Options and settings — pandas 2.2.3 documentation (original) (raw)

Overview#

pandas has an options API configure and customize global behavior related toDataFrame 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 pandasnamespace:

• 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.

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 ~/work/pandas/pandas/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 ~/work/pandas/pandas/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 ~/work/pandas/pandas/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 () 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, callset_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 aDataFrame 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 exceedmax_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: 928.0 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: 928.0 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: 928.0 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_colslimit 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: 928.0 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: 928.0 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 aSeries 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 thedisplay.html.table_schema option:

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

Only 'display.max_rows' are serialized and published.