.. currentmodule:: pandas
.. ipython:: python :suppress: import pandas as pd import numpy as np np.random.seed(123456)
pandas has an options system that lets you customize some aspects of it's behaviour, display-related options being those the user is most likely to adjust.
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
There is also an API composed of 5 relevant functions, available directly from the pandas
namespace, and they are:
- :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.py for more info.
All of the functions above accept a regexp pattern (re.search style) as an argument,
and so passing in a substring will work - as long as it is unambiguous :
.. ipython:: python
pd.get_option("display.max_rows")
pd.set_option("display.max_rows",101)
pd.get_option("display.max_rows")
pd.set_option("max_r",102)
pd.get_option("display.max_rows")
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:
try:
pd.get_option("column")
except KeyError as e:
print(e)
Note: Using this form of shorthand may cause your code to break if new options with similar names are added in future versions.
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.
.. ipython:: python
:suppress:
pd.reset_option("all")
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)
.. ipython:: python
pd.get_option('mode.sim_interactive')
pd.set_option('mode.sim_interactive', True)
pd.get_option('mode.sim_interactive')
Note: that the option 'mode.sim_interactive' is mostly used for debugging purposes.
All options also have a default value, and you can use reset_option to do just that:
.. 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
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:
.. 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"))
The following is a walkthrough of 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('max_rows', 7)
df
pd.set_option('max_rows', 5)
df
pd.reset_option('max_rows')
display.expand_frame_repr allows for the the representation of
dataframes to stretch across pages, wrapped over the full column vs row-wise.
.. 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 lets you select whether to display dataframes that exceed
max_columns or max_rows as a truncated frame, or as a summary.
.. ipython:: python
df=pd.DataFrame(np.random.randn(10,10))
pd.set_option('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('max_rows')
display.max_columnwidth 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 when by-column info
will be given.
.. 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: 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 then specified.
.. 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. This is only a
suggestion.
.. ipython:: python
df=pd.DataFrame(np.random.randn(5,5))
pd.set_option('precision',7)
df
pd.set_option('precision',4)
df
display.chop_threshold sets at what level pandas rounds to zero when
it displays a Series of DataFrame. Note, this does not effect 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', .5)
df
pd.reset_option('chop_threshold')
display.colheader_justify controls the justification of the headers.
Options are 'right', and 'left'.
.. ipython:: python
df=pd.DataFrame(np.array([np.random.randn(6), np.random.randint(1,9,6)*.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')
| Option | Default | Function |
|---|---|---|
| display.chop_threshold | None | If set to a float value, all float values smaller then the given threshold will be displayed as exactly 0 by repr and friends. |
| display.colheader_justify | right | Controls the justification of column headers. used by DataFrameFormatter. |
| display.column_space | 12 | No description available. |
| display.date_dayfirst | False | When True, prints and parses dates with the day first, eg 20/01/2005 |
| display.date_yearfirst | False | When True, prints and parses dates with the year first, eg 2005/01/20 |
| display.encoding | UTF-8 | 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. |
| display.expand_frame_repr | True | 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 it's width exceeds display.width. |
| display.float_format | None | 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 core.format.EngFormatter for an example. |
| display.height | 60 | Deprecated. Use display.max_rows instead. |
| display.large_repr | truncate | For DataFrames exceeding max_rows/max_cols, the repr (and HTML repr) can show a truncated table (the default from 0.13), or switch to the view from df.info() (the behaviour in earlier versions of pandas). allowable settings, ['truncate', 'info'] |
| display.line_width | 80 | Deprecated. Use display.width instead. |
| display.max_columns | 20 | max_rows and max_columns are used in __repr__() methods to decide if to_string() or info() is used to render an object to a string. In case python/IPython is running in a terminal this can be set to 0 and pandas will correctly auto-detect the width the terminal and swap to a smaller format in case all columns would not fit vertically. The IPython notebook, IPython qtconsole, or IDLE do not run in a terminal and hence it is not possible to do correct auto-detection. 'None' value means unlimited. |
| display.max_colwidth | 50 | 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. |
| display.max_info_columns | 100 | max_info_columns is used in DataFrame.info method to decide if per column information will be printed. |
| display.max_info_rows | 1690785 | 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 then specified. |
| display.max_rows | 60 | This sets the maximum number of rows pandas should output when printing out various output. For example, this value determines whether the repr() for a dataframe prints out fully or just a summary repr. 'None' value means unlimited. |
| display.max_seq_items | 100 | 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. |
| display.mpl_style | None | Setting this to 'default' will modify the rcParams used by matplotlib to give plots a more pleasing visual style by default. Setting this to None/False restores the values to their initial value. |
| display.multi_sparse | True | "Sparsify" MultiIndex display (don't display repeated elements in outer levels within groups) |
| display.notebook_repr_html | True | When True, IPython notebook will use html representation for pandas objects (if it is available). |
| display.pprint_nest_depth | 3 | Controls the number of nested levels to process when pretty-printing |
| display.precision | 7 | Floating point output precision (number of significant digits). This is only a suggestion |
| display.show_dimensions | 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) |
| display.width | 80 | 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. |
| io.excel.xls.writer | xlwt | The default Excel writer engine for 'xls' files. |
| io.excel.xlsm.writer | openpyxl | The default Excel writer engine for 'xlsm' files. Available options: 'openpyxl' (the default). |
| io.excel.xlsx.writer | openpyxl | The default Excel writer engine for 'xlsx' files. |
| io.hdf.default_format | None | default format writing format, if None, then put will default to 'fixed' and append will default to 'table' |
| io.hdf.dropna_table | True | drop ALL nan rows when appending to a table |
| mode.chained_assignment | warn | Raise an exception, warn, or no action if trying to use chained assignment, The default is warn |
| mode.sim_interactive | False | Whether to simulate interactive mode for purposes of testing |
| mode.use_inf_as_null | False | True means treat None, NaN, -INF, INF as null (old way), False means None and NaN are null, but INF, -INF are not null (new way). |
pandas also allow 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.
For instance:
.. 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.e3 s/1.e6
.. ipython:: python
:suppress:
pd.reset_option('^display\.')