Read excel pandas as text

.. currentmodule:: pandas

IO tools (text, CSV, HDF5, …)

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

:ref:`Here <io.perf>` is an informal performance comparison for some of these IO methods.

CSV & text files

The workhorse function for reading text files (a.k.a. flat files) is
:func:`read_csv`. See the :ref:`cookbook<cookbook.csv>` for some advanced strategies.

Parsing options

:func:`read_csv` accepts the following common arguments:

Basic

filepath_or_buffer : various

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

sep : str, defaults to ',' for :func:`read_csv`, t for :func:`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,
:class:`python: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.

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 MultiIndex 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 are not allowed.

index_col : int, str, sequence of int / str, or False, optional, default None

Column(s) to use as the row labels of the DataFrame, either given as
string name or column index. If a sequence of int / str is given, a
MultiIndex is used.

Note

index_col=False can be used to force pandas to not use the first
column as the index, e.g. when you have a malformed file with delimiters at
the end of each line.

The default value of None instructs pandas to guess. If the number of
fields in the column header row is equal to the number of fields in the body
of the data file, then a default index is used. If it is larger, then
the first columns are used as index so that the remaining number of fields in
the body are equal to the number of fields in the header.

The first row after the header is used to determine the number of columns,
which will go into the index. If the subsequent rows contain less columns
than the first row, they are filled with NaN.

This can be avoided through usecols. This ensures that the columns are
taken as is and the trailing data are ignored.

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). If names are given, the document
header row(s) are not taken into account. 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:

.. ipython:: python

   import pandas as pd
   from io import StringIO

   data = "col1,col2,col3na,b,1na,b,2nc,d,3"
   pd.read_csv(StringIO(data))
   pd.read_csv(StringIO(data), usecols=lambda x: x.upper() in ["COL1", "COL3"])

Using this parameter results in much faster parsing time and lower memory usage
when using the c engine. The Python engine loads the data first before deciding
which columns to drop.

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, 'c': 'Int64'}
Use str or object together with suitable na_values settings to preserve
and not interpret dtype. If converters are specified, they will be applied INSTEAD
of dtype conversion.

.. versionadded:: 1.5.0

   Support for defaultdict was added. Specify a defaultdict as input where
   the default determines the dtype of the columns which are not explicitly
   listed.

dtype_backend : {«numpy_nullable», «pyarrow»}, defaults to NumPy backed DataFrames

Which dtype_backend to use, e.g. whether a DataFrame should have NumPy
arrays, nullable dtypes are used for all dtypes that have a nullable
implementation when «numpy_nullable» is set, pyarrow is used for all
dtypes if «pyarrow» is set.

The dtype_backends are still experimential.

.. versionadded:: 2.0

engine : {'c', 'python', 'pyarrow'}

Parser engine to use. The C and pyarrow engines are faster, while the python engine
is currently more feature-complete. Multithreading is currently only supported by
the pyarrow engine.

.. versionadded:: 1.4.0

   The "pyarrow" engine was added as an *experimental* engine, and some features
   are unsupported, or may not work correctly, with this engine.

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:

.. ipython:: python

   data = "col1,col2,col3na,b,1na,b,2nc,d,3"
   pd.read_csv(StringIO(data))
   pd.read_csv(StringIO(data), skiprows=lambda x: x % 2 != 0)

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 :ref:`na values const <io.navaluesconst>` 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’.

Note

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.

.. deprecated:: 2.0.0
 A strict version of this argument is now the default, passing it has no effect.

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.

.. deprecated:: 2.0.0
 Use ``date_format`` instead, or read in as ``object`` and then apply
 :func:`to_datetime` as-needed.

date_format : str or dict of column -> format, default None

If used in conjunction with parse_dates, will parse dates according to this
format. For anything more complex,
please read in as object and then apply :func:`to_datetime` as-needed.

.. versionadded:: 2.0.0

dayfirst : boolean, default False

DD/MM format dates, international and European format.

cache_dates : boolean, default True

If True, use a cache of unique, converted dates to apply the datetime
conversion. May produce significant speed-up when parsing duplicate
date strings, especially ones with timezone offsets.

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 :ref:`iterating and chunking
<io.chunking>` below.

Quoting, compression, and file format

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

For on-the-fly decompression of on-disk data. If ‘infer’, then use gzip,
bz2, zip, xz, or zstandard if filepath_or_buffer is path-like ending in ‘.gz’, ‘.bz2’,
‘.zip’, ‘.xz’, ‘.zst’, 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. Can also be a dict with key 'method'
set to one of {'zip', 'gzip', 'bz2', 'zstd'} and other key-value pairs are
forwarded to zipfile.ZipFile, gzip.GzipFile, bz2.BZ2File, or zstandard.ZstdDecompressor.
As an example, the following could be passed for faster compression and to
create a reproducible gzip archive:
compression={'method': 'gzip', 'compresslevel': 1, 'mtime': 1}.

.. versionchanged:: 1.1.0 dict option extended to support ``gzip`` and ``bz2``.

.. versionchanged:: 1.2.0 Previous versions forwarded dict entries for 'gzip' to ``gzip.open``.

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 ‘#emptyna,b,cn1,2,3’ 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 :class:`python: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 :class:`python:csv.Dialect`
documentation for more details.

Error handling

on_bad_lines : {{‘error’, ‘warn’, ‘skip’}}, default ‘error’

Specifies what to do upon encountering a bad line (a line with too many fields).
Allowed values are :

• ‘error’, raise an ParserError when a bad line is encountered.
• ‘warn’, print a warning when a bad line is encountered and skip that line.
• ‘skip’, skip bad lines without raising or warning when they are encountered.

.. versionadded:: 1.3.0

Specifying column data types

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

.. ipython:: python

    import numpy as np

    data = "a,b,c,dn1,2,3,4n5,6,7,8n9,10,11"
    print(data)

    df = pd.read_csv(StringIO(data), dtype=object)
    df
    df["a"][0]
    df = pd.read_csv(StringIO(data), dtype={"b": object, "c": np.float64, "d": "Int64"})
    df.dtypes

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 :ref:`here<basics.dtypes>` to learn more about dtypes, and
:ref:`here<basics.object_conversion>` to learn more about object conversion in
pandas.

For instance, you can use the converters argument
of :func:`~pandas.read_csv`:

.. ipython:: python

    data = "col_1n1n2n'A'n4.22"
    df = pd.read_csv(StringIO(data), converters={"col_1": str})
    df
    df["col_1"].apply(type).value_counts()

Or you can use the :func:`~pandas.to_numeric` function to coerce the
dtypes after reading in the data,

.. ipython:: python

    df2 = pd.read_csv(StringIO(data))
    df2["col_1"] = pd.to_numeric(df2["col_1"], errors="coerce")
    df2
    df2["col_1"].apply(type).value_counts()

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 :func:`~pandas.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 :func:`~pandas.read_csv` would certainly be
worth trying.

.. ipython:: python
     :okwarning:

     col_1 = list(range(500000)) + ["a", "b"] + list(range(500000))
     df = pd.DataFrame({"col_1": col_1})
     df.to_csv("foo.csv")
     mixed_df = pd.read_csv("foo.csv")
     mixed_df["col_1"].apply(type).value_counts()
     mixed_df["col_1"].dtype

.. ipython:: python
   :suppress:

   import os

   os.remove("foo.csv")

Setting dtype_backend="numpy_nullable" will result in nullable dtypes for every column.

.. ipython:: python

   data = """a,b,c,d,e,f,g,h,i,j
   1,2.5,True,a,,,,,12-31-2019,
   3,4.5,False,b,6,7.5,True,a,12-31-2019,
   """

   df = pd.read_csv(StringIO(data), dtype_backend="numpy_nullable", parse_dates=["i"])
   df
   df.dtypes

Specifying categorical dtype

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

.. ipython:: python

   data = "col1,col2,col3na,b,1na,b,2nc,d,3"

   pd.read_csv(StringIO(data))
   pd.read_csv(StringIO(data)).dtypes
   pd.read_csv(StringIO(data), dtype="category").dtypes

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

.. ipython:: python

   pd.read_csv(StringIO(data), dtype={"col1": "category"}).dtypes

Specifying dtype='category' 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
:class:`~pandas.api.types.CategoricalDtype` ahead of time, and pass that for
that column’s dtype.

.. ipython:: python

   from pandas.api.types import CategoricalDtype

   dtype = CategoricalDtype(["d", "c", "b", "a"], ordered=True)
   pd.read_csv(StringIO(data), dtype={"col1": dtype}).dtypes

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

.. ipython:: python

   dtype = CategoricalDtype(["a", "b", "d"])  # No 'c'
   pd.read_csv(StringIO(data), dtype={"col1": dtype}).col1

This matches the behavior of :meth:`Categorical.set_categories`.

.. ipython:: python

   df = pd.read_csv(StringIO(data), dtype="category")
   df.dtypes
   df["col3"]
   new_categories = pd.to_numeric(df["col3"].cat.categories)
   df["col3"] = df["col3"].cat.rename_categories(new_categories)
   df["col3"]

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:

.. ipython:: python

    data = "a,b,cn1,2,3n4,5,6n7,8,9"
    print(data)
    pd.read_csv(StringIO(data))

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):

.. ipython:: python

    print(data)
    pd.read_csv(StringIO(data), names=["foo", "bar", "baz"], header=0)
    pd.read_csv(StringIO(data), names=["foo", "bar", "baz"], header=None)

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

.. ipython:: python

    data = "skip this skip itna,b,cn1,2,3n4,5,6n7,8,9"
    pd.read_csv(StringIO(data), header=1)

Duplicate names parsing

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

.. ipython:: python

   data = "a,b,an0,1,2n3,4,5"
   pd.read_csv(StringIO(data))

There is no more duplicate data because duplicate columns ‘X’, …, ‘X’ become
‘X’, ‘X.1’, …, ‘X.N’.

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:

.. ipython:: python

    data = "a,b,c,dn1,2,3,foon4,5,6,barn7,8,9,baz"
    pd.read_csv(StringIO(data))
    pd.read_csv(StringIO(data), usecols=["b", "d"])
    pd.read_csv(StringIO(data), usecols=[0, 2, 3])
    pd.read_csv(StringIO(data), usecols=lambda x: x.upper() in ["A", "C"])

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

.. ipython:: python

   pd.read_csv(StringIO(data), usecols=lambda x: x not in ["a", "c"])

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.

.. ipython:: python

   data = "na,b,cn  n# commented linen1,2,3nn4,5,6"
   print(data)
   pd.read_csv(StringIO(data), comment="#")

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

.. ipython:: python

   data = "a,b,cnn1,2,3nnn4,5,6"
   pd.read_csv(StringIO(data), skip_blank_lines=False)

.. ipython:: python

   data = "#commentna,b,cnA,B,Cn1,2,3"
   pd.read_csv(StringIO(data), comment="#", header=1)
   data = "A,B,Cn#commentna,b,cn1,2,3"
   pd.read_csv(StringIO(data), comment="#", skiprows=2)

.. ipython:: python

   data = (
       "# emptyn"
       "# second empty linen"
       "# third emptylinen"
       "X,Y,Zn"
       "1,2,3n"
       "A,B,Cn"
       "1,2.,4.n"
       "5.,NaN,10.0n"
   )
   print(data)
   pd.read_csv(StringIO(data), comment="#", skiprows=4, header=1)

Comments

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

.. ipython:: python
   :suppress:

   data = (
       "ID,level,categoryn"
       "Patient1,123000,x # really unpleasantn"
       "Patient2,23000,y # wouldn't take his medicinen"
       "Patient3,1234018,z # awesome"
   )

   with open("tmp.csv", "w") as fh:
       fh.write(data)

.. ipython:: python

   print(open("tmp.csv").read())

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

.. ipython:: python

   df = pd.read_csv("tmp.csv")
   df

We can suppress the comments using the comment keyword:

.. ipython:: python

   df = pd.read_csv("tmp.csv", comment="#")
   df

.. ipython:: python
   :suppress:

   os.remove("tmp.csv")

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:

.. ipython:: python

   from io import BytesIO

   data = b"word,lengthn" b"Trxc3xa4umen,7n" b"Grxc3xbcxc3x9fe,5"
   data = data.decode("utf8").encode("latin-1")
   df = pd.read_csv(BytesIO(data), encoding="latin-1")
   df
   df["word"][1]

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:

.. ipython:: python

    data = "a,b,cn4,apple,bat,5.7n8,orange,cow,10"
    pd.read_csv(StringIO(data))

.. ipython:: python

    data = "index,a,b,cn4,apple,bat,5.7n8,orange,cow,10"
    pd.read_csv(StringIO(data), index_col=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:

.. ipython:: python

    data = "a,b,cn4,apple,bat,n8,orange,cow,"
    print(data)
    pd.read_csv(StringIO(data))
    pd.read_csv(StringIO(data), index_col=False)

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.

.. ipython:: python

    data = "a,b,cn4,apple,bat,n8,orange,cow,"
    print(data)
    pd.read_csv(StringIO(data), usecols=["b", "c"])
    pd.read_csv(StringIO(data), usecols=["b", "c"], index_col=0)

Date Handling

Specifying date columns

To better facilitate working with datetime data, :func:`read_csv`
uses the keyword arguments parse_dates and date_format
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:

.. ipython:: python

   with open("foo.csv", mode="w") as f:
       f.write("date,A,B,Cn20090101,a,1,2n20090102,b,3,4n20090103,c,4,5")

   # Use a column as an index, and parse it as dates.
   df = pd.read_csv("foo.csv", index_col=0, parse_dates=True)
   df

   # These are Python datetime objects
   df.index

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:

.. ipython:: python

   data = (
       "KORD,19990127, 19:00:00, 18:56:00, 0.8100n"
       "KORD,19990127, 20:00:00, 19:56:00, 0.0100n"
       "KORD,19990127, 21:00:00, 20:56:00, -0.5900n"
       "KORD,19990127, 21:00:00, 21:18:00, -0.9900n"
       "KORD,19990127, 22:00:00, 21:56:00, -0.5900n"
       "KORD,19990127, 23:00:00, 22:56:00, -0.5900"
   )

   with open("tmp.csv", "w") as fh:
       fh.write(data)

    df = pd.read_csv("tmp.csv", header=None, parse_dates=[[1, 2], [1, 3]])
    df

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

.. ipython:: python

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

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=[1, 2] 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:

.. ipython:: python

   date_spec = {"nominal": [1, 2], "actual": [1, 3]}
   df = pd.read_csv("tmp.csv", header=None, parse_dates=date_spec)
   df

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:

.. ipython:: python

   date_spec = {"nominal": [1, 2], "actual": [1, 3]}
   df = pd.read_csv(
       "tmp.csv", header=None, parse_dates=date_spec, index_col=0
   )  # index is the nominal column
   df

Date parsing functions

Finally, the parser allows you to specify a custom date_format.
Performance-wise, you should try these methods of parsing dates in order:

1. If you know the format, use date_format, e.g.:
   date_format="%d/%m/%Y" or date_format={column_name: "%d/%m/%Y"}.
2. If you different formats for different columns, or want to pass any extra options (such
   as utc) to to_datetime, then you should read in your data as object dtype, and
   then use to_datetime.

.. ipython:: python
   :suppress:

   os.remove("tmp.csv")

Parsing a CSV with mixed timezones

pandas cannot natively represent a column or index with mixed timezones. If your CSV
file contains columns with a mixture of timezones, the default result will be
an object-dtype column with strings, even with parse_dates.

.. ipython:: python

   content = """
   a
   2000-01-01T00:00:00+05:00
   2000-01-01T00:00:00+06:00"""
   df = pd.read_csv(StringIO(content), parse_dates=["a"])
   df["a"]

To parse the mixed-timezone values as a datetime column, read in as object dtype and
then call :func:`to_datetime` with utc=True.

.. ipython:: python

   df = pd.read_csv(StringIO(content))
   df["a"] = pd.to_datetime(df["a"], utc=True)
   df["a"]

Inferring datetime format

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 format inference 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.

If you try to parse a column of date strings, pandas will attempt to guess the format
from the first non-NaN element, and will then parse the rest of the column with that
format. If pandas fails to guess the format (for example if your first string is
'01 December US/Pacific 2000'), then a warning will be raised and each
row will be parsed individually by dateutil.parser.parse. The safest
way to parse dates is to explicitly set format=.

.. ipython:: python

   df = pd.read_csv(
       "foo.csv",
       index_col=0,
       parse_dates=True,
   )
   df

In the case that you have mixed datetime formats within the same column, you can
pass format='mixed'

.. ipython:: python

   data = io.StringIO("daten12 Jan 2000n2000-01-13n")
   df = pd.read_csv(data)
   df['date'] = pd.to_datetime(df['date'], format='mixed')
   df

or, if your datetime formats are all ISO8601 (possibly not identically-formatted):

.. ipython:: python

   data = io.StringIO("daten2020-01-01n2020-01-01 03:00n")
   df = pd.read_csv(data)
   df['date'] = pd.to_datetime(df['date'], format='ISO8601')
   df

.. ipython:: python
   :suppress:

   os.remove("foo.csv")

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:

.. ipython:: python

   data = "date,value,catn1/6/2000,5,an2/6/2000,10,bn3/6/2000,15,c"
   print(data)
   with open("tmp.csv", "w") as fh:
       fh.write(data)

   pd.read_csv("tmp.csv", parse_dates=[0])
   pd.read_csv("tmp.csv", dayfirst=True, parse_dates=[0])

.. ipython:: python
   :suppress:

   os.remove("tmp.csv")

Writing CSVs to binary file objects

.. versionadded:: 1.2.0

df.to_csv(..., mode="wb") allows writing a CSV to a file object
opened binary mode. In most cases, it is not necessary to specify
mode as Pandas will auto-detect whether the file object is
opened in text or binary mode.

.. ipython:: python

   import io

   data = pd.DataFrame([0, 1, 2])
   buffer = io.BytesIO()
   data.to_csv(buffer, encoding="utf-8", compression="gzip")

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:

.. ipython:: python

   val = "0.3066101993807095471566981359501369297504425048828125"
   data = "a,b,cn1,2,{0}".format(val)
   abs(
       pd.read_csv(
           StringIO(data),
           engine="c",
           float_precision=None,
       )["c"][0] - float(val)
   )
   abs(
       pd.read_csv(
           StringIO(data),
           engine="c",
           float_precision="high",
       )["c"][0] - float(val)
   )
   abs(
       pd.read_csv(StringIO(data), engine="c", float_precision="round_trip")["c"][0]
       - float(val)
   )

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:

.. ipython:: python

   data = (
       "ID|level|categoryn"
       "Patient1|123,000|xn"
       "Patient2|23,000|yn"
       "Patient3|1,234,018|z"
   )

   with open("tmp.csv", "w") as fh:
       fh.write(data)

    df = pd.read_csv("tmp.csv", sep="|")
    df

    df.level.dtype

The thousands keyword allows integers to be parsed correctly:

.. ipython:: python

    df = pd.read_csv("tmp.csv", sep="|", thousands=",")
    df

    df.level.dtype

.. ipython:: python
   :suppress:

   os.remove("tmp.csv")

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>', '#NA', 'NULL', 'null', 'NaN', '-NaN', 'nan', '-nan', 'None', ''].

Let us consider some examples:

pd.read_csv("path_to_file.csv", 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.

pd.read_csv("path_to_file.csv", keep_default_na=False, na_values=[""])

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

pd.read_csv("path_to_file.csv", keep_default_na=False, na_values=["NA", "0"])

Above, both NA and 0 as strings are NaN.

pd.read_csv("path_to_file.csv", 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.

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:

.. ipython:: python

    data = "a,b,cn1,Yes,2n3,No,4"
    print(data)
    pd.read_csv(StringIO(data))
    pd.read_csv(StringIO(data), true_values=["Yes"], false_values=["No"])

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:

.. ipython:: python
    :okexcept:

    data = "a,b,cn1,2,3n4,5,6,7n8,9,10"
    pd.read_csv(StringIO(data))

You can elect to skip bad lines:

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

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

Or pass a callable function to handle the bad line if engine="python".
The bad line will be a list of strings that was split by the sep:

In [29]: external_list = []

In [30]: def bad_lines_func(line):
    ...:     external_list.append(line)
    ...:     return line[-3:]

In [31]: pd.read_csv(StringIO(data), on_bad_lines=bad_lines_func, engine="python")
Out[31]:
   a  b   c
0  1  2   3
1  5  6   7
2  8  9  10

In [32]: external_list
Out[32]: [4, 5, 6, 7]

.. versionadded:: 1.4.0

Note that the callable function will handle only a line with too many fields.
Bad lines caused by other errors will be silently skipped.

For example:

def bad_lines_func(line):
   print(line)

data = 'name,typenname a,a is of type anname b,"b" is of type b"'
data
pd.read_csv(data, on_bad_lines=bad_lines_func, engine="python")

The line was not processed in this case, as a «bad line» here is caused by an escape character.

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

In [33]: pd.read_csv(StringIO(data), usecols=[0, 1, 2])

 Out[33]:
    a  b   c
 0  1  2   3
 1  4  5   6
 2  8  9  10

In case you want to keep all data including the lines with too many fields, you can
specify a sufficient number of names. This ensures that lines with not enough
fields are filled with NaN.

In [34]: pd.read_csv(StringIO(data), names=['a', 'b', 'c', 'd'])

Out[34]:
    a  b   c  d
 0  1  2   3  NaN
 1  4  5   6  7
 2  8  9  10  NaN

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 :class:`python:csv.Dialect` instance.

Suppose you had data with unenclosed quotes:

.. ipython:: python

   data = "label1,label2,label3n" 'index1,"a,c,en' "index2,b,d,f"
   print(data)

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:

.. ipython:: python
   :okwarning:

   import csv

   dia = csv.excel()
   dia.quoting = csv.QUOTE_NONE
   pd.read_csv(StringIO(data), dialect=dia)

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

.. ipython:: python

    data = "a,b,c~1,2,3~4,5,6"
    pd.read_csv(StringIO(data), lineterminator="~")

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

.. ipython:: python

   data = "a, b, cn1, 2, 3n4, 5, 6"
   print(data)
   pd.read_csv(StringIO(data), skipinitialspace=True)

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:

.. ipython:: python

   data = 'a,bn"hello, \"Bob\", nice to see you",5'
   print(data)
   pd.read_csv(StringIO(data), escapechar="\")

Files with fixed width columns

While :func:`read_csv` reads delimited data, the :func:`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:

.. ipython:: python

   data1 = (
       "id8141    360.242940   149.910199   11950.7n"
       "id1594    444.953632   166.985655   11788.4n"
       "id1849    364.136849   183.628767   11806.2n"
       "id1230    413.836124   184.375703   11916.8n"
       "id1948    502.953953   173.237159   12468.3"
   )
   with open("bar.csv", "w") as f:
       f.write(data1)

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:

.. ipython:: python

   # Column specifications are a list of half-intervals
   colspecs = [(0, 6), (8, 20), (21, 33), (34, 43)]
   df = pd.read_fwf("bar.csv", colspecs=colspecs, header=None, index_col=0)
   df

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:

.. ipython:: python

   # Widths are a list of integers
   widths = [6, 14, 13, 10]
   df = pd.read_fwf("bar.csv", widths=widths, header=None)
   df

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).

.. ipython:: python

   df = pd.read_fwf("bar.csv", header=None, index_col=0)
   df

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

.. ipython:: python

   pd.read_fwf("bar.csv", header=None, index_col=0).dtypes
   pd.read_fwf("bar.csv", header=None, dtype={2: "object"}).dtypes

.. ipython:: python
   :suppress:

   os.remove("bar.csv")

Indexes

Files with an «implicit» index column

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

.. ipython:: python

   data = "A,B,Cn20090101,a,1,2n20090102,b,3,4n20090103,c,4,5"
   print(data)
   with open("foo.csv", "w") as f:
       f.write(data)

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

.. ipython:: python

   pd.read_csv("foo.csv")

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

.. ipython:: python

   df = pd.read_csv("foo.csv", parse_dates=True)
   df.index

.. ipython:: python
   :suppress:

   os.remove("foo.csv")

Reading an index with a MultiIndex

Suppose you have data indexed by two columns:

.. ipython:: python

   data = 'year,indiv,zit,xitn1977,"A",1.2,.6n1977,"B",1.5,.5'
   print(data)
   with open("mindex_ex.csv", mode="w") as f:
       f.write(data)

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

.. ipython:: python

   df = pd.read_csv("mindex_ex.csv", index_col=[0, 1])
   df
   df.loc[1977]

.. ipython:: python
   :suppress:

   os.remove("mindex_ex.csv")

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.

.. ipython:: python

   from pandas._testing import makeCustomDataframe as mkdf

   df = mkdf(5, 3, r_idx_nlevels=2, c_idx_nlevels=4)
   df.to_csv("mi.csv")
   print(open("mi.csv").read())
   pd.read_csv("mi.csv", header=[0, 1, 2, 3], index_col=[0, 1])

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

.. ipython:: python

   data = ",a,a,a,b,c,cn,q,r,s,t,u,vnone,1,2,3,4,5,6ntwo,7,8,9,10,11,12"
   print(data)
   with open("mi2.csv", "w") as fh:
       fh.write(data)

   pd.read_csv("mi2.csv", header=[0, 1], index_col=0)

.. ipython:: python
   :suppress:

   os.remove("mi.csv")
   os.remove("mi2.csv")

Automatically «sniffing» the delimiter

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

.. ipython:: python

   df = pd.DataFrame(np.random.randn(10, 4))
   df.to_csv("tmp.csv", sep="|")
   df.to_csv("tmp2.csv", sep=":")
   pd.read_csv("tmp2.csv", sep=None, engine="python")

.. ipython:: python
   :suppress:

   os.remove("tmp2.csv")

Reading multiple files to create a single DataFrame

It’s best to use :func:`~pandas.concat` to combine multiple files.
See the :ref:`cookbook<cookbook.csv.multiple_files>` 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:

.. ipython:: python

   df = pd.DataFrame(np.random.randn(10, 4))
   df.to_csv("tmp.csv", sep="|")
   table = pd.read_csv("tmp.csv", sep="|")
   table

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

.. ipython:: python

   with pd.read_csv("tmp.csv", sep="|", chunksize=4) as reader:
       reader
       for chunk in reader:
           print(chunk)

.. versionchanged:: 1.2

  ``read_csv/json/sas`` return a context-manager when iterating through a file.

Specifying iterator=True will also return the TextFileReader object:

.. ipython:: python

   with pd.read_csv("tmp.csv", sep="|", iterator=True) as reader:
       reader.get_chunk(5)

.. ipython:: python
   :suppress:

   os.remove("tmp.csv")

Specifying the parser engine

Pandas currently supports three engines, the C engine, the python engine, and an experimental
pyarrow engine (requires the pyarrow package). In general, the pyarrow engine is fastest
on larger workloads and is equivalent in speed to the C engine on most other workloads.
The python engine tends to be slower than the pyarrow and C engines on most workloads. However,
the pyarrow engine is much less robust than the C engine, which lacks a few features compared to the
Python engine.

Where possible, pandas uses the C parser (specified as engine='c'), but it may fall
back to Python if C-unsupported options are specified.

Currently, options unsupported by the C and pyarrow engines 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'.

Options that are unsupported by the pyarrow engine which are not covered by the list above include:

• float_precision
• chunksize
• comment
• nrows
• thousands
• memory_map
• dialect
• on_bad_lines
• delim_whitespace
• quoting
• lineterminator
• converters
• decimal
• iterator
• dayfirst
• infer_datetime_format
• verbose
• skipinitialspace
• low_memory

Specifying these options with engine='pyarrow' will raise a ValueError.

Reading/writing remote files

You can pass in a URL to read or write remote files to many of pandas’ IO
functions — the following example shows reading a CSV file:

df = pd.read_csv("https://download.bls.gov/pub/time.series/cu/cu.item", sep="t")

.. versionadded:: 1.3.0

A custom header can be sent alongside HTTP(s) requests by passing a dictionary
of header key value mappings to the storage_options keyword argument as shown below:

headers = {"User-Agent": "pandas"}
df = pd.read_csv(
    "https://download.bls.gov/pub/time.series/cu/cu.item",
    sep="t",
    storage_options=headers
)

All URLs which are not local files or HTTP(s) are handled by
fsspec, if installed, and its various filesystem implementations
(including Amazon S3, Google Cloud, SSH, FTP, webHDFS…).
Some of these implementations will require additional packages to be
installed, for example
S3 URLs require the s3fs library:

df = pd.read_json("s3://pandas-test/adatafile.json")

When dealing with remote storage systems, you might need
extra configuration with environment variables or config files in
special locations. For example, to access data in your S3 bucket,
you will need to define credentials in one of the several ways listed in
the S3Fs documentation. The same is true
for several of the storage backends, and you should follow the links
at fsimpl1 for implementations built into fsspec and fsimpl2
for those not included in the main fsspec
distribution.

You can also pass parameters directly to the backend driver. For example,
if you do not have S3 credentials, you can still access public data by
specifying an anonymous connection, such as

.. versionadded:: 1.2.0

pd.read_csv(
    "s3://ncei-wcsd-archive/data/processed/SH1305/18kHz/SaKe2013"
    "-D20130523-T080854_to_SaKe2013-D20130523-T085643.csv",
    storage_options={"anon": True},
)

fsspec also allows complex URLs, for accessing data in compressed
archives, local caching of files, and more. To locally cache the above
example, you would modify the call to

pd.read_csv(
    "simplecache::s3://ncei-wcsd-archive/data/processed/SH1305/18kHz/"
    "SaKe2013-D20130523-T080854_to_SaKe2013-D20130523-T085643.csv",
    storage_options={"s3": {"anon": True}},
)

where we specify that the «anon» parameter is meant for the «s3» part of
the implementation, not to the caching implementation. Note that this caches to a temporary
directory for the duration of the session only, but you can also specify
a permanent store.

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 file object. If a file object it must be opened with newline=''
• 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
• columns: 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
• lineterminator: Character sequence denoting line end (default os.linesep)
• 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
• 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
  table	adhering to the JSON Table Schema

• 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.

• mode : string, writer mode when writing to path. ‘w’ for write, ‘a’ for append. Default ‘w’

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.

.. ipython:: python

   dfj = pd.DataFrame(np.random.randn(5, 2), columns=list("AB"))
   json = dfj.to_json()
   json

Orient options

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

.. ipython:: python

  dfjo = pd.DataFrame(
      dict(A=range(1, 4), B=range(4, 7), C=range(7, 10)),
      columns=list("ABC"),
      index=list("xyz"),
  )
  dfjo
  sjo = pd.Series(dict(x=15, y=16, z=17), name="D")
  sjo

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

.. ipython:: python

  dfjo.to_json(orient="columns")
  # Not available for Series

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

.. ipython:: python

  dfjo.to_json(orient="index")
  sjo.to_json(orient="index")

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:

.. ipython:: python

  dfjo.to_json(orient="records")
  sjo.to_json(orient="records")

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

.. ipython:: python

  dfjo.to_json(orient="values")
  # 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:

.. ipython:: python

  dfjo.to_json(orient="split")
  sjo.to_json(orient="split")

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

Date handling

Writing in ISO date format:

.. ipython:: python

   dfd = pd.DataFrame(np.random.randn(5, 2), columns=list("AB"))
   dfd["date"] = pd.Timestamp("20130101")
   dfd = dfd.sort_index(axis=1, ascending=False)
   json = dfd.to_json(date_format="iso")
   json

Writing in ISO date format, with microseconds:

.. ipython:: python

   json = dfd.to_json(date_format="iso", date_unit="us")
   json

Epoch timestamps, in seconds:

.. ipython:: python

   json = dfd.to_json(date_format="epoch", date_unit="s")
   json

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

.. ipython:: python

   dfj2 = dfj.copy()
   dfj2["date"] = pd.Timestamp("20130101")
   dfj2["ints"] = list(range(5))
   dfj2["bools"] = True
   dfj2.index = pd.date_range("20130101", periods=5)
   dfj2.to_json("test.json")

   with open("test.json") as fh:
       print(fh.read())

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:

.. ipython:: python

   pd.DataFrame([1.0, 2.0, complex(1.0, 2.0)]).to_json(default_handler=str)

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.

• 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.

• engine: Either "ujson", the built-in JSON parser, or "pyarrow" which dispatches to pyarrow’s pyarrow.json.read_json.
  The "pyarrow" is only available when lines=True

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.

Reading from a JSON string:

.. ipython:: python

   pd.read_json(json)

Reading from a file:

.. ipython:: python

   pd.read_json("test.json")

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

.. ipython:: python

   pd.read_json("test.json", dtype=object).dtypes

Specify dtypes for conversion:

.. ipython:: python

   pd.read_json("test.json", dtype={"A": "float32", "bools": "int8"}).dtypes

Preserve string indices:

.. ipython:: python

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

   sij = pd.read_json(json, convert_axes=False)
   sij
   sij.index
   sij.columns

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

.. ipython:: python

   json = dfj2.to_json(date_unit="ns")

   # Try to parse timestamps as milliseconds -> Won't Work
   dfju = pd.read_json(json, date_unit="ms")
   dfju

   # Let pandas detect the correct precision
   dfju = pd.read_json(json)
   dfju

   # Or specify that all timestamps are in nanoseconds
   dfju = pd.read_json(json, date_unit="ns")
   dfju

Normalization

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

.. ipython:: python

   data = [
       {"id": 1, "name": {"first": "Coleen", "last": "Volk"}},
       {"name": {"given": "Mark", "family": "Regner"}},
       {"id": 2, "name": "Faye Raker"},
   ]
   pd.json_normalize(data)

.. ipython:: python

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

   pd.json_normalize(data, "county", ["state", "shortname", ["info", "governor"]])

The max_level parameter provides more control over which level to end normalization.
With max_level=1 the following snippet normalizes until 1st nesting level of the provided dict.

.. ipython:: python

    data = [
        {
            "CreatedBy": {"Name": "User001"},
            "Lookup": {
                "TextField": "Some text",
                "UserField": {"Id": "ID001", "Name": "Name001"},
            },
            "Image": {"a": "b"},
        }
    ]
    pd.json_normalize(data, max_level=1)

Line delimited json

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

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.

.. ipython:: python

  jsonl = """
      {"a": 1, "b": 2}
      {"a": 3, "b": 4}
  """
  df = pd.read_json(jsonl, lines=True)
  df
  df.to_json(orient="records", lines=True)

  # reader is an iterator that returns ``chunksize`` lines each iteration
  with pd.read_json(StringIO(jsonl), lines=True, chunksize=1) as reader:
      reader
      for chunk in reader:
          print(chunk)

Line-limited json can also be read using the pyarrow reader by specifying engine="pyarrow".

.. ipython:: python

   from io import BytesIO
   df = pd.read_json(BytesIO(jsonl.encode()), lines=True, engine="pyarrow")
   df

.. versionadded:: 2.0.0

Table schema

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.

.. ipython:: python

   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"),
   )
   df
   df.to_json(orient="table", date_format="iso")

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:

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

  .. ipython:: python
  
     from pandas.io.json import build_table_schema
  
     s = pd.Series(pd.date_range("2016", periods=4))
     build_table_schema(s)
  
  

• datetimes with a timezone (before serializing), include an additional field
  tz with the time zone name (e.g. 'US/Central').

  .. ipython:: python
  
     s_tz = pd.Series(pd.date_range("2016", periods=12, tz="US/Central"))
     build_table_schema(s_tz)
  
  

• 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'.

  .. ipython:: python
  
     s_per = pd.Series(1, index=pd.period_range("2016", freq="A-DEC", periods=4))
     build_table_schema(s_per)
  
  

• Categoricals use the any type and an enum constraint listing
  the set of possible values. Additionally, an ordered field is included:

  .. ipython:: python
  
     s_cat = pd.Series(pd.Categorical(["a", "b", "a"]))
     build_table_schema(s_cat)
  
  

• A primaryKey field, containing an array of labels, is included
  if the index is unique:

  .. ipython:: python
  
     s_dupe = pd.Series([1, 2], index=[1, 1])
     build_table_schema(s_dupe)
  
  

• The primaryKey behavior is the same with MultiIndexes, but in this
  case the primaryKey is an array:

  .. ipython:: python
  
     s_multi = pd.Series(1, index=pd.MultiIndex.from_product([("a", "b"), (0, 1)]))
     build_table_schema(s_multi)
  
  

• 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.

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

.. ipython:: python

   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"),
   )
   df
   df.dtypes

   df.to_json("test.json", orient="table")
   new_df = pd.read_json("test.json", orient="table")
   new_df
   new_df.dtypes

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

.. ipython:: python
   :okwarning:

   df.index.name = "index"
   df.to_json("test.json", orient="table")
   new_df = pd.read_json("test.json", orient="table")
   print(new_df.index.name)

.. ipython:: python
   :suppress:

   os.remove("test.json")

When using orient='table' along with user-defined ExtensionArray,
the generated schema will contain an additional extDtype key in the respective
fields element. This extra key is not standard but does enable JSON roundtrips
for extension types (e.g. read_json(df.to_json(orient="table"), orient="table")).

The extDtype key carries the name of the extension, if you have properly registered
the ExtensionDtype, pandas will use said name to perform a lookup into the registry
and re-convert the serialized data into your custom dtype.

HTML

Reading HTML content

The top-level :func:`~pandas.io.html.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.

Read a URL with no options:

In [320]: "https://www.fdic.gov/resources/resolutions/bank-failures/failed-bank-list"
In [321]: pd.read_html(url)
Out[321]:
[                         Bank NameBank           CityCity StateSt  ...              Acquiring InstitutionAI Closing DateClosing FundFund
 0                    Almena State Bank             Almena      KS  ...                          Equity Bank    October 23, 2020    10538
 1           First City Bank of Florida  Fort Walton Beach      FL  ...            United Fidelity Bank, fsb    October 16, 2020    10537
 2                 The First State Bank      Barboursville      WV  ...                       MVB Bank, Inc.       April 3, 2020    10536
 3                   Ericson State Bank            Ericson      NE  ...           Farmers and Merchants Bank   February 14, 2020    10535
 4     City National Bank of New Jersey             Newark      NJ  ...                      Industrial Bank    November 1, 2019    10534
 ..                                 ...                ...     ...  ...                                  ...                 ...      ...
 558                 Superior Bank, FSB           Hinsdale      IL  ...                Superior Federal, FSB       July 27, 2001     6004
 559                Malta National Bank              Malta      OH  ...                    North Valley Bank         May 3, 2001     4648
 560    First Alliance Bank & Trust Co.         Manchester      NH  ...  Southern New Hampshire Bank & Trust    February 2, 2001     4647
 561  National State Bank of Metropolis         Metropolis      IL  ...              Banterra Bank of Marion   December 14, 2000     4646
 562                   Bank of Honolulu           Honolulu      HI  ...                   Bank of the Orient    October 13, 2000     4645

 [563 rows x 7 columns]]

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

.. ipython:: python

   html_str = """
            <table>
                <tr>
                    <th>A</th>
                    <th colspan="1">B</th>
                    <th rowspan="1">C</th>
                </tr>
                <tr>
                    <td>a</td>
                    <td>b</td>
                    <td>c</td>
                </tr>
            </table>
        """

   with open("tmp.html", "w") as f:
       f.write(html_str)
   df = pd.read_html("tmp.html")
   df[0]

.. ipython:: python
   :suppress:

   os.remove("tmp.html")

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

.. ipython:: python

   dfs = pd.read_html(StringIO(html_str))
   dfs[0]

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 (range 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"])

Specify whether to keep the default set of NaN values:

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

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},
)

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(np.random.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"])

Links can be extracted from cells along with the text using extract_links="all".

.. ipython:: python

    html_table = """
    <table>
      <tr>
        <th>GitHub</th>
      </tr>
      <tr>
        <td><a href="https://github.com/pandas-dev/pandas">pandas</a></td>
      </tr>
    </table>
    """

    df = pd.read_html(
        html_table,
        extract_links="all"
    )[0]
    df
    df[("GitHub", None)]
    df[("GitHub", None)].str[1]

.. versionadded:: 1.5.0

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.

.. ipython:: python

   from IPython.display import display, HTML

   df = pd.DataFrame(np.random.randn(2, 2))
   df
   html = df.to_html()
   print(html)  # raw html
   display(HTML(html))

The columns argument will limit the columns shown:

.. ipython:: python

   html = df.to_html(columns=[0])
   print(html)
   display(HTML(html))

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

.. ipython:: python

   html = df.to_html(float_format="{0:.10f}".format)
   print(html)
   display(HTML(html))

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

.. ipython:: python

   html = df.to_html(bold_rows=False)
   print(html)
   display(HTML(html))

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.

.. ipython:: python

   print(df.to_html(classes=["awesome_table_class", "even_more_awesome_class"]))

The render_links argument provides the ability to add hyperlinks to cells
that contain URLs.

.. ipython:: python

   url_df = pd.DataFrame(
       {
           "name": ["Python", "pandas"],
           "url": ["https://www.python.org/", "https://pandas.pydata.org"],
       }
   )
   html = url_df.to_html(render_links=True)
   print(html)
   display(HTML(html))

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

.. ipython:: python

   df = pd.DataFrame({"a": list("&<>"), "b": np.random.randn(3)})

Escaped:

.. ipython:: python

   html = df.to_html()
   print(html)
   display(HTML(html))

Not escaped:

.. ipython:: python

   html = df.to_html(escape=False)
   print(html)
   display(HTML(html))

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.

LaTeX

.. versionadded:: 1.3.0

Currently there are no methods to read from LaTeX, only output methods.

Writing to LaTeX files

Review the documentation for Styler.to_latex,
which gives examples of conditional styling and explains the operation of its keyword
arguments.

For simple application the following pattern is sufficient.

.. ipython:: python

   df = pd.DataFrame([[1, 2], [3, 4]], index=["a", "b"], columns=["c", "d"])
   print(df.style.to_latex())

To format values before output, chain the Styler.format
method.

.. ipython:: python

   print(df.style.format("€ {}").to_latex())

XML

Reading XML

.. versionadded:: 1.3.0

The top-level :func:`~pandas.io.xml.read_xml` function can accept an XML
string/file/URL and will parse nodes and attributes into a pandas DataFrame.

Let’s look at a few examples.

Read an XML string:

.. ipython:: python

   xml = """<?xml version="1.0" encoding="UTF-8"?>
   <bookstore>
     <book category="cooking">
       <title lang="en">Everyday Italian</title>
       <author>Giada De Laurentiis</author>
       <year>2005</year>
       <price>30.00</price>
     </book>
     <book category="children">
       <title lang="en">Harry Potter</title>
       <author>J K. Rowling</author>
       <year>2005</year>
       <price>29.99</price>
     </book>
     <book category="web">
       <title lang="en">Learning XML</title>
       <author>Erik T. Ray</author>
       <year>2003</year>
       <price>39.95</price>
     </book>
   </bookstore>"""

   df = pd.read_xml(xml)
   df

Read a URL with no options:

.. ipython:: python

   df = pd.read_xml("https://www.w3schools.com/xml/books.xml")
   df

Read in the content of the «books.xml» file and pass it to read_xml
as a string:

.. ipython:: python

   file_path = "books.xml"
   with open(file_path, "w") as f:
       f.write(xml)

   with open(file_path, "r") as f:
       df = pd.read_xml(f.read())
   df

Read in the content of the «books.xml» as instance of StringIO or
BytesIO and pass it to read_xml:

.. ipython:: python

   with open(file_path, "r") as f:
       sio = StringIO(f.read())

   df = pd.read_xml(sio)
   df

.. ipython:: python

   with open(file_path, "rb") as f:
       bio = BytesIO(f.read())

   df = pd.read_xml(bio)
   df

Even read XML from AWS S3 buckets such as NIH NCBI PMC Article Datasets providing
Biomedical and Life Science Jorurnals:

.. ipython:: python
   :okwarning:

   df = pd.read_xml(
       "s3://pmc-oa-opendata/oa_comm/xml/all/PMC1236943.xml",
       xpath=".//journal-meta",
   )
   df

With lxml as default parser, you access the full-featured XML library
that extends Python’s ElementTree API. One powerful tool is ability to query
nodes selectively or conditionally with more expressive XPath:

.. ipython:: python

   df = pd.read_xml(file_path, xpath="//book[year=2005]")
   df

Specify only elements or only attributes to parse:

.. ipython:: python

   df = pd.read_xml(file_path, elems_only=True)
   df

.. ipython:: python

   df = pd.read_xml(file_path, attrs_only=True)
   df

.. ipython:: python
   :suppress:

   os.remove("books.xml")

XML documents can have namespaces with prefixes and default namespaces without
prefixes both of which are denoted with a special attribute xmlns. In order
to parse by node under a namespace context, xpath must reference a prefix.

For example, below XML contains a namespace with prefix, doc, and URI at
https://example.com. In order to parse doc:row nodes,
namespaces must be used.

.. ipython:: python

   xml = """<?xml version='1.0' encoding='utf-8'?>
   <doc:data xmlns:doc="https://example.com">
     <doc:row>
       <doc:shape>square</doc:shape>
       <doc:degrees>360</doc:degrees>
       <doc:sides>4.0</doc:sides>
     </doc:row>
     <doc:row>
       <doc:shape>circle</doc:shape>
       <doc:degrees>360</doc:degrees>
       <doc:sides/>
     </doc:row>
     <doc:row>
       <doc:shape>triangle</doc:shape>
       <doc:degrees>180</doc:degrees>
       <doc:sides>3.0</doc:sides>
     </doc:row>
   </doc:data>"""

   df = pd.read_xml(xml,
                    xpath="//doc:row",
                    namespaces={"doc": "https://example.com"})
   df

Similarly, an XML document can have a default namespace without prefix. Failing
to assign a temporary prefix will return no nodes and raise a ValueError.
But assigning any temporary name to correct URI allows parsing by nodes.

.. ipython:: python

   xml = """<?xml version='1.0' encoding='utf-8'?>
   <data xmlns="https://example.com">
    <row>
      <shape>square</shape>
      <degrees>360</degrees>
      <sides>4.0</sides>
    </row>
    <row>
      <shape>circle</shape>
      <degrees>360</degrees>
      <sides/>
    </row>
    <row>
      <shape>triangle</shape>
      <degrees>180</degrees>
      <sides>3.0</sides>
    </row>
   </data>"""

   df = pd.read_xml(xml,
                    xpath="//pandas:row",
                    namespaces={"pandas": "https://example.com"})
   df

However, if XPath does not reference node names such as default, /*, then
namespaces is not required.

.. ipython:: python
     :okwarning:

   xml = """
   <data>
     <row>
       <shape sides="4">square</shape>
       <degrees>360</degrees>
     </row>
     <row>
       <shape sides="0">circle</shape>
       <degrees>360</degrees>
     </row>
     <row>
       <shape sides="3">triangle</shape>
       <degrees>180</degrees>
     </row>
   </data>"""

   df = pd.read_xml(xml, xpath="./row")
   df

With lxml as parser, you can flatten nested XML documents with an XSLT
script which also can be string/file/URL types. As background, XSLT is
a special-purpose language written in a special XML file that can transform
original XML documents into other XML, HTML, even text (CSV, JSON, etc.)
using an XSLT processor.

For example, consider this somewhat nested structure of Chicago «L» Rides
where station and rides elements encapsulate data in their own sections.
With below XSLT, lxml can transform original nested document into a flatter
output (as shown below for demonstration) for easier parse into DataFrame:

.. ipython:: python

   xml = """<?xml version='1.0' encoding='utf-8'?>
    <response>
     <row>
       <station id="40850" name="Library"/>
       <month>2020-09-01T00:00:00</month>
       <rides>
         <avg_weekday_rides>864.2</avg_weekday_rides>
         <avg_saturday_rides>534</avg_saturday_rides>
         <avg_sunday_holiday_rides>417.2</avg_sunday_holiday_rides>
       </rides>
     </row>
     <row>
       <station id="41700" name="Washington/Wabash"/>
       <month>2020-09-01T00:00:00</month>
       <rides>
         <avg_weekday_rides>2707.4</avg_weekday_rides>
         <avg_saturday_rides>1909.8</avg_saturday_rides>
         <avg_sunday_holiday_rides>1438.6</avg_sunday_holiday_rides>
       </rides>
     </row>
     <row>
       <station id="40380" name="Clark/Lake"/>
       <month>2020-09-01T00:00:00</month>
       <rides>
         <avg_weekday_rides>2949.6</avg_weekday_rides>
         <avg_saturday_rides>1657</avg_saturday_rides>
         <avg_sunday_holiday_rides>1453.8</avg_sunday_holiday_rides>
       </rides>
     </row>
    </response>"""

   xsl = """<xsl:stylesheet version="1.0" xmlns:xsl="http://www.w3.org/1999/XSL/Transform">
      <xsl:output method="xml" omit-xml-declaration="no" indent="yes"/>
      <xsl:strip-space elements="*"/>
      <xsl:template match="/response">
         <xsl:copy>
           <xsl:apply-templates select="row"/>
         </xsl:copy>
      </xsl:template>
      <xsl:template match="row">
         <xsl:copy>
           <station_id><xsl:value-of select="station/@id"/></station_id>
           <station_name><xsl:value-of select="station/@name"/></station_name>
           <xsl:copy-of select="month|rides/*"/>
         </xsl:copy>
      </xsl:template>
    </xsl:stylesheet>"""

   output = """<?xml version='1.0' encoding='utf-8'?>
    <response>
      <row>
         <station_id>40850</station_id>
         <station_name>Library</station_name>
         <month>2020-09-01T00:00:00</month>
         <avg_weekday_rides>864.2</avg_weekday_rides>
         <avg_saturday_rides>534</avg_saturday_rides>
         <avg_sunday_holiday_rides>417.2</avg_sunday_holiday_rides>
      </row>
      <row>
         <station_id>41700</station_id>
         <station_name>Washington/Wabash</station_name>
         <month>2020-09-01T00:00:00</month>
         <avg_weekday_rides>2707.4</avg_weekday_rides>
         <avg_saturday_rides>1909.8</avg_saturday_rides>
         <avg_sunday_holiday_rides>1438.6</avg_sunday_holiday_rides>
      </row>
      <row>
         <station_id>40380</station_id>
         <station_name>Clark/Lake</station_name>
         <month>2020-09-01T00:00:00</month>
         <avg_weekday_rides>2949.6</avg_weekday_rides>
         <avg_saturday_rides>1657</avg_saturday_rides>
         <avg_sunday_holiday_rides>1453.8</avg_sunday_holiday_rides>
      </row>
    </response>"""

   df = pd.read_xml(xml, stylesheet=xsl)
   df

For very large XML files that can range in hundreds of megabytes to gigabytes, :func:`pandas.read_xml`
supports parsing such sizeable files using lxml’s iterparse and etree’s iterparse
which are memory-efficient methods to iterate through an XML tree and extract specific elements and attributes.
without holding entire tree in memory.

.. versionadded:: 1.5.0

To use this feature, you must pass a physical XML file path into read_xml and use the iterparse argument.
Files should not be compressed or point to online sources but stored on local disk. Also, iterparse should be
a dictionary where the key is the repeating nodes in document (which become the rows) and the value is a list of
any element or attribute that is a descendant (i.e., child, grandchild) of repeating node. Since XPath is not
used in this method, descendants do not need to share same relationship with one another. Below shows example
of reading in Wikipedia’s very large (12 GB+) latest article data dump.

In [1]: df = pd.read_xml(
...         "/path/to/downloaded/enwikisource-latest-pages-articles.xml",
...         iterparse = {"page": ["title", "ns", "id"]}
...     )
...     df
Out[2]:
                                                     title   ns        id
0                                       Gettysburg Address    0     21450
1                                                Main Page    0     42950
2                            Declaration by United Nations    0      8435
3             Constitution of the United States of America    0      8435
4                     Declaration of Independence (Israel)    0     17858
...                                                    ...  ...       ...
3578760               Page:Black cat 1897 07 v2 n10.pdf/17  104    219649
3578761               Page:Black cat 1897 07 v2 n10.pdf/43  104    219649
3578762               Page:Black cat 1897 07 v2 n10.pdf/44  104    219649
3578763      The History of Tom Jones, a Foundling/Book IX    0  12084291
3578764  Page:Shakespeare of Stratford (1926) Yale.djvu/91  104     21450

[3578765 rows x 3 columns]

Writing XML

.. versionadded:: 1.3.0

DataFrame objects have an instance method to_xml which renders the
contents of the DataFrame as an XML document.

Let’s look at a few examples.

Write an XML without options:

.. ipython:: python

   geom_df = pd.DataFrame(
       {
           "shape": ["square", "circle", "triangle"],
           "degrees": [360, 360, 180],
           "sides": [4, np.nan, 3],
       }
   )

   print(geom_df.to_xml())

Write an XML with new root and row name:

.. ipython:: python

   print(geom_df.to_xml(root_name="geometry", row_name="objects"))

Write an attribute-centric XML:

.. ipython:: python

   print(geom_df.to_xml(attr_cols=geom_df.columns.tolist()))

Write a mix of elements and attributes:

.. ipython:: python

   print(
       geom_df.to_xml(
           index=False,
           attr_cols=['shape'],
           elem_cols=['degrees', 'sides'])
   )

Any DataFrames with hierarchical columns will be flattened for XML element names
with levels delimited by underscores:

.. ipython:: python

   ext_geom_df = pd.DataFrame(
       {
           "type": ["polygon", "other", "polygon"],
           "shape": ["square", "circle", "triangle"],
           "degrees": [360, 360, 180],
           "sides": [4, np.nan, 3],
       }
   )

   pvt_df = ext_geom_df.pivot_table(index='shape',
                                    columns='type',
                                    values=['degrees', 'sides'],
                                    aggfunc='sum')
   pvt_df

   print(pvt_df.to_xml())

Write an XML with default namespace:

.. ipython:: python

   print(geom_df.to_xml(namespaces={"": "https://example.com"}))

Write an XML with namespace prefix:

.. ipython:: python

   print(
       geom_df.to_xml(namespaces={"doc": "https://example.com"},
                      prefix="doc")
   )

Write an XML without declaration or pretty print:

.. ipython:: python

   print(
       geom_df.to_xml(xml_declaration=False,
                      pretty_print=False)
   )

Write an XML and transform with stylesheet:

.. ipython:: python

   xsl = """<xsl:stylesheet version="1.0" xmlns:xsl="http://www.w3.org/1999/XSL/Transform">
      <xsl:output method="xml" omit-xml-declaration="no" indent="yes"/>
      <xsl:strip-space elements="*"/>
      <xsl:template match="/data">
        <geometry>
          <xsl:apply-templates select="row"/>
        </geometry>
      </xsl:template>
      <xsl:template match="row">
        <object index="{index}">
          <xsl:if test="shape!='circle'">
              <xsl:attribute name="type">polygon</xsl:attribute>
          </xsl:if>
          <xsl:copy-of select="shape"/>
          <property>
              <xsl:copy-of select="degrees|sides"/>
          </property>
        </object>
      </xsl:template>
    </xsl:stylesheet>"""

   print(geom_df.to_xml(stylesheet=xsl))

XML Final Notes

• All XML documents adhere to W3C specifications. Both etree and lxml
  parsers will fail to parse any markup document that is not well-formed or
  follows XML syntax rules. Do be aware HTML is not an XML document unless it
  follows XHTML specs. However, other popular markup types including KML, XAML,
  RSS, MusicML, MathML are compliant XML schemas.
• For above reason, if your application builds XML prior to pandas operations,
  use appropriate DOM libraries like etree and lxml to build the necessary
  document and not by string concatenation or regex adjustments. Always remember
  XML is a special text file with markup rules.
• With very large XML files (several hundred MBs to GBs), XPath and XSLT
  can become memory-intensive operations. Be sure to have enough available
  RAM for reading and writing to large XML files (roughly about 5 times the
  size of text).
• Because XSLT is a programming language, use it with caution since such scripts
  can pose a security risk in your environment and can run large or infinite
  recursive operations. Always test scripts on small fragments before full run.
• The etree parser supports all functionality of both read_xml and
  to_xml except for complex XPath and any XSLT. Though limited in features,
  etree is still a reliable and capable parser and tree builder. Its
  performance may trail lxml to a certain degree for larger files but
  relatively unnoticeable on small to medium size files.

Excel files

The :func:`~pandas.read_excel` method can read Excel 2007+ (.xlsx) files
using the openpyxl Python module. Excel 2003 (.xls) files
can be read using xlrd. Binary Excel (.xlsb)
files can be read using pyxlsb.
The :meth:`~DataFrame.to_excel` instance method is used for
saving a DataFrame to Excel. Generally the semantics are
similar to working with :ref:`csv<io.read_csv_table>` data.
See the :ref:`cookbook<cookbook.excel>` 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.

When using the engine_kwargs parameter, pandas will pass these arguments to the
engine. For this, it is important to know which function pandas is
using internally.

• For the engine openpyxl, pandas is using :func:`openpyxl.load_workbook` to read in (.xlsx) and (.xlsm) files.
• For the engine xlrd, pandas is using :func:`xlrd.open_workbook` to read in (.xls) files.
• For the engine pyxlsb, pandas is using :func:`pyxlsb.open_workbook` to read in (.xlsb) files.
• For the engine odf, pandas is using :func:`odf.opendocument.load` to read in (.ods) files.

# Returns a DataFrame
pd.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"] = pd.read_excel(xls, "Sheet1", index_col=None, na_values=["NA"])
    data["Sheet2"] = pd.read_excel(xls, "Sheet2", index_col=None, na_values=["NA"])

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

ExcelFile can also be called with a xlrd.book.Book object
as a parameter. This allows the user to control how the excel file is read.
For example, sheets can be loaded on demand by calling xlrd.open_workbook()
with on_demand=True.

import xlrd

xlrd_book = xlrd.open_workbook("path_to_file.xls", on_demand=True)
with pd.ExcelFile(xlrd_book) as xls:
    df1 = pd.read_excel(xls, "Sheet1")
    df2 = pd.read_excel(xls, "Sheet2")

Specifying 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
pd.read_excel("path_to_file.xls", "Sheet1", index_col=None, na_values=["NA"])

Using the sheet index:

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

Using all default values:

# Returns a DataFrame
pd.read_excel("path_to_file.xls")

Using None to get all sheets:

# Returns a dictionary of DataFrames
pd.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.
pd.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:

.. ipython:: python

   df = pd.DataFrame(
       {"a": [1, 2, 3, 4], "b": [5, 6, 7, 8]},
       index=pd.MultiIndex.from_product([["a", "b"], ["c", "d"]]),
   )
   df.to_excel("path_to_file.xlsx")
   df = pd.read_excel("path_to_file.xlsx", index_col=[0, 1])
   df

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

.. ipython:: python

   df.index = df.index.set_names(["lvl1", "lvl2"])
   df.to_excel("path_to_file.xlsx")
   df = pd.read_excel("path_to_file.xlsx", index_col=[0, 1])
   df

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

.. ipython:: python

   df.columns = pd.MultiIndex.from_product([["a"], ["b", "d"]], names=["c1", "c2"])
   df.to_excel("path_to_file.xlsx")
   df = pd.read_excel("path_to_file.xlsx", index_col=[0, 1], header=[0, 1])
   df

.. ipython:: python
   :suppress:

   os.remove("path_to_file.xlsx")

Missing values in columns specified in index_col will be forward filled to
allow roundtripping with to_excel for merged_cells=True. To avoid forward
filling the missing values use set_index after reading the data instead of
index_col.

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.

You can specify a comma-delimited set of Excel columns and ranges as a string:

pd.read_excel("path_to_file.xls", "Sheet1", usecols="A,C:E")

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

pd.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].

If usecols is a list of strings, it is assumed that each string corresponds
to a column name provided either by the user in names or inferred from the
document header row(s). Those strings define which columns will be parsed:

pd.read_excel("path_to_file.xls", "Sheet1", usecols=["foo", "bar"])

Element order is ignored, so usecols=['baz', 'joe'] is the same as ['joe', 'baz'].

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

pd.read_excel("path_to_file.xls", "Sheet1", usecols=lambda x: x.isalpha())

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:

pd.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:

pd.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:

def cfun(x):
    return int(x) if x else -1


pd.read_excel("path_to_file.xls", "Sheet1", converters={"MyInts": cfun})

Dtype specifications

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.

pd.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
.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 :class:`~pandas.io.excel.ExcelWriter`.

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

Writing Excel files to memory

pandas supports writing Excel files to buffer-like objects such as StringIO or
BytesIO using :class:`~pandas.io.excel.ExcelWriter`.

from io import BytesIO

bio = BytesIO()

# By setting the 'engine' in the ExcelWriter constructor.
writer = pd.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()

Excel writer engines

pandas chooses an Excel writer via two methods:

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

By default, pandas uses the XlsxWriter for .xlsx, openpyxl
for .xlsm. If you have multiple
engines installed, you can set the default engine through :ref:`setting the
config options <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

# By setting the 'engine' in the DataFrame 'to_excel()' methods.
df.to_excel("path_to_file.xlsx", sheet_name="Sheet1", engine="xlsxwriter")

# By setting the 'engine' in the ExcelWriter constructor.
writer = pd.ExcelWriter("path_to_file.xlsx", engine="xlsxwriter")

# Or via pandas configuration.
from pandas import options  # noqa: E402

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).

Using the Xlsxwriter engine provides many options for controlling the
format of an Excel worksheet created with the to_excel method. Excellent examples can be found in the
Xlsxwriter documentation here: https://xlsxwriter.readthedocs.io/working_with_pandas.html

OpenDocument Spreadsheets

The io methods for Excel files also support reading and writing OpenDocument spreadsheets
using the odfpy module. The semantics and features for reading and writing
OpenDocument spreadsheets match what can be done for Excel files using
engine='odf'. The optional dependency ‘odfpy’ needs to be installed.

The :func:`~pandas.read_excel` method can read OpenDocument spreadsheets

# Returns a DataFrame
pd.read_excel("path_to_file.ods", engine="odf")

.. versionadded:: 1.1.0

Similarly, the :func:`~pandas.to_excel` method can write OpenDocument spreadsheets

# Writes DataFrame to a .ods file
df.to_excel("path_to_file.ods", engine="odf")

Binary Excel (.xlsb) files

The :func:`~pandas.read_excel` method can also read binary Excel files
using the pyxlsb module. The semantics and features for reading
binary Excel files mostly match what can be done for Excel files using
engine='pyxlsb'. pyxlsb does not recognize datetime types
in files and will return floats instead.

# Returns a DataFrame
pd.read_excel("path_to_file.xlsb", engine="pyxlsb")

Clipboard

A handy way to grab data is to use the :meth:`~DataFrame.read_clipboard` method,
which takes the contents of the clipboard buffer and passes them to the
read_csv 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()
>>> clipdf
  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.

>>> df = pd.DataFrame(
...     {"A": [1, 2, 3], "B": [4, 5, 6], "C": ["p", "q", "r"]}, index=["x", "y", "z"]
... )

>>> df
  A B C
x 1 4 p
y 2 5 q
z 3 6 r
>>> df.to_clipboard()
>>> pd.read_clipboard()
  A B C
x 1 4 p
y 2 5 q
z 3 6 r

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

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.

.. ipython:: python

   df
   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:

.. ipython:: python

   pd.read_pickle("foo.pkl")

.. ipython:: python
   :suppress:

   os.remove("foo.pkl")

Compressed pickle files

:func:`read_pickle`, :meth:`DataFrame.to_pickle` and :meth:`Series.to_pickle` can read
and write compressed pickle files. The compression types of gzip, bz2, xz, zstd 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, xz, zstd if filename ends in '.gz', '.bz2', '.zip',
'.xz', or '.zst', respectively.

The compression parameter can also be a dict in order to pass options to the
compression protocol. It must have a 'method' key set to the name
of the compression protocol, which must be one of
{'zip', 'gzip', 'bz2', 'xz', 'zstd'}. All other key-value pairs are passed to
the underlying compression library.

.. ipython:: python

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

Using an explicit compression type:

.. ipython:: python

   df.to_pickle("data.pkl.compress", compression="gzip")
   rt = pd.read_pickle("data.pkl.compress", compression="gzip")
   rt

Inferring compression type from the extension:

.. ipython:: python

   df.to_pickle("data.pkl.xz", compression="infer")
   rt = pd.read_pickle("data.pkl.xz", compression="infer")
   rt

The default is to ‘infer’:

.. ipython:: python

   df.to_pickle("data.pkl.gz")
   rt = pd.read_pickle("data.pkl.gz")
   rt

   df["A"].to_pickle("s1.pkl.bz2")
   rt = pd.read_pickle("s1.pkl.bz2")
   rt

Passing options to the compression protocol in order to speed up compression:

.. ipython:: python

   df.to_pickle("data.pkl.gz", compression={"method": "gzip", "compresslevel": 1})

.. ipython:: python
   :suppress:

   os.remove("data.pkl.compress")
   os.remove("data.pkl.xz")
   os.remove("data.pkl.gz")
   os.remove("s1.pkl.bz2")

msgpack

pandas support for msgpack has been removed in version 1.0.0. It is
recommended to use :ref:`pickle <io.pickle>` instead.

Alternatively, you can also the Arrow IPC serialization format for on-the-wire
transmission of pandas objects. For documentation on pyarrow, see
here.

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 :ref:`cookbook <cookbook.hdf>`
for some advanced strategies

.. ipython:: python
   :suppress:
   :okexcept:

   os.remove("store.h5")

.. ipython:: python

   store = pd.HDFStore("store.h5")
   print(store)

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

.. ipython:: python

   index = pd.date_range("1/1/2000", periods=8)
   s = pd.Series(np.random.randn(5), index=["a", "b", "c", "d", "e"])
   df = pd.DataFrame(np.random.randn(8, 3), index=index, columns=["A", "B", "C"])

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

   store["df"] = df

   store

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

.. ipython:: python

   # store.get('df') is an equivalent method
   store["df"]

   # dotted (attribute) access provides get as well
   store.df

Deletion of the object specified by the key:

.. ipython:: python

   # store.remove('df') is an equivalent method
   del store["df"]

   store

Closing a Store and using a context manager:

.. ipython:: python

   store.close()
   store
   store.is_open

   # Working with, and automatically closing the store using a context manager
   with pd.HDFStore("store.h5") as store:
       store.keys()

.. ipython:: python
   :suppress:

   store.close()
   os.remove("store.h5")

Read/write API

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

.. ipython:: python

   df_tl = pd.DataFrame({"A": list(range(5)), "B": list(range(5))})
   df_tl.to_hdf("store_tl.h5", "table", append=True)
   pd.read_hdf("store_tl.h5", "table", where=["index>2"])

.. ipython:: python
   :suppress:
   :okexcept:

   os.remove("store_tl.h5")

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

.. ipython:: python

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

   df_with_missing.to_hdf("file.h5", "df_with_missing", format="table", mode="w")

   pd.read_hdf("file.h5", "df_with_missing")

   df_with_missing.to_hdf(
       "file.h5", "df_with_missing", format="table", mode="w", dropna=True
   )
   pd.read_hdf("file.h5", "df_with_missing")

.. ipython:: python
   :suppress:

   os.remove("file.h5")

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'.

>>> pd.DataFrame(np.random.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.

.. ipython:: python
   :suppress:
   :okexcept:

   os.remove("store.h5")

.. ipython:: python

   store = pd.HDFStore("store.h5")
   df1 = df[0:4]
   df2 = df[4:]

   # append data (creates a table automatically)
   store.append("df", df1)
   store.append("df", df2)
   store

   # select the entire object
   store.select("df")

   # the type of stored data
   store.root.df._v_attrs.pandas_type

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 without 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.

.. ipython:: python

   store.put("foo/bar/bah", df)
   store.append("food/orange", df)
   store.append("food/apple", df)
   store

   # a list of keys are returned
   store.keys()

   # remove all nodes under this level
   store.remove("food")
   store

You can walk through the group hierarchy using the walk method which
will yield a tuple for each group key along with the relative keys of its contents.

.. ipython:: python

   for (path, subgroups, subkeys) in store.walk():
       for subgroup in subgroups:
           print("GROUP: {}/{}".format(path, subgroup))
       for subkey in subkeys:
           key = "/".join([path, subkey])
           print("KEY: {}".format(key))
           print(store.get(key))

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)]

.. ipython:: python

   store["foo/bar/bah"]

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.

.. ipython:: python

    df_mixed = pd.DataFrame(
        {
            "A": np.random.randn(8),
            "B": np.random.randn(8),
            "C": np.array(np.random.randn(8), dtype="float32"),
            "string": "string",
            "int": 1,
            "bool": True,
            "datetime64": pd.Timestamp("20010102"),
        },
        index=list(range(8)),
    )
    df_mixed.loc[df_mixed.index[3:5], ["A", "B", "string", "datetime64"]] = np.nan

    store.append("df_mixed", df_mixed, min_itemsize={"values": 50})
    df_mixed1 = store.select("df_mixed")
    df_mixed1
    df_mixed1.dtypes.value_counts()

    # we have provided a minimum string column size
    store.root.df_mixed.table

Storing MultiIndex DataFrames

Storing MultiIndex DataFrames as tables is very similar to
storing/selecting from homogeneous index DataFrames.

.. ipython:: python

        index = pd.MultiIndex(
            levels=[["foo", "bar", "baz", "qux"], ["one", "two", "three"]],
            codes=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3], [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
            names=["foo", "bar"],
        )
        df_mi = pd.DataFrame(np.random.randn(10, 3), index=index, columns=["A", "B", "C"])
        df_mi

        store.append("df_mi", df_mi)
        store.select("df_mi")

        # the levels are automatically included as data columns
        store.select("df_mi", "foo=bar")

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 DataFrames.
• if data_columns are specified, these can be used as additional indexers.
• level name in a MultiIndex, with default name level_0, level_1, … if not provided.

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.

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

string = "HolyMoly'"
store.select("df", "index == string")

string = "HolyMoly'"
store.select('df', f'index == {string}')

store.select("df", "index == %r" % string)

Here are some examples:

.. ipython:: python

    dfq = pd.DataFrame(
        np.random.randn(10, 4),
        columns=list("ABCD"),
        index=pd.date_range("20130101", periods=10),
    )
    store.append("dfq", dfq, format="table", data_columns=True)

Use boolean expressions, with in-line function evaluation.

.. ipython:: python

    store.select("dfq", "index>pd.Timestamp('20130104') & columns=['A', 'B']")

Use inline column reference.

.. ipython:: python

   store.select("dfq", where="A>0 or C>0")

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':

.. ipython:: python

   store.select("df", "columns=['A', 'B']")

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.

Query 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:

.. ipython:: python

   from datetime import timedelta

   dftd = pd.DataFrame(
       {
           "A": pd.Timestamp("20130101"),
           "B": [
               pd.Timestamp("20130101") + timedelta(days=i, seconds=10)
               for i in range(10)
           ],
       }
   )
   dftd["C"] = dftd["A"] - dftd["B"]
   dftd
   store.append("dftd", dftd, data_columns=True)
   store.select("dftd", "C<'-3.5D'")

Query MultiIndex

Selecting from a MultiIndex can be achieved by using the name of the level.

.. ipython:: python

   df_mi.index.names
   store.select("df_mi", "foo=baz and bar=two")

If the MultiIndex levels names are None, the levels are automatically made available via
the level_n keyword with n the level of the MultiIndex you want to select from.

.. ipython:: python

   index = pd.MultiIndex(
       levels=[["foo", "bar", "baz", "qux"], ["one", "two", "three"]],
       codes=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3], [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
   )
   df_mi_2 = pd.DataFrame(np.random.randn(10, 3), index=index, columns=["A", "B", "C"])
   df_mi_2

   store.append("df_mi_2", df_mi_2)

   # the levels are automatically included as data columns with keyword level_n
   store.select("df_mi_2", "level_0=foo and level_1=two")

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.

.. ipython:: python

   # we have automagically already created an index (in the first section)
   i = store.root.df.table.cols.index.index
   i.optlevel, i.kind

   # change an index by passing new parameters
   store.create_table_index("df", optlevel=9, kind="full")
   i = store.root.df.table.cols.index.index
   i.optlevel, i.kind

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.

.. ipython:: python

   df_1 = pd.DataFrame(np.random.randn(10, 2), columns=list("AB"))
   df_2 = pd.DataFrame(np.random.randn(10, 2), columns=list("AB"))

   st = pd.HDFStore("appends.h5", mode="w")
   st.append("df", df_1, data_columns=["B"], index=False)
   st.append("df", df_2, data_columns=["B"], index=False)
   st.get_storer("df").table

Then create the index when finished appending.

.. ipython:: python

   st.create_table_index("df", columns=["B"], optlevel=9, kind="full")
   st.get_storer("df").table

   st.close()

.. ipython:: python
   :suppress:
   :okexcept:

   os.remove("appends.h5")

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.

.. ipython:: python

   df_dc = df.copy()
   df_dc["string"] = "foo"
   df_dc.loc[df_dc.index[4:6], "string"] = np.nan
   df_dc.loc[df_dc.index[7:9], "string"] = "bar"
   df_dc["string2"] = "cool"
   df_dc.loc[df_dc.index[1:3], ["B", "C"]] = 1.0
   df_dc

   # on-disk operations
   store.append("df_dc", df_dc, data_columns=["B", "C", "string", "string2"])
   store.select("df_dc", where="B > 0")

   # getting creative
   store.select("df_dc", "B > 0 & C > 0 & string == foo")

   # this is in-memory version of this type of selection
   df_dc[(df_dc.B > 0) & (df_dc.C > 0) & (df_dc.string == "foo")]

   # we have automagically created this index and the B/C/string/string2
   # columns are stored separately as ``PyTables`` columns
   store.root.df_dc.table

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.

.. ipython:: python

   for df in store.select("df", chunksize=3):
       print(df)

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.

.. ipython:: python

   dfeq = pd.DataFrame({"number": np.arange(1, 11)})
   dfeq

   store.append("dfeq", dfeq, data_columns=["number"])

   def chunks(l, n):
       return [l[i: i + n] for i in range(0, len(l), n)]

   evens = [2, 4, 6, 8, 10]
   coordinates = store.select_as_coordinates("dfeq", "number=evens")
   for c in chunks(coordinates, 2):
       print(store.select("dfeq", where=c))

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.

.. ipython:: python

   store.select_column("df_dc", "index")
   store.select_column("df_dc", "string")

Selecting coordinates

Sometimes you want to get the coordinates (a.k.a the index locations) of your query. This returns an
Index of the resulting locations. These coordinates can also be passed to subsequent
where operations.

.. ipython:: python

   df_coord = pd.DataFrame(
       np.random.randn(1000, 2), index=pd.date_range("20000101", periods=1000)
   )
   store.append("df_coord", df_coord)
   c = store.select_as_coordinates("df_coord", "index > 20020101")
   c
   store.select("df_coord", where=c)

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.

.. ipython:: python

   df_mask = pd.DataFrame(
       np.random.randn(1000, 2), index=pd.date_range("20000101", periods=1000)
   )
   store.append("df_mask", df_mask)
   c = store.select_column("df_mask", "index")
   where = c[pd.DatetimeIndex(c).month == 5].index
   store.select("df_mask", where=where)

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.

.. ipython:: python

   store.get_storer("df_dc").nrows

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.

.. ipython:: python

   df_mt = pd.DataFrame(
       np.random.randn(8, 6),
       index=pd.date_range("1/1/2000", periods=8),
       columns=["A", "B", "C", "D", "E", "F"],
   )
   df_mt["foo"] = "bar"
   df_mt.loc[df_mt.index[1], ("A", "B")] = np.nan

   # you can also create the tables individually
   store.append_to_multiple(
       {"df1_mt": ["A", "B"], "df2_mt": None}, df_mt, selector="df1_mt"
   )
   store

   # individual tables were created
   store.select("df1_mt")
   store.select("df2_mt")

   # as a multiple
   store.select_as_multiple(
       ["df1_mt", "df2_mt"],
       where=["A>0", "B>0"],
       selector="df1_mt",
   )

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. 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.

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.

    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.

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

• If you use locks to manage write access between multiple processes, you
  may want to use :py:func:`~os.fsync` before releasing write locks. For
  convenience you can use store.flush(fsync=True) to do this for you.
• Once a table is created 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.

DataTypes

HDFStore will map an object dtype to the PyTables underlying
dtype. This means the following types are known to work:

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.

.. ipython:: python

   dfcat = pd.DataFrame(
       {"A": pd.Series(list("aabbcdba")).astype("category"), "B": np.random.randn(8)}
   )
   dfcat
   dfcat.dtypes
   cstore = pd.HDFStore("cats.h5", mode="w")
   cstore.append("dfcat", dfcat, format="table", data_columns=["A"])
   result = cstore.select("dfcat", where="A in ['b', 'c']")
   result
   result.dtypes

.. ipython:: python
   :suppress:
   :okexcept:

   cstore.close()
   os.remove("cats.h5")

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.

.. ipython:: python

   dfs = pd.DataFrame({"A": "foo", "B": "bar"}, index=list(range(5)))
   dfs

   # A and B have a size of 30
   store.append("dfs", dfs, min_itemsize=30)
   store.get_storer("dfs").table

   # A is created as a data_column with a size of 30
   # B is size is calculated
   store.append("dfs2", dfs, min_itemsize={"A": 30})
   store.get_storer("dfs2").table

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.

.. ipython:: python

   dfss = pd.DataFrame({"A": ["foo", "bar", "nan"]})
   dfss

   store.append("dfss", dfss)
   store.select("dfss")

   # here you need to specify a different nan rep
   store.append("dfss2", dfss, nan_rep="_nan_")
   store.select("dfss2")

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 rows that PyTables will expect.
  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.

.. ipython:: python
   :suppress:

   store.close()
   os.remove("store.h5")

Feather

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:

• 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
• Actual Python objects in object dtype columns are not supported. These will
  raise a helpful error message on an attempt at serialization.

See the Full Documentation.

.. ipython:: python

   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"),
       }
   )

   df
   df.dtypes

Write to a feather file.

.. ipython:: python

   df.to_feather("example.feather")

Read from a feather file.

.. ipython:: python
   :okwarning:

   result = pd.read_feather("example.feather")
   result

   # we preserve dtypes
   result.dtypes

.. ipython:: python
   :suppress:

   os.remove("example.feather")

Parquet

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.
• The pyarrow engine always writes the index to the output, but fastparquet only writes non-default
  indexes. This extra column can cause problems for non-pandas consumers that are not expecting it. You can
  force including or omitting indexes with the index argument, regardless of the underlying engine.
• Index level names, if specified, must be strings.
• In the pyarrow engine, categorical dtypes for non-string types can be serialized to parquet, but will de-serialize as their primitive dtype.
• The pyarrow engine preserves the ordered flag of categorical dtypes with string types. fastparquet does not preserve the ordered flag.
• Non supported types include Interval and actual Python object types. These will raise a helpful error message
  on an attempt at serialization. Period type is supported with pyarrow >= 0.16.0.
• The pyarrow engine preserves extension data types such as the nullable integer and string data
  type (requiring pyarrow >= 0.16.0, and requiring the extension type to implement the needed protocols,
  see the :ref:`extension types documentation <extending.extension.arrow>`).

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.

.. ipython:: python

   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"),
           "h": pd.Categorical(list("abc")),
           "i": pd.Categorical(list("abc"), ordered=True),
       }
   )

   df
   df.dtypes

Write to a parquet file.

.. ipython:: python
   :okwarning:

   df.to_parquet("example_pa.parquet", engine="pyarrow")
   df.to_parquet("example_fp.parquet", engine="fastparquet")

Read from a parquet file.

.. ipython:: python
   :okwarning:

   result = pd.read_parquet("example_fp.parquet", engine="fastparquet")
   result = pd.read_parquet("example_pa.parquet", engine="pyarrow")

   result.dtypes

Read only certain columns of a parquet file.

.. ipython:: python
   :okwarning:

   result = pd.read_parquet(
       "example_fp.parquet",
       engine="fastparquet",
       columns=["a", "b"],
   )
   result = pd.read_parquet(
       "example_pa.parquet",
       engine="pyarrow",
       columns=["a", "b"],
   )
   result.dtypes

.. ipython:: python
   :suppress:

   os.remove("example_pa.parquet")
   os.remove("example_fp.parquet")

Handling indexes

Serializing a DataFrame to parquet may include the implicit index as one or
more columns in the output file. Thus, this code:

.. ipython:: python

    df = pd.DataFrame({"a": [1, 2], "b": [3, 4]})
    df.to_parquet("test.parquet", engine="pyarrow")

creates a parquet file with three columns if you use pyarrow for serialization:
a, b, and __index_level_0__. If you’re using fastparquet, the
index may or may not
be written to the file.

This unexpected extra column causes some databases like Amazon Redshift to reject
the file, because that column doesn’t exist in the target table.

If you want to omit a dataframe’s indexes when writing, pass index=False to
:func:`~pandas.DataFrame.to_parquet`:

.. ipython:: python

    df.to_parquet("test.parquet", index=False)

This creates a parquet file with just the two expected columns, a and b.
If your DataFrame has a custom index, you won’t get it back when you load
this file into a DataFrame.

Passing index=True will always write the index, even if that’s not the
underlying engine’s default behavior.

.. ipython:: python
   :suppress:

   os.remove("test.parquet")

Partitioning Parquet files

Parquet supports partitioning of data based on the values of one or more columns.

.. ipython:: python

    df = pd.DataFrame({"a": [0, 0, 1, 1], "b": [0, 1, 0, 1]})
    df.to_parquet(path="test", engine="pyarrow", partition_cols=["a"], compression=None)

The path specifies the parent directory to which data will be saved.
The partition_cols are the column names by which the dataset will be partitioned.
Columns are partitioned in the order they are given. The partition splits are
determined by the unique values in the partition columns.
The above example creates a partitioned dataset that may look like:

test
├── a=0
│   ├── 0bac803e32dc42ae83fddfd029cbdebc.parquet
│   └──  ...
└── a=1
    ├── e6ab24a4f45147b49b54a662f0c412a3.parquet
    └── ...

.. ipython:: python
   :suppress:

   from shutil import rmtree

   try:
       rmtree("test")
   except OSError:
       pass

ORC

Similar to the :ref:`parquet <io.parquet>` format, the ORC Format is a binary columnar serialization
for data frames. It is designed to make reading data frames efficient. pandas provides both the reader and the writer for the
ORC format, :func:`~pandas.read_orc` and :func:`~pandas.DataFrame.to_orc`. This requires the pyarrow library.

.. ipython:: python

   df = pd.DataFrame(
       {
           "a": list("abc"),
           "b": list(range(1, 4)),
           "c": np.arange(4.0, 7.0, dtype="float64"),
           "d": [True, False, True],
           "e": pd.date_range("20130101", periods=3),
       }
   )

   df
   df.dtypes

Write to an orc file.

.. ipython:: python
   :okwarning:

   df.to_orc("example_pa.orc", engine="pyarrow")

Read from an orc file.

.. ipython:: python
   :okwarning:

   result = pd.read_orc("example_pa.orc")

   result.dtypes

Read only certain columns of an orc file.

.. ipython:: python

   result = pd.read_orc(
       "example_pa.orc",
       columns=["a", "b"],
   )
   result.dtypes

.. ipython:: python
   :suppress:

   os.remove("example_pa.orc")

SQL queries

The :mod:`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, you can use a :class:`sqlite3.Connection` in place of
a SQLAlchemy engine, connection, or URI string.

See also some :ref:`cookbook examples <cookbook.sql>` for some advanced strategies.

The key functions are:

.. autosummary::

    read_sql_table
    read_sql_query
    read_sql
    DataFrame.to_sql

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 :func:`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 :func:`create_engine` and the URI formatting, see the examples
below and the SQLAlchemy documentation

.. ipython:: python

   from sqlalchemy import create_engine

   # Create your engine.
   engine = create_engine("sqlite:///:memory:")

If you want to manage your own connections you can pass one of those instead. The example below opens a
connection to the database using a Python context manager that automatically closes the connection after
the block has completed.
See the SQLAlchemy docs
for an explanation of how the database connection is handled.

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 :func:`~pandas.DataFrame.to_sql`.

.. ipython:: python

   import datetime

   c = ["id", "Date", "Col_1", "Col_2", "Col_3"]
   d = [
       (26, datetime.datetime(2010, 10, 18), "X", 27.5, True),
       (42, datetime.datetime(2010, 10, 19), "Y", -12.5, False),
       (63, datetime.datetime(2010, 10, 20), "Z", 5.73, True),
   ]

   data = pd.DataFrame(d, columns=c)

   data
   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:

.. ipython:: python

    data.to_sql("data_chunked", engine, chunksize=1000)

SQL data types

:func:`~pandas.DataFrame.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:

.. ipython:: python

    from sqlalchemy.types import String

    data.to_sql("data_dtype", engine, dtype={"Col_1": String})

Datetime data types

Using SQLAlchemy, :func:`~pandas.DataFrame.to_sql` is capable of writing
datetime data that is timezone naive or timezone aware. However, the resulting
data stored in the database ultimately depends on the supported data type
for datetime data of the database system being used.

The following table lists supported data types for datetime data for some
common databases. Other database dialects may have different data types for
datetime data.

When writing timezone aware data to databases that do not support timezones,
the data will be written as timezone naive timestamps that are in local time
with respect to the timezone.

:func:`~pandas.read_sql_table` is also capable of reading datetime data that is
timezone aware or naive. When reading TIMESTAMP WITH TIME ZONE types, pandas
will convert the data to UTC.

Insertion method

The parameter method controls the SQL insertion clause used.
Possible values are:

• None: Uses standard SQL INSERT clause (one per row).
• 'multi': Pass multiple values in a single INSERT clause.
  It uses a special SQL syntax not supported by all backends.
  This usually provides better performance for analytic databases
  like Presto and Redshift, but has worse performance for
  traditional SQL backend if the table contains many columns.
  For more information check the SQLAlchemy documentation.
• callable with signature (pd_table, conn, keys, data_iter):
  This can be used to implement a more performant insertion method based on
  specific backend dialect features.

Example of a callable using PostgreSQL COPY clause:

# Alternative to_sql() *method* for DBs that support COPY FROM
import csv
from io import StringIO

def psql_insert_copy(table, conn, keys, data_iter):
    """
    Execute SQL statement inserting data

    Parameters
    ----------
    table : pandas.io.sql.SQLTable
    conn : sqlalchemy.engine.Engine or sqlalchemy.engine.Connection
    keys : list of str
        Column names
    data_iter : Iterable that iterates the values to be inserted
    """
    # gets a DBAPI connection that can provide a cursor
    dbapi_conn = conn.connection
    with dbapi_conn.cursor() as cur:
        s_buf = StringIO()
        writer = csv.writer(s_buf)
        writer.writerows(data_iter)
        s_buf.seek(0)

        columns = ', '.join(['"{}"'.format(k) for k in keys])
        if table.schema:
            table_name = '{}.{}'.format(table.schema, table.name)
        else:
            table_name = table.name

        sql = 'COPY {} ({}) FROM STDIN WITH CSV'.format(
            table_name, columns)
        cur.copy_expert(sql=sql, file=s_buf)

Reading tables

:func:`~pandas.read_sql_table` will read a database table given the
table name and optionally a subset of columns to read.

.. ipython:: python

   pd.read_sql_table("data", engine)

You can also specify the name of the column as the DataFrame index,
and specify a subset of columns to be read.

.. ipython:: python

   pd.read_sql_table("data", engine, index_col="id")
   pd.read_sql_table("data", engine, columns=["Col_1", "Col_2"])

And you can explicitly force columns to be parsed as dates:

.. ipython:: python

   pd.read_sql_table("data", engine, parse_dates=["Date"])

If needed you can explicitly specify a format string, or a dict of arguments
to pass to :func:`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 :func:`~pandas.io.sql.has_table`

Schema support

Reading from and writing to different schema’s is supported through the schema
keyword in the :func:`~pandas.read_sql_table` and :func:`~pandas.DataFrame.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 :func:`~pandas.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.

.. ipython:: python

   pd.read_sql_query("SELECT * FROM data", engine)

Of course, you can specify a more «complex» query.

.. ipython:: python

   pd.read_sql_query("SELECT id, Col_1, Col_2 FROM data WHERE id = 42;", engine)

The :func:`~pandas.read_sql_query` function supports a chunksize argument.
Specifying this will return an iterator through chunks of the query result:

.. ipython:: python

    df = pd.DataFrame(np.random.randn(20, 3), columns=list("abc"))
    df.to_sql("data_chunks", engine, index=False)

.. ipython:: python

    for chunk in pd.read_sql_query("SELECT * FROM data_chunks", engine, chunksize=5):
        print(chunk)

Engine connection examples

To connect with SQLAlchemy you use the :func:`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:tiger@127.0.0.1:1521/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 :func:`sqlalchemy.text` to specify query parameters in a backend-neutral way

.. ipython:: python

   import sqlalchemy as sa

   pd.read_sql(
       sa.text("SELECT * FROM data where Col_1=:col1"), engine, params={"col1": "X"}
   )

If you have an SQLAlchemy description of your database you can express where conditions using SQLAlchemy expressions

.. ipython:: python

   metadata = sa.MetaData()
   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),
   )

   pd.read_sql(sa.select(data_table).where(data_table.c.Col_3 is True), engine)

You can combine SQLAlchemy expressions with parameters passed to :func:`read_sql` using :func:`sqlalchemy.bindparam`

.. ipython:: python

    import datetime as dt

    expr = sa.select(data_table).where(data_table.c.Date > sa.bindparam("date"))
    pd.read_sql(expr, engine, params={"date": dt.datetime(2010, 10, 18)})

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", con)
pd.read_sql_query("SELECT * FROM data", con)

Google BigQuery

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 :func:`~pandas.core.frame.DataFrame.to_stata` will write a DataFrame
into a .dta file. The format version of this file is always 115 (Stata 12).

.. ipython:: python

   df = pd.DataFrame(np.random.randn(10, 2), columns=list("AB"))
   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).

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.

Reading from Stata format

The top-level function read_stata will read a dta file and return
either a DataFrame or a :class:`~pandas.io.stata.StataReader` that can
be used to read the file incrementally.

.. ipython:: python

   pd.read_stata("stata.dta")

Specifying a chunksize yields a
:class:`~pandas.io.stata.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.

.. ipython:: python

  with pd.read_stata("stata.dta", chunksize=3) as reader:
      for df in reader:
          print(df.shape)

For more fine-grained control, use iterator=True and specify
chunksize with each call to
:func:`~pandas.io.stata.StataReader.read`.

.. ipython:: python

  with pd.read_stata("stata.dta", iterator=True) as reader:
      chunk1 = reader.read(5)
      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 :func:`~pandas.io.stata.StataReader.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.

.. ipython:: python
   :suppress:

   os.remove("stata.dta")

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.

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.

SAS formats

The top-level function :func:`read_sas` can read (but not write) SAS
XPORT (.xpt) and 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:

def do_something(chunk):
    pass


with pd.read_sas("sas_xport.xpt", chunk=100000) as rdr:
    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.

SPSS formats

The top-level function :func:`read_spss` can read (but not write) SPSS
SAV (.sav) and ZSAV (.zsav) format files.

SPSS files contain column names. By default the
whole file is read, categorical columns are converted into pd.Categorical,
and a DataFrame with all columns is returned.

Specify the usecols parameter to obtain a subset of columns. Specify convert_categoricals=False
to avoid converting categorical columns into pd.Categorical.

Read an SPSS file:

df = pd.read_spss("spss_data.sav")

Extract a subset of columns contained in usecols from an SPSS file and
avoid converting categorical columns into pd.Categorical:

df = pd.read_spss(
    "spss_data.sav",
    usecols=["foo", "bar"],
    convert_categoricals=False,
)

More information about the SAV and ZSAV file formats is available here.

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
0.24.2. Timings are machine dependent and small differences should be
ignored.

In [1]: sz = 1000000
In [2]: df = pd.DataFrame({'A': np.random.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

The following test functions will be used below to compare the performance of several IO methods:

import numpy as np

import os

sz = 1000000
df = pd.DataFrame({"A": np.random.randn(sz), "B": [1] * sz})

sz = 1000000
np.random.seed(42)
df = pd.DataFrame({"A": np.random.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")


def test_parquet_write(df):
    df.to_parquet("test.parquet")


def test_parquet_read():
    pd.read_parquet("test.parquet")

When writing, the top three functions in terms of speed are test_feather_write, test_hdf_fixed_write and test_hdf_fixed_write_compress.

In [4]: %timeit test_sql_write(df)
3.29 s ± 43.2 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

In [5]: %timeit test_hdf_fixed_write(df)
19.4 ms ± 560 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)

In [6]: %timeit test_hdf_fixed_write_compress(df)
19.6 ms ± 308 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

In [7]: %timeit test_hdf_table_write(df)
449 ms ± 5.61 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

In [8]: %timeit test_hdf_table_write_compress(df)
448 ms ± 11.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

In [9]: %timeit test_csv_write(df)
3.66 s ± 26.2 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

In [10]: %timeit test_feather_write(df)
9.75 ms ± 117 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

In [11]: %timeit test_pickle_write(df)
30.1 ms ± 229 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

In [12]: %timeit test_pickle_write_compress(df)
4.29 s ± 15.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

In [13]: %timeit test_parquet_write(df)
67.6 ms ± 706 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

When reading, the top three functions in terms of speed are test_feather_read, test_pickle_read and
test_hdf_fixed_read.

In [14]: %timeit test_sql_read()
1.77 s ± 17.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

In [15]: %timeit test_hdf_fixed_read()
19.4 ms ± 436 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

In [16]: %timeit test_hdf_fixed_read_compress()
19.5 ms ± 222 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

In [17]: %timeit test_hdf_table_read()
38.6 ms ± 857 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

In [18]: %timeit test_hdf_table_read_compress()
38.8 ms ± 1.49 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

In [19]: %timeit test_csv_read()
452 ms ± 9.04 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

In [20]: %timeit test_feather_read()
12.4 ms ± 99.7 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

In [21]: %timeit test_pickle_read()
18.4 ms ± 191 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

In [22]: %timeit test_pickle_read_compress()
915 ms ± 7.48 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

In [23]: %timeit test_parquet_read()
24.4 ms ± 146 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

The files test.pkl.compress, test.parquet and test.feather took the least space on disk (in bytes).

29519500 Oct 10 06:45 test.csv
16000248 Oct 10 06:45 test.feather
8281983  Oct 10 06:49 test.parquet
16000857 Oct 10 06:47 test.pkl
7552144  Oct 10 06:48 test.pkl.compress
34816000 Oct 10 06:42 test.sql
24009288 Oct 10 06:43 test_fixed.hdf
24009288 Oct 10 06:43 test_fixed_compress.hdf
24458940 Oct 10 06:44 test_table.hdf
24458940 Oct 10 06:44 test_table_compress.hdf