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# Options and settings

## Overview

pandas has an options API configure and customize global behavior related to `DataFrame` display, data behavior and more.

Options have a full “dotted-style”, case-insensitive name (e.g. `display.max_rows`). You can get/set options directly as attributes of the top-level `options` attribute:

    In [1]: import pandas as pd

    In [2]: pd.options.display.max_rows
    Out[2]: 15

    In [3]: pd.options.display.max_rows = 999

    In [4]: pd.options.display.max_rows
    Out[4]: 999

The API is composed of 5 relevant functions, available directly from the `pandas` namespace:

- `get_option()` / `set_option()` - get/set the value of a single option.

- `reset_option()` - reset one or more options to their default value.

- `describe_option()` - print the descriptions of one or more options.

- `option_context()` - execute a codeblock with a set of options that revert to prior settings after execution.

Note

Developers can check out pandas/core/config_init.py for more information.

All of the functions above accept a regexp pattern (`re.search` style) as an argument, to match an unambiguous substring:

    In [5]: pd.get_option("display.chop_threshold")

    In [6]: pd.set_option("display.chop_threshold", 2)

    In [7]: pd.get_option("display.chop_threshold")
    Out[7]: 2

    In [8]: pd.set_option("chop", 4)

    In [9]: pd.get_option("display.chop_threshold")
    Out[9]: 4

The following will not work because it matches multiple option names, e.g. `display.max_colwidth`, `display.max_rows`, `display.max_columns`:

    In [10]: pd.get_option("max")
    ---------------------------------------------------------------------------
    OptionError                               Traceback (most recent call last)
    Cell In[10], line 1
    ----> 1 pd.get_option("max")

    File /usr/lib/python3/dist-packages/pandas/_config/config.py:274, in CallableDynamicDoc.__call__(self, *args, **kwds)
        273 def __call__(self, *args, **kwds) -> T:
    --> 274     return self.__func__(*args, **kwds)

    File /usr/lib/python3/dist-packages/pandas/_config/config.py:146, in _get_option(pat, silent)
        145 def _get_option(pat: str, silent: bool = False) -> Any:
    --> 146     key = _get_single_key(pat, silent)
        148     # walk the nested dict
        149     root, k = _get_root(key)

    File /usr/lib/python3/dist-packages/pandas/_config/config.py:134, in _get_single_key(pat, silent)
        132     raise OptionError(f"No such keys(s): {repr(pat)}")
        133 if len(keys) > 1:
    --> 134     raise OptionError("Pattern matched multiple keys")
        135 key = keys[0]
        137 if not silent:

    OptionError: Pattern matched multiple keys

Warning

Using this form of shorthand may cause your code to break if new options with similar names are added in future versions.

## Available options

You can get a list of available options and their descriptions with `describe_option()`. When called with no argument `describe_option()` will print out the descriptions for all available options.

    In [11]: pd.describe_option()
    compute.use_bottleneck : bool
        Use the bottleneck library to accelerate if it is installed,
        the default is True
        Valid values: False,True
        [default: True] [currently: True]
    compute.use_numba : bool
        Use the numba engine option for select operations if it is installed,
        the default is False
        Valid values: False,True
        [default: False] [currently: False]
    compute.use_numexpr : bool
        Use the numexpr library to accelerate computation if it is installed,
        the default is True
        Valid values: False,True
        [default: True] [currently: True]
    display.chop_threshold : float or None
        if set to a float value, all float values smaller than the given threshold
        will be displayed as exactly 0 by repr and friends.
        [default: None] [currently: None]
    display.colheader_justify : 'left'/'right'
        Controls the justification of column headers. used by DataFrameFormatter.
        [default: right] [currently: right]
    display.date_dayfirst : boolean
        When True, prints and parses dates with the day first, eg 20/01/2005
        [default: False] [currently: False]
    display.date_yearfirst : boolean
        When True, prints and parses dates with the year first, eg 2005/01/20
        [default: False] [currently: False]
    display.encoding : str/unicode
        Defaults to the detected encoding of the console.
        Specifies the encoding to be used for strings returned by to_string,
        these are generally strings meant to be displayed on the console.
        [default: utf-8] [currently: utf8]
    display.expand_frame_repr : boolean
        Whether to print out the full DataFrame repr for wide DataFrames across
        multiple lines, `max_columns` is still respected, but the output will
        wrap-around across multiple "pages" if its width exceeds `display.width`.
        [default: True] [currently: True]
    display.float_format : callable
        The callable should accept a floating point number and return
        a string with the desired format of the number. This is used
        in some places like SeriesFormatter.
        See formats.format.EngFormatter for an example.
        [default: None] [currently: None]
    display.html.border : int
        A ``border=value`` attribute is inserted in the ``<table>`` tag
        for the DataFrame HTML repr.
        [default: 1] [currently: 1]
    display.html.table_schema : boolean
        Whether to publish a Table Schema representation for frontends
        that support it.
        (default: False)
        [default: False] [currently: False]
    display.html.use_mathjax : boolean
        When True, Jupyter notebook will process table contents using MathJax,
        rendering mathematical expressions enclosed by the dollar symbol.
        (default: True)
        [default: True] [currently: True]
    display.large_repr : 'truncate'/'info'
        For DataFrames exceeding max_rows/max_cols, the repr (and HTML repr) can
        show a truncated table, or switch to the view from
        df.info() (the behaviour in earlier versions of pandas).
        [default: truncate] [currently: truncate]
    display.max_categories : int
        This sets the maximum number of categories pandas should output when
        printing out a `Categorical` or a Series of dtype "category".
        [default: 8] [currently: 8]
    display.max_columns : int
        If max_cols is exceeded, switch to truncate view. Depending on
        `large_repr`, objects are either centrally truncated or printed as
        a summary view. 'None' value means unlimited.

        In case python/IPython is running in a terminal and `large_repr`
        equals 'truncate' this can be set to 0 or None and pandas will auto-detect
        the width of the terminal and print a truncated object which fits
        the screen width. The IPython notebook, IPython qtconsole, or IDLE
        do not run in a terminal and hence it is not possible to do
        correct auto-detection and defaults to 20.
        [default: 0] [currently: 0]
    display.max_colwidth : int or None
        The maximum width in characters of a column in the repr of
        a pandas data structure. When the column overflows, a "..."
        placeholder is embedded in the output. A 'None' value means unlimited.
        [default: 50] [currently: 50]
    display.max_dir_items : int
        The number of items that will be added to `dir(...)`. 'None' value means
        unlimited. Because dir is cached, changing this option will not immediately
        affect already existing dataframes until a column is deleted or added.

        This is for instance used to suggest columns from a dataframe to tab
        completion.
        [default: 100] [currently: 100]
    display.max_info_columns : int
        max_info_columns is used in DataFrame.info method to decide if
        per column information will be printed.
        [default: 100] [currently: 100]
    display.max_info_rows : int
        df.info() will usually show null-counts for each column.
        For large frames this can be quite slow. max_info_rows and max_info_cols
        limit this null check only to frames with smaller dimensions than
        specified.
        [default: 1690785] [currently: 1690785]
    display.max_rows : int
        If max_rows is exceeded, switch to truncate view. Depending on
        `large_repr`, objects are either centrally truncated or printed as
        a summary view. 'None' value means unlimited.

        In case python/IPython is running in a terminal and `large_repr`
        equals 'truncate' this can be set to 0 and pandas will auto-detect
        the height of the terminal and print a truncated object which fits
        the screen height. The IPython notebook, IPython qtconsole, or
        IDLE do not run in a terminal and hence it is not possible to do
        correct auto-detection.
        [default: 60] [currently: 60]
    display.max_seq_items : int or None
        When pretty-printing a long sequence, no more then `max_seq_items`
        will be printed. If items are omitted, they will be denoted by the
        addition of "..." to the resulting string.

        If set to None, the number of items to be printed is unlimited.
        [default: 100] [currently: 100]
    display.memory_usage : bool, string or None
        This specifies if the memory usage of a DataFrame should be displayed when
        df.info() is called. Valid values True,False,'deep'
        [default: True] [currently: True]
    display.min_rows : int
        The numbers of rows to show in a truncated view (when `max_rows` is
        exceeded). Ignored when `max_rows` is set to None or 0. When set to
        None, follows the value of `max_rows`.
        [default: 10] [currently: 10]
    display.multi_sparse : boolean
        "sparsify" MultiIndex display (don't display repeated
        elements in outer levels within groups)
        [default: True] [currently: True]
    display.notebook_repr_html : boolean
        When True, IPython notebook will use html representation for
        pandas objects (if it is available).
        [default: True] [currently: True]
    display.pprint_nest_depth : int
        Controls the number of nested levels to process when pretty-printing
        [default: 3] [currently: 3]
    display.precision : int
        Floating point output precision in terms of number of places after the
        decimal, for regular formatting as well as scientific notation. Similar
        to ``precision`` in :meth:`numpy.set_printoptions`.
        [default: 6] [currently: 6]
    display.show_dimensions : boolean or 'truncate'
        Whether to print out dimensions at the end of DataFrame repr.
        If 'truncate' is specified, only print out the dimensions if the
        frame is truncated (e.g. not display all rows and/or columns)
        [default: truncate] [currently: truncate]
    display.unicode.ambiguous_as_wide : boolean
        Whether to use the Unicode East Asian Width to calculate the display text
        width.
        Enabling this may affect to the performance (default: False)
        [default: False] [currently: False]
    display.unicode.east_asian_width : boolean
        Whether to use the Unicode East Asian Width to calculate the display text
        width.
        Enabling this may affect to the performance (default: False)
        [default: False] [currently: False]
    display.width : int
        Width of the display in characters. In case python/IPython is running in
        a terminal this can be set to None and pandas will correctly auto-detect
        the width.
        Note that the IPython notebook, IPython qtconsole, or IDLE do not run in a
        terminal and hence it is not possible to correctly detect the width.
        [default: 80] [currently: 80]
    future.infer_string Whether to infer sequence of str objects as pyarrow string dtype, which will be the default in pandas 3.0 (at which point this option will be deprecated).
        [default: False] [currently: False]
    future.no_silent_downcasting Whether to opt-in to the future behavior which will *not* silently downcast results from Series and DataFrame `where`, `mask`, and `clip` methods. Silent downcasting will be removed in pandas 3.0 (at which point this option will be deprecated).
        [default: False] [currently: False]
    io.excel.ods.reader : string
        The default Excel reader engine for 'ods' files. Available options:
        auto, odf, calamine.
        [default: auto] [currently: auto]
    io.excel.ods.writer : string
        The default Excel writer engine for 'ods' files. Available options:
        auto, odf.
        [default: auto] [currently: auto]
    io.excel.xls.reader : string
        The default Excel reader engine for 'xls' files. Available options:
        auto, xlrd, calamine.
        [default: auto] [currently: auto]
    io.excel.xlsb.reader : string
        The default Excel reader engine for 'xlsb' files. Available options:
        auto, pyxlsb, calamine.
        [default: auto] [currently: auto]
    io.excel.xlsm.reader : string
        The default Excel reader engine for 'xlsm' files. Available options:
        auto, xlrd, openpyxl, calamine.
        [default: auto] [currently: auto]
    io.excel.xlsm.writer : string
        The default Excel writer engine for 'xlsm' files. Available options:
        auto, openpyxl.
        [default: auto] [currently: auto]
    io.excel.xlsx.reader : string
        The default Excel reader engine for 'xlsx' files. Available options:
        auto, xlrd, openpyxl, calamine.
        [default: auto] [currently: auto]
    io.excel.xlsx.writer : string
        The default Excel writer engine for 'xlsx' files. Available options:
        auto, openpyxl, xlsxwriter.
        [default: auto] [currently: auto]
    io.hdf.default_format : format
        default format writing format, if None, then
        put will default to 'fixed' and append will default to 'table'
        [default: None] [currently: None]
    io.hdf.dropna_table : boolean
        drop ALL nan rows when appending to a table
        [default: False] [currently: False]
    io.parquet.engine : string
        The default parquet reader/writer engine. Available options:
        'auto', 'pyarrow', 'fastparquet', the default is 'auto'
        [default: auto] [currently: auto]
    io.sql.engine : string
        The default sql reader/writer engine. Available options:
        'auto', 'sqlalchemy', the default is 'auto'
        [default: auto] [currently: auto]
    mode.chained_assignment : string
        Raise an exception, warn, or no action if trying to use chained assignment,
        The default is warn
        [default: warn] [currently: warn]
    mode.copy_on_write : bool
        Use new copy-view behaviour using Copy-on-Write. Defaults to False,
        unless overridden by the 'PANDAS_COPY_ON_WRITE' environment variable
        (if set to "1" for True, needs to be set before pandas is imported).
        [default: False] [currently: False]
    mode.data_manager : string
        Internal data manager type; can be "block" or "array". Defaults to "block",
        unless overridden by the 'PANDAS_DATA_MANAGER' environment variable (needs
        to be set before pandas is imported).
        [default: block] [currently: block]
        (Deprecated, use `` instead.)
    mode.sim_interactive : boolean
        Whether to simulate interactive mode for purposes of testing
        [default: False] [currently: False]
    mode.string_storage : string
        The default storage for StringDtype. This option is ignored if
        ``future.infer_string`` is set to True.
        [default: python] [currently: python]
    mode.use_inf_as_na : boolean
        True means treat None, NaN, INF, -INF as NA (old way),
        False means None and NaN are null, but INF, -INF are not NA
        (new way).

        This option is deprecated in pandas 2.1.0 and will be removed in 3.0.
        [default: False] [currently: False]
        (Deprecated, use `` instead.)
    plotting.backend : str
        The plotting backend to use. The default value is "matplotlib", the
        backend provided with pandas. Other backends can be specified by
        providing the name of the module that implements the backend.
        [default: matplotlib] [currently: matplotlib]
    plotting.matplotlib.register_converters : bool or 'auto'.
        Whether to register converters with matplotlib's units registry for
        dates, times, datetimes, and Periods. Toggling to False will remove
        the converters, restoring any converters that pandas overwrote.
        [default: auto] [currently: auto]
    styler.format.decimal : str
        The character representation for the decimal separator for floats and complex.
        [default: .] [currently: .]
    styler.format.escape : str, optional
        Whether to escape certain characters according to the given context; html or latex.
        [default: None] [currently: None]
    styler.format.formatter : str, callable, dict, optional
        A formatter object to be used as default within ``Styler.format``.
        [default: None] [currently: None]
    styler.format.na_rep : str, optional
        The string representation for values identified as missing.
        [default: None] [currently: None]
    styler.format.precision : int
        The precision for floats and complex numbers.
        [default: 6] [currently: 6]
    styler.format.thousands : str, optional
        The character representation for thousands separator for floats, int and complex.
        [default: None] [currently: None]
    styler.html.mathjax : bool
        If False will render special CSS classes to table attributes that indicate Mathjax
        will not be used in Jupyter Notebook.
        [default: True] [currently: True]
    styler.latex.environment : str
        The environment to replace ``\begin{table}``. If "longtable" is used results
        in a specific longtable environment format.
        [default: None] [currently: None]
    styler.latex.hrules : bool
        Whether to add horizontal rules on top and bottom and below the headers.
        [default: False] [currently: False]
    styler.latex.multicol_align : {"r", "c", "l", "naive-l", "naive-r"}
        The specifier for horizontal alignment of sparsified LaTeX multicolumns. Pipe
        decorators can also be added to non-naive values to draw vertical
        rules, e.g. "\|r" will draw a rule on the left side of right aligned merged cells.
        [default: r] [currently: r]
    styler.latex.multirow_align : {"c", "t", "b"}
        The specifier for vertical alignment of sparsified LaTeX multirows.
        [default: c] [currently: c]
    styler.render.encoding : str
        The encoding used for output HTML and LaTeX files.
        [default: utf-8] [currently: utf-8]
    styler.render.max_columns : int, optional
        The maximum number of columns that will be rendered. May still be reduced to
        satisfy ``max_elements``, which takes precedence.
        [default: None] [currently: None]
    styler.render.max_elements : int
        The maximum number of data-cell (<td>) elements that will be rendered before
        trimming will occur over columns, rows or both if needed.
        [default: 262144] [currently: 262144]
    styler.render.max_rows : int, optional
        The maximum number of rows that will be rendered. May still be reduced to
        satisfy ``max_elements``, which takes precedence.
        [default: None] [currently: None]
    styler.render.repr : str
        Determine which output to use in Jupyter Notebook in {"html", "latex"}.
        [default: html] [currently: html]
    styler.sparse.columns : bool
        Whether to sparsify the display of hierarchical columns. Setting to False will
        display each explicit level element in a hierarchical key for each column.
        [default: True] [currently: True]
    styler.sparse.index : bool
        Whether to sparsify the display of a hierarchical index. Setting to False will
        display each explicit level element in a hierarchical key for each row.
        [default: True] [currently: True]

## Getting and setting options

As described above, `get_option()` and `set_option()` are available from the pandas namespace. To change an option, call `set_option('option regex', new_value)`.

    In [12]: pd.get_option("mode.sim_interactive")
    Out[12]: False

    In [13]: pd.set_option("mode.sim_interactive", True)

    In [14]: pd.get_option("mode.sim_interactive")
    Out[14]: True

Note

The option `'mode.sim_interactive'` is mostly used for debugging purposes.

You can use `reset_option()` to revert to a setting’s default value

    In [15]: pd.get_option("display.max_rows")
    Out[15]: 60

    In [16]: pd.set_option("display.max_rows", 999)

    In [17]: pd.get_option("display.max_rows")
    Out[17]: 999

    In [18]: pd.reset_option("display.max_rows")

    In [19]: pd.get_option("display.max_rows")
    Out[19]: 60

It’s also possible to reset multiple options at once (using a regex):

    In [20]: pd.reset_option("^display")

`option_context()` context manager has been exposed through the top-level API, allowing you to execute code with given option values. Option values are restored automatically when you exit the `with` block:

    In [21]: with pd.option_context("display.max_rows", 10, "display.max_columns", 5):
       ....:     print(pd.get_option("display.max_rows"))
       ....:     print(pd.get_option("display.max_columns"))
       ....:
    10
    5

    In [22]: print(pd.get_option("display.max_rows"))
    60

    In [23]: print(pd.get_option("display.max_columns"))
    0

## Setting startup options in Python/IPython environment

Using startup scripts for the Python/IPython environment to import pandas and set options makes working with pandas more efficient. To do this, create a `.py` or `.ipy` script in the startup directory of the desired profile. An example where the startup folder is in a default IPython profile can be found at:

    $IPYTHONDIR/profile_default/startup

More information can be found in the IPython documentation. An example startup script for pandas is displayed below:

    import pandas as pd

    pd.set_option("display.max_rows", 999)
    pd.set_option("display.precision", 5)

## Frequently used options

The following is a demonstrates the more frequently used display options.

`display.max_rows` and `display.max_columns` sets the maximum number of rows and columns displayed when a frame is pretty-printed. Truncated lines are replaced by an ellipsis.

    In [24]: df = pd.DataFrame(np.random.randn(7, 2))

    In [25]: pd.set_option("display.max_rows", 7)

    In [26]: df
    Out[26]:
              0         1
    0  0.469112 -0.282863
    1 -1.509059 -1.135632
    2  1.212112 -0.173215
    3  0.119209 -1.044236
    4 -0.861849 -2.104569
    5 -0.494929  1.071804
    6  0.721555 -0.706771

    In [27]: pd.set_option("display.max_rows", 5)

    In [28]: df
    Out[28]:
               0         1
    0   0.469112 -0.282863
    1  -1.509059 -1.135632
    ..       ...       ...
    5  -0.494929  1.071804
    6   0.721555 -0.706771

    [7 rows x 2 columns]

    In [29]: pd.reset_option("display.max_rows")

Once the `display.max_rows` is exceeded, the `display.min_rows` options determines how many rows are shown in the truncated repr.

    In [30]: pd.set_option("display.max_rows", 8)

    In [31]: pd.set_option("display.min_rows", 4)

    # below max_rows -> all rows shown
    In [32]: df = pd.DataFrame(np.random.randn(7, 2))

    In [33]: df
    Out[33]:
              0         1
    0 -1.039575  0.271860
    1 -0.424972  0.567020
    2  0.276232 -1.087401
    3 -0.673690  0.113648
    4 -1.478427  0.524988
    5  0.404705  0.577046
    6 -1.715002 -1.039268

    # above max_rows -> only min_rows (4) rows shown
    In [34]: df = pd.DataFrame(np.random.randn(9, 2))

    In [35]: df
    Out[35]:
               0         1
    0  -0.370647 -1.157892
    1  -1.344312  0.844885
    ..       ...       ...
    7   0.276662 -0.472035
    8  -0.013960 -0.362543

    [9 rows x 2 columns]

    In [36]: pd.reset_option("display.max_rows")

    In [37]: pd.reset_option("display.min_rows")

`display.expand_frame_repr` allows for the representation of a `DataFrame` to stretch across pages, wrapped over the all the columns.

    In [38]: df = pd.DataFrame(np.random.randn(5, 10))

    In [39]: pd.set_option("expand_frame_repr", True)

    In [40]: df
    Out[40]:
              0         1         2  ...         7         8         9
    0 -0.006154 -0.923061  0.895717  ...  1.340309 -1.170299 -0.226169
    1  0.410835  0.813850  0.132003  ... -1.436737 -1.413681  1.607920
    2  1.024180  0.569605  0.875906  ... -0.078638  0.545952 -1.219217
    3 -1.226825  0.769804 -1.281247  ...  0.341734  0.959726 -1.110336
    4 -0.619976  0.149748 -0.732339  ...  0.301624 -2.179861 -1.369849

    [5 rows x 10 columns]

    In [41]: pd.set_option("expand_frame_repr", False)

    In [42]: df
    Out[42]:
              0         1         2         3         4         5         6         7         8         9
    0 -0.006154 -0.923061  0.895717  0.805244 -1.206412  2.565646  1.431256  1.340309 -1.170299 -0.226169
    1  0.410835  0.813850  0.132003 -0.827317 -0.076467 -1.187678  1.130127 -1.436737 -1.413681  1.607920
    2  1.024180  0.569605  0.875906 -2.211372  0.974466 -2.006747 -0.410001 -0.078638  0.545952 -1.219217
    3 -1.226825  0.769804 -1.281247 -0.727707 -0.121306 -0.097883  0.695775  0.341734  0.959726 -1.110336
    4 -0.619976  0.149748 -0.732339  0.687738  0.176444  0.403310 -0.154951  0.301624 -2.179861 -1.369849

    In [43]: pd.reset_option("expand_frame_repr")

`display.large_repr` displays a `DataFrame` that exceed `max_columns` or `max_rows` as a truncated frame or summary.

    In [44]: df = pd.DataFrame(np.random.randn(10, 10))

    In [45]: pd.set_option("display.max_rows", 5)

    In [46]: pd.set_option("large_repr", "truncate")

    In [47]: df
    Out[47]:
               0         1         2  ...         7         8         9
    0  -0.954208  1.462696 -1.743161  ...  0.995761  2.396780  0.014871
    1   3.357427 -0.317441 -1.236269  ...  0.380396  0.084844  0.432390
    ..       ...       ...       ...  ...       ...       ...       ...
    8  -0.303421 -0.858447  0.306996  ...  0.476720  0.473424 -0.242861
    9  -0.014805 -0.284319  0.650776  ...  1.613616  0.464000  0.227371

    [10 rows x 10 columns]

    In [48]: pd.set_option("large_repr", "info")

    In [49]: df
    Out[49]:
    <class 'pandas.core.frame.DataFrame'>
    RangeIndex: 10 entries, 0 to 9
    Data columns (total 10 columns):
     #   Column  Non-Null Count  Dtype
    ---  ------  --------------  -----
     0   0       10 non-null     float64
     1   1       10 non-null     float64
     2   2       10 non-null     float64
     3   3       10 non-null     float64
     4   4       10 non-null     float64
     5   5       10 non-null     float64
     6   6       10 non-null     float64
     7   7       10 non-null     float64
     8   8       10 non-null     float64
     9   9       10 non-null     float64
    dtypes: float64(10)
    memory usage: 932.0 bytes

    In [50]: pd.reset_option("large_repr")

    In [51]: pd.reset_option("display.max_rows")

`display.max_colwidth` sets the maximum width of columns. Cells of this length or longer will be truncated with an ellipsis.

    In [52]: df = pd.DataFrame(
       ....:     np.array(
       ....:         [
       ....:             ["foo", "bar", "bim", "uncomfortably long string"],
       ....:             ["horse", "cow", "banana", "apple"],
       ....:         ]
       ....:     )
       ....: )
       ....:

    In [53]: pd.set_option("max_colwidth", 40)

    In [54]: df
    Out[54]:
           0    1       2                          3
    0    foo  bar     bim  uncomfortably long string
    1  horse  cow  banana                      apple

    In [55]: pd.set_option("max_colwidth", 6)

    In [56]: df
    Out[56]:
           0    1      2      3
    0    foo  bar    bim  un...
    1  horse  cow  ba...  apple

    In [57]: pd.reset_option("max_colwidth")

`display.max_info_columns` sets a threshold for the number of columns displayed when calling `info()`.

    In [58]: df = pd.DataFrame(np.random.randn(10, 10))

    In [59]: pd.set_option("max_info_columns", 11)

    In [60]: df.info()
    <class 'pandas.core.frame.DataFrame'>
    RangeIndex: 10 entries, 0 to 9
    Data columns (total 10 columns):
     #   Column  Non-Null Count  Dtype
    ---  ------  --------------  -----
     0   0       10 non-null     float64
     1   1       10 non-null     float64
     2   2       10 non-null     float64
     3   3       10 non-null     float64
     4   4       10 non-null     float64
     5   5       10 non-null     float64
     6   6       10 non-null     float64
     7   7       10 non-null     float64
     8   8       10 non-null     float64
     9   9       10 non-null     float64
    dtypes: float64(10)
    memory usage: 932.0 bytes

    In [61]: pd.set_option("max_info_columns", 5)

    In [62]: df.info()
    <class 'pandas.core.frame.DataFrame'>
    RangeIndex: 10 entries, 0 to 9
    Columns: 10 entries, 0 to 9
    dtypes: float64(10)
    memory usage: 932.0 bytes

    In [63]: pd.reset_option("max_info_columns")

`display.max_info_rows`: `info()` will usually show null-counts for each column. For a large `DataFrame`, this can be quite slow. `max_info_rows` and `max_info_cols` limit this null check to the specified rows and columns respectively. The `info()` keyword argument `show_counts=True` will override this.

    In [64]: df = pd.DataFrame(np.random.choice([0, 1, np.nan], size=(10, 10)))

    In [65]: df
    Out[65]:
         0    1    2    3    4    5    6    7    8    9
    0  0.0  NaN  1.0  NaN  NaN  0.0  NaN  0.0  NaN  1.0
    1  1.0  NaN  1.0  1.0  1.0  1.0  NaN  0.0  0.0  NaN
    2  0.0  NaN  1.0  0.0  0.0  NaN  NaN  NaN  NaN  0.0
    3  NaN  NaN  NaN  0.0  1.0  1.0  NaN  1.0  NaN  1.0
    4  0.0  NaN  NaN  NaN  0.0  NaN  NaN  NaN  1.0  0.0
    5  0.0  1.0  1.0  1.0  1.0  0.0  NaN  NaN  1.0  0.0
    6  1.0  1.0  1.0  NaN  1.0  NaN  1.0  0.0  NaN  NaN
    7  0.0  0.0  1.0  0.0  1.0  0.0  1.0  1.0  0.0  NaN
    8  NaN  NaN  NaN  0.0  NaN  NaN  NaN  NaN  1.0  NaN
    9  0.0  NaN  0.0  NaN  NaN  0.0  NaN  1.0  1.0  0.0

    In [66]: pd.set_option("max_info_rows", 11)

    In [67]: df.info()
    <class 'pandas.core.frame.DataFrame'>
    RangeIndex: 10 entries, 0 to 9
    Data columns (total 10 columns):
     #   Column  Non-Null Count  Dtype
    ---  ------  --------------  -----
     0   0       8 non-null      float64
     1   1       3 non-null      float64
     2   2       7 non-null      float64
     3   3       6 non-null      float64
     4   4       7 non-null      float64
     5   5       6 non-null      float64
     6   6       2 non-null      float64
     7   7       6 non-null      float64
     8   8       6 non-null      float64
     9   9       6 non-null      float64
    dtypes: float64(10)
    memory usage: 932.0 bytes

    In [68]: pd.set_option("max_info_rows", 5)

    In [69]: df.info()
    <class 'pandas.core.frame.DataFrame'>
    RangeIndex: 10 entries, 0 to 9
    Data columns (total 10 columns):
     #   Column  Dtype
    ---  ------  -----
     0   0       float64
     1   1       float64
     2   2       float64
     3   3       float64
     4   4       float64
     5   5       float64
     6   6       float64
     7   7       float64
     8   8       float64
     9   9       float64
    dtypes: float64(10)
    memory usage: 932.0 bytes

    In [70]: pd.reset_option("max_info_rows")

`display.precision` sets the output display precision in terms of decimal places.

    In [71]: df = pd.DataFrame(np.random.randn(5, 5))

    In [72]: pd.set_option("display.precision", 7)

    In [73]: df
    Out[73]:
               0          1          2          3          4
    0 -1.1506406 -0.7983341 -0.5576966  0.3813531  1.3371217
    1 -1.5310949  1.3314582 -0.5713290 -0.0266708 -1.0856630
    2 -1.1147378 -0.0582158 -0.4867681  1.6851483  0.1125723
    3 -1.4953086  0.8984347 -0.1482168 -1.5960698  0.1596530
    4  0.2621358  0.0362196  0.1847350 -0.2550694 -0.2710197

    In [74]: pd.set_option("display.precision", 4)

    In [75]: df
    Out[75]:
            0       1       2       3       4
    0 -1.1506 -0.7983 -0.5577  0.3814  1.3371
    1 -1.5311  1.3315 -0.5713 -0.0267 -1.0857
    2 -1.1147 -0.0582 -0.4868  1.6851  0.1126
    3 -1.4953  0.8984 -0.1482 -1.5961  0.1597
    4  0.2621  0.0362  0.1847 -0.2551 -0.2710

`display.chop_threshold` sets the rounding threshold to zero when displaying a `Series` or `DataFrame`. This setting does not change the precision at which the number is stored.

    In [76]: df = pd.DataFrame(np.random.randn(6, 6))

    In [77]: pd.set_option("chop_threshold", 0)

    In [78]: df
    Out[78]:
            0       1       2       3       4       5
    0  1.2884  0.2946 -1.1658  0.8470 -0.6856  0.6091
    1 -0.3040  0.6256 -0.0593  0.2497  1.1039 -1.0875
    2  1.9980 -0.2445  0.1362  0.8863 -1.3507 -0.8863
    3 -1.0133  1.9209 -0.3882 -2.3144  0.6655  0.4026
    4  0.3996 -1.7660  0.8504  0.3881  0.9923  0.7441
    5 -0.7398 -1.0549 -0.1796  0.6396  1.5850  1.9067

    In [79]: pd.set_option("chop_threshold", 0.5)

    In [80]: df
    Out[80]:
            0       1       2       3       4       5
    0  1.2884  0.0000 -1.1658  0.8470 -0.6856  0.6091
    1  0.0000  0.6256  0.0000  0.0000  1.1039 -1.0875
    2  1.9980  0.0000  0.0000  0.8863 -1.3507 -0.8863
    3 -1.0133  1.9209  0.0000 -2.3144  0.6655  0.0000
    4  0.0000 -1.7660  0.8504  0.0000  0.9923  0.7441
    5 -0.7398 -1.0549  0.0000  0.6396  1.5850  1.9067

    In [81]: pd.reset_option("chop_threshold")

`display.colheader_justify` controls the justification of the headers. The options are `'right'`, and `'left'`.

    In [82]: df = pd.DataFrame(
       ....:     np.array([np.random.randn(6), np.random.randint(1, 9, 6) * 0.1, np.zeros(6)]).T,
       ....:     columns=["A", "B", "C"],
       ....:     dtype="float",
       ....: )
       ....:

    In [83]: pd.set_option("colheader_justify", "right")

    In [84]: df
    Out[84]:
            A    B    C
    0  0.1040  0.1  0.0
    1  0.1741  0.5  0.0
    2 -0.4395  0.4  0.0
    3 -0.7413  0.8  0.0
    4 -0.0797  0.4  0.0
    5 -0.9229  0.3  0.0

    In [85]: pd.set_option("colheader_justify", "left")

    In [86]: df
    Out[86]:
       A       B    C
    0  0.1040  0.1  0.0
    1  0.1741  0.5  0.0
    2 -0.4395  0.4  0.0
    3 -0.7413  0.8  0.0
    4 -0.0797  0.4  0.0
    5 -0.9229  0.3  0.0

    In [87]: pd.reset_option("colheader_justify")

## Number formatting

pandas also allows you to set how numbers are displayed in the console. This option is not set through the `set_options` API.

Use the `set_eng_float_format` function to alter the floating-point formatting of pandas objects to produce a particular format.

    In [88]: import numpy as np

    In [89]: pd.set_eng_float_format(accuracy=3, use_eng_prefix=True)

    In [90]: s = pd.Series(np.random.randn(5), index=["a", "b", "c", "d", "e"])

    In [91]: s / 1.0e3
    Out[91]:
    a    303.638u
    b   -721.084u
    c   -622.696u
    d    648.250u
    e     -1.945m
    dtype: float64

    In [92]: s / 1.0e6
    Out[92]:
    a    303.638n
    b   -721.084n
    c   -622.696n
    d    648.250n
    e     -1.945u
    dtype: float64

Use `round()` to specifically control rounding of an individual `DataFrame`

## Unicode formatting

Warning

Enabling this option will affect the performance for printing of DataFrame and Series (about 2 times slower). Use only when it is actually required.

Some East Asian countries use Unicode characters whose width corresponds to two Latin characters. If a DataFrame or Series contains these characters, the default output mode may not align them properly.

    In [93]: df = pd.DataFrame({"国籍": ["UK", "日本"], "名前": ["Alice", "しのぶ"]})

    In [94]: df
    Out[94]:
       国籍     名前
    0  UK  Alice
    1  日本    しのぶ

Enabling `display.unicode.east_asian_width` allows pandas to check each character’s “East Asian Width” property. These characters can be aligned properly by setting this option to `True`. However, this will result in longer render times than the standard `len` function.

    In [95]: pd.set_option("display.unicode.east_asian_width", True)

    In [96]: df
    Out[96]:
       国籍    名前
    0    UK   Alice
    1  日本  しのぶ

In addition, Unicode characters whose width is “ambiguous” can either be 1 or 2 characters wide depending on the terminal setting or encoding. The option `display.unicode.ambiguous_as_wide` can be used to handle the ambiguity.

By default, an “ambiguous” character’s width, such as “¡” (inverted exclamation) in the example below, is taken to be 1.

    In [97]: df = pd.DataFrame({"a": ["xxx", "¡¡"], "b": ["yyy", "¡¡"]})

    In [98]: df
    Out[98]:
         a    b
    0  xxx  yyy
    1   ¡¡   ¡¡

Enabling `display.unicode.ambiguous_as_wide` makes pandas interpret these characters’ widths to be 2. (Note that this option will only be effective when `display.unicode.east_asian_width` is enabled.)

However, setting this option incorrectly for your terminal will cause these characters to be aligned incorrectly:

    In [99]: pd.set_option("display.unicode.ambiguous_as_wide", True)

    In [100]: df
    Out[100]:
          a     b
    0   xxx   yyy
    1  ¡¡  ¡¡

## Table schema display

`DataFrame` and `Series` will publish a Table Schema representation by default. This can be enabled globally with the `display.html.table_schema` option:

    In [101]: pd.set_option("display.html.table_schema", True)

Only `'display.max_rows'` are serialized and published.