
column as a Series:

In[126]: print(open('tmp.csv').read())
level
Patient1,123000
Patient2,23000
Patient3,1234018

In[127]: output =  pd.read_csv('tmp.csv', squeeze=True)

In[128]: output
Out[128]: 
Patient1     123000
Patient2      23000
Patient3    1234018
Name: level, dtype: int64

In[129]: type(output)
Out[129]: pandas.core.series.Series

Boolean values¶

The common values True, False, TRUE, and FALSE are all
recognized as boolean. Occasionally you might want to recognize other
values as being boolean. To do this, use the true_values and
false_values options as follows:

In[130]: data= 'a,b,c\n1,Yes,2\n3,No,4'

In[131]: print(data)
a,b,c
1,Yes,2
3,No,4

In[132]: pd.read_csv(StringIO(data))
Out[132]: 
   a    b  c
0  1  Yes  2
1  3   No  4

In[133]: pd.read_csv(StringIO(data), true_values=['Yes'], false_values=['No'])
Out[133]: 
   a      b  c
0  1   True  2
1  3  False  4

Handling “bad” lines¶

Some files may have malformed lines with too few fields or too many.
Lines with too few fields will have NA values filled in the trailing
fields. Lines with too many fields will raise an error by default:

In[27]: data = 'a,b,c\n1,2,3\n4,5,6,7\n8,9,10'

In[28]: pd.read_csv(StringIO(data))
---------------------------------------------------------------------------
ParserError                              Traceback (most recent call last)
ParserError: Error tokenizing data. C error: Expected 3 fields in line 3, saw 4

You can elect to skip bad lines:

In[29]: pd.read_csv(StringIO(data), error_bad_lines=False)
Skipping line 3: expected 3 fields, saw 4

Out[29]:
   a  b   c
0  1  2   3
1  8  9  10

You can also use the usecols parameter to eliminate extraneous column
data that appear in some lines but not others:

In[30]: pd.read_csv(StringIO(data), usecols=[0, 1, 2])

 Out[30]:
    a  b   c
 0  1  2   3
 1  4  5   6
 2  8  9  10

Dialect¶

The dialect keyword gives greater flexibility in specifying the file
format. By default it uses the Excel dialect but you can specify either
the dialect name or a
csv.Dialect
instance.

Suppose you had data with unenclosed quotes:

In[134]: print(data)
label1,label2,label3
index1,"a,c,e
index2,b,d,f

By default, read_csv uses the Excel dialect and treats the double
quote as the quote character, which causes it to fail when it finds a
newline before it finds the closing double quote.

We can get around this using dialect:

In[135]: dia = csv.excel()

In[136]: dia.quoting = csv.QUOTE_NONE

In[137]: pd.read_csv(StringIO(data), dialect=dia)
Out[137]: 
       label1 label2 label3
index1     "a      c      e
index2      b      d      f

All of the dialect options can be specified separately by keyword
arguments:

In[138]: data = 'a,b,c~1,2,3~4,5,6'

In[139]: pd.read_csv(StringIO(data), lineterminator='~')
Out[139]: 
   a  b  c
0  1  2  3
1  4  5  6

Another common dialect option is skipinitialspace, to skip any
whitespace after a delimiter:

In[140]: data = 'a, b, c\n1, 2, 3\n4, 5, 6'

In[141]: print(data)
a, b, c
1, 2, 3
4, 5, 6

In[142]: pd.read_csv(StringIO(data), skipinitialspace=True)
Out[142]: 
   a  b  c
0  1  2  3
1  4  5  6

The parsers make every attempt to “do the right thing” and not be
fragile. Type inference is a pretty big deal. If a column can be coerced
to integer dtype without altering the contents, the parser will do so.
Any non-numeric columns will come through as object dtype as with the
rest of pandas objects.

Quoting and Escape Characters¶

Quotes (and other escape characters) in embedded fields can be handled
in any number of ways. One way is to use backslashes; to properly parse
this data, you should pass the escapechar option:

In[143]: data = 'a,b\n"hello, \\"Bob\\", nice to see you",5'

In[144]: print(data)
a,b
"hello, \"Bob\", nice to see you",5

In[145]: pd.read_csv(StringIO(data), escapechar='\\')
Out[145]: 
                               a  b
0  hello, "Bob", nice to see you  5

Files with Fixed Width Columns¶

While
read_csv()
reads delimited data, the
read_fwf()
function works with data files that have known and fixed column widths.
The function parameters to read_fwf are largely the same as read_csv
with two extra parameters, and a different usage of the delimiter
parameter:

colspecs: A list of pairs (tuples) giving the extents of the fixed-width fields of each line as half-open intervals (i.e., [from, to[ ). String value ‘infer’ can be used to instruct the parser to try detecting the column specifications from the first 100 rows of the data. Default behavior, if not specified, is to infer.

widths: A list of field widths which can be used instead of ‘colspecs’ if the intervals are contiguous.

delimiter: Characters to consider as filler characters in the fixed-width file. Can be used to specify the filler character of the fields if it is not spaces (e.g., ‘~’).

Consider a typical fixed-width data file:

In[146]: print(open('bar.csv').read())
id8141    360.242940   149.910199   11950.7
id1594    444.953632   166.985655   11788.4
id1849    364.136849   183.628767   11806.2
id1230    413.836124   184.375703   11916.8
id1948    502.953953   173.237159   12468.3

In order to parse this file into a DataFrame, we simply need to supply
the column specifications to the read_fwf function along with the file
name:

# Column specifications are a list of half-intervals
In[147]: colspecs = [(0, 6), (8, 20), (21, 33), (34, 43)]

In[148]: df = pd.read_fwf('bar.csv', colspecs=colspecs, header=None, index_col=0)

In[149]: df
Out[149]: 
                 1           2        3
0                                      
id8141  360.242940  149.910199  11950.7
id1594  444.953632  166.985655  11788.4
id1849  364.136849  183.628767  11806.2
id1230  413.836124  184.375703  11916.8
id1948  502.953953  173.237159  12468.3

Note how the parser automatically picks column names X.<column
number> when header=None argument is specified. Alternatively, you
can supply just the column widths for contiguous columns:

# Widths are a list of integers
In[150]: widths = [6, 14, 13, 10]

In[151]: df = pd.read_fwf('bar.csv', widths=widths, header=None)

In[152]: df
Out[152]: 
        0           1           2        3
0  id8141  360.242940  149.910199  11950.7
1  id1594  444.953632  166.985655  11788.4
2  id1849  364.136849  183.628767  11806.2
3  id1230  413.836124  184.375703  11916.8
4  id1948  502.953953  173.237159  12468.3

The parser will take care of extra white spaces around the columns so
it’s ok to have extra separation between the columns in the file.

By default, read_fwf will try to infer the file’s colspecs by using
the first 100 rows of the file. It can do it only in cases when the
columns are aligned and correctly separated by the provided delimiter
(default delimiter is whitespace).

In[153]: df = pd.read_fwf('bar.csv', header=None, index_col=0)

In[154]: df
Out[154]: 
                 1           2        3
0                                      
id8141  360.242940  149.910199  11950.7
id1594  444.953632  166.985655  11788.4
id1849  364.136849  183.628767  11806.2
id1230  413.836124  184.375703  11916.8
id1948  502.953953  173.237159  12468.3

New in version 0.20.0.

read_fwf supports the dtype parameter for specifying the types of
parsed columns to be different from the inferred type.

In[155]: pd.read_fwf('bar.csv', header=None, index_col=0).dtypes
Out[155]: 
1    float64
2    float64
3    float64
dtype: object

In[156]: pd.read_fwf('bar.csv', header=None, dtype={2: 'object'}).dtypes
Out[156]: 
0     object
1    float64
2     object
3    float64
dtype: object

Indexes¶

Files with an “implicit” index column¶

Consider a file with one less entry in the header than the number of
data column:

In[157]: print(open('foo.csv').read())
A,B,C
20090101,a,1,2
20090102,b,3,4
20090103,c,4,5

In this special case, read_csv assumes that the first column is to be
used as the index of the DataFrame:

In[158]: pd.read_csv('foo.csv')
Out[158]: 
          A  B  C
20090101  a  1  2
20090102  b  3  4
20090103  c  4  5

Note that the dates weren’t automatically parsed. In that case you would
need to do as before:

In[159]: df = pd.read_csv('foo.csv', parse_dates=True)

In[160]: df.index
Out[160]: DatetimeIndex(['2009-01-01', '2009-01-02', '2009-01-03'], dtype='datetime64[ns]', freq=None)

Reading an index with a `MultiIndex`¶

Suppose you have data indexed by two columns:

In[161]: print(open('data/mindex_ex.csv').read())
year,indiv,zit,xit
1977,"A",1.2,.6
1977,"B",1.5,.5
1977,"C",1.7,.8
1978,"A",.2,.06
1978,"B",.7,.2
1978,"C",.8,.3
1978,"D",.9,.5
1978,"E",1.4,.9
1979,"C",.2,.15
1979,"D",.14,.05
1979,"E",.5,.15
1979,"F",1.2,.5
1979,"G",3.4,1.9
1979,"H",5.4,2.7
1979,"I",6.4,1.2

The index_col argument to read_csv and read_table can take a list
of column numbers to turn multiple columns into a MultiIndex for the
index of the returned object:

In[162]: df = pd.read_csv("data/mindex_ex.csv", index_col=[0,1])

In[163]: df
Out[163]: 
             zit   xit
year indiv            
1977 A      1.20  0.60
     B      1.50  0.50
     C      1.70  0.80
1978 A      0.20  0.06
     B      0.70  0.20
     C      0.80  0.30
     D      0.90  0.50
     E      1.40  0.90
1979 C      0.20  0.15
     D      0.14  0.05
     E      0.50  0.15
     F      1.20  0.50
     G      3.40  1.90
     H      5.40  2.70
     I      6.40  1.20

In[164]: df.loc[1978]
Out[164]: 
       zit   xit
indiv           
A      0.2  0.06
B      0.7  0.20
C      0.8  0.30
D      0.9  0.50
E      1.4  0.90

Reading columns with a `MultiIndex`¶

By specifying list of row locations for the header argument, you can
read in a MultiIndex for the columns. Specifying non-consecutive rows
will skip the intervening rows.

In[165]: from pandas.util.testing import makeCustomDataframe as mkdf

In[166]: df = mkdf(5, 3, r_idx_nlevels=2, c_idx_nlevels=4)

In[167]: df.to_csv('mi.csv')

In[168]: print(open('mi.csv').read())
C0,,C_l0_g0,C_l0_g1,C_l0_g2
C1,,C_l1_g0,C_l1_g1,C_l1_g2
C2,,C_l2_g0,C_l2_g1,C_l2_g2
C3,,C_l3_g0,C_l3_g1,C_l3_g2
R0,R1,,,
R_l0_g0,R_l1_g0,R0C0,R0C1,R0C2
R_l0_g1,R_l1_g1,R1C0,R1C1,R1C2
R_l0_g2,R_l1_g2,R2C0,R2C1,R2C2
R_l0_g3,R_l1_g3,R3C0,R3C1,R3C2
R_l0_g4,R_l1_g4,R4C0,R4C1,R4C2


In[169]: pd.read_csv('mi.csv', header=[0, 1, 2, 3], index_col=[0, 1])
Out[169]: 
C0              C_l0_g0 C_l0_g1 C_l0_g2
C1              C_l1_g0 C_l1_g1 C_l1_g2
C2              C_l2_g0 C_l2_g1 C_l2_g2
C3              C_l3_g0 C_l3_g1 C_l3_g2
R0      R1                             
R_l0_g0 R_l1_g0    R0C0    R0C1    R0C2
R_l0_g1 R_l1_g1    R1C0    R1C1    R1C2
R_l0_g2 R_l1_g2    R2C0    R2C1    R2C2
R_l0_g3 R_l1_g3    R3C0    R3C1    R3C2
R_l0_g4 R_l1_g4    R4C0    R4C1    R4C2

read_csv is also able to interpret a more common format of
multi-columns indices.

In[170]: print(open('mi2.csv').read())
,a,a,a,b,c,c
,q,r,s,t,u,v
one,1,2,3,4,5,6
two,7,8,9,10,11,12

In[171]: pd.read_csv('mi2.csv', header=[0, 1], index_col=0)
Out[171]: 
     a         b   c    
     q  r  s   t   u   v
one  1  2  3   4   5   6
two  7  8  9  10  11  12

Note: If an index_col is not specified (e.g. you don’t have an index,
or wrote it with df.to_csv(..., index=False), then any names on the
columns index will be lost.

Automatically “sniffing” the delimiter¶

read_csv is capable of inferring delimited (not necessarily
comma-separated) files, as pandas uses the
csv.Sniffer
class of the csv module. For this, you have to specify sep=None.

In[172]: print(open('tmp2.sv').read())
:0:1:2:3
0:0.4691122999071863:-0.2828633443286633:-1.5090585031735124:-1.1356323710171934
1:1.2121120250208506:-0.17321464905330858:0.11920871129693428:-1.0442359662799567
2:-0.8618489633477999:-2.1045692188948086:-0.4949292740687813:1.071803807037338
3:0.7215551622443669:-0.7067711336300845:-1.0395749851146963:0.27185988554282986
4:-0.42497232978883753:0.567020349793672:0.27623201927771873:-1.0874006912859915
5:-0.6736897080883706:0.1136484096888855:-1.4784265524372235:0.5249876671147047
6:0.4047052186802365:0.5770459859204836:-1.7150020161146375:-1.0392684835147725
7:-0.3706468582364464:-1.1578922506419993:-1.344311812731667:0.8448851414248841
8:1.0757697837155533:-0.10904997528022223:1.6435630703622064:-1.4693879595399115
9:0.35702056413309086:-0.6746001037299882:-1.776903716971867:-0.9689138124473498


In[173]: pd.read_csv('tmp2.sv', sep=None, engine='python')
Out[173]: 
   Unnamed: 0         0         1         2         3
0           0  0.469112 -0.282863 -1.509059 -1.135632
1           1  1.212112 -0.173215  0.119209 -1.044236
2           2 -0.861849 -2.104569 -0.494929  1.071804
3           3  0.721555 -0.706771 -1.039575  0.271860
4           4 -0.424972  0.567020  0.276232 -1.087401
5           5 -0.673690  0.113648 -1.478427  0.524988
6           6  0.404705  0.577046 -1.715002 -1.039268
7           7 -0.370647 -1.157892 -1.344312  0.844885
8           8  1.075770 -0.109050  1.643563 -1.469388
9           9  0.357021 -0.674600 -1.776904 -0.968914

Reading multiple files to create a single DataFrame¶

It’s best to use
concat()
to combine multiple files. See the
cookbook
for an example.

Iterating through files chunk by chunk¶

Suppose you wish to iterate through a (potentially very large) file
lazily rather than reading the entire file into memory, such as the
following:

In[174]: print(open('tmp.sv').read())
|0|1|2|3
0|0.4691122999071863|-0.2828633443286633|-1.5090585031735124|-1.1356323710171934
1|1.2121120250208506|-0.17321464905330858|0.11920871129693428|-1.0442359662799567
2|-0.8618489633477999|-2.1045692188948086|-0.4949292740687813|1.071803807037338
3|0.7215551622443669|-0.7067711336300845|-1.0395749851146963|0.27185988554282986
4|-0.42497232978883753|0.567020349793672|0.27623201927771873|-1.0874006912859915
5|-0.6736897080883706|0.1136484096888855|-1.4784265524372235|0.5249876671147047
6|0.4047052186802365|0.5770459859204836|-1.7150020161146375|-1.0392684835147725
7|-0.3706468582364464|-1.1578922506419993|-1.344311812731667|0.8448851414248841
8|1.0757697837155533|-0.10904997528022223|1.6435630703622064|-1.4693879595399115
9|0.35702056413309086|-0.6746001037299882|-1.776903716971867|-0.9689138124473498


In[175]: table = pd.read_table('tmp.sv', sep='|')

In[176]: table
Out[176]: 
   Unnamed: 0         0         1         2         3
0           0  0.469112 -0.282863 -1.509059 -1.135632
1           1  1.212112 -0.173215  0.119209 -1.044236
2           2 -0.861849 -2.104569 -0.494929  1.071804
3           3  0.721555 -0.706771 -1.039575  0.271860
4           4 -0.424972  0.567020  0.276232 -1.087401
5           5 -0.673690  0.113648 -1.478427  0.524988
6           6  0.404705  0.577046 -1.715002 -1.039268
7           7 -0.370647 -1.157892 -1.344312  0.844885
8           8  1.075770 -0.109050  1.643563 -1.469388
9           9  0.357021 -0.674600 -1.776904 -0.968914

By specifying a chunksize to read_csv or read_table, the return
value will be an iterable object of type TextFileReader:

In[177]: reader = pd.read_table('tmp.sv', sep='|', chunksize=4)

In[178]: reader
Out[178]: <pandas.io.parsers.TextFileReader at 0x1c30400780>

In[179]: for chunk in reader:
   .....:     print(chunk)
   .....: 
   Unnamed: 0         0         1         2         3
0           0  0.469112 -0.282863 -1.509059 -1.135632
1           1  1.212112 -0.173215  0.119209 -1.044236
2           2 -0.861849 -2.104569 -0.494929  1.071804
3           3  0.721555 -0.706771 -1.039575  0.271860
   Unnamed: 0         0         1         2         3
4           4 -0.424972  0.567020  0.276232 -1.087401
5           5 -0.673690  0.113648 -1.478427  0.524988
6           6  0.404705  0.577046 -1.715002 -1.039268
7           7 -0.370647 -1.157892 -1.344312  0.844885
   Unnamed: 0         0        1         2         3
8           8  1.075770 -0.10905  1.643563 -1.469388
9           9  0.357021 -0.67460 -1.776904 -0.968914

Specifying iterator=True will also return the TextFileReader object:

In[180]: reader = pd.read_table('tmp.sv', sep='|', iterator=True)

In[181]: reader.get_chunk(5)
Out[181]: 
   Unnamed: 0         0         1         2         3
0           0  0.469112 -0.282863 -1.509059 -1.135632
1           1  1.212112 -0.173215  0.119209 -1.044236
2           2 -0.861849 -2.104569 -0.494929  1.071804
3           3  0.721555 -0.706771 -1.039575  0.271860
4           4 -0.424972  0.567020  0.276232 -1.087401

Specifying the parser engine¶

Under the hood pandas uses a fast and efficient parser implemented in C
as well as a Python implementation which is currently more
feature-complete. Where possible pandas uses the C parser (specified as
engine='c'), but may fall back to Python if C-unsupported options are
specified. Currently, C-unsupported options include:

sep other than a single character (e.g. regex separators)
skipfooter
sep=None with delim_whitespace=False

Specifying any of the above options will produce a ParserWarning
unless the python engine is selected explicitly using engine='python'.

Reading remote files¶

You can pass in a URL to a CSV file:

df = pd.read_csv('https://download.bls.gov/pub/time.series/cu/cu.item',
                 sep='\t')

S3 URLs are handled as well:

df = pd.read_csv('s3://pandas-test/tips.csv')

Writing out Data¶

Writing to CSV format¶

The Series and DataFrame objects have an instance method to_csv
which allows storing the contents of the object as a
comma-separated-values file. The function takes a number of arguments.
Only the first is required.

path_or_buf: A string path to the file to write or a StringIO

sep : Field delimiter for the output file (default “,”)

na_rep: A string representation of a missing value (default ‘’)

float_format: Format string for floating point numbers

cols: Columns to write (default None)

header: Whether to write out the column names (default True)

index: whether to write row (index) names (default True)

index_label: Column label(s) for index column(s) if desired. If None (default), and header and index are True, then the index names are used. (A sequence should be given if the DataFrame uses MultiIndex).

mode : Python write mode, default ‘w’

encoding: a string representing the encoding to use if the contents are non-ASCII, for Python versions prior to 3

line_terminator: Character sequence denoting line end (default ‘\n’)

quoting: Set quoting rules as in csv module (default csv.QUOTE_MINIMAL). Note that if you have set a float_format then floats are converted to strings and csv.QUOTE_NONNUMERIC will treat them as non-numeric

quotechar: Character used to quote fields (default ‘”’)

doublequote: Control quoting of quotechar in fields (default True)

escapechar: Character used to escape sep and quotechar when appropriate (default None)

chunksize: Number of rows to write at a time

tupleize_cols: If False (default), write as a list of tuples, otherwise write in an expanded line format suitable for read_csv

date_format: Format string for datetime objects

Writing a formatted string¶

The DataFrame object has an instance method to_string which allows
control over the string representation of the object. All arguments are
optional:

buf default None, for example a StringIO object

columns default None, which columns to write

col_space default None, minimum width of each column.

na_rep default NaN, representation of NA value

formatters default None, a dictionary (by column) of functions each of which takes a single argument and returns a formatted string

float_format default None, a function which takes a single (float) argument and returns a formatted string; to be applied to floats in the DataFrame.

sparsify default True, set to False for a DataFrame with a hierarchical index to print every multiindex key at each row.

index_names default True, will print the names of the indices

index default True, will print the index (ie, row labels)

header default True, will print the column labels

justify default left, will print column headers left- or right-justified

The Series object also has a to_string method, but with only the
buf, na_rep, float_format arguments. There is also a length
argument which, if set to True, will additionally output the length of
the Series.

JSON¶

Read and write JSON format files and strings.

Writing JSON¶

A Series or DataFrame can be converted to a valid JSON string. Use
to_json with optional parameters:

path_or_buf : the pathname or buffer to write the output This can

be None in which case a JSON string is returned

orient :

Series:

default is index
allowed values are {split, records, index}

DataFrame:

default is columns
allowed values are {split, records, index, columns,

values, table}

The format of the JSON string

`split`	dict like {index -> [index], columns -> [columns], data -> [values]}
`records`	list like [{column -> value}, … , {column -> value}]
`index`	dict like {index -> {column -> value}}
`columns`	dict like {column -> {index -> value}}
`values`	just the values array

date_format : string, type of date conversion, ‘epoch’ for

timestamp, ‘iso’ for ISO8601.
double_precision : The number of decimal places to use when

encoding floating point values, default 10.
force_ascii : force encoded string to be ASCII, default True.
date_unit : The time unit to encode to, governs timestamp and

ISO8601 precision. One of ‘s’, ‘ms’, ‘us’ or ‘ns’ for seconds,
milliseconds, microseconds and nanoseconds respectively. Default
‘ms’.
default_handler : The handler to call if an object cannot

otherwise be converted to a suitable format for JSON. Takes a single
argument, which is the object to convert, and returns a serializable
object.
lines : If records orient, then will write each record per line

as json.

Note NaN’s, NaT’s and None will be converted to null and
datetime objects will be converted based on the date_format and
date_unit parameters.

In[182]: dfj = pd.DataFrame(randn(5, 2), columns=list('AB'))

In[183]: json = dfj.to_json()

In[184]: json
Out[184]: '{"A":{"0":-1.2945235903,"1":0.2766617129,"2":-0.0139597524,"3":-0.0061535699,"4":0.8957173022},"B":{"0":0.4137381054,"1":-0.472034511,"2":-0.3625429925,"3":-0.923060654,"4":0.8052440254}}'

Orient Options¶

There are a number of different options for the format of the resulting
JSON file / string. Consider the following DataFrame and Series:

In[185]: dfjo = pd.DataFrame(dict(A=range(1, 4), B=range(4, 7), C=range(7, 10)),
   .....:                     columns=list('ABC'), index=list('xyz'))
   .....: 

In[186]: dfjo
Out[186]: 
   A  B  C
x  1  4  7
y  2  5  8
z  3  6  9

In[187]: sjo = pd.Series(dict(x=15, y=16, z=17), name='D')

In[188]: sjo
Out[188]: 
x    15
y    16
z    17
Name: D, dtype: int64

Column oriented (the default for DataFrame) serializes the data as
nested JSON objects with column labels acting as the primary index:

In[189]: dfjo.to_json(orient="columns")
Out[189]: '{"A":{"x":1,"y":2,"z":3},"B":{"x":4,"y":5,"z":6},"C":{"x":7,"y":8,"z":9}}'

# Not available for Series

Index oriented (the default for Series) similar to column oriented
but the index labels are now primary:

In[190]: dfjo.to_json(orient="index")
Out[190]: '{"x":{"A":1,"B":4,"C":7},"y":{"A":2,"B":5,"C":8},"z":{"A":3,"B":6,"C":9}}'

In[191]: sjo.to_json(orient="index")
Out[191]: '{"x":15,"y":16,"z":17}'

Record oriented serializes the data to a JSON array of column ->
value records, index labels are not included. This is useful for passing
DataFrame data to plotting libraries, for example the JavaScript
library d3.js:

In[192]: dfjo.to_json(orient="records")
Out[192]: '[{"A":1,"B":4,"C":7},{"A":2,"B":5,"C":8},{"A":3,"B":6,"C":9}]'

In[193]: sjo.to_json(orient="records")
Out[193]: '[15,16,17]'

Value oriented is a bare-bones option which serializes to nested
JSON arrays of values only, column and index labels are not included:

In[194]: dfjo.to_json(orient="values")
Out[194]: '[[1,4,7],[2,5,8],[3,6,9]]'

# Not available for Series

Split oriented serializes to a JSON object containing separate
entries for values, index and columns. Name is also included for
Series:

In[195]: dfjo.to_json(orient="split")
Out[195]: '{"columns":["A","B","C"],"index":["x","y","z"],"data":[[1,4,7],[2,5,8],[3,6,9]]}'

In[196]: sjo.to_json(orient="split")
Out[196]: '{"name":"D","index":["x","y","z"],"data":[15,16,17]}'

Table oriented serializes to the JSON
Table Schema,
allowing for the preservation of metadata including but not limited to
dtypes and index names.

Note

Any orient option that encodes to a JSON object will not preserve the
ordering of index and column labels during round-trip serialization. If
you wish to preserve label ordering use the split option as it uses
ordered containers.

Date Handling¶

Writing in ISO date format:

In[197]: dfd = pd.DataFrame(randn(5, 2), columns=list('AB'))

In[198]: dfd['date'] = pd.Timestamp('20130101')

In[199]: dfd = dfd.sort_index(1, ascending=False)

In[200]: json = dfd.to_json(date_format='iso')

In[201]: json
Out[201]: '{"date":{"0":"2013-01-01T00:00:00.000Z","1":"2013-01-01T00:00:00.000Z","2":"2013-01-01T00:00:00.000Z","3":"2013-01-01T00:00:00.000Z","4":"2013-01-01T00:00:00.000Z"},"B":{"0":2.5656459463,"1":1.3403088498,"2":-0.2261692849,"3":0.8138502857,"4":-0.8273169356},"A":{"0":-1.2064117817,"1":1.4312559863,"2":-1.1702987971,"3":0.4108345112,"4":0.1320031703}}'

Writing in ISO date format, with microseconds:

In[202]: json = dfd.to_json(date_format='iso', date_unit='us')

In[203]: json
Out[203]: '{"date":{"0":"2013-01-01T00:00:00.000000Z","1":"2013-01-01T00:00:00.000000Z","2":"2013-01-01T00:00:00.000000Z","3":"2013-01-01T00:00:00.000000Z","4":"2013-01-01T00:00:00.000000Z"},"B":{"0":2.5656459463,"1":1.3403088498,"2":-0.2261692849,"3":0.8138502857,"4":-0.8273169356},"A":{"0":-1.2064117817,"1":1.4312559863,"2":-1.1702987971,"3":0.4108345112,"4":0.1320031703}}'

Epoch timestamps, in seconds:

In[204]: json = dfd.to_json(date_format='epoch', date_unit='s')

In[205]: json
Out[205]: '{"date":{"0":1356998400,"1":1356998400,"2":1356998400,"3":1356998400,"4":1356998400},"B":{"0":2.5656459463,"1":1.3403088498,"2":-0.2261692849,"3":0.8138502857,"4":-0.8273169356},"A":{"0":-1.2064117817,"1":1.4312559863,"2":-1.1702987971,"3":0.4108345112,"4":0.1320031703}}'

Writing to a file, with a date index and a date column:

In[206]: dfj2 = dfj.copy()

In[207]: dfj2['date'] = pd.Timestamp('20130101')

In[208]: dfj2['ints'] = list(range(5))

In[209]: dfj2['bools'] = True

In[210]: dfj2.index = pd.date_range('20130101', periods=5)

In[211]: dfj2.to_json('test.json')

In[212]: open('test.json').read()
Out[212]: '{"A":{"1356998400000":-1.2945235903,"1357084800000":0.2766617129,"1357171200000":-0.0139597524,"1357257600000":-0.0061535699,"1357344000000":0.8957173022},"B":{"1356998400000":0.4137381054,"1357084800000":-0.472034511,"1357171200000":-0.3625429925,"1357257600000":-0.923060654,"1357344000000":0.8052440254},"date":{"1356998400000":1356998400000,"1357084800000":1356998400000,"1357171200000":1356998400000,"1357257600000":1356998400000,"1357344000000":1356998400000},"ints":{"1356998400000":0,"1357084800000":1,"1357171200000":2,"1357257600000":3,"1357344000000":4},"bools":{"1356998400000":true,"1357084800000":true,"1357171200000":true,"1357257600000":true,"1357344000000":true}}'

Fallback Behavior¶

If the JSON serializer cannot handle the container contents directly it
will fall back in the following manner:

if the dtype is unsupported (e.g. np.complex) then the

default_handler, if provided, will be called for each value,
otherwise an exception is raised.
if an object is unsupported it will attempt the following:
- check if the object has defined a toDict method and call it. A
  
  toDict method should return a dict which will then be JSON
  serialized.
- invoke the default_handler if one was provided.
- convert the object to a dict by traversing its contents.
  
  However this will often fail with an OverflowError or give
  unexpected results.

In general the best approach for unsupported objects or dtypes is to
provide a default_handler. For example:

DataFrame([1.0, 2.0, complex(1.0, 2.0)]).to_json()  # raises

RuntimeError: Unhandled numpy dtype 15

can be dealt with by specifying a simple default_handler:

In[213]: pd.DataFrame([1.0, 2.0, complex(1.0, 2.0)]).to_json(default_handler=str)
Out[213]: '{"0":{"0":"(1+0j)","1":"(2+0j)","2":"(1+2j)"}}'

Reading JSON¶

Reading a JSON string to pandas object can take a number of parameters.
The parser will try to parse a DataFrame if typ is not supplied or
is None. To explicitly force Series parsing, pass typ=series

filepath_or_buffer : a VALID JSON string or file handle /

StringIO. The string could be a URL. Valid URL schemes include http,
ftp, S3, and file. For file URLs, a host is expected. For instance,
a local file could be file ://localhost/path/to/table.json
typ : type of object to recover (series or frame), default ‘frame’

orient :

Series :

default is index
allowed values are {split, records, index}

DataFrame

default is columns
allowed values are {split, records, index, columns,

values, table}

The format of the JSON string

`split`	dict like {index -> [index], columns -> [columns], data -> [values]}
`records`	list like [{column -> value}, … , {column -> value}]
`index`	dict like {index -> {column -> value}}
`columns`	dict like {column -> {index -> value}}
`values`	just the values array
`table`	adhering to the JSON Table Schema

dtype : if True, infer dtypes, if a dict of column to dtype, then

use those, if False, then don’t infer dtypes at all, default is
True, apply only to the data.
convert_axes : boolean, try to convert the axes to the proper

dtypes, default is True
convert_dates : a list of columns to parse for dates; If True,

then try to parse date-like columns, default is True.
keep_default_dates : boolean, default True. If parsing dates,

then parse the default date-like columns.
numpy : direct decoding to NumPy arrays. default is False;

Supports numeric data only, although labels may be non-numeric. Also
note that the JSON ordering MUST be the same for each term if
numpy=True.
precise_float : boolean, default False. Set to enable usage of

higher precision (strtod) function when decoding string to double
values. Default (False) is to use fast but less precise builtin
functionality.
date_unit : string, the timestamp unit to detect if converting

dates. Default None. By default the timestamp precision will be
detected, if this is not desired then pass one of ‘s’, ‘ms’, ‘us’ or
‘ns’ to force timestamp precision to seconds, milliseconds,
microseconds or nanoseconds respectively.
lines : reads file as one json object per line.
encoding : The encoding to use to decode py3 bytes.
chunksize : when used in combination with lines=True, return a

JsonReader which reads in chunksize lines per iteration.

The parser will raise one of ValueError/TypeError/AssertionError if
the JSON is not parseable.

If a non-default orient was used when encoding to JSON be sure to pass
the same option here so that decoding produces sensible results, see
Orient Options
for an overview.

Data Conversion¶

The default of convert_axes=True, dtype=True, and
convert_dates=True will try to parse the axes, and all of the data
into appropriate types, including dates. If you need to override
specific dtypes, pass a dict to dtype. convert_axes should only be
set to False if you need to preserve string-like numbers (e.g. ‘1’,
‘2’) in an axes.

Note

Large integer values may be converted to dates if convert_dates=True
and the data and / or column labels appear ‘date-like’. The exact
threshold depends on the date_unit specified. ‘date-like’ means that
the column label meets one of the following criteria:

it ends with '_at'

it ends with '_time'

it begins with 'timestamp'

it is 'modified'

it is 'date'

Warning

When reading JSON data, automatic coercing into dtypes has some quirks:

an index can be reconstructed in a different order from serialization, that is, the returned order is not guaranteed to be the same as before serialization

a column that was float data will be converted to integer if it can be done safely, e.g. a column of 1.

bool columns will be converted to integer on reconstruction

Thus there are times where you may want to specify specific dtypes via
the dtype keyword argument.

Reading from a JSON string:

In[214]: pd.read_json(json)
Out[214]: 
        date         B         A
0 2013-01-01  2.565646 -1.206412
1 2013-01-01  1.340309  1.431256
2 2013-01-01 -0.226169 -1.170299
3 2013-01-01  0.813850  0.410835
4 2013-01-01 -0.827317  0.132003

Reading from a file:

In[215]: pd.read_json('test.json')
Out[215]: 
                   A         B       date  ints  bools
2013-01-01 -1.294524  0.413738 2013-01-01     0   True
2013-01-02  0.276662 -0.472035 2013-01-01     1   True
2013-01-03 -0.013960 -0.362543 2013-01-01     2   True
2013-01-04 -0.006154 -0.923061 2013-01-01     3   True
2013-01-05  0.895717  0.805244 2013-01-01     4   True

Don’t convert any data (but still convert axes and dates):

In[216]: pd.read_json('test.json', dtype=object).dtypes
Out[216]: 
A        object
B        object
date     object
ints     object
bools    object
dtype: object

Specify dtypes for conversion:

In[217]: pd.read_json('test.json', dtype={'A': 'float32', 'bools': 'int8'}).dtypes
Out[217]: 
A               float32
B               float64
date     datetime64[ns]
ints              int64
bools              int8
dtype: object

Preserve string indices:

In[218]: si = pd.DataFrame(np.zeros((4, 4)),
   .....:          columns=list(range(4)),
   .....:          index=[str(i) for i in range(4)])
   .....: 

In[219]: si
Out[219]: 
     0    1    2    3
0  0.0  0.0  0.0  0.0
1  0.0  0.0  0.0  0.0
2  0.0  0.0  0.0  0.0
3  0.0  0.0  0.0  0.0

In[220]: si.index
Out[220]: Index(['0', '1', '2', '3'], dtype='object')

In[221]: si.columns
Out[221]: Int64Index([0, 1, 2, 3], dtype='int64')

In[222]: json = si.to_json()

In[223]: sij = pd.read_json(json, convert_axes=False)

In[224]: sij
Out[224]: 
   0  1  2  3
0  0  0  0  0
1  0  0  0  0
2  0  0  0  0
3  0  0  0  0

In[225]: sij.index
Out[225]: Index(['0', '1', '2', '3'], dtype='object')

In[226]: sij.columns
Out[226]: Index(['0', '1', '2', '3'], dtype='object')

Dates written in nanoseconds need to be read back in nanoseconds:

In[227]: json = dfj2.to_json(date_unit='ns')

# Try to parse timestamps as millseconds -> Won't Work
In[228]: dfju = pd.read_json(json, date_unit='ms')

In[229]: dfju
Out[229]: 
                            A         B                 date  ints  bools
1356998400000000000 -1.294524  0.413738  1356998400000000000     0   True
1357084800000000000  0.276662 -0.472035  1356998400000000000     1   True
1357171200000000000 -0.013960 -0.362543  1356998400000000000     2   True
1357257600000000000 -0.006154 -0.923061  1356998400000000000     3   True
1357344000000000000  0.895717  0.805244  1356998400000000000     4   True

# Let pandas detect the correct precision
In[230]: dfju = pd.read_json(json)

In[231]: dfju
Out[231]: 
                   A         B       date  ints  bools
2013-01-01 -1.294524  0.413738 2013-01-01     0   True
2013-01-02  0.276662 -0.472035 2013-01-01     1   True
2013-01-03 -0.013960 -0.362543 2013-01-01     2   True
2013-01-04 -0.006154 -0.923061 2013-01-01     3   True
2013-01-05  0.895717  0.805244 2013-01-01     4   True

# Or specify that all timestamps are in nanoseconds
In[232]: dfju = pd.read_json(json, date_unit='ns')

In[233]: dfju
Out[233]: 
                   A         B       date  ints  bools
2013-01-01 -1.294524  0.413738 2013-01-01     0   True
2013-01-02  0.276662 -0.472035 2013-01-01     1   True
2013-01-03 -0.013960 -0.362543 2013-01-01     2   True
2013-01-04 -0.006154 -0.923061 2013-01-01     3   True
2013-01-05  0.895717  0.805244 2013-01-01     4   True

The Numpy Parameter¶

Note

This supports numeric data only. Index and columns labels may be
non-numeric, e.g. strings, dates etc.

If numpy=True is passed to read_json an attempt will be made to
sniff an appropriate dtype during deserialization and to subsequently
decode directly to NumPy arrays, bypassing the need for intermediate
Python objects.

This can provide speedups if you are deserialising a large amount of
numeric data:

In[234]: randfloats = np.random.uniform(-100, 1000, 10000)

In[235]: randfloats.shape = (1000, 10)

In[236]: dffloats = pd.DataFrame(randfloats, columns=list('ABCDEFGHIJ'))

In[237]: jsonfloats = dffloats.to_json()

In[238]: timeit pd.read_json(jsonfloats)
15.3 ms +- 2.24 ms per loop (mean +- std. dev. of 7 runs, 100 loops each)

In[239]: timeit pd.read_json(jsonfloats, numpy=True)
11.6 ms +- 1.2 ms per loop (mean +- std. dev. of 7 runs, 100 loops each)

The speedup is less noticeable for smaller datasets:

In[240]: jsonfloats = dffloats.head(100).to_json()

In[241]: timeit pd.read_json(jsonfloats)
8.24 ms +- 453 us per loop (mean +- std. dev. of 7 runs, 100 loops each)

In[242]: timeit pd.read_json(jsonfloats, numpy=True)
7.59 ms +- 565 us per loop (mean +- std. dev. of 7 runs, 100 loops each)

Warning

Direct NumPy decoding makes a number of assumptions and may fail or
produce unexpected output if these assumptions are not satisfied:

data is numeric.

data is uniform. The dtype is sniffed from the first value decoded. A ValueError may be raised, or incorrect output may be produced if this condition is not satisfied.

labels are ordered. Labels are only read from the first container, it is assumed that each subsequent row / column has been encoded in the same order. This should be satisfied if the data was encoded using to_json but may not be the case if the JSON is from another source.

Normalization¶

pandas provides a utility function to take a dict or list of dicts and
normalize this semi-structured data into a flat table.

In[243]: from pandas.io.json import json_normalize

In[244]: data = [{'id': 1, 'name': {'first': 'Coleen', 'last': 'Volk'}},
   .....:         {'name': {'given': 'Mose', 'family': 'Regner'}},
   .....:         {'id': 2, 'name': 'Faye Raker'}]
   .....: 

In[245]: json_normalize(data)
Out[245]: 
    id        name name.family name.first name.given name.last
0  1.0         NaN         NaN     Coleen        NaN      Volk
1  NaN         NaN      Regner        NaN       Mose       NaN
2  2.0  Faye Raker         NaN        NaN        NaN       NaN

In[246]: data = [{'state': 'Florida',
   .....:           'shortname': 'FL',
   .....:           'info': {
   .....:                'governor': 'Rick Scott'
   .....:           },
   .....:           'counties': [{'name': 'Dade', 'population': 12345},
   .....:                       {'name': 'Broward', 'population': 40000},
   .....:                       {'name': 'Palm Beach', 'population': 60000}]},
   .....:          {'state': 'Ohio',
   .....:           'shortname': 'OH',
   .....:           'info': {
   .....:                'governor': 'John Kasich'
   .....:           },
   .....:           'counties': [{'name': 'Summit', 'population': 1234},
   .....:                        {'name': 'Cuyahoga', 'population': 1337}]}]
   .....: 

In[247]: json_normalize(data, 'counties', ['state', 'shortname', ['info', 'governor']])
Out[247]: 
         name  population    state shortname info.governor
0        Dade       12345  Florida        FL    Rick Scott
1     Broward       40000  Florida        FL    Rick Scott
2  Palm Beach       60000  Florida        FL    Rick Scott
3      Summit        1234     Ohio        OH   John Kasich
4    Cuyahoga        1337     Ohio        OH   John Kasich

Line delimited json¶

New in version 0.19.0.

pandas is able to read and write line-delimited json files that are
common in data processing pipelines using Hadoop or Spark.

New in version 0.21.0.

For line-delimited json files, pandas can also return an iterator which
reads in chunksize lines at a time. This can be useful for large files
or to read from a stream.

In[248]: jsonl = '''
   .....:     {"a": 1, "b": 2}
   .....:     {"a": 3, "b": 4}
   .....: '''
   .....: 

In[249]: df = pd.read_json(jsonl, lines=True)

In[250]: df
Out[250]: 
   a  b
0  1  2
1  3  4

In[251]: df.to_json(orient='records', lines=True)
Out[251]: '{"a":1,"b":2}\n{"a":3,"b":4}'

# reader is an iterator that returns `chunksize` lines each iteration
In[252]: reader = pd.read_json(StringIO(jsonl), lines=True, chunksize=1)

In[253]: reader
Out[253]: <pandas.io.json.json.JsonReader at 0x1c31c5a208>

In[254]: for chunk in reader:
   .....:     print(chunk)
   .....: 
Empty DataFrame
Columns: []
Index: []
   a  b
0  1  2
   a  b
1  3  4

Table Schema¶

New in version 0.20.0.

Table Schema
is a spec for describing tabular datasets as a JSON object. The JSON
includes information on the field names, types, and other attributes.
You can use the orient table to build a JSON string with two fields,
schema and data.

In[255]: df = pd.DataFrame(
   .....:     {'A': [1, 2, 3],
   .....:      'B': ['a', 'b', 'c'],
   .....:      'C': pd.date_range('2016-01-01', freq='d', periods=3),
   .....:     }, index=pd.Index(range(3), name='idx'))
   .....: 

In[256]: df
Out[256]: 
     A  B          C
idx                 
0    1  a 2016-01-01
1    2  b 2016-01-02
2    3  c 2016-01-03

In[257]: df.to_json(orient='table', date_format="iso")
Out[257]: '{"schema": {"fields":[{"name":"idx","type":"integer"},{"name":"A","type":"integer"},{"name":"B","type":"string"},{"name":"C","type":"datetime"}],"primaryKey":["idx"],"pandas_version":"0.20.0"}, "data": [{"idx":0,"A":1,"B":"a","C":"2016-01-01T00:00:00.000Z"},{"idx":1,"A":2,"B":"b","C":"2016-01-02T00:00:00.000Z"},{"idx":2,"A":3,"B":"c","C":"2016-01-03T00:00:00.000Z"}]}'

The schema field contains the fields key, which itself contains a
list of column name to type pairs, including the Index or MultiIndex
(see below for a list of types). The schema field also contains a
primaryKey field if the (Multi)index is unique.

The second field, data, contains the serialized data with the
records orient. The index is included, and any datetimes are ISO 8601
formatted, as required by the Table Schema spec.

The full list of types supported are described in the Table Schema spec.
This table shows the mapping from pandas types:

Pandas type	Table Schema type
int64	integer
float64	number
bool	boolean
datetime64[ns]	datetime
timedelta64[ns]	duration
categorical	any
object	str

A few notes on the generated table schema:

The schema object contains a pandas_version field. This contains

the version of pandas’ dialect of the schema, and will be
incremented with each revision.

All dates are converted to UTC when serializing. Even timezone naïve

values, which are treated as UTC with an offset of 0.

In[258]: from pandas.io.json import build_table_schema

In[259]: s = pd.Series(pd.date_range('2016', periods=4))

In[260]: build_table_schema(s)
Out[260]: 
{'fields': [{'name': 'index', 'type': 'integer'},
  {'name': 'values', 'type': 'datetime'}],
 'primaryKey': ['index'],
 'pandas_version': '0.20.0'}

datetimes with a timezone (before serializing), include an

additional field tz with the time zone name (e.g. 'US/Central').

In[261]: s_tz = pd.Series(pd.date_range('2016', periods=12,
   .....:                                tz='US/Central'))
   .....: 

In[262]: build_table_schema(s_tz)
Out[262]: 
{'fields': [{'name': 'index', 'type': 'integer'},
  {'name': 'values', 'type': 'datetime', 'tz': 'US/Central'}],
 'primaryKey': ['index'],
 'pandas_version': '0.20.0'}

Periods are converted to timestamps before serialization, and so

have the same behavior of being converted to UTC. In addition,
periods will contain and additional field freq with the period’s
frequency, e.g. 'A-DEC'.

In[263]: s_per = pd.Series(1, index=pd.period_range('2016', freq='A-DEC',
   .....:                                            periods=4))
   .....: 

In[264]: build_table_schema(s_per)
Out[264]: 
{'fields': [{'name': 'index', 'type': 'datetime', 'freq': 'A-DEC'},
  {'name': 'values', 'type': 'integer'}],
 'primaryKey': ['index'],
 'pandas_version': '0.20.0'}

Categoricals use the any type and an enum constraint listing the

set of possible values. Additionally, an ordered field is
included:

In[265]: s_cat = pd.Series(pd.Categorical(['a', 'b', 'a']))

In[266]: build_table_schema(s_cat)
Out[266]: 
{'fields': [{'name': 'index', 'type': 'integer'},
  {'name': 'values',
   'type': 'any',
   'constraints': {'enum': ['a', 'b']},
   'ordered': False}],
 'primaryKey': ['index'],
 'pandas_version': '0.20.0'}

A primaryKey field, containing an array of labels, is included *if

the index is unique*:

In[267]: s_dupe = pd.Series([1, 2], index=[1, 1])

In[268]: build_table_schema(s_dupe)
Out[268]: 
{'fields': [{'name': 'index', 'type': 'integer'},
  {'name': 'values', 'type': 'integer'}],
 'pandas_version': '0.20.0'}

The primaryKey behavior is the same with MultiIndexes, but in this

case the primaryKey is an array:

In[269]: s_multi = pd.Series(1, index=pd.MultiIndex.from_product([('a', 'b'),
   .....:                                                          (0, 1)]))
   .....: 

In[270]: build_table_schema(s_multi)
Out[270]: 
{'fields': [{'name': 'level_0', 'type': 'string'},
  {'name': 'level_1', 'type': 'integer'},
  {'name': 'values', 'type': 'integer'}],
 'primaryKey': FrozenList(['level_0', 'level_1']),
 'pandas_version': '0.20.0'}

The default naming roughly follows these rules:
- For series, the object.name is used. If that’s none, then the
  
  name is values
- For DataFrames, the stringified version of the column name is
  
  used
- For Index (not MultiIndex), index.name is used, with a
  
  fallback to index if that is None.
- For MultiIndex, mi.names is used. If any level has no name,
  
  then level_<i> is used.

New in version 0.23.0.

read_json also accepts orient='table' as an argument. This allows
for the preserveration of metadata such as dtypes and index names in a
round-trippable manner.

In[271]: df = pd.DataFrame({'foo': [1, 2, 3, 4],
   .....:                    'bar': ['a', 'b', 'c', 'd'],
   .....:                    'baz': pd.date_range('2018-01-01', freq='d', periods=4),
   .....:                    'qux': pd.Categorical(['a', 'b', 'c', 'c'])
   .....:                    }, index=pd.Index(range(4), name='idx'))
   .....: 
In[272]: df
Out[272]:
foo bar baz qux
idx

0 1 a 2018-01-01 a
1 2 b 2018-01-02 b
2 3 c 2018-01-03 c
3 4 d 2018-01-04 c

In[273]: df.dtypes
Out[273]:
foo int64
bar object
baz datetime64
qux category
dtype: object

In[274]: df.to_json('test.json', orient='table')

In[275]: new_df = pd.read_json('test.json', orient='table')

In[276]: new_df
Out[276]:
foo bar baz qux
idx

0 1 a 2018-01-01 a
1 2 b 2018-01-02 b
2 3 c 2018-01-03 c
3 4 d 2018-01-04 c

In[277]: new_df.dtypes
Out[277]:
foo int64
bar object
baz datetime64
qux category
dtype: object

Please note that the literal string ‘index’ as the name of an
Index
is not round-trippable, nor are any names beginning with 'level_'
within a
MultiIndex.
These are used by default in
DataFrame.to_json()
to indicate missing values and the subsequent read cannot distinguish
the intent.

In[278]: df.index.name = 'index'

In[279]: df.to_json('test.json', orient='table')

In[280]: new_df = pd.read_json('test.json', orient='table')

In[281]: print(new_df.index.name)
None

HTML¶

Reading HTML Content¶

Warning

We highly encourage you to read the
HTML Table Parsing gotchas
below regarding the issues surrounding the BeautifulSoup4/html5lib/lxml
parsers.

The top-level read_html() function can accept an HTML string/file/URL
and will parse HTML tables into list of pandas DataFrames. Let’s look
at a few examples.

Note

read_html returns a list of DataFrame objects, even if there is
only a single table contained in the HTML content.

Read a URL with no options:

In[282]: url = 'http://www.fdic.gov/bank/individual/failed/banklist.html'

In[283]: dfs = pd.read_html(url)

In[284]: dfs
Out[284]: 
[                                             Bank Name                City         ...               Closing Date        Updated Date
 0                  Washington Federal Bank for Savings             Chicago         ...          December 15, 2017   February 21, 2018
 1      The Farmers and Merchants State Bank of Argonia             Argonia         ...           October 13, 2017   February 21, 2018
 2                                  Fayette County Bank          Saint Elmo         ...               May 26, 2017       July 26, 2017
 3    Guaranty Bank, (d/b/a BestBank in Georgia & Mi...           Milwaukee         ...                May 5, 2017      March 22, 2018
 4                                       First NBC Bank         New Orleans         ...             April 28, 2017    December 5, 2017
 5                                        Proficio Bank  Cottonwood Heights         ...              March 3, 2017       March 7, 2018
 6                        Seaway Bank and Trust Company             Chicago         ...           January 27, 2017        May 18, 2017
 ..                                                 ...                 ...         ...                        ...                 ...
 548                      Hamilton Bank, NA  En Espanol               Miami         ...           January 11, 2002  September 21, 2015
 549                             Sinclair National Bank            Gravette         ...          September 7, 2001     October 6, 2017
 550                                 Superior Bank, FSB            Hinsdale         ...              July 27, 2001     August 19, 2014
 551                                Malta National Bank               Malta         ...                May 3, 2001   November 18, 2002
 552                    First Alliance Bank & Trust Co.          Manchester         ...           February 2, 2001   February 18, 2003
 553                  National State Bank of Metropolis          Metropolis         ...          December 14, 2000      March 17, 2005
 554                                   Bank of Honolulu            Honolulu         ...           October 13, 2000      March 17, 2005

 [555 rows x 7 columns]]

Note

The data from the above URL changes every Monday so the resulting data
above and the data below may be slightly different.

Read in the content of the file from the above URL and pass it to
read_html as a string:

In[285]: with open(file_path, 'r') as f:
   .....:     dfs = pd.read_html(f.read())
   .....: 

In[286]: dfs
Out[286]: 
[                                    Bank Name          City  ST        ...                        Acquiring Institution       Closing Date       Updated Date
 0    Banks of Wisconsin d/b/a Bank of Kenosha       Kenosha  WI        ...                        North Shore Bank, FSB       May 31, 2013       May 31, 2013
 1                        Central Arizona Bank    Scottsdale  AZ        ...                           Western State Bank       May 14, 2013       May 20, 2013
 2                                Sunrise Bank      Valdosta  GA        ...                                 Synovus Bank       May 10, 2013       May 21, 2013
 3                       Pisgah Community Bank     Asheville  NC        ...                           Capital Bank, N.A.       May 10, 2013       May 14, 2013
 4                         Douglas County Bank  Douglasville  GA        ...                          Hamilton State Bank     April 26, 2013       May 16, 2013
 5                                Parkway Bank        Lenoir  NC        ...             CertusBank, National Association     April 26, 2013       May 17, 2013
 6                      Chipola Community Bank      Marianna  FL        ...                First Federal Bank of Florida     April 19, 2013       May 16, 2013
 ..                                        ...           ...  ..        ...                                          ...                ...                ...
 498               Hamilton Bank, NAEn Espanol         Miami  FL        ...             Israel Discount Bank of New York   January 11, 2002       June 5, 2012
 499                    Sinclair National Bank      Gravette  AR        ...                           Delta Trust & Bank  September 7, 2001  February 10, 2004
 500                        Superior Bank, FSB      Hinsdale  IL        ...                        Superior Federal, FSB      July 27, 2001       June 5, 2012
 501                       Malta National Bank         Malta  OH        ...                            North Valley Bank        May 3, 2001  November 18, 2002
 502           First Alliance Bank & Trust Co.    Manchester  NH        ...          Southern New Hampshire Bank & Trust   February 2, 2001  February 18, 2003
 503         National State Bank of Metropolis    Metropolis  IL        ...                      Banterra Bank of Marion  December 14, 2000     March 17, 2005
 504                          Bank of Honolulu      Honolulu  HI        ...                           Bank of the Orient   October 13, 2000     March 17, 2005

 [505 rows x 7 columns]]

You can even pass in an instance of StringIO if you so desire:

In[287]: with open(file_path, 'r') as f:
   .....:     sio = StringIO(f.read())
   .....: 

In[288]: dfs = pd.read_html(sio)

In[289]: dfs
Out[289]: 
[                                    Bank Name          City  ST        ...                        Acquiring Institution       Closing Date       Updated Date
 0    Banks of Wisconsin d/b/a Bank of Kenosha       Kenosha  WI        ...                        North Shore Bank, FSB       May 31, 2013       May 31, 2013
 1                        Central Arizona Bank    Scottsdale  AZ        ...                           Western State Bank       May 14, 2013       May 20, 2013
 2                                Sunrise Bank      Valdosta  GA        ...                                 Synovus Bank       May 10, 2013       May 21, 2013
 3                       Pisgah Community Bank     Asheville  NC        ...                           Capital Bank, N.A.       May 10, 2013       May 14, 2013
 4                         Douglas County Bank  Douglasville  GA        ...                          Hamilton State Bank     April 26, 2013       May 16, 2013
 5                                Parkway Bank        Lenoir  NC        ...             CertusBank, National Association     April 26, 2013       May 17, 2013
 6                      Chipola Community Bank      Marianna  FL        ...                First Federal Bank of Florida     April 19, 2013       May 16, 2013
 ..                                        ...           ...  ..        ...                                          ...                ...                ...
 498               Hamilton Bank, NAEn Espanol         Miami  FL        ...             Israel Discount Bank of New York   January 11, 2002       June 5, 2012
 499                    Sinclair National Bank      Gravette  AR        ...                           Delta Trust & Bank  September 7, 2001  February 10, 2004
 500                        Superior Bank, FSB      Hinsdale  IL        ...                        Superior Federal, FSB      July 27, 2001       June 5, 2012
 501                       Malta National Bank         Malta  OH        ...                            North Valley Bank        May 3, 2001  November 18, 2002
 502           First Alliance Bank & Trust Co.    Manchester  NH        ...          Southern New Hampshire Bank & Trust   February 2, 2001  February 18, 2003
 503         National State Bank of Metropolis    Metropolis  IL        ...                      Banterra Bank of Marion  December 14, 2000     March 17, 2005
 504                          Bank of Honolulu      Honolulu  HI        ...                           Bank of the Orient   October 13, 2000     March 17, 2005

 [505 rows x 7 columns]]

Note

The following examples are not run by the IPython evaluator due to the
fact that having so many network-accessing functions slows down the
documentation build. If you spot an error or an example that doesn’t
run, please do not hesitate to report it over on
pandas GitHub issues page.

Read a URL and match a table that contains specific text:

match = 'Metcalf Bank'
df_list = pd.read_html(url, match=match)

Specify a header row (by default <th> or <td> elements located
within a <thead> are used to form the column index, if multiple rows
are contained within <thead> then a multiindex is created); if
specified, the header row is taken from the data minus the parsed header
elements (<th> elements).

dfs = pd.read_html(url, header=0)

Specify an index column:

dfs = pd.read_html(url, index_col=0)

Specify a number of rows to skip:

dfs = pd.read_html(url, skiprows=0)

Specify a number of rows to skip using a list (xrange (Python 2 only)
works as well):

dfs = pd.read_html(url, skiprows=range(2))

Specify an HTML attribute:

dfs1 = pd.read_html(url, attrs={'id': 'table'})
dfs2 = pd.read_html(url, attrs={'class': 'sortable'})
print(np.array_equal(dfs1[0], dfs2[0]))  # Should be True

Specify values that should be converted to NaN:

dfs = pd.read_html(url, na_values=['No Acquirer'])

New in version 0.19.

Specify whether to keep the default set of NaN values:

dfs = pd.read_html(url, keep_default_na=False)

New in version 0.19.

Specify converters for columns. This is useful for numerical text data
that has leading zeros. By default columns that are numerical are cast
to numeric types and the leading zeros are lost. To avoid this, we can
convert these columns to strings.

url_mcc = 'https://en.wikipedia.org/wiki/Mobile_country_code'
dfs = pd.read_html(url_mcc, match='Telekom Albania', header=0, converters={'MNC':
str})

New in version 0.19.

Use some combination of the above:

dfs = pd.read_html(url, match='Metcalf Bank', index_col=0)

Read in pandas to_html output (with some loss of floating point
precision):

df = pd.DataFrame(randn(2, 2))
s = df.to_html(float_format='{0:.40g}'.format)
dfin = pd.read_html(s, index_col=0)

The lxml backend will raise an error on a failed parse if that is the
only parser you provide. If you only have a single parser you can
provide just a string, but it is considered good practice to pass a list
with one string if, for example, the function expects a sequence of
strings. You may use:

dfs = pd.read_html(url, 'Metcalf Bank', index_col=0, flavor=['lxml'])

Or you could pass flavor='lxml' without a list:

dfs = pd.read_html(url, 'Metcalf Bank', index_col=0, flavor='lxml')

However, if you have bs4 and html5lib installed and pass None or
['lxml', 'bs4'] then the parse will most likely succeed. Note that as
soon as a parse succeeds, the function will return.

dfs = pd.read_html(url, 'Metcalf Bank', index_col=0, flavor=['lxml', 'bs4'])

Writing to HTML files¶

DataFrame objects have an instance method to_html which renders the
contents of the DataFrame as an HTML table. The function arguments are
as in the method to_string described above.

Note

Not all of the possible options for DataFrame.to_html are shown here
for brevity’s sake. See to_html() for the full set of options.

In[290]: df = pd.DataFrame(randn(2, 2))

In[291]: df
Out[291]: 
          0         1
0 -0.184744  0.496971
1 -0.856240  1.857977

In[292]: print(df.to_html())  # raw html
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>0</th>
      <th>1</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>-0.184744</td>
      <td>0.496971</td>
    </tr>
    <tr>
      <th>1</th>
      <td>-0.856240</td>
      <td>1.857977</td>
    </tr>
  </tbody>
</table>

HTML:

	0	1
0	-0.184744	0.496971
1	-0.856240	1.857977

The columns argument will limit the columns shown:

In[293]: print(df.to_html(columns=[0]))
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>0</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>-0.184744</td>
    </tr>
    <tr>
      <th>1</th>
      <td>-0.856240</td>
    </tr>
  </tbody>
</table>

HTML:

	0
0	-0.184744
1	-0.856240

float_format takes a Python callable to control the precision of
floating point values:

In[294]: print(df.to_html(float_format='{0:.10f}'.format))
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>0</th>
      <th>1</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>-0.1847438576</td>
      <td>0.4969711327</td>
    </tr>
    <tr>
      <th>1</th>
      <td>-0.8562396763</td>
      <td>1.8579766508</td>
    </tr>
  </tbody>
</table>

HTML:

	0	1
0	-0.1847438576	0.4969711327
1	-0.8562396763	1.8579766508

bold_rows will make the row labels bold by default, but you can turn
that off:

In[295]: print(df.to_html(bold_rows=False))
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>0</th>
      <th>1</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>0</td>
      <td>-0.184744</td>
      <td>0.496971</td>
    </tr>
    <tr>
      <td>1</td>
      <td>-0.856240</td>
      <td>1.857977</td>
    </tr>
  </tbody>
</table>

	0	1
0	-0.184744	0.496971
1	-0.856240	1.857977

The classes argument provides the ability to give the resulting HTML
table CSS classes. Note that these classes are appended to the
existing 'dataframe' class.

In[296]: print(df.to_html(classes=['awesome_table_class', 'even_more_awesome_class']))
<table border="1" class="dataframe awesome_table_class even_more_awesome_class">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>0</th>
      <th>1</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>-0.184744</td>
      <td>0.496971</td>
    </tr>
    <tr>
      <th>1</th>
      <td>-0.856240</td>
      <td>1.857977</td>
    </tr>
  </tbody>
</table>

Finally, the escape argument allows you to control whether the “<”,
“>” and “&” characters escaped in the resulting HTML (by default it
is True). So to get the HTML without escaped characters pass
escape=False

In[297]: df = pd.DataFrame({'a': list('&<>'), 'b': randn(3)})

Escaped:

In[298]: print(df.to_html())
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>a</th>
      <th>b</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>&amp;</td>
      <td>-0.474063</td>
    </tr>
    <tr>
      <th>1</th>
      <td>&lt;</td>
      <td>-0.230305</td>
    </tr>
    <tr>
      <th>2</th>
      <td>&gt;</td>
      <td>-0.400654</td>
    </tr>
  </tbody>
</table>

	a	b
0	&	-0.474063
1	<	-0.230305
2	>	-0.400654

Not escaped:

In[299]: print(df.to_html(escape=False))
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>a</th>
      <th>b</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>&</td>
      <td>-0.474063</td>
    </tr>
    <tr>
      <th>1</th>
      <td><</td>
      <td>-0.230305</td>
    </tr>
    <tr>
      <th>2</th>
      <td>></td>
      <td>-0.400654</td>
    </tr>
  </tbody>
</table>

	a	b
0	&	-0.474063
1	<	-0.230305
2	>	-0.400654

Note

Some browsers may not show a difference in the rendering of the previous
two HTML tables.

HTML Table Parsing Gotchas¶

There are some versioning issues surrounding the libraries that are used
to parse HTML tables in the top-level pandas io function read_html.

Issues with
lxml

Benefits

lxml is very fast.

lxml requires Cython to install correctly.

Drawbacks

lxml does not make any guarantees about the results of its parse unless it is given strictly valid markup.

In light of the above, we have chosen to allow you, the user, to use the lxml backend, but this backend will use html5lib if lxml fails to parse

It is therefore highly recommended that you install both BeautifulSoup4 and html5lib, so that you will still get a valid result (provided everything else is valid) even if lxml fails.

Issues with
BeautifulSoup4
using
lxml
as a backend

The above issues hold here as well since BeautifulSoup4 is essentially just a wrapper around a parser backend.

Issues with
BeautifulSoup4
using
html5lib
as a backend

Benefits

html5lib is far more lenient than lxml and consequently deals with real-life markup in a much saner way rather than just, e.g., dropping an element without notifying you.

html5lib generates valid HTML5 markup from invalid markup automatically. This is extremely important for parsing HTML tables, since it guarantees a valid document. However, that does NOT mean that it is “correct”, since the process of fixing markup does not have a single definition.

html5lib is pure Python and requires no additional build steps beyond its own installation.

Drawbacks

The biggest drawback to using html5lib is that it is slow as molasses. However consider the fact that many tables on the web are not big enough for the parsing algorithm runtime to matter. It is more likely that the bottleneck will be in the process of reading the raw text from the URL over the web, i.e., IO (input-output). For very large tables, this might not be true.

Excel files¶

The
read_excel()
method can read Excel 2003 (.xls) and Excel 2007+ (.xlsx) files
using the xlrd Python module. The
to_excel()
instance method is used for saving a DataFrame to Excel. Generally the
semantics are similar to working with
csv
data. See the
cookbook
for some advanced strategies.

Reading Excel Files¶

In the most basic use-case, read_excel takes a path to an Excel file,
and the sheet_name indicating which sheet to parse.

# Returns a DataFrame
read_excel('path_to_file.xls', sheet_name='Sheet1')

`ExcelFile` class¶

To facilitate working with multiple sheets from the same file, the
ExcelFile class can be used to wrap the file and can be passed into
read_excel There will be a performance benefit for reading multiple
sheets as the file is read into memory only once.

xlsx = pd.ExcelFile('path_to_file.xls')
df = pd.read_excel(xlsx, 'Sheet1')

The ExcelFile class can also be used as a context manager.

with pd.ExcelFile('path_to_file.xls') as xls:
    df1 = pd.read_excel(xls, 'Sheet1')
    df2 = pd.read_excel(xls, 'Sheet2')

The sheet_names property will generate a list of the sheet names in
the file.

The primary use-case for an ExcelFile is parsing multiple sheets with
different parameters:

data = {}
# For when Sheet1's format differs from Sheet2
with pd.ExcelFile('path_to_file.xls') as xls:
    data['Sheet1'] = pd.read_excel(xls, 'Sheet1', index_col=None, na_values=['NA'])
    data['Sheet2'] = pd.read_excel(xls, 'Sheet2', index_col=1)

Note that if the same parsing parameters are used for all sheets, a list
of sheet names can simply be passed to read_excel with no loss in
performance.

# using the ExcelFile class
data = {}
with pd.ExcelFile('path_to_file.xls') as xls:
    data['Sheet1'] = read_excel(xls, 'Sheet1', index_col=None, na_values=['NA'])
    data['Sheet2'] = read_excel(xls, 'Sheet2', index_col=None, na_values=['NA'])

# equivalent using the read_excel function
data = read_excel('path_to_file.xls', ['Sheet1', 'Sheet2'], index_col=None, na_values=['NA'])

Specifying Sheets¶

Note

The second argument is sheet_name, not to be confused with
ExcelFile.sheet_names.

Note

An ExcelFile’s attribute sheet_names provides access to a list of
sheets.

The arguments sheet_name allows specifying the sheet or sheets to

read.
The default value for sheet_name is 0, indicating to read the

first sheet
Pass a string to refer to the name of a particular sheet in the
workbook.
Pass an integer to refer to the index of a sheet. Indices follow
Python convention, beginning at 0.
Pass a list of either strings or integers, to return a dictionary of
specified sheets.
Pass a None to return a dictionary of all available sheets.

Returns a DataFrame

read_excel('path_to_file.xls', 'Sheet1', index_col=None, na_values=['NA'])

Using the sheet index:

# Returns a DataFrame
read_excel('path_to_file.xls', 0, index_col=None, na_values=['NA'])

Using all default values:

# Returns a DataFrame
read_excel('path_to_file.xls')

Using None to get all sheets:

# Returns a dictionary of DataFrames
read_excel('path_to_file.xls', sheet_name=None)

Using a list to get multiple sheets:

# Returns the 1st and 4th sheet, as a dictionary of DataFrames.
read_excel('path_to_file.xls', sheet_name=['Sheet1', 3])

read_excel can read more than one sheet, by setting sheet_name to
either a list of sheet names, a list of sheet positions, or None to
read all sheets. Sheets can be specified by sheet index or sheet name,
using an integer or string, respectively.

Reading a `MultiIndex`¶

read_excel can read a MultiIndex index, by passing a list of columns
to index_col and a MultiIndex column by passing a list of rows to
header. If either the index or columns have serialized level names
those will be read in as well by specifying the rows/columns that make
up the levels.

For example, to read in a MultiIndex index without names:

In[300]: df = pd.DataFrame({'a':[1, 2, 3, 4], 'b':[5, 6, 7, 8]},
   .....:                   index=pd.MultiIndex.from_product([['a', 'b'],['c', 'd']]))
   .....: 

In[301]: df.to_excel('path_to_file.xlsx')

In[302]: df = pd.read_excel('path_to_file.xlsx', index_col=[0, 1])

In[303]: df
Out[303]: 
     a  b
a c  1  5
  d  2  6
b c  3  7
  d  4  8

If the index has level names, they will parsed as well, using the same
parameters.

In[304]: df.index = df.index.set_names(['lvl1', 'lvl2'])

In[305]: df.to_excel('path_to_file.xlsx')

In[306]: df = pd.read_excel('path_to_file.xlsx', index_col=[0, 1])

In[307]: df
Out[307]: 
           a  b
lvl1 lvl2      
a    c     1  5
     d     2  6
b    c     3  7
     d     4  8

If the source file has both MultiIndex index and columns, lists
specifying each should be passed to index_col and header:

In[308]: df.columns = pd.MultiIndex.from_product([['a'], ['b', 'd']], names=['c1', 'c2'])

In[309]: df.to_excel('path_to_file.xlsx')

In[310]: df = pd.read_excel('path_to_file.xlsx', index_col=[0, 1], header=[0, 1])

In[311]: df
Out[311]: 
c1         a   
c2         b  d
lvl1 lvl2      
a    c     1  5
     d     2  6
b    c     3  7
     d     4  8

Parsing Specific Columns¶

It is often the case that users will insert columns to do temporary
computations in Excel and you may not want to read in those columns.
read_excel takes a usecols keyword to allow you to specify a subset
of columns to parse.

If usecols is an integer, then it is assumed to indicate the last
column to be parsed.

read_excel('path_to_file.xls', 'Sheet1', usecols=2)

If usecols is a list of integers, then it is assumed to be the file
column indices to be parsed.

read_excel('path_to_file.xls', 'Sheet1', usecols=[0, 2, 3])

Element order is ignored, so usecols=[0, 1] is the same as [1, 0].

Parsing Dates¶

Datetime-like values are normally automatically converted to the
appropriate dtype when reading the excel file. But if you have a column
of strings that look like dates (but are not actually formatted as
dates in excel), you can use the parse_dates keyword to parse those
strings to datetimes:

read_excel('path_to_file.xls', 'Sheet1', parse_dates=['date_strings'])

Cell Converters¶

It is possible to transform the contents of Excel cells via the
converters option. For instance, to convert a column to boolean:

read_excel('path_to_file.xls', 'Sheet1', converters={'MyBools': bool})

This options handles missing values and treats exceptions in the
converters as missing data. Transformations are applied cell by cell
rather than to the column as a whole, so the array dtype is not
guaranteed. For instance, a column of integers with missing values
cannot be transformed to an array with integer dtype, because NaN is
strictly a float. You can manually mask missing data to recover integer
dtype:

cfun = lambda x: int(x) if x else -1
read_excel('path_to_file.xls', 'Sheet1', converters={'MyInts': cfun})

dtype Specifications¶

New in version 0.20.

As an alternative to converters, the type for an entire column can be
specified using the dtype keyword, which takes a dictionary mapping
column names to types. To interpret data with no type inference, use the
type str or object.

read_excel('path_to_file.xls', dtype={'MyInts': 'int64', 'MyText': str})

Writing Excel Files¶

Writing Excel Files to Disk¶

To write a DataFrame object to a sheet of an Excel file, you can use
the to_excel instance method. The arguments are largely the same as
to_csv described above, the first argument being the name of the excel
file, and the optional second argument the name of the sheet to which
the DataFrame should be written. For example:

df.to_excel('path_to_file.xlsx', sheet_name='Sheet1')

Files with a .xls extension will be written using xlwt and those
with a .xlsx extension will be written using xlsxwriter (if
available) or openpyxl.

The DataFrame will be written in a way that tries to mimic the REPL
output. The index_label will be placed in the second row instead of
the first. You can place it in the first row by setting the
merge_cells option in to_excel() to False:

df.to_excel('path_to_file.xlsx', index_label='label', merge_cells=False)

In order to write separate DataFrames to separate sheets in a single
Excel file, one can pass an ExcelWriter.

with ExcelWriter('path_to_file.xlsx') as writer:
    df1.to_excel(writer, sheet_name='Sheet1')
    df2.to_excel(writer, sheet_name='Sheet2')

Note

Wringing a little more performance out of read_excel Internally, Excel
stores all numeric data as floats. Because this can produce unexpected
behavior when reading in data, pandas defaults to trying to convert
integers to floats if it doesn’t lose information (1.0 --> 1). You can
pass convert_float=False to disable this behavior, which may give a
slight performance improvement.

Writing Excel Files to Memory¶

Pandas supports writing Excel files to buffer-like objects such as
StringIO or BytesIO using ExcelWriter.

# Safe import for either Python 2.x or 3.x
try:
    from io import BytesIO
except ImportError:
    from cStringIO import StringIO as BytesIO

bio = BytesIO()

# By setting the 'engine' in the ExcelWriter constructor.
writer = ExcelWriter(bio, engine='xlsxwriter')
df.to_excel(writer, sheet_name='Sheet1')

# Save the workbook
writer.save()

# Seek to the beginning and read to copy the workbook to a variable in memory
bio.seek(0)
workbook = bio.read()

Note

engine is optional but recommended. Setting the engine determines the
version of workbook produced. Setting engine='xlrd' will produce an
Excel 2003-format workbook (xls). Using either 'openpyxl' or
'xlsxwriter' will produce an Excel 2007-format workbook (xlsx). If
omitted, an Excel 2007-formatted workbook is produced.

Excel writer engines¶

Pandas chooses an Excel writer via two methods:

the engine keyword argument
the filename extension (via the default specified in config options)

By default, pandas uses the
XlsxWriter
for .xlsx,
openpyxl
for .xlsm, and
xlwt
for .xls files. If you have multiple engines installed, you can set
the default engine through
setting the config options
io.excel.xlsx.writer and io.excel.xls.writer. pandas will fall back
on
openpyxl
for .xlsx files if
Xlsxwriter
is not available.

To specify which writer you want to use, you can pass an engine keyword
argument to to_excel and to ExcelWriter. The built-in engines are:

openpyxl: version 2.4 or higher is required
xlsxwriter
xlwt

By setting the 'engine' in the DataFrame and Panel 'to_excel()' methods.

df.to_excel('path_to_file.xlsx', sheet_name='Sheet1', engine='xlsxwriter')

By setting the 'engine' in the ExcelWriter constructor.

writer = ExcelWriter('path_to_file.xlsx', engine='xlsxwriter')

Or via pandas configuration.

from pandas import options
options.io.excel.xlsx.writer = 'xlsxwriter'

df.to_excel('path_to_file.xlsx', sheet_name='Sheet1')

Style and Formatting¶

The look and feel of Excel worksheets created from pandas can be
modified using the following parameters on the DataFrame’s to_excel
method.

float_format : Format string for floating point numbers (default

None).
freeze_panes : A tuple of two integers representing the bottommost

row and rightmost column to freeze. Each of these parameters is
one-based, so (1, 1) will freeze the first row and first column
(default None).

Clipboard¶

A handy way to grab data is to use the read_clipboard() method, which
takes the contents of the clipboard buffer and passes them to the
read_table method. For instance, you can copy the following text to
the clipboard (CTRL-C on many operating systems):

  A B C
x 1 4 p
y 2 5 q
z 3 6 r

And then import the data directly to a DataFrame by calling:

clipdf = pd.read_clipboard()

In[312]: clipdf
Out[312]: 
   A  B  C
x  1  4  p
y  2  5  q
z  3  6  r

The to_clipboard method can be used to write the contents of a
DataFrame to the clipboard. Following which you can paste the
clipboard contents into other applications (CTRL-V on many operating
systems). Here we illustrate writing a DataFrame into clipboard and
reading it back.

In[313]: df = pd.DataFrame(randn(5, 3))

In[314]: df
Out[314]: 
          0         1         2
0 -0.288267 -0.084905  0.004772
1  1.382989  0.343635 -1.253994
2 -0.124925  0.212244  0.496654
3  0.525417  1.238640 -1.210543
4 -1.175743 -0.172372 -0.734129

In[315]: df.to_clipboard()

In[316]: pd.read_clipboard()
Out[316]: 
          0         1         2
0 -0.288267 -0.084905  0.004772
1  1.382989  0.343635 -1.253994
2 -0.124925  0.212244  0.496654
3  0.525417  1.238640 -1.210543
4 -1.175743 -0.172372 -0.734129

We can see that we got the same content back, which we had earlier
written to the clipboard.

Note

You may need to install xclip or xsel (with gtk, PyQt5, PyQt4 or qtpy)
on Linux to use these methods.

Pickling¶

All pandas objects are equipped with to_pickle methods which use
Python’s cPickle module to save data structures to disk using the
pickle format.

In[317]: df
Out[317]: 
          0         1         2
0 -0.288267 -0.084905  0.004772
1  1.382989  0.343635 -1.253994
2 -0.124925  0.212244  0.496654
3  0.525417  1.238640 -1.210543
4 -1.175743 -0.172372 -0.734129

In[318]: df.to_pickle('foo.pkl')

The read_pickle function in the pandas namespace can be used to load
any pickled pandas object (or any other pickled object) from file:

In[319]: pd.read_pickle('foo.pkl')
Out[319]: 
          0         1         2
0 -0.288267 -0.084905  0.004772
1  1.382989  0.343635 -1.253994
2 -0.124925  0.212244  0.496654
3  0.525417  1.238640 -1.210543
4 -1.175743 -0.172372 -0.734129

Warning

Loading pickled data received from untrusted sources can be unsafe.

See:
https://docs.python.org/3/library/pickle.html

Warning

Several internal refactorings have been done while still preserving
compatibility with pickles created with older versions of pandas.
However, for such cases, pickled DataFrames, Series etc, must be
read with pd.read_pickle, rather than pickle.load.

See
here
and
here
for some examples of compatibility-breaking changes. See
this question
for a detailed explanation.

Compressed pickle files¶

New in version 0.20.0.

read_pickle(),
DataFrame.to_pickle()
and
Series.to_pickle()
can read and write compressed pickle files. The compression types of
gzip, bz2, xz are supported for reading and writing. The zip
file format only supports reading and must contain only one data file to
be read.

The compression type can be an explicit parameter or be inferred from
the file extension. If ‘infer’, then use gzip, bz2, zip, or xz
if filename ends in '.gz', '.bz2', '.zip', or '.xz',
respectively.

In[320]: df = pd.DataFrame({
   .....:     'A': np.random.randn(1000),
   .....:     'B': 'foo',
   .....:     'C': pd.date_range('20130101', periods=1000, freq='s')})
   .....: 

In[321]: df
Out[321]: 
            A    B                   C
0    0.478412  foo 2013-01-01 00:00:00
1   -0.783748  foo 2013-01-01 00:00:01
2    1.403558  foo 2013-01-01 00:00:02
3   -0.539282  foo 2013-01-01 00:00:03
4   -1.651012  foo 2013-01-01 00:00:04
5    0.692072  foo 2013-01-01 00:00:05
6    1.022171  foo 2013-01-01 00:00:06
..        ...  ...                 ...
993 -1.613932  foo 2013-01-01 00:16:33
994  1.088104  foo 2013-01-01 00:16:34
995 -0.632963  foo 2013-01-01 00:16:35
996 -0.585314  foo 2013-01-01 00:16:36
997 -0.275038  foo 2013-01-01 00:16:37
998 -0.937512  foo 2013-01-01 00:16:38
999  0.632369  foo 2013-01-01 00:16:39

[1000 rows x 3 columns]

Using an explicit compression type:

In[322]: df.to_pickle("data.pkl.compress", compression="gzip")

In[323]: rt = pd.read_pickle("data.pkl.compress", compression="gzip")

In[324]: rt
Out[324]: 
            A    B                   C
0    0.478412  foo 2013-01-01 00:00:00
1   -0.783748  foo 2013-01-01 00:00:01
2    1.403558  foo 2013-01-01 00:00:02
3   -0.539282  foo 2013-01-01 00:00:03
4   -1.651012  foo 2013-01-01 00:00:04
5    0.692072  foo 2013-01-01 00:00:05
6    1.022171  foo 2013-01-01 00:00:06
..        ...  ...                 ...
993 -1.613932  foo 2013-01-01 00:16:33
994  1.088104  foo 2013-01-01 00:16:34
995 -0.632963  foo 2013-01-01 00:16:35
996 -0.585314  foo 2013-01-01 00:16:36
997 -0.275038  foo 2013-01-01 00:16:37
998 -0.937512  foo 2013-01-01 00:16:38
999  0.632369  foo 2013-01-01 00:16:39

[1000 rows x 3 columns]

Inferring compression type from the extension:

In[325]: df.to_pickle("data.pkl.xz", compression="infer")

In[326]: rt = pd.read_pickle("data.pkl.xz", compression="infer")

In[327]: rt
Out[327]: 
            A    B                   C
0    0.478412  foo 2013-01-01 00:00:00
1   -0.783748  foo 2013-01-01 00:00:01
2    1.403558  foo 2013-01-01 00:00:02
3   -0.539282  foo 2013-01-01 00:00:03
4   -1.651012  foo 2013-01-01 00:00:04
5    0.692072  foo 2013-01-01 00:00:05
6    1.022171  foo 2013-01-01 00:00:06
..        ...  ...                 ...
993 -1.613932  foo 2013-01-01 00:16:33
994  1.088104  foo 2013-01-01 00:16:34
995 -0.632963  foo 2013-01-01 00:16:35
996 -0.585314  foo 2013-01-01 00:16:36
997 -0.275038  foo 2013-01-01 00:16:37
998 -0.937512  foo 2013-01-01 00:16:38
999  0.632369  foo 2013-01-01 00:16:39

[1000 rows x 3 columns]

The default is to ‘infer’:

In[328]: df.to_pickle("data.pkl.gz")

In[329]: rt = pd.read_pickle("data.pkl.gz")

In[330]: rt
Out[330]: 
            A    B                   C
0    0.478412  foo 2013-01-01 00:00:00
1   -0.783748  foo 2013-01-01 00:00:01
2    1.403558  foo 2013-01-01 00:00:02
3   -0.539282  foo 2013-01-01 00:00:03
4   -1.651012  foo 2013-01-01 00:00:04
5    0.692072  foo 2013-01-01 00:00:05
6    1.022171  foo 2013-01-01 00:00:06
..        ...  ...                 ...
993 -1.613932  foo 2013-01-01 00:16:33
994  1.088104  foo 2013-01-01 00:16:34
995 -0.632963  foo 2013-01-01 00:16:35
996 -0.585314  foo 2013-01-01 00:16:36
997 -0.275038  foo 2013-01-01 00:16:37
998 -0.937512  foo 2013-01-01 00:16:38
999  0.632369  foo 2013-01-01 00:16:39

[1000 rows x 3 columns]

In[331]: df["A"].to_pickle("s1.pkl.bz2")

In[332]: rt = pd.read_pickle("s1.pkl.bz2")

In[333]: rt
Out[333]: 
0      0.478412
1     -0.783748
2      1.403558
3     -0.539282
4     -1.651012
5      0.692072
6      1.022171
         ...   
993   -1.613932
994    1.088104
995   -0.632963
996   -0.585314
997   -0.275038
998   -0.937512
999    0.632369
Name: A, Length: 1000, dtype: float64

msgpack¶

pandas supports the msgpack format for object serialization. This is a
lightweight portable binary format, similar to binary JSON, that is
highly space efficient, and provides good performance both on the
writing (serialization), and reading (deserialization).

Warning

This is a very new feature of pandas. We intend to provide certain
optimizations in the io of the msgpack data. Since this is marked as
an EXPERIMENTAL LIBRARY, the storage format may not be stable until a
future release.

In[334]: df = pd.DataFrame(np.random.rand(5, 2), columns=list('AB'))

In[335]: df.to_msgpack('foo.msg')

In[336]: pd.read_msgpack('foo.msg')
Out[336]: 
          A         B
0  0.170801  0.895366
1  0.838238  0.052592
2  0.664140  0.289750
3  0.449593  0.872087
4  0.983618  0.744359

In[337]: s = pd.Series(np.random.rand(5), index=pd.date_range('20130101', periods=5))

You can pass a list of objects and you will receive them back on
deserialization.

In[338]: pd.to_msgpack('foo.msg', df, 'foo', np.array([1, 2, 3]), s)

In[339]: pd.read_msgpack('foo.msg')
Out[339]: 
[          A         B
 0  0.170801  0.895366
 1  0.838238  0.052592
 2  0.664140  0.289750
 3  0.449593  0.872087
 4  0.983618  0.744359, 'foo', array([1, 2, 3]), 2013-01-01    0.548134
 2013-01-02    0.503447
 2013-01-03    0.348438
 2013-01-04    0.707267
 2013-01-05    0.261656
 Freq: D, dtype: float64]

You can pass iterator=True to iterate over the unpacked results:

In[340]: for o in pd.read_msgpack('foo.msg', iterator=True):
   .....:     print(o)
   .....: 
          A         B
0  0.170801  0.895366
1  0.838238  0.052592
2  0.664140  0.289750
3  0.449593  0.872087
4  0.983618  0.744359
foo
[1 2 3]
2013-01-01    0.548134
2013-01-02    0.503447
2013-01-03    0.348438
2013-01-04    0.707267
2013-01-05    0.261656
Freq: D, dtype: float64

You can pass append=True to the writer to append to an existing pack:

In[341]: df.to_msgpack('foo.msg', append=True)

In[342]: pd.read_msgpack('foo.msg')
Out[342]: 
[          A         B
 0  0.170801  0.895366
 1  0.838238  0.052592
 2  0.664140  0.289750
 3  0.449593  0.872087
 4  0.983618  0.744359, 'foo', array([1, 2, 3]), 2013-01-01    0.548134
 2013-01-02    0.503447
 2013-01-03    0.348438
 2013-01-04    0.707267
 2013-01-05    0.261656
 Freq: D, dtype: float64,           A         B
 0  0.170801  0.895366
 1  0.838238  0.052592
 2  0.664140  0.289750
 3  0.449593  0.872087
 4  0.983618  0.744359]

Unlike other io methods, to_msgpack is available on both a per-object
basis, df.to_msgpack() and using the top-level pd.to_msgpack(...)
where you can pack arbitrary collections of Python lists, dicts,
scalars, while intermixing pandas objects.

In[343]: pd.to_msgpack('foo2.msg', {'dict': [{ 'df': df }, {'string': 'foo'},
   .....:                                     {'scalar': 1.}, {'s': s}]})
   .....: 

In[344]: pd.read_msgpack('foo2.msg')
Out[344]: 
{'dict': ({'df':           A         B
   0  0.170801  0.895366
   1  0.838238  0.052592
   2  0.664140  0.289750
   3  0.449593  0.872087
   4  0.983618  0.744359},
  {'string': 'foo'},
  {'scalar': 1.0},
  {'s': 2013-01-01    0.548134
   2013-01-02    0.503447
   2013-01-03    0.348438
   2013-01-04    0.707267
   2013-01-05    0.261656
   Freq: D, dtype: float64})}

Read/Write API¶

Msgpacks can also be read from and written to strings.

In[345]: df.to_msgpack()
Out[345]: b'\x84\xa3typ\xadblock_manager\xa5klass\xa9DataFrame\xa4axes\x92\x86\xa3typ\xa5index\xa5klass\xa5Index\xa4name\xc0\xa5dtype\xa6object\xa4data\x92\xa1A\xa1B\xa8compress\xc0\x86\xa3typ\xabrange_index\xa5klass\xaaRangeIndex\xa4name\xc0\xa5start\x00\xa4stop\x05\xa4step\x01\xa6blocks\x91\x86\xa4locs\x86\xa3typ\xa7ndarray\xa5shape\x91\x02\xa4ndim\x01\xa5dtype\xa5int64\xa4data\xd8\x00\x00\x00\x00\x00\x00\x00\x00\x00\x01\x00\x00\x00\x00\x00\x00\x00\xa8compress\xc0\xa6values\xc7P\x00<\xfd\xd2f\xcf\xdc\xc5?0\x15\xebN\xd9\xd2\xea?,\x9c\x16A\xa2@\xe5?\xd8/\xdd\xf4"\xc6\xdc?\x11\x1e\x97\x1b\xcdy\xef?&\x1e<\xee\xd6\xa6\xec?p\xd3;\xb2N\xed\xaa?h\xcb\xb1\xbdB\x8b\xd2?\xaf4\x01r"\xe8\xeb?)G6\xd9\xc9\xd1\xe7?\xa5shape\x92\x02\x05\xa5dtype\xa7float64\xa5klass\xaaFloatBlock\xa8compress\xc0'

Furthermore you can concatenate the strings to produce a list of the
original objects.

In[346]: pd.read_msgpack(df.to_msgpack() + s.to_msgpack())
Out[346]: 
[          A         B
 0  0.170801  0.895366
 1  0.838238  0.052592
 2  0.664140  0.289750
 3  0.449593  0.872087
 4  0.983618  0.744359, 2013-01-01    0.548134
 2013-01-02    0.503447
 2013-01-03    0.348438
 2013-01-04    0.707267
 2013-01-05    0.261656
 Freq: D, dtype: float64]

HDF5 (PyTables)¶

HDFStore is a dict-like object which reads and writes pandas using the
high performance HDF5 format using the excellent
PyTables
library. See the
cookbook
for some advanced strategies

Warning

pandas requires PyTables >= 3.0.0. There is a indexing bug in
PyTables < 3.2 which may appear when querying stores using an
index. If you see a subset of results being returned, upgrade to
PyTables >= 3.2. Stores created previously will need to be
rewritten using the updated version.

In[347]: store = pd.HDFStore('store.h5')

In[348]: print(store)
<class 'pandas.io.pytables.HDFStore'>
File path: store.h5

Objects can be written to the file just like adding key-value pairs to a
dict:

In[349]: np.random.seed(1234)

In[350]: index = pd.date_range('1/1/2000', periods=8)

In[351]: s = pd.Series(randn(5), index=['a', 'b', 'c', 'd', 'e'])

In[352]: df = pd.DataFrame(randn(8, 3), index=index,
   .....:                   columns=['A', 'B', 'C'])
   .....: 

In[353]: wp = pd.Panel(randn(2, 5, 4), items=['Item1', 'Item2'],
   .....:               major_axis=pd.date_range('1/1/2000', periods=5),
   .....:               minor_axis=['A', 'B', 'C', 'D'])
   .....: 

# store.put('s', s) is an equivalent method
In[354]: store['s'] = s

In[355]: store['df'] = df

In[356]: store['wp'] = wp

# the type of stored data
In[357]: store.root.wp._v_attrs.pandas_type
Out[357]: 'wide'

In[358]: store
Out[358]: 
<class 'pandas.io.pytables.HDFStore'>
File path: store.h5

In a current or later Python session, you can retrieve stored objects:

# store.get('df') is an equivalent method
In[359]: store['df']
Out[359]: 
                   A         B         C
2000-01-01  0.887163  0.859588 -0.636524
2000-01-02  0.015696 -2.242685  1.150036
2000-01-03  0.991946  0.953324 -2.021255
2000-01-04 -0.334077  0.002118  0.405453
2000-01-05  0.289092  1.321158 -1.546906
2000-01-06 -0.202646 -0.655969  0.193421
2000-01-07  0.553439  1.318152 -0.469305
2000-01-08  0.675554 -1.817027 -0.183109

# dotted (attribute) access provides get as well
In[360]: store.df
Out[360]: 
                   A         B         C
2000-01-01  0.887163  0.859588 -0.636524
2000-01-02  0.015696 -2.242685  1.150036
2000-01-03  0.991946  0.953324 -2.021255
2000-01-04 -0.334077  0.002118  0.405453
2000-01-05  0.289092  1.321158 -1.546906
2000-01-06 -0.202646 -0.655969  0.193421
2000-01-07  0.553439  1.318152 -0.469305
2000-01-08  0.675554 -1.817027 -0.183109

Deletion of the object specified by the key:

# store.remove('wp') is an equivalent method
In[361]: del store['wp']

In[362]: store
Out[362]: 
<class 'pandas.io.pytables.HDFStore'>
File path: store.h5

Closing a Store and using a context manager:

In[363]: store.close()

In[364]: store
Out[364]: 
<class 'pandas.io.pytables.HDFStore'>
File path: store.h5

In[365]: store.is_open
Out[365]: False

# Working with, and automatically closing the store using a context manager
In[366]: with pd.HDFStore('store.h5') as store:
   .....:     store.keys()
   .....:

Read/Write API¶

HDFStore supports an top-level API using read_hdf for reading and
to_hdf for writing, similar to how read_csv and to_csv work.

In[367]: df_tl = pd.DataFrame(dict(A=list(range(5)), B=list(range(5))))

In[368]: df_tl.to_hdf('store_tl.h5','table', append=True)

In[369]: pd.read_hdf('store_tl.h5', 'table', where=['index>2'])
Out[369]: 
   A  B
3  3  3
4  4  4

HDFStore will by default not drop rows that are all missing. This
behavior can be changed by setting dropna=True.

In[370]: df_with_missing = pd.DataFrame({'col1': [0, np.nan, 2],
   .....:                                 'col2': [1, np.nan, np.nan]})
   .....: 

In[371]: df_with_missing
Out[371]: 
   col1  col2
0   0.0   1.0
1   NaN   NaN
2   2.0   NaN

In[372]: df_with_missing.to_hdf('file.h5', 'df_with_missing',
   .....:                         format='table', mode='w')
   .....: 

In[373]: pd.read_hdf('file.h5', 'df_with_missing')
Out[373]: 
   col1  col2
0   0.0   1.0
1   NaN   NaN
2   2.0   NaN

In[374]: df_with_missing.to_hdf('file.h5', 'df_with_missing',
   .....:                         format='table', mode='w', dropna=True)
   .....: 

In[375]: pd.read_hdf('file.h5', 'df_with_missing')
Out[375]: 
   col1  col2
0   0.0   1.0
2   2.0   NaN

This is also true for the major axis of a Panel:

In[376]: matrix = [[[np.nan, np.nan, np.nan], [1, np.nan, np.nan]],
   .....:          [[np.nan, np.nan, np.nan], [np.nan, 5, 6]],
   .....:          [[np.nan, np.nan, np.nan], [np.nan, 3, np.nan]]]
   .....: 

In[377]: panel_with_major_axis_all_missing=pd.Panel(matrix,
   .....:         items=['Item1', 'Item2', 'Item3'],
   .....:         major_axis=[1, 2],
   .....:         minor_axis=['A', 'B', 'C'])
   .....: 

In[378]: panel_with_major_axis_all_missing
Out[378]: 
<class 'pandas.core.panel.Panel'>
Dimensions: 3 (items) x 2 (major_axis) x 3 (minor_axis)
Items axis: Item1 to Item3
Major_axis axis: 1 to 2
Minor_axis axis: A to C

In[379]: panel_with_major_axis_all_missing.to_hdf('file.h5', 'panel',
   .....:                                          dropna=True,
   .....:                                          format='table',
   .....:                                          mode='w')
   .....: 

In[380]: reloaded = pd.read_hdf('file.h5', 'panel')

In[381]: reloaded
Out[381]: 
<class 'pandas.core.panel.Panel'>
Dimensions: 3 (items) x 1 (major_axis) x 3 (minor_axis)
Items axis: Item1 to Item3
Major_axis axis: 2 to 2
Minor_axis axis: A to C

Fixed Format¶

The examples above show storing using put, which write the HDF5 to
PyTables in a fixed array format, called the fixed format. These
types of stores are not appendable once written (though you can
simply remove them and rewrite). Nor are they queryable; they must
be retrieved in their entirety. They also do not support dataframes with
non-unique column names. The fixed format stores offer very fast
writing and slightly faster reading than table stores. This format is
specified by default when using put or to_hdf or by format='fixed'
or format='f'.

Warning

A fixed format will raise a TypeError if you try to retrieve using a
where:

pd.DataFrame(randn(10, 2)).to_hdf('test_fixed.h5', 'df')

pd.read_hdf('test_fixed.h5', 'df', where='index>5')
TypeError: cannot pass a where specification when reading a fixed format.
           this store must be selected in its entirety

Table Format¶

HDFStore supports another PyTables format on disk, the table
format. Conceptually a table is shaped very much like a DataFrame,
with rows and columns. A table may be appended to in the same or other
sessions. In addition, delete and query type operations are supported.
This format is specified by format='table' or format='t' to append
or put or to_hdf.

This format can be set as an option as well
pd.set_option('io.hdf.default_format','table') to enable
put/append/to_hdf to by default store in the table format.

In[382]: store = pd.HDFStore('store.h5')

In[383]: df1 = df[0:4]

In[384]: df2 = df[4:]

# append data (creates a table automatically)
In[385]: store.append('df', df1)

In[386]: store.append('df', df2)

In[387]: store
Out[387]: 
<class 'pandas.io.pytables.HDFStore'>
File path: store.h5

# select the entire object
In[388]: store.select('df')
Out[388]: 
                   A         B         C
2000-01-01  0.887163  0.859588 -0.636524
2000-01-02  0.015696 -2.242685  1.150036
2000-01-03  0.991946  0.953324 -2.021255
2000-01-04 -0.334077  0.002118  0.405453
2000-01-05  0.289092  1.321158 -1.546906
2000-01-06 -0.202646 -0.655969  0.193421
2000-01-07  0.553439  1.318152 -0.469305
2000-01-08  0.675554 -1.817027 -0.183109

# the type of stored data
In[389]: store.root.df._v_attrs.pandas_type
Out[389]: 'frame_table'

Note

You can also create a table by passing format='table' or
format='t' to a put operation.

Hierarchical Keys¶

Keys to a store can be specified as a string. These can be in a
hierarchical path-name like format (e.g. foo/bar/bah), which will
generate a hierarchy of sub-stores (or Groups in PyTables parlance).
Keys can be specified with out the leading ‘/’ and are always
absolute (e.g. ‘foo’ refers to ‘/foo’). Removal operations can remove
everything in the sub-store and below, so be careful.

In[390]: store.put('foo/bar/bah', df)

In[391]: store.append('food/orange', df)

In[392]: store.append('food/apple',  df)

In[393]: store
Out[393]: 
<class 'pandas.io.pytables.HDFStore'>
File path: store.h5

# a list of keys are returned
In[394]: store.keys()
Out[394]: ['/df', '/food/apple', '/food/orange', '/foo/bar/bah']

# remove all nodes under this level
In[395]: store.remove('food')

In[396]: store
Out[396]: 
<class 'pandas.io.pytables.HDFStore'>
File path: store.h5

Warning

Hierarchical keys cannot be retrieved as dotted (attribute) access as
described above for items stored under the root node.

In[8]: store.foo.bar.bah
AttributeError: 'HDFStore' object has no attribute 'foo'

# you can directly access the actual PyTables node but using the root node
In[9]: store.root.foo.bar.bah
Out[9]:
/foo/bar/bah (Group) ''
  children := ['block0_items' (Array), 'block0_values' (Array), 'axis0' (Array), 'axis1' (Array)]

Instead, use explicit string based keys:

In[397]: store['foo/bar/bah']
Out[397]: 
                   A         B         C
2000-01-01  0.887163  0.859588 -0.636524
2000-01-02  0.015696 -2.242685  1.150036
2000-01-03  0.991946  0.953324 -2.021255
2000-01-04 -0.334077  0.002118  0.405453
2000-01-05  0.289092  1.321158 -1.546906
2000-01-06 -0.202646 -0.655969  0.193421
2000-01-07  0.553439  1.318152 -0.469305
2000-01-08  0.675554 -1.817027 -0.183109

Storing Types¶

Storing Mixed Types in a Table¶

Storing mixed-dtype data is supported. Strings are stored as a
fixed-width using the maximum size of the appended column. Subsequent
attempts at appending longer strings will raise a ValueError.

Passing min_itemsize={`values`: size} as a parameter to append
will set a larger minimum for the string columns. Storing
floats, strings, ints, bools, datetime64 are currently supported. For
string columns, passing nan_rep = 'nan' to append will change the
default nan representation on disk (which converts to/from np.nan), this
defaults to nan.

In[398]: df_mixed = pd.DataFrame({'A': randn(8),
   .....:                          'B': randn(8),
   .....:                          'C': np.array(randn(8), dtype='float32'),
   .....:                          'string':'string',
   .....:                          'int': 1,
   .....:                          'bool': True,
   .....:                          'datetime64': pd.Timestamp('20010102')},
   .....:                         index=list(range(8)))
   .....: 

In[399]: df_mixed.loc[df_mixed.index[3:5], ['A', 'B', 'string', 'datetime64']] = np.nan

In[400]: store.append('df_mixed', df_mixed, min_itemsize = {'values': 50})

In[401]: df_mixed1 = store.select('df_mixed')

In[402]: df_mixed1
Out[402]: 
          A         B         C  string  int  bool datetime64
0  0.704721 -1.152659 -0.430096  string    1  True 2001-01-02
1 -0.785435  0.631979  0.767369  string    1  True 2001-01-02
2  0.462060  0.039513  0.984920  string    1  True 2001-01-02
3       NaN       NaN  0.270836     NaN    1  True        NaT
4       NaN       NaN  1.391986     NaN    1  True        NaT
5 -0.926254  1.321106  0.079842  string    1  True 2001-01-02
6  2.007843  0.152631 -0.399965  string    1  True 2001-01-02
7  0.226963  0.164530 -1.027851  string    1  True 2001-01-02

In[403]: df_mixed1.get_dtype_counts()
Out[403]: 
float64           2
float32           1
object            1
int64             1
bool              1
datetime64[ns]    1
dtype: int64

# we have provided a minimum string column size
In[404]: store.root.df_mixed.table
Out[404]: 
/df_mixed/table (Table(8,)) ''
  description := {
  "index": Int64Col(shape=(), dflt=0, pos=0),
  "values_block_0": Float64Col(shape=(2,), dflt=0.0, pos=1),
  "values_block_1": Float32Col(shape=(1,), dflt=0.0, pos=2),
  "values_block_2": Int64Col(shape=(1,), dflt=0, pos=3),
  "values_block_3": Int64Col(shape=(1,), dflt=0, pos=4),
  "values_block_4": BoolCol(shape=(1,), dflt=False, pos=5),
  "values_block_5": StringCol(itemsize=50, shape=(1,), dflt=b'', pos=6)}
  byteorder := 'little'
  chunkshape := (689,)
  autoindex := True
  colindexes := {
    "index": Index(6, medium, shuffle, zlib(1)).is_csi=False}

Storing Multi-Index DataFrames¶

Storing multi-index DataFrames as tables is very similar to
storing/selecting from homogeneous index DataFrames.

In[405]: index = pd.MultiIndex(levels=[['foo', 'bar', 'baz', 'qux'],
   .....:                               ['one', 'two', 'three']],
   .....:                       labels=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3],
   .....:                               [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
   .....:                       names=['foo', 'bar'])
   .....: 

In[406]: df_mi = pd.DataFrame(np.random.randn(10, 3), index=index,
   .....:                      columns=['A', 'B', 'C'])
   .....: 

In[407]: df_mi
Out[407]: 
                  A         B         C
foo bar                                
foo one   -0.584718  0.816594 -0.081947
    two   -0.344766  0.528288 -1.068989
    three -0.511881  0.291205  0.566534
bar one    0.503592  0.285296  0.484288
    two    1.363482 -0.781105 -0.468018
baz two    1.224574 -1.281108  0.875476
    three -1.710715 -0.450765  0.749164
qux one   -0.203933 -0.182175  0.680656
    two   -1.818499  0.047072  0.394844
    three -0.248432 -0.617707 -0.682884

In[408]: store.append('df_mi', df_mi)

In[409]: store.select('df_mi')
Out[409]: 
                  A         B         C
foo bar                                
foo one   -0.584718  0.816594 -0.081947
    two   -0.344766  0.528288 -1.068989
    three -0.511881  0.291205  0.566534
bar one    0.503592  0.285296  0.484288
    two    1.363482 -0.781105 -0.468018
baz two    1.224574 -1.281108  0.875476
    three -1.710715 -0.450765  0.749164
qux one   -0.203933 -0.182175  0.680656
    two   -1.818499  0.047072  0.394844
    three -0.248432 -0.617707 -0.682884

# the levels are automatically included as data columns
In[410]: store.select('df_mi', 'foo=bar')
Out[410]: 
                A         B         C
foo bar                              
bar one  0.503592  0.285296  0.484288
    two  1.363482 -0.781105 -0.468018

Querying¶

Querying a Table¶

select and delete operations have an optional criterion that can be
specified to select/delete only a subset of the data. This allows one to
have a very large on-disk table and retrieve only a portion of the data.

A query is specified using the Term class under the hood, as a boolean
expression.

index and columns are supported indexers of a DataFrames.
major_axis, minor_axis, and items are supported indexers of

the Panel.
if data_columns are specified, these can be used as additional

indexers.

Valid comparison operators are:

=, ==, !=, >, >=, <, <=

Valid boolean expressions are combined with:

| : or
& : and
( and ) : for grouping

These rules are similar to how boolean expressions are used in pandas
for indexing.

Note

= will be automatically expanded to the comparison operator ==
~ is the not operator, but can only be used in very limited

circumstances
If a list/tuple of expressions is passed they will be combined via
&

The following are valid expressions:

'index >= date'
"columns = ['A', 'D']"
"columns in ['A', 'D']"
'columns = A'
'columns == A'
"~(columns = ['A', 'B'])"
'index > df.index[3] & string = "bar"'
'(index > df.index[3] & index <= df.index[6]) | string = "bar"'
"ts >= Timestamp('2012-02-01')"
"major_axis>=20130101"

The indexers are on the left-hand side of the sub-expression:

columns, major_axis, ts

The right-hand side of the sub-expression (after a comparison operator)
can be:

functions that will be evaluated, e.g. Timestamp('2012-02-01')
strings, e.g. "bar"
date-like, e.g. 20130101, or "20130101"
lists, e.g. "['A', 'B']"
variables that are defined in the local names space, e.g. date

Note

Passing a string to a query by interpolating it into the query
expression is not recommended. Simply assign the string of interest to a
variable and use that variable in an expression. For example, do this

string = "HolyMoly'"
store.select('df', 'index == string')

instead of this

string = "HolyMoly'"
store.select('df',  'index == %s' % string)

The latter will not work and will raise a SyntaxError.Note that
there’s a single quote followed by a double quote in the string
variable.

If you must interpolate, use the '%r' format specifier

store.select('df', 'index == %r' % string)

which will quote string.

Here are some examples:

In[411]: dfq = pd.DataFrame(randn(10, 4), columns=list('ABCD'),
   .....:                    index=pd.date_range('20130101', periods=10))
   .....: 

In[412]: store.append('dfq', dfq, format='table', data_columns=True)

Use boolean expressions, with in-line function evaluation.

In[413]: store.select('dfq', "index>pd.Timestamp('20130104') & columns=['A', 'B']")
Out[413]: 
                   A         B
2013-01-05  1.210384  0.797435
2013-01-06 -0.850346  1.176812
2013-01-07  0.984188 -0.121728
2013-01-08  0.796595 -0.474021
2013-01-09 -0.804834 -2.123620
2013-01-10  0.334198  0.536784

Use and inline column reference

In[414]: store.select('dfq', where="A>0 or C>0")
Out[414]: 
                   A         B         C         D
2013-01-01  0.436258 -1.703013  0.393711 -0.479324
2013-01-02 -0.299016  0.694103  0.678630  0.239556
2013-01-03  0.151227  0.816127  1.893534  0.639633
2013-01-04 -0.962029 -2.085266  1.930247 -1.735349
2013-01-05  1.210384  0.797435 -0.379811  0.702562
2013-01-07  0.984188 -0.121728  2.365769  0.496143
2013-01-08  0.796595 -0.474021 -0.056696  1.357797
2013-01-10  0.334198  0.536784 -0.743830 -0.320204

Works with a Panel as well.

In[415]: store.append('wp', wp)

In[416]: store
Out[416]: 
<class 'pandas.io.pytables.HDFStore'>
File path: store.h5

In[417]: store.select('wp', "major_axis>pd.Timestamp('20000102') & minor_axis=['A', 'B']")
Out[417]: 
<class 'pandas.core.panel.Panel'>
Dimensions: 2 (items) x 3 (major_axis) x 2 (minor_axis)
Items axis: Item1 to Item2
Major_axis axis: 2000-01-03 00:00:00 to 2000-01-05 00:00:00
Minor_axis axis: A to B

The columns keyword can be supplied to select a list of columns to be
returned, this is equivalent to passing a
'columns=list_of_columns_to_filter':

In[418]: store.select('df', "columns=['A', 'B']")
Out[418]: 
                   A         B
2000-01-01  0.887163  0.859588
2000-01-02  0.015696 -2.242685
2000-01-03  0.991946  0.953324
2000-01-04 -0.334077  0.002118
2000-01-05  0.289092  1.321158
2000-01-06 -0.202646 -0.655969
2000-01-07  0.553439  1.318152
2000-01-08  0.675554 -1.817027

start and stop parameters can be specified to limit the total search
space. These are in terms of the total number of rows in a table.

# this is effectively what the storage of a Panel looks like
In[419]: wp.to_frame()
Out[419]: 
                     Item1     Item2
major      minor                    
2000-01-01 A      1.058969  0.215269
           B     -0.397840  0.841009
           C      0.337438 -1.445810
           D      1.047579 -1.401973
2000-01-02 A      1.045938 -0.100918
           B      0.863717 -0.548242
           C     -0.122092 -0.144620
...                    ...       ...
2000-01-04 B      0.036142  0.307969
           C     -2.074978 -0.208499
           D      0.247792  1.033801
2000-01-05 A     -0.897157 -2.400454
           B     -0.136795  2.030604
           C      0.018289 -1.142631
           D      0.755414  0.211883

[20 rows x 2 columns]

# limiting the search
In[420]: store.select('wp', "major_axis>20000102 & minor_axis=['A', 'B']",
   .....:              start=0, stop=10)
   .....: 
Out[420]: 
<class 'pandas.core.panel.Panel'>
Dimensions: 2 (items) x 1 (major_axis) x 2 (minor_axis)
Items axis: Item1 to Item2
Major_axis axis: 2000-01-03 00:00:00 to 2000-01-03 00:00:00
Minor_axis axis: A to B

Note

select will raise a ValueError if the query expression has an
unknown variable reference. Usually this means that you are trying to
select on a column that is not a data_column.

select will raise a SyntaxError if the query expression is not
valid.

Using timedelta64[ns]¶

You can store and query using the timedelta64[ns] type. Terms can be
specified in the format: <float>(<unit>), where float may be signed
(and fractional), and unit can be D,s,ms,us,ns for the timedelta.
Here’s an example:

In[421]: from datetime import timedelta

In[422]: dftd = pd.DataFrame(dict(A = pd.Timestamp('20130101'), B = [ pd.Timestamp('20130101') + timedelta(days=i, seconds=10) for i in range(10) ]))

In[423]: dftd['C'] = dftd['A'] - dftd['B']

In[424]: dftd
Out[424]: 
           A                   B                  C
0 2013-01-01 2013-01-01 00:00:10  -1 days +23:59:50
1 2013-01-01 2013-01-02 00:00:10  -2 days +23:59:50
2 2013-01-01 2013-01-03 00:00:10  -3 days +23:59:50
3 2013-01-01 2013-01-04 00:00:10  -4 days +23:59:50
4 2013-01-01 2013-01-05 00:00:10  -5 days +23:59:50
5 2013-01-01 2013-01-06 00:00:10  -6 days +23:59:50
6 2013-01-01 2013-01-07 00:00:10  -7 days +23:59:50
7 2013-01-01 2013-01-08 00:00:10  -8 days +23:59:50
8 2013-01-01 2013-01-09 00:00:10  -9 days +23:59:50
9 2013-01-01 2013-01-10 00:00:10 -10 days +23:59:50

In[425]: store.append('dftd', dftd, data_columns=True)

In[426]: store.select('dftd', "C<'-3.5D'")
Out[426]: 
           A                   B                  C
4 2013-01-01 2013-01-05 00:00:10  -5 days +23:59:50
5 2013-01-01 2013-01-06 00:00:10  -6 days +23:59:50
6 2013-01-01 2013-01-07 00:00:10  -7 days +23:59:50
7 2013-01-01 2013-01-08 00:00:10  -8 days +23:59:50
8 2013-01-01 2013-01-09 00:00:10  -9 days +23:59:50
9 2013-01-01 2013-01-10 00:00:10 -10 days +23:59:50

Indexing¶

You can create/modify an index for a table with create_table_index
after data is already in the table (after and append/put operation).
Creating a table index is highly encouraged. This will speed your
queries a great deal when you use a select with the indexed dimension
as the where.

Note

Indexes are automagically created on the indexables and any data columns
you specify. This behavior can be turned off by passing index=False to
append.

# we have automagically already created an index (in the first section)
In[427]: i = store.root.df.table.cols.index.index

In[428]: i.optlevel, i.kind
Out[428]: (6, 'medium')

# change an index by passing new parameters
In[429]: store.create_table_index('df', optlevel=9, kind='full')

In[430]: i = store.root.df.table.cols.index.index

In[431]: i.optlevel, i.kind
Out[431]: (9, 'full')

Oftentimes when appending large amounts of data to a store, it is useful
to turn off index creation for each append, then recreate at the end.

In[432]: df_1 = pd.DataFrame(randn(10, 2), columns=list('AB'))

In[433]: df_2 = pd.DataFrame(randn(10, 2), columns=list('AB'))

In[434]: st = pd.HDFStore('appends.h5', mode='w')

In[435]: st.append('df', df_1, data_columns=['B'], index=False)

In[436]: st.append('df', df_2, data_columns=['B'], index=False)

In[437]: st.get_storer('df').table
Out[437]: 
/df/table (Table(20,)) ''
  description := {
  "index": Int64Col(shape=(), dflt=0, pos=0),
  "values_block_0": Float64Col(shape=(1,), dflt=0.0, pos=1),
  "B": Float64Col(shape=(), dflt=0.0, pos=2)}
  byteorder := 'little'
  chunkshape := (2730,)

Then create the index when finished appending.

In[438]: st.create_table_index('df', columns=['B'], optlevel=9, kind='full')

In[439]: st.get_storer('df').table
Out[439]: 
/df/table (Table(20,)) ''
  description := {
  "index": Int64Col(shape=(), dflt=0, pos=0),
  "values_block_0": Float64Col(shape=(1,), dflt=0.0, pos=1),
  "B": Float64Col(shape=(), dflt=0.0, pos=2)}
  byteorder := 'little'
  chunkshape := (2730,)
  autoindex := True
  colindexes := {
    "B": Index(9, full, shuffle, zlib(1)).is_csi=True}

In[440]: st.close()

See
here
for how to create a completely-sorted-index (CSI) on an existing store.

Query via Data Columns¶

You can designate (and index) certain columns that you want to be able
to perform queries (other than the indexable columns, which you can
always query). For instance say you want to perform this common
operation, on-disk, and return just the frame that matches this query.
You can specify data_columns = True to force all columns to be
data_columns.

In[441]: df_dc = df.copy()

In[442]: df_dc['string'] = 'foo'

In[443]: df_dc.loc[df_dc.index[4: 6], 'string'] = np.nan

In[444]: df_dc.loc[df_dc.index[7: 9], 'string'] = 'bar'

In[445]: df_dc['string2'] = 'cool'

In[446]: df_dc.loc[df_dc.index[1: 3], ['B', 'C']] = 1.0

In[447]: df_dc
Out[447]: 
                   A         B         C string string2
2000-01-01  0.887163  0.859588 -0.636524    foo    cool
2000-01-02  0.015696  1.000000  1.000000    foo    cool
2000-01-03  0.991946  1.000000  1.000000    foo    cool
2000-01-04 -0.334077  0.002118  0.405453    foo    cool
2000-01-05  0.289092  1.321158 -1.546906    NaN    cool
2000-01-06 -0.202646 -0.655969  0.193421    NaN    cool
2000-01-07  0.553439  1.318152 -0.469305    foo    cool
2000-01-08  0.675554 -1.817027 -0.183109    bar    cool

# on-disk operations
In[448]: store.append('df_dc', df_dc, data_columns = ['B', 'C', 'string', 'string2'])

In[449]: store.select('df_dc', where='B > 0')
Out[449]: 
                   A         B         C string string2
2000-01-01  0.887163  0.859588 -0.636524    foo    cool
2000-01-02  0.015696  1.000000  1.000000    foo    cool
2000-01-03  0.991946  1.000000  1.000000    foo    cool
2000-01-04 -0.334077  0.002118  0.405453    foo    cool
2000-01-05  0.289092  1.321158 -1.546906    NaN    cool
2000-01-07  0.553439  1.318152 -0.469305    foo    cool

# getting creative
In[450]: store.select('df_dc', 'B > 0 & C > 0 & string == foo')
Out[450]: 
                   A         B         C string string2
2000-01-02  0.015696  1.000000  1.000000    foo    cool
2000-01-03  0.991946  1.000000  1.000000    foo    cool
2000-01-04 -0.334077  0.002118  0.405453    foo    cool

# this is in-memory version of this type of selection
In[451]: df_dc[(df_dc.B > 0) & (df_dc.C > 0) & (df_dc.string == 'foo')]
Out[451]: 
                   A         B         C string string2
2000-01-02  0.015696  1.000000  1.000000    foo    cool
2000-01-03  0.991946  1.000000  1.000000    foo    cool
2000-01-04 -0.334077  0.002118  0.405453    foo    cool

# we have automagically created this index and the B/C/string/string2
# columns are stored separately as ``PyTables`` columns
In[452]: store.root.df_dc.table
Out[452]: 
/df_dc/table (Table(8,)) ''
  description := {
  "index": Int64Col(shape=(), dflt=0, pos=0),
  "values_block_0": Float64Col(shape=(1,), dflt=0.0, pos=1),
  "B": Float64Col(shape=(), dflt=0.0, pos=2),
  "C": Float64Col(shape=(), dflt=0.0, pos=3),
  "string": StringCol(itemsize=3, shape=(), dflt=b'', pos=4),
  "string2": StringCol(itemsize=4, shape=(), dflt=b'', pos=5)}
  byteorder := 'little'
  chunkshape := (1680,)
  autoindex := True
  colindexes := {
    "index": Index(6, medium, shuffle, zlib(1)).is_csi=False,
    "B": Index(6, medium, shuffle, zlib(1)).is_csi=False,
    "C": Index(6, medium, shuffle, zlib(1)).is_csi=False,
    "string": Index(6, medium, shuffle, zlib(1)).is_csi=False,
    "string2": Index(6, medium, shuffle, zlib(1)).is_csi=False}

There is some performance degradation by making lots of columns into
data columns, so it is up to the user to designate these. In addition,
you cannot change data columns (nor indexables) after the first
append/put operation (Of course you can simply read in the data and
create a new table!).

Iterator¶

You can pass iterator=True or chunksize=number_in_a_chunk to
select and select_as_multiple to return an iterator on the results.
The default is 50,000 rows returned in a chunk.

In[453]: for df in store.select('df', chunksize=3):
   .....:    print(df)
   .....: 
                   A         B         C
2000-01-01  0.887163  0.859588 -0.636524
2000-01-02  0.015696 -2.242685  1.150036
2000-01-03  0.991946  0.953324 -2.021255
                   A         B         C
2000-01-04 -0.334077  0.002118  0.405453
2000-01-05  0.289092  1.321158 -1.546906
2000-01-06 -0.202646 -0.655969  0.193421
                   A         B         C
2000-01-07  0.553439  1.318152 -0.469305
2000-01-08  0.675554 -1.817027 -0.183109

Note

You can also use the iterator with read_hdf which will open, then
automatically close the store when finished iterating.

for df in pd.read_hdf('store.h5','df', chunksize=3):
    print(df)

Note, that the chunksize keyword applies to the source rows. So if
you are doing a query, then the chunksize will subdivide the total rows
in the table and the query applied, returning an iterator on potentially
unequal sized chunks.

Here is a recipe for generating a query and using it to create equal
sized return chunks.

In[454]: dfeq = pd.DataFrame({'number': np.arange(1, 11)})

In[455]: dfeq
Out[455]: 
   number
0       1
1       2
2       3
3       4
4       5
5       6
6       7
7       8
8       9
9      10

In[456]: store.append('dfeq', dfeq, data_columns=['number'])

In[457]: def chunks(l, n):
   .....:      return [l[i: i+n] for i in range(0, len(l), n)]
   .....: 

In[458]: evens = [2, 4, 6, 8, 10]

In[459]: coordinates = store.select_as_coordinates('dfeq', 'number=evens')

In[460]: for c in chunks(coordinates, 2):
   .....:      print(store.select('dfeq', where=c))
   .....: 
   number
1       2
3       4
   number
5       6
7       8
   number
9      10

Advanced Queries¶

Select a Single Column¶

To retrieve a single indexable or data column, use the method
select_column. This will, for example, enable you to get the index
very quickly. These return a Series of the result, indexed by the row
number. These do not currently accept the where selector.

In[461]: store.select_column('df_dc', 'index')
Out[461]: 
0   2000-01-01
1   2000-01-02
2   2000-01-03
3   2000-01-04
4   2000-01-05
5   2000-01-06
6   2000-01-07
7   2000-01-08
Name: index, dtype: datetime64[ns]

In[462]: store.select_column('df_dc', 'string')
Out[462]: 
0    foo
1    foo
2    foo
3    foo
4    NaN
5    NaN
6    foo
7    bar
Name: string, dtype: object

Selecting coordinates¶

Sometimes you want to get the coordinates (a.k.a the index locations) of
your query. This returns an Int64Index of the resulting locations.
These coordinates can also be passed to subsequent where operations.

In[463]: df_coord = pd.DataFrame(np.random.randn(1000, 2),
   .....:                         index=pd.date_range('20000101', periods=1000))
   .....: 

In[464]: store.append('df_coord', df_coord)

In[465]: c = store.select_as_coordinates('df_coord', 'index > 20020101')

In[466]: c
Out[466]: 
Int64Index([732, 733, 734, 735, 736, 737, 738, 739, 740, 741,
            ...
            990, 991, 992, 993, 994, 995, 996, 997, 998, 999],
           dtype='int64', length=268)

In[467]: store.select('df_coord', where=c)
Out[467]: 
                   0         1
2002-01-02 -0.178266 -0.064638
2002-01-03 -1.204956 -3.880898
2002-01-04  0.974470  0.415160
2002-01-05  1.751967  0.485011
2002-01-06 -0.170894  0.748870
2002-01-07  0.629793  0.811053
2002-01-08  2.133776  0.238459
...              ...       ...
2002-09-20 -0.181434  0.612399
2002-09-21 -0.763324 -0.354962
2002-09-22 -0.261776  0.812126
2002-09-23  0.482615 -0.886512
2002-09-24 -0.037757 -0.562953
2002-09-25  0.897706  0.383232
2002-09-26 -1.324806  1.139269

[268 rows x 2 columns]

Selecting using a where mask¶

Sometime your query can involve creating a list of rows to select.
Usually this mask would be a resulting index from an indexing
operation. This example selects the months of a datetimeindex which are
5.

In[468]: df_mask = pd.DataFrame(np.random.randn(1000, 2),
   .....:                        index=pd.date_range('20000101', periods=1000))
   .....: 

In[469]: store.append('df_mask', df_mask)

In[470]: c = store.select_column('df_mask', 'index')

In[471]: where = c[pd.DatetimeIndex(c).month == 5].index

In[472]: store.select('df_mask', where=where)
Out[472]: 
                   0         1
2000-05-01 -1.006245 -0.616759
2000-05-02  0.218940  0.717838
2000-05-03  0.013333  1.348060
2000-05-04  0.662176 -1.050645
2000-05-05 -1.034870 -0.243242
2000-05-06 -0.753366 -1.454329
2000-05-07 -1.022920 -0.476989
...              ...       ...
2002-05-25 -0.509090 -0.389376
2002-05-26  0.150674  1.164337
2002-05-27 -0.332944  0.115181
2002-05-28 -1.048127 -0.605733
2002-05-29  1.418754 -0.442835
2002-05-30 -0.433200  0.835001
2002-05-31 -1.041278  1.401811

[93 rows x 2 columns]

Storer Object¶

If you want to inspect the stored object, retrieve via get_storer. You
could use this programmatically to say get the number of rows in an
object.

In[473]: store.get_storer('df_dc').nrows
Out[473]: 8

Multiple Table Queries¶

The methods append_to_multiple and select_as_multiple can perform
appending/selecting from multiple tables at once. The idea is to have
one table (call it the selector table) that you index most/all of the
columns, and perform your queries. The other table(s) are data tables
with an index matching the selector table’s index. You can then perform
a very fast query on the selector table, yet get lots of data back. This
method is similar to having a very wide table, but enables more
efficient queries.

The append_to_multiple method splits a given single DataFrame into
multiple tables according to d, a dictionary that maps the table names
to a list of ‘columns’ you want in that table. If None is used in place
of a list, that table will have the remaining unspecified columns of the
given DataFrame. The argument selector defines which table is the
selector table (which you can make queries from). The argument dropna
will drop rows from the input DataFrame to ensure tables are
synchronized. This means that if a row for one of the tables being
written to is entirely np.NaN, that row will be dropped from all
tables.

If dropna is False, THE USER IS RESPONSIBLE FOR SYNCHRONIZING THE
TABLES. Remember that entirely np.Nan rows are not written to the
HDFStore, so if you choose to call dropna=False, some tables may have
more rows than others, and therefore select_as_multiple may not work
or it may return unexpected results.

In[474]: df_mt = pd.DataFrame(randn(8, 6), index=pd.date_range('1/1/2000', periods=8),
   .....:                                   columns=['A', 'B', 'C', 'D', 'E', 'F'])
   .....: 

In[475]: df_mt['foo'] = 'bar'

In[476]: df_mt.loc[df_mt.index[1], ('A', 'B')] = np.nan

# you can also create the tables individually
In[477]: store.append_to_multiple({'df1_mt': ['A', 'B'], 'df2_mt': None },
   .....:                           df_mt, selector='df1_mt')
   .....: 

In[478]: store
Out[478]: 
<class 'pandas.io.pytables.HDFStore'>
File path: store.h5

# individual tables were created
In[479]: store.select('df1_mt')
Out[479]: 
                   A         B
2000-01-01  0.714697  0.318215
2000-01-02       NaN       NaN
2000-01-03 -0.086919  0.416905
2000-01-04  0.489131 -0.253340
2000-01-05 -0.382952 -0.397373
2000-01-06  0.538116  0.226388
2000-01-07 -2.073479 -0.115926
2000-01-08 -0.695400  0.402493

In[480]: store.select('df2_mt')
Out[480]: 
                   C         D         E         F  foo
2000-01-01  0.607460  0.790907  0.852225  0.096696  bar
2000-01-02  0.811031 -0.356817  1.047085  0.664705  bar
2000-01-03 -0.764381 -0.287229 -0.089351 -1.035115  bar
2000-01-04 -1.948100 -0.116556  0.800597 -0.796154  bar
2000-01-05 -0.717627  0.156995 -0.344718 -0.171208  bar
2000-01-06  1.541729  0.205256  1.998065  0.953591  bar
2000-01-07  1.391070  0.303013  1.093347 -0.101000  bar
2000-01-08 -1.507639  0.089575  0.658822 -1.037627  bar

# as a multiple
In[481]: store.select_as_multiple(['df1_mt', 'df2_mt'], where=['A>0', 'B>0'],
   .....:                           selector = 'df1_mt')
   .....: 
Out[481]: 
                   A         B         C         D         E         F  foo
2000-01-01  0.714697  0.318215  0.607460  0.790907  0.852225  0.096696  bar
2000-01-06  0.538116  0.226388  1.541729  0.205256  1.998065  0.953591  bar

Delete from a Table¶

You can delete from a table selectively by specifying a where. In
deleting rows, it is important to understand the PyTables deletes rows
by erasing the rows, then moving the following data. Thus deleting
can potentially be a very expensive operation depending on the
orientation of your data. This is especially true in higher dimensional
objects (Panel and Panel4D). To get optimal performance, it’s
worthwhile to have the dimension you are deleting be the first of the
indexables.

Data is ordered (on the disk) in terms of the indexables. Here’s a
simple use case. You store panel-type data, with dates in the
major_axis and ids in the minor_axis. The data is then interleaved
like this:

date_1 - id_1 - id_2 - . - id_n
date_2 - id_1 - . - id_n

It should be clear that a delete operation on the major_axis will be
fairly quick, as one chunk is removed, then the following data moved. On
the other hand a delete operation on the minor_axis will be very
expensive. In this case it would almost certainly be faster to rewrite
the table using a where that selects all but the missing data.

# returns the number of rows deleted
In[482]: store.remove('wp', 'major_axis > 20000102' )
Out[482]: 12

In[483]: store.select('wp')
Out[483]: 
<class 'pandas.core.panel.Panel'>
Dimensions: 2 (items) x 2 (major_axis) x 4 (minor_axis)
Items axis: Item1 to Item2
Major_axis axis: 2000-01-01 00:00:00 to 2000-01-02 00:00:00
Minor_axis axis: A to D

Warning

Please note that HDF5 DOES NOT RECLAIM SPACE in the h5 files
automatically. Thus, repeatedly deleting (or removing nodes) and adding
again, WILL TEND TO INCREASE THE FILE SIZE.

To repack and clean the file, use
ptrepack.

Notes & Caveats¶

Compression¶

PyTables allows the stored data to be compressed. This applies to all
kinds of stores, not just tables. Two parameters are used to control
compression: complevel and complib.

complevel specifies if and how hard data is to be compressed.

complevel=0 and complevel=None disables compression and
0<complevel<10 enables compression.

complib specifies which compression library to use. If nothing is

specified the default library zlib is used. A compression library
usually optimizes for either good compression rates or speed and the
results will depend on the type of data. Which type of compression to
choose depends on your specific needs and data. The list of supported
compression libraries:

zlib: The default compression library. A classic in terms of compression, achieves good compression rates but is somewhat slow.

lzo: Fast compression and decompression.

bzip2: Good compression rates.

blosc: Fast compression and decompression. New in version 0.20.2: Support for alternative blosc compressors:

blosc:blosclz This is the default compressor for blosc

blosc:lz4: A compact, very popular and fast compressor.

blosc:lz4hc: A tweaked version of LZ4, produces better compression ratios at the expense of speed.

blosc:snappy: A popular compressor used in many places.

blosc:zlib: A classic; somewhat slower than the previous ones, but achieving better compression ratios.

blosc:zstd: An extremely well balanced codec; it provides the best compression ratios among the others above, and at reasonably fast speed. If complib is defined as something other than the listed libraries a ValueError exception is issued.

Note

If the library specified with the complib option is missing on your
platform, compression defaults to zlib without further ado.

Enable compression for all objects within the file:

store_compressed = pd.HDFStore('store_compressed.h5', complevel=9,
                               complib='blosc:blosclz')

Or on-the-fly compression (this only applies to tables) in stores where
compression is not enabled:

store.append('df', df, complib='zlib', complevel=5)

ptrepack¶

PyTables offers better write performance when tables are compressed
after they are written, as opposed to turning on compression at the very
beginning. You can use the supplied PyTables utility ptrepack. In
addition, ptrepack can change compression levels after the fact.

ptrepack --chunkshape=auto --propindexes --complevel=9 --complib=blosc in.h5 out.h5

Furthermore ptrepack in.h5 out.h5 will repack the file to allow you
to reuse previously deleted space. Alternatively, one can simply remove
the file and write again, or use the copy method.

Caveats¶

Warning

HDFStore is not-threadsafe for writing. The underlying PyTables
only supports concurrent reads (via threading or processes). If you need
reading and writing at the same time, you need to serialize these
operations in a single thread in a single process. You will corrupt your
data otherwise. See the
(GH2397)
for more information.

If you use locks to manage write access between multiple processes,
you may want to use
fsync()
before releasing write locks. For convenience you can use
store.flush(fsync=True) to do this for you.
Once a table is created its items (Panel) / columns (DataFrame)

are fixed; only exactly the same columns can be appended
Be aware that timezones (e.g., pytz.timezone('US/Eastern')) are

not necessarily equal across timezone versions. So if data is
localized to a specific timezone in the HDFStore using one version
of a timezone library and that data is updated with another version,
the data will be converted to UTC since these timezones are not
considered equal. Either use the same version of timezone library or
use tz_convert with the updated timezone definition.

Warning

PyTables will show a NaturalNameWarning if a column name cannot be
used as an attribute selector. Natural identifiers contain only
letters, numbers, and underscores, and may not begin with a number.
Other identifiers cannot be used in a where clause and are generally a
bad idea.

DataTypes¶

HDFStore will map an object dtype to the PyTables underlying dtype.
This means the following types are known to work:

Type	Represents missing values
floating : `float64, float32, float16`	`np.nan`
integer : `int64, int32, int8, uint64,uint32, uint8`
boolean
`datetime64[ns]`	`NaT`
`timedelta64[ns]`	`NaT`
categorical : see the section below
object : `strings`	`np.nan`

unicode columns are not supported, and WILL FAIL.

Categorical Data¶

You can write data that contains category dtypes to a HDFStore.
Queries work the same as if it was an object array. However, the
category dtyped data is stored in a more efficient manner.

In[484]: dfcat = pd.DataFrame({'A': pd.Series(list('aabbcdba')).astype('category'),
   .....:                       'B': np.random.randn(8) })
   .....: 

In[485]: dfcat
Out[485]: 
   A         B
0  a  0.603273
1  a  0.262554
2  b -0.979586
3  b  2.132387
4  c  0.892485
5  d  1.996474
6  b  0.231425
7  a  0.980070

In[486]: dfcat.dtypes
Out[486]: 
A    category
B     float64
dtype: object

In[487]: cstore = pd.HDFStore('cats.h5', mode='w')

In[488]: cstore.append('dfcat', dfcat, format='table', data_columns=['A'])

In[489]: result = cstore.select('dfcat', where="A in ['b', 'c']")

In[490]: result
Out[490]: 
   A         B
2  b -0.979586
3  b  2.132387
4  c  0.892485
6  b  0.231425

In[491]: result.dtypes
Out[491]: 
A    category
B     float64
dtype: object

String Columns¶

min_itemsize

The underlying implementation of HDFStore uses a fixed column width
(itemsize) for string columns. A string column itemsize is calculated as
the maximum of the length of data (for that column) that is passed to
the HDFStore, in the first append. Subsequent appends, may
introduce a string for a column larger than the column can hold, an
Exception will be raised (otherwise you could have a silent truncation
of these columns, leading to loss of information). In the future we may
relax this and allow a user-specified truncation to occur.

Pass min_itemsize on the first table creation to a-priori specify the
minimum length of a particular string column. min_itemsize can be an
integer, or a dict mapping a column name to an integer. You can pass
values as a key to allow all indexables or data_columns to have
this min_itemsize.

Passing a min_itemsize dict will cause all passed columns to be
created as data_columns automatically.

Note

If you are not passing any data_columns, then the min_itemsize will
be the maximum of the length of any string passed

In[492]: dfs = pd.DataFrame(dict(A='foo', B='bar'), index=list(range(5)))

In[493]: dfs
Out[493]: 
     A    B
0  foo  bar
1  foo  bar
2  foo  bar
3  foo  bar
4  foo  bar

# A and B have a size of 30
In[494]: store.append('dfs', dfs, min_itemsize=30)

In[495]: store.get_storer('dfs').table
Out[495]: 
/dfs/table (Table(5,)) ''
  description := {
  "index": Int64Col(shape=(), dflt=0, pos=0),
  "values_block_0": StringCol(itemsize=30, shape=(2,), dflt=b'', pos=1)}
  byteorder := 'little'
  chunkshape := (963,)
  autoindex := True
  colindexes := {
    "index": Index(6, medium, shuffle, zlib(1)).is_csi=False}

# A is created as a data_column with a size of 30
# B is size is calculated
In[496]: store.append('dfs2', dfs, min_itemsize={'A': 30})

In[497]: store.get_storer('dfs2').table
Out[497]: 
/dfs2/table (Table(5,)) ''
  description := {
  "index": Int64Col(shape=(), dflt=0, pos=0),
  "values_block_0": StringCol(itemsize=3, shape=(1,), dflt=b'', pos=1),
  "A": StringCol(itemsize=30, shape=(), dflt=b'', pos=2)}
  byteorder := 'little'
  chunkshape := (1598,)
  autoindex := True
  colindexes := {
    "index": Index(6, medium, shuffle, zlib(1)).is_csi=False,
    "A": Index(6, medium, shuffle, zlib(1)).is_csi=False}

nan_rep

String columns will serialize a np.nan (a missing value) with the
nan_rep string representation. This defaults to the string value
nan. You could inadvertently turn an actual nan value into a missing
value.

In[498]: dfss = pd.DataFrame(dict(A=['foo', 'bar', 'nan']))

In[499]: dfss
Out[499]: 
     A
0  foo
1  bar
2  nan

In[500]: store.append('dfss', dfss)

In[501]: store.select('dfss')
Out[501]: 
     A
0  foo
1  bar
2  NaN

# here you need to specify a different nan rep
In[502]: store.append('dfss2', dfss, nan_rep='_nan_')

In[503]: store.select('dfss2')
Out[503]: 
     A
0  foo
1  bar
2  nan

External Compatibility¶

HDFStore writes table format objects in specific formats suitable
for producing loss-less round trips to pandas objects. For external
compatibility, HDFStore can read native PyTables format tables.

It is possible to write an HDFStore object that can easily be imported
into R using the rhdf5 library
(Package website).
Create a table format store like this:

In[504]: np.random.seed(1)

In[505]: df_for_r = pd.DataFrame({"first": np.random.rand(100),
   .....:                          "second": np.random.rand(100),
   .....:                          "class": np.random.randint(0, 2, (100, ))},
   .....:                          index=range(100))
   .....: 

In[506]: df_for_r.head()
Out[506]: 
      first    second  class
0  0.417022  0.326645      0
1  0.720324  0.527058      0
2  0.000114  0.885942      1
3  0.302333  0.357270      1
4  0.146756  0.908535      1

In[507]: store_export = pd.HDFStore('export.h5')

In[508]: store_export.append('df_for_r', df_for_r, data_columns=df_dc.columns)

In[509]: store_export
Out[509]: 
<class 'pandas.io.pytables.HDFStore'>
File path: export.h5

In R this file can be read into a data.frame object using the rhdf5
library. The following example function reads the corresponding column
names and data values from the values and assembles them into a
data.frame:

# Load values and column names for all datasets from corresponding nodes and
# insert them into one data.frame object.

library(rhdf5)

loadhdf5data <- function(h5File) {

listing <- h5ls(h5File)
# Find all data nodes, values are stored in *_values and corresponding column
# titles in *_items
data_nodes <- grep("_values", listing$name)
name_nodes <- grep("_items", listing$name)
data_paths = paste(listing$group[data_nodes], listing$name[data_nodes], sep = "/")
name_paths = paste(listing$group[name_nodes], listing$name[name_nodes], sep = "/")
columns = list()
for (idx in seq(data_paths)) {
  # NOTE: matrices returned by h5read have to be transposed to obtain
  # required Fortran order!
  data <- data.frame(t(h5read(h5File, data_paths[idx])))
  names <- t(h5read(h5File, name_paths[idx]))
  entry <- data.frame(data)
  colnames(entry) <- names
  columns <- append(columns, entry)
}

data <- data.frame(columns)

return(data)
}

Now you can import the DataFrame into R:

> data = loadhdf5data("transfer.hdf5")
> head(data)
         first    second class
1 0.4170220047 0.3266449     0
2 0.7203244934 0.5270581     0
3 0.0001143748 0.8859421     1
4 0.3023325726 0.3572698     1
5 0.1467558908 0.9085352     1
6 0.0923385948 0.6233601     1

Note

The R function lists the entire HDF5 file’s contents and assembles the
data.frame object from all matching nodes, so use this only as a
starting point if you have stored multiple DataFrame objects to a
single HDF5 file.

Performance¶

tables format come with a writing performance penalty as compared

to fixed stores. The benefit is the ability to append/delete and
query (potentially very large amounts of data). Write times are
generally longer as compared with regular stores. Query times can be
quite fast, especially on an indexed axis.
You can pass chunksize=<int> to append, specifying the write

chunksize (default is 50000). This will significantly lower your
memory usage on writing.
You can pass expectedrows=<int> to the first append, to set the

TOTAL number of expected rows that PyTables will expected. This
will optimize read/write performance.
Duplicate rows can be written to tables, but are filtered out in
selection (with the last items being selected; thus a table is
unique on major, minor pairs)
A PerformanceWarning will be raised if you are attempting to store

types that will be pickled by PyTables (rather than stored as
endemic types). See
Here
for more information and some solutions.

Feather¶

New in version 0.20.0.

Feather provides binary columnar serialization for data frames. It is
designed to make reading and writing data frames efficient, and to make
sharing data across data analysis languages easy.

Feather is designed to faithfully serialize and de-serialize DataFrames,
supporting all of the pandas dtypes, including extension dtypes such as
categorical and datetime with tz.

Several caveats.

This is a newer library, and the format, though stable, is not
guaranteed to be backward compatible to the earlier versions.
The format will NOT write an Index, or MultiIndex for the

DataFrame and will raise an error if a non-default one is
provided. You can .reset_index() to store the index or
.reset_index(drop=True) to ignore it.
Duplicate column names and non-string columns names are not
supported
Non supported types include Period and actual Python object types.

These will raise a helpful error message on an attempt at
serialization.

See the
Full Documentation.

In[510]: df = pd.DataFrame({'a': list('abc'),
   .....:                    'b': list(range(1, 4)),
   .....:                    'c': np.arange(3, 6).astype('u1'),
   .....:                    'd': np.arange(4.0, 7.0, dtype='float64'),
   .....:                    'e': [True, False, True],
   .....:                    'f': pd.Categorical(list('abc')),
   .....:                    'g': pd.date_range('20130101', periods=3),
   .....:                    'h': pd.date_range('20130101', periods=3, tz='US/Eastern'),
   .....:                    'i': pd.date_range('20130101', periods=3, freq='ns')})
   .....: 

In[511]: df
Out[511]: 
   a  b  c    d      e  f          g                         h                             i
0  a  1  3  4.0   True  a 2013-01-01 2013-01-01 00:00:00-05:00 2013-01-01 00:00:00.000000000
1  b  2  4  5.0  False  b 2013-01-02 2013-01-02 00:00:00-05:00 2013-01-01 00:00:00.000000001
2  c  3  5  6.0   True  c 2013-01-03 2013-01-03 00:00:00-05:00 2013-01-01 00:00:00.000000002

In[512]: df.dtypes
Out[512]: 
a                        object
b                         int64
c                         uint8
d                       float64
e                          bool
f                      category
g                datetime64[ns]
h    datetime64[ns, US/Eastern]
i                datetime64[ns]
dtype: object

Write to a feather file.

In[513]: df.to_feather('example.feather')

Read from a feather file.

In[514]: result = pd.read_feather('example.feather')

In[515]: result
Out[515]: 
   a  b  c    d      e  f          g                         h                             i
0  a  1  3  4.0   True  a 2013-01-01 2013-01-01 00:00:00-05:00 2013-01-01 00:00:00.000000000
1  b  2  4  5.0  False  b 2013-01-02 2013-01-02 00:00:00-05:00 2013-01-01 00:00:00.000000001
2  c  3  5  6.0   True  c 2013-01-03 2013-01-03 00:00:00-05:00 2013-01-01 00:00:00.000000002

# we preserve dtypes
In[516]: result.dtypes
Out[516]: 
a                        object
b                         int64
c                         uint8
d                       float64
e                          bool
f                      category
g                datetime64[ns]
h    datetime64[ns, US/Eastern]
i                datetime64[ns]
dtype: object

Parquet¶

New in version 0.21.0.

Apache Parquet
provides a partitioned binary columnar serialization for data frames. It
is designed to make reading and writing data frames efficient, and to
make sharing data across data analysis languages easy. Parquet can use a
variety of compression techniques to shrink the file size as much as
possible while still maintaining good read performance.

Parquet is designed to faithfully serialize and de-serialize DataFrame
s, supporting all of the pandas dtypes, including extension dtypes such
as datetime with tz.

Several caveats.

Duplicate column names and non-string columns names are not
supported.
Index level names, if specified, must be strings.
Categorical dtypes can be serialized to parquet, but will
de-serialize as object dtype.
Non supported types include Period and actual Python object types.

These will raise a helpful error message on an attempt at
serialization.

You can specify an engine to direct the serialization. This can be one
of pyarrow, or fastparquet, or auto. If the engine is NOT
specified, then the pd.options.io.parquet.engine option is checked; if
this is also auto, then pyarrow is tried, and falling back to
fastparquet.

See the documentation for
pyarrow
and
fastparquet.

Note

These engines are very similar and should read/write nearly identical
parquet format files. Currently pyarrow does not support timedelta
data, fastparquet>=0.1.4 supports timezone aware datetimes. These
libraries differ by having different underlying dependencies
(fastparquet by using numba, while pyarrow uses a c-library).

In[517]: df = pd.DataFrame({'a': list('abc'),
   .....:                    'b': list(range(1, 4)),
   .....:                    'c': np.arange(3, 6).astype('u1'),
   .....:                    'd': np.arange(4.0, 7.0, dtype='float64'),
   .....:                    'e': [True, False, True],
   .....:                    'f': pd.date_range('20130101', periods=3),
   .....:                    'g': pd.date_range('20130101', periods=3, tz='US/Eastern')})
   .....: 

In[518]: df
Out[518]: 
   a  b  c    d      e          f                         g
0  a  1  3  4.0   True 2013-01-01 2013-01-01 00:00:00-05:00
1  b  2  4  5.0  False 2013-01-02 2013-01-02 00:00:00-05:00
2  c  3  5  6.0   True 2013-01-03 2013-01-03 00:00:00-05:00

In[519]: df.dtypes
Out[519]: 
a                        object
b                         int64
c                         uint8
d                       float64
e                          bool
f                datetime64[ns]
g    datetime64[ns, US/Eastern]
dtype: object

Write to a parquet file.

In[520]: df.to_parquet('example_pa.parquet', engine='pyarrow')

In[521]: df.to_parquet('example_fp.parquet', engine='fastparquet')

Read from a parquet file.

In[522]: result = pd.read_parquet('example_fp.parquet', engine='fastparquet')

In[523]: result = pd.read_parquet('example_pa.parquet', engine='pyarrow')

In[524]: result.dtypes
Out[524]: 
a                        object
b                         int64
c                         uint8
d                       float64
e                          bool
f                datetime64[ns]
g    datetime64[ns, US/Eastern]
dtype: object

Read only certain columns of a parquet file.

In[525]: result = pd.read_parquet('example_fp.parquet',
   .....:                          engine='fastparquet', columns=['a', 'b'])
   .....: 

In[526]: result.dtypes
Out[526]: 
a    object
b     int64
dtype: object

SQL Queries¶

The pandas.io.sql module provides a collection of query wrappers to
both facilitate data retrieval and to reduce dependency on DB-specific
API. Database abstraction is provided by SQLAlchemy if installed. In
addition you will need a driver library for your database. Examples of
such drivers are
psycopg2
for PostgreSQL or
pymysql
for MySQL. For
SQLite
this is included in Python’s standard library by default. You can find
an overview of supported drivers for each SQL dialect in the
SQLAlchemy docs.

If SQLAlchemy is not installed, a fallback is only provided for sqlite
(and for mysql for backwards compatibility, but this is deprecated and
will be removed in a future version). This mode requires a Python
database adapter which respect the
Python DB-API.

See also some
cookbook examples
for some advanced strategies.

The key functions are:

`read_sql_table`(table_name, con[, schema, …])	Read SQL database table into a DataFrame.
`read_sql_query`(sql, con[, index_col, …])	Read SQL query into a DataFrame.
`read_sql`(sql, con[, index_col, …])	Read SQL query or database table into a DataFrame.
`DataFrame.to_sql`(name, con[, schema, …])	Write records stored in a DataFrame to a SQL database.

Note

The function
read_sql()
is a convenience wrapper around
read_sql_table()
and
read_sql_query()
(and for backward compatibility) and will delegate to specific function
depending on the provided input (database table name or sql query).
Table names do not need to be quoted if they have special characters.

In the following example, we use the
SQlite
SQL database engine. You can use a temporary SQLite database where data
are stored in “memory”.

To connect with SQLAlchemy you use the create_engine() function to
create an engine object from database URI. You only need to create the
engine once per database you are connecting to. For more information on
create_engine() and the URI formatting, see the examples below and the
SQLAlchemy
documentation

In[527]: from sqlalchemy import create_engine

# Create your engine.
In[528]: engine = create_engine('sqlite:///:memory:')

If you want to manage your own connections you can pass one of those
instead:

with engine.connect() as conn, conn.begin():
    data = pd.read_sql_table('data', conn)

Writing DataFrames¶

Assuming the following data is in a DataFrame data, we can insert it
into the database using
to_sql().

id	Date	Col_1	Col_2	Col_3
26	2012-10-18	X	25.7	True
42	2012-10-19	Y	-12.4	False
63	2012-10-20	Z	5.73	True

In[529]: data.to_sql('data', engine)

With some databases, writing large DataFrames can result in errors due
to packet size limitations being exceeded. This can be avoided by
setting the chunksize parameter when calling to_sql. For example,
the following writes data to the database in batches of 1000 rows at a
time:

In[530]: data.to_sql('data_chunked', engine, chunksize=1000)

to_sql()
will try to map your data to an appropriate SQL data type based on the
dtype of the data. When you have columns of dtype object, pandas will
try to infer the data type.

You can always override the default type by specifying the desired SQL
type of any of the columns by using the dtype argument. This argument
needs a dictionary mapping column names to SQLAlchemy types (or strings
for the sqlite3 fallback mode). For example, specifying to use the
sqlalchemy String type instead of the default Text type for string
columns:

In[531]: from sqlalchemy.types import String

In[532]: data.to_sql('data_dtype', engine, dtype={'Col_1': String})

Note

Due to the limited support for timedelta’s in the different database
flavors, columns with type timedelta64 will be written as integer
values as nanoseconds to the database and a warning will be raised.

Note

Columns of category dtype will be converted to the dense
representation as you would get with np.asarray(categorical) (e.g. for
string categories this gives an array of strings). Because of this,
reading the database table back in does not generate a categorical.

Reading Tables¶

read_sql_table()
will read a database table given the table name and optionally a subset
of columns to read.

Note

In order to use
read_sql_table(),
you must have the SQLAlchemy optional dependency installed.

In[533]: pd.read_sql_table('data', engine)
Out[533]: 
   index  id       Date Col_1  Col_2  Col_3
0      0  26 2010-10-18     X  27.50   True
1      1  42 2010-10-19     Y -12.50  False
2      2  63 2010-10-20     Z   5.73   True

You can also specify the name of the column as the DataFrame index,
and specify a subset of columns to be read.

In[534]: pd.read_sql_table('data', engine, index_col='id')
Out[534]: 
    index       Date Col_1  Col_2  Col_3
id                                      
26      0 2010-10-18     X  27.50   True
42      1 2010-10-19     Y -12.50  False
63      2 2010-10-20     Z   5.73   True

In[535]: pd.read_sql_table('data', engine, columns=['Col_1', 'Col_2'])
Out[535]: 
  Col_1  Col_2
0     X  27.50
1     Y -12.50
2     Z   5.73

And you can explicitly force columns to be parsed as dates:

In[536]: pd.read_sql_table('data', engine, parse_dates=['Date'])
Out[536]: 
   index  id       Date Col_1  Col_2  Col_3
0      0  26 2010-10-18     X  27.50   True
1      1  42 2010-10-19     Y -12.50  False
2      2  63 2010-10-20     Z   5.73   True

If needed you can explicitly specify a format string, or a dict of
arguments to pass to
pandas.to_datetime():

pd.read_sql_table('data', engine, parse_dates={'Date': '%Y-%m-%d'})
pd.read_sql_table('data', engine, parse_dates={'Date': {'format': '%Y-%m-%d %H:%M:%S'}})

You can check if a table exists using has_table()

Schema support¶

Reading from and writing to different schema’s is supported through the
schema keyword in the
read_sql_table()
and
to_sql()
functions. Note however that this depends on the database flavor (sqlite
does not have schema’s). For example:

df.to_sql('table', engine, schema='other_schema')
pd.read_sql_table('table', engine, schema='other_schema')

Querying¶

You can query using raw SQL in the
read_sql_query()
function. In this case you must use the SQL variant appropriate for your
database. When using SQLAlchemy, you can also pass SQLAlchemy Expression
language constructs, which are database-agnostic.

In[537]: pd.read_sql_query('SELECT * FROM data', engine)
Out[537]: 
   index  id                        Date Col_1  Col_2  Col_3
0      0  26  2010-10-18 00:00:00.000000     X  27.50      1
1      1  42  2010-10-19 00:00:00.000000     Y -12.50      0
2      2  63  2010-10-20 00:00:00.000000     Z   5.73      1

Of course, you can specify a more “complex” query.

In[538]: pd.read_sql_query("SELECT id, Col_1, Col_2 FROM data WHERE id = 42;", engine)
Out[538]: 
   id Col_1  Col_2
0  42     Y  -12.5

The
read_sql_query()
function supports a chunksize argument. Specifying this will return an
iterator through chunks of the query result:

In[539]: df = pd.DataFrame(np.random.randn(20, 3), columns=list('abc'))

In[540]: df.to_sql('data_chunks', engine, index=False)

In[541]: for chunk in pd.read_sql_query("SELECT * FROM data_chunks", engine, chunksize=5):
   .....:     print(chunk)
   .....: 
          a         b         c
0  0.280665 -0.073113  1.160339
1  0.369493  1.904659  1.111057
2  0.659050 -1.627438  0.602319
3  0.420282  0.810952  1.044442
4 -0.400878  0.824006 -0.562305
          a         b         c
0  1.954878 -1.331952 -1.760689
1 -1.650721 -0.890556 -1.119115
2  1.956079 -0.326499 -1.342676
3  1.114383 -0.586524 -1.236853
4  0.875839  0.623362 -0.434957
          a         b         c
0  1.407540  0.129102  1.616950
1  0.502741  1.558806  0.109403
2 -1.219744  2.449369 -0.545774
3 -0.198838 -0.700399 -0.203394
4  0.242669  0.201830  0.661020
          a         b         c
0  1.792158 -0.120465 -1.233121
1 -1.182318 -0.665755 -1.674196
2  0.825030 -0.498214 -0.310985
3 -0.001891 -1.396620 -0.861316
4  0.674712  0.618539 -0.443172

You can also run a plain query without creating a DataFrame with
execute(). This is useful for queries that don’t return values, such
as INSERT. This is functionally equivalent to calling execute on the
SQLAlchemy engine or db connection object. Again, you must use the SQL
syntax variant appropriate for your database.

from pandas.io import sql
sql.execute('SELECT * FROM table_name', engine)
sql.execute('INSERT INTO table_name VALUES(?, ?, ?)', engine,
            params=[('id', 1, 12.2, True)])

Engine connection examples¶

To connect with SQLAlchemy you use the create_engine() function to
create an engine object from database URI. You only need to create the
engine once per database you are connecting to.

from sqlalchemy import create_engine

engine = create_engine('postgresql://scott:tiger@localhost:5432/mydatabase')

engine = create_engine('mysql+mysqldb://scott:tiger@localhost/foo')

engine = create_engine('oracle://scott:[email protected]:1521/sidname')

engine = create_engine('mssql+pyodbc://mydsn')

# sqlite://<nohostname>/<path>
# where <path> is relative:
engine = create_engine('sqlite:///foo.db')

# or absolute, starting with a slash:
engine = create_engine('sqlite:////absolute/path/to/foo.db')

For more information see the examples the SQLAlchemy
documentation

Advanced SQLAlchemy queries¶

You can use SQLAlchemy constructs to describe your query.

Use sqlalchemy.text() to specify query parameters in a backend-neutral
way

In[542]: import sqlalchemy as sa

In[543]: pd.read_sql(sa.text('SELECT * FROM data where Col_1=:col1'),
   .....:             engine, params={'col1': 'X'})
   .....: 
Out[543]: 
   index  id                        Date Col_1  Col_2  Col_3
0      0  26  2010-10-18 00:00:00.000000     X   27.5      1

If you have an SQLAlchemy description of your database you can express
where conditions using SQLAlchemy expressions

In[544]: metadata = sa.MetaData()

In[545]: data_table = sa.Table('data', metadata,
   .....:     sa.Column('index', sa.Integer),
   .....:     sa.Column('Date', sa.DateTime),
   .....:     sa.Column('Col_1', sa.String),
   .....:     sa.Column('Col_2', sa.Float),
   .....:     sa.Column('Col_3', sa.Boolean),
   .....: )
   .....: 

In[546]: pd.read_sql(sa.select([data_table]).where(data_table.c.Col_3 == True), engine)
Out[546]: 
   index       Date Col_1  Col_2  Col_3
0      0 2010-10-18     X  27.50   True
1      2 2010-10-20     Z   5.73   True

You can combine SQLAlchemy expressions with parameters passed to
read_sql()
using sqlalchemy.bindparam()

In[547]: import datetime as dt

In[548]: expr = sa.select([data_table]).where(data_table.c.Date > sa.bindparam('date'))

In[549]: pd.read_sql(expr, engine, params={'date': dt.datetime(2010, 10, 18)})
Out[549]: 
   index       Date Col_1  Col_2  Col_3
0      1 2010-10-19     Y -12.50  False
1      2 2010-10-20     Z   5.73   True

Sqlite fallback¶

The use of sqlite is supported without using SQLAlchemy. This mode
requires a Python database adapter which respect the
Python DB-API.

You can create connections like so:

import sqlite3
con = sqlite3.connect(':memory:')

And then issue the following queries:

data.to_sql('data', cnx)
pd.read_sql_query("SELECT * FROM data", con)

Google BigQuery¶

Warning

Starting in 0.20.0, pandas has split off Google BigQuery support into
the separate package pandas-gbq. You can pip install pandas-gbq to
get it.

The pandas-gbq package provides functionality to read/write from
Google BigQuery.

pandas integrates with this external package. if pandas-gbq is
installed, you can use the pandas methods pd.read_gbq and
DataFrame.to_gbq, which will call the respective functions from
pandas-gbq.

Full documentation can be found
here.

Stata Format¶

Writing to Stata format¶

The method to_stata() will write a DataFrame into a .dta file. The
format version of this file is always 115 (Stata 12).

In[550]: df = pd.DataFrame(randn(10, 2), columns=list('AB'))

In[551]: df.to_stata('stata.dta')

Stata data files have limited data type support; only strings with 244
or fewer characters, int8, int16, int32, float32 and float64
can be stored in .dta files. Additionally, Stata reserves certain
values to represent missing data. Exporting a non-missing value that is
outside of the permitted range in Stata for a particular data type will
retype the variable to the next larger size. For example, int8 values
are restricted to lie between -127 and 100 in Stata, and so variables
with values above 100 will trigger a conversion to int16. nan values
in floating points data types are stored as the basic missing data type
(. in Stata).

Note

It is not possible to export missing data values for integer data types.

The Stata writer gracefully handles other data types including
int64, bool, uint8, uint16, uint32 by casting to the smallest
supported type that can represent the data. For example, data with a
type of uint8 will be cast to int8 if all values are less than 100
(the upper bound for non-missing int8 data in Stata), or, if values
are outside of this range, the variable is cast to int16.

Warning

Conversion from int64 to float64 may result in a loss of precision
if int64 values are larger than 2**53.

Warning

StataWriter and to_stata() only support fixed width strings
containing up to 244 characters, a limitation imposed by the version 115
dta file format. Attempting to write Stata dta files with strings
longer than 244 characters raises a ValueError.

Reading from Stata format¶

The top-level function read_stata will read a dta file and return
either a DataFrame or a StataReader that can be used to read the
file incrementally.

In[552]: pd.read_stata('stata.dta')
Out[552]: 
   index         A         B
0      0  1.810535 -1.305727
1      1 -0.344987 -0.230840
2      2 -2.793085  1.937529
3      3  0.366332 -1.044589
4      4  2.051173  0.585662
5      5  0.429526 -0.606998
6      6  0.106223 -1.525680
7      7  0.795026 -0.374438
8      8  0.134048  1.202055
9      9  0.284748  0.262467

Specifying a chunksize yields a StataReader instance that can be
used to read chunksize lines from the file at a time. The
StataReader object can be used as an iterator.

In[553]: reader = pd.read_stata('stata.dta', chunksize=3)

In[554]: for df in reader:
   .....:     print(df.shape)
   .....: 
(3, 3)
(3, 3)
(3, 3)
(1, 3)

For more fine-grained control, use iterator=True and specify
chunksize with each call to read().

In[555]: reader = pd.read_stata('stata.dta', iterator=True)

In[556]: chunk1 = reader.read(5)

In[557]: chunk2 = reader.read(5)

Currently the index is retrieved as a column.

The parameter convert_categoricals indicates whether value labels
should be read and used to create a Categorical variable from them.
Value labels can also be retrieved by the function value_labels, which
requires read() to be called before use.

The parameter convert_missing indicates whether missing value
representations in Stata should be preserved. If False (the default),
missing values are represented as np.nan. If True, missing values
are represented using StataMissingValue objects, and columns
containing missing values will have object data type.

Note

read_stata()
and StataReader support .dta formats 113-115 (Stata 10-12), 117 (Stata
13), and 118 (Stata 14).

Note

Setting preserve_dtypes=False will upcast to the standard pandas data
types: int64 for all integer types and float64 for floating point
data. By default, the Stata data types are preserved when importing.

Categorical Data¶

Categorical data can be exported to Stata data files as value
labeled data. The exported data consists of the underlying category
codes as integer data values and the categories as value labels. Stata
does not have an explicit equivalent to a Categorical and information
about whether the variable is ordered is lost when exporting.

Warning

Stata only supports string value labels, and so str is called on the
categories when exporting data. Exporting Categorical variables with
non-string categories produces a warning, and can result a loss of
information if the str representations of the categories are not
unique.

Labeled data can similarly be imported from Stata data files as
Categorical variables using the keyword argument
convert_categoricals (True by default). The keyword argument
order_categoricals (True by default) determines whether imported
Categorical variables are ordered.

Note

When importing categorical data, the values of the variables in the
Stata data file are not preserved since Categorical variables always
use integer data types between -1 and n-1 where n is the number of
categories. If the original values in the Stata data file are
required, these can be imported by setting convert_categoricals=False,
which will import original data (but not the variable labels). The
original values can be matched to the imported categorical data since
there is a simple mapping between the original Stata data values and
the category codes of imported Categorical variables: missing values are
assigned code -1, and the smallest original value is assigned 0, the
second smallest is assigned 1 and so on until the largest original
value is assigned the code n-1.

Note

Stata supports partially labeled series. These series have value
labels for some but not all data values. Importing a partially labeled
series will produce a Categorical with string categories for the
values that are labeled and numeric categories for values with no label.

SAS Formats¶

The top-level function
read_sas()
can read (but not write) SAS xport (.XPT) and (since v0.18.0) SAS7BDAT
(.sas7bdat) format files.

SAS files only contain two value types: ASCII text and floating point
values (usually 8 bytes but sometimes truncated). For xport files, there
is no automatic type conversion to integers, dates, or categoricals. For
SAS7BDAT files, the format codes may allow date variables to be
automatically converted to dates. By default the whole file is read and
returned as a DataFrame.

Specify a chunksize or use iterator=True to obtain reader objects
(XportReader or SAS7BDATReader) for incrementally reading the file.
The reader objects also have attributes that contain additional
information about the file and its variables.

Read a SAS7BDAT file:

df = pd.read_sas('sas_data.sas7bdat')

Obtain an iterator and read an XPORT file 100,000 lines at a time:

rdr = pd.read_sas('sas_xport.xpt', chunk=100000)
for chunk in rdr:
    do_something(chunk)

The
specification
for the xport file format is available from the SAS web site.

No official documentation is available for the SAS7BDAT format.

Other file formats¶

pandas itself only supports IO with a limited set of file formats that
map cleanly to its tabular data model. For reading and writing other
file formats into and from pandas, we recommend these packages from the
broader community.

netCDF¶

xarray
provides data structures inspired by the pandas DataFrame for working
with multi-dimensional datasets, with a focus on the netCDF file format
and easy conversion to and from pandas.

Performance Considerations¶

This is an informal comparison of various IO methods, using pandas
0.20.3. Timings are machine dependent and small differences should be
ignored.

In[1]: sz = 1000000
In[2]: df = pd.DataFrame({'A': randn(sz), 'B': [1] * sz})

In[3]: df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000000 entries, 0 to 999999
Data columns (total 2 columns):
A    1000000 non-null float64
B    1000000 non-null int64
dtypes: float64(1), int64(1)
memory usage: 15.3 MB

When writing, the top-three functions in terms of speed are are
test_pickle_write, test_feather_write and
test_hdf_fixed_write_compress.

In[14]: %timeit test_sql_write(df)
2.37 s ± 36.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

In[15]: %timeit test_hdf_fixed_write(df)
194 ms ± 65.9 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

In[26]: %timeit test_hdf_fixed_write_compress(df)
119 ms ± 2.15 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

In[16]: %timeit test_hdf_table_write(df)
623 ms ± 125 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

In[27]: %timeit test_hdf_table_write_compress(df)
563 ms ± 23.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

In[17]: %timeit test_csv_write(df)
3.13 s ± 49.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

In[30]: %timeit test_feather_write(df)
103 ms ± 5.88 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

In[31]: %timeit test_pickle_write(df)
109 ms ± 3.72 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

In[32]: %timeit test_pickle_write_compress(df)
3.33 s ± 55.2 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

When reading, the top three are test_feather_read, test_pickle_read
and test_hdf_fixed_read.

In[18]: %timeit test_sql_read()
1.35 s ± 14.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

In[19]: %timeit test_hdf_fixed_read()
14.3 ms ± 438 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

In[28]: %timeit test_hdf_fixed_read_compress()
23.5 ms ± 672 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

In[20]: %timeit test_hdf_table_read()
35.4 ms ± 314 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

In[29]: %timeit test_hdf_table_read_compress()
42.6 ms ± 2.1 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

In[22]: %timeit test_csv_read()
516 ms ± 27.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

In[33]: %timeit test_feather_read()
4.06 ms ± 115 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

In[34]: %timeit test_pickle_read()
6.5 ms ± 172 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

In[35]: %timeit test_pickle_read_compress()
588 ms ± 3.57 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

Space on disk (in bytes)

34816000 Aug 21 18:00 test.sql
24009240 Aug 21 18:00 test_fixed.hdf
 7919610 Aug 21 18:00 test_fixed_compress.hdf
24458892 Aug 21 18:00 test_table.hdf
 8657116 Aug 21 18:00 test_table_compress.hdf
28520770 Aug 21 18:00 test.csv
16000248 Aug 21 18:00 test.feather
16000848 Aug 21 18:00 test.pkl
 7554108 Aug 21 18:00 test.pkl.compress

And here’s the code:

import os
import pandas as pd
import sqlite3
from numpy.random import randn
from pandas.io import sql

sz = 1000000
df = pd.DataFrame({'A': randn(sz), 'B': [1] * sz})

def test_sql_write(df):
    if os.path.exists('test.sql'):
        os.remove('test.sql')
    sql_db = sqlite3.connect('test.sql')
    df.to_sql(name='test_table', con=sql_db)
    sql_db.close()

def test_sql_read():
    sql_db = sqlite3.connect('test.sql')
    pd.read_sql_query("select * from test_table", sql_db)
    sql_db.close()

def test_hdf_fixed_write(df):
    df.to_hdf('test_fixed.hdf', 'test', mode='w')

def test_hdf_fixed_read():
    pd.read_hdf('test_fixed.hdf', 'test')

def test_hdf_fixed_write_compress(df):
    df.to_hdf('test_fixed_compress.hdf', 'test', mode='w', complib='blosc')

def test_hdf_fixed_read_compress():
    pd.read_hdf('test_fixed_compress.hdf', 'test')

def test_hdf_table_write(df):
    df.to_hdf('test_table.hdf', 'test', mode='w', format='table')

def test_hdf_table_read():
    pd.read_hdf('test_table.hdf', 'test')

def test_hdf_table_write_compress(df):
    df.to_hdf('test_table_compress.hdf', 'test', mode='w', complib='blosc', format='table')

def test_hdf_table_read_compress():
    pd.read_hdf('test_table_compress.hdf', 'test')

def test_csv_write(df):
    df.to_csv('test.csv', mode='w')

def test_csv_read():
    pd.read_csv('test.csv', index_col=0)

def test_feather_write(df):
    df.to_feather('test.feather')

def test_feather_read():
    pd.read_feather('test.feather')

def test_pickle_write(df):
    df.to_pickle('test.pkl')

def test_pickle_read():
    pd.read_pickle('test.pkl')

def test_pickle_write_compress(df):
    df.to_pickle('test.pkl.compress', compression='xz')

def test_pickle_read_compress():
    pd.read_pickle('test.pkl.compress', compression='xz')

Comments

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By setting the 'engine' in the DataFrame and Panel 'to_excel()' methods.

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