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pandas has an options API configure and customize global behavior related to :class:`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:
.. ipython:: python import pandas as pd pd.options.display.max_rows pd.options.display.max_rows = 999 pd.options.display.max_rows
The API is composed of 5 relevant functions, available directly from the pandas
namespace:
- :func:`~pandas.get_option` / :func:`~pandas.set_option` - get/set the value of a single option.
- :func:`~pandas.reset_option` - reset one or more options to their default value.
- :func:`~pandas.describe_option` - print the descriptions of one or more options.
- :func:`~pandas.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:
.. ipython:: python
pd.get_option("display.chop_threshold")
pd.set_option("display.chop_threshold", 2)
pd.get_option("display.chop_threshold")
pd.set_option("chop", 4)
pd.get_option("display.chop_threshold")
The following will not work because it matches multiple option names, e.g.
display.max_colwidth, display.max_rows, display.max_columns:
.. ipython:: python
:okexcept:
pd.get_option("max")
Warning
Using this form of shorthand may cause your code to break if new options with similar names are added in future versions.
.. ipython:: python
:suppress:
:okwarning:
pd.reset_option("all")
You can get a list of available options and their descriptions with :func:`~pandas.describe_option`. When called with no argument :func:`~pandas.describe_option` will print out the descriptions for all available options.
.. ipython:: python pd.describe_option()
As described above, :func:`~pandas.get_option` and :func:`~pandas.set_option`
are available from the pandas namespace. To change an option, call
set_option('option regex', new_value).
.. ipython:: python
pd.get_option("mode.sim_interactive")
pd.set_option("mode.sim_interactive", True)
pd.get_option("mode.sim_interactive")
Note
The option 'mode.sim_interactive' is mostly used for debugging purposes.
You can use :func:`~pandas.reset_option` to revert to a setting's default value
.. ipython:: python
:suppress:
pd.reset_option("display.max_rows")
.. ipython:: python
pd.get_option("display.max_rows")
pd.set_option("display.max_rows", 999)
pd.get_option("display.max_rows")
pd.reset_option("display.max_rows")
pd.get_option("display.max_rows")
It's also possible to reset multiple options at once (using a regex):
.. ipython:: python
:okwarning:
pd.reset_option("^display")
:func:`~pandas.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:
.. ipython:: python
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"))
print(pd.get_option("display.max_rows"))
print(pd.get_option("display.max_columns"))
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)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.
.. ipython:: python
df = pd.DataFrame(np.random.randn(7, 2))
pd.set_option("display.max_rows", 7)
df
pd.set_option("display.max_rows", 5)
df
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.
.. ipython:: python
pd.set_option("display.max_rows", 8)
pd.set_option("display.min_rows", 4)
# below max_rows -> all rows shown
df = pd.DataFrame(np.random.randn(7, 2))
df
# above max_rows -> only min_rows (4) rows shown
df = pd.DataFrame(np.random.randn(9, 2))
df
pd.reset_option("display.max_rows")
pd.reset_option("display.min_rows")
display.expand_frame_repr allows for the representation of a
:class:`DataFrame` to stretch across pages, wrapped over the all the columns.
.. ipython:: python
df = pd.DataFrame(np.random.randn(5, 10))
pd.set_option("expand_frame_repr", True)
df
pd.set_option("expand_frame_repr", False)
df
pd.reset_option("expand_frame_repr")
display.large_repr displays a :class:`DataFrame` that exceed
max_columns or max_rows as a truncated frame or summary.
.. ipython:: python
df = pd.DataFrame(np.random.randn(10, 10))
pd.set_option("display.max_rows", 5)
pd.set_option("large_repr", "truncate")
df
pd.set_option("large_repr", "info")
df
pd.reset_option("large_repr")
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.
.. ipython:: python
df = pd.DataFrame(
np.array(
[
["foo", "bar", "bim", "uncomfortably long string"],
["horse", "cow", "banana", "apple"],
]
)
)
pd.set_option("max_colwidth", 40)
df
pd.set_option("max_colwidth", 6)
df
pd.reset_option("max_colwidth")
display.max_info_columns sets a threshold for the number of columns
displayed when calling :meth:`~pandas.DataFrame.info`.
.. ipython:: python
df = pd.DataFrame(np.random.randn(10, 10))
pd.set_option("max_info_columns", 11)
df.info()
pd.set_option("max_info_columns", 5)
df.info()
pd.reset_option("max_info_columns")
display.max_info_rows: :meth:`~pandas.DataFrame.info` will usually show null-counts for each column.
For a large :class:`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 :meth:`~pandas.DataFrame.info`
keyword argument null_counts=True will override this.
.. ipython:: python
df = pd.DataFrame(np.random.choice([0, 1, np.nan], size=(10, 10)))
df
pd.set_option("max_info_rows", 11)
df.info()
pd.set_option("max_info_rows", 5)
df.info()
pd.reset_option("max_info_rows")
display.precision sets the output display precision in terms of decimal places.
.. ipython:: python
df = pd.DataFrame(np.random.randn(5, 5))
pd.set_option("display.precision", 7)
df
pd.set_option("display.precision", 4)
df
display.chop_threshold sets the rounding threshold to zero when displaying a
:class:`Series` or :class:`DataFrame`. This setting does not change the
precision at which the number is stored.
.. ipython:: python
df = pd.DataFrame(np.random.randn(6, 6))
pd.set_option("chop_threshold", 0)
df
pd.set_option("chop_threshold", 0.5)
df
pd.reset_option("chop_threshold")
display.colheader_justify controls the justification of the headers.
The options are 'right', and 'left'.
.. ipython:: python
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",
)
pd.set_option("colheader_justify", "right")
df
pd.set_option("colheader_justify", "left")
df
pd.reset_option("colheader_justify")
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.
.. ipython:: python import numpy as np pd.set_eng_float_format(accuracy=3, use_eng_prefix=True) s = pd.Series(np.random.randn(5), index=["a", "b", "c", "d", "e"]) s / 1.0e3 s / 1.0e6
.. ipython:: python
:suppress:
:okwarning:
pd.reset_option("^display")
Use :meth:`~pandas.DataFrame.round` to specifically control rounding of an individual :class:`DataFrame`
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.
.. ipython:: python
df = pd.DataFrame({"国籍": ["UK", "日本"], "名前": ["Alice", "しのぶ"]})
df
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.
.. ipython:: python
pd.set_option("display.unicode.east_asian_width", True)
df
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.
.. ipython:: python
df = pd.DataFrame({"a": ["xxx", "¡¡"], "b": ["yyy", "¡¡"]})
df
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:
.. ipython:: python
pd.set_option("display.unicode.ambiguous_as_wide", True)
df
.. ipython:: python
:suppress:
pd.set_option("display.unicode.east_asian_width", False)
pd.set_option("display.unicode.ambiguous_as_wide", False)
:class:`DataFrame` and :class:`Series` will publish a Table Schema representation
by default. This can be enabled globally with the
display.html.table_schema option:
.. ipython:: python
pd.set_option("display.html.table_schema", True)
Only 'display.max_rows' are serialized and published.
.. ipython:: python
:suppress:
pd.reset_option("display.html.table_schema")