# Options and Settings

## Overview

pandas has an options system that lets you customize some aspects of its 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:

    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

There is also an API 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.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 :

    In [5]: pd.get_option("display.max_rows")
    Out[5]: 999

    In [6]: pd.set_option("display.max_rows",101)

    In [7]: pd.get_option("display.max_rows")
    Out[7]: 101

    In [8]: pd.set_option("max_r",102)

    In [9]: pd.get_option("display.max_rows")
    Out[9]: 102

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

    In [10]: try:
       ....:     pd.get_option("column")
       ....: except KeyError as e:
       ....:     print(e)
       ....:
    'Pattern matched multiple keys'

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.

## 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 [11]: pd.get_option('mode.sim_interactive')
    Out[11]: False

    In [12]: pd.set_option('mode.sim_interactive', True)

    In [13]: pd.get_option('mode.sim_interactive')
    Out[13]: True

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:

    In [14]: pd.get_option("display.max_rows")
    Out[14]: 60

    In [15]: pd.set_option("display.max_rows",999)

    In [16]: pd.get_option("display.max_rows")
    Out[16]: 999

    In [17]: pd.reset_option("display.max_rows")

    In [18]: pd.get_option("display.max_rows")
    Out[18]: 60

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

    In [19]: pd.reset_option("^display")
    height has been deprecated.

    line_width has been deprecated, use display.width instead (currently both are
    identical)

`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 [20]: 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 [21]: print(pd.get_option("display.max_rows"))
    60

    In [22]: print(pd.get_option("display.max_columns"))
    20

## 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('precision', 5)

## Frequently Used Options

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.

    In [23]: df = pd.DataFrame(np.random.randn(7,2))

    In [24]: pd.set_option('max_rows', 7)

    In [25]: df
    Out[25]:
              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 [26]: pd.set_option('max_rows', 5)

    In [27]: df
    Out[27]:
               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 [28]: 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.

    In [29]: df = pd.DataFrame(np.random.randn(5,10))

    In [30]: pd.set_option('expand_frame_repr', True)

    In [31]: df
    Out[31]:
              0         1         2         3         4         5         6  \
    0 -1.039575  0.271860 -0.424972  0.567020  0.276232 -1.087401 -0.673690
    1  0.404705  0.577046 -1.715002 -1.039268 -0.370647 -1.157892 -1.344312
    2  1.643563 -1.469388  0.357021 -0.674600 -1.776904 -0.968914 -1.294524
    3 -0.013960 -0.362543 -0.006154 -0.923061  0.895717  0.805244 -1.206412
    4 -1.170299 -0.226169  0.410835  0.813850  0.132003 -0.827317 -0.076467

              7         8         9
    0  0.113648 -1.478427  0.524988
    1  0.844885  1.075770 -0.109050
    2  0.413738  0.276662 -0.472035
    3  2.565646  1.431256  1.340309
    4 -1.187678  1.130127 -1.436737

    In [32]: pd.set_option('expand_frame_repr', False)

    In [33]: df
    Out[33]:
              0         1         2         3         4         5         6         7         8         9
    0 -1.039575  0.271860 -0.424972  0.567020  0.276232 -1.087401 -0.673690  0.113648 -1.478427  0.524988
    1  0.404705  0.577046 -1.715002 -1.039268 -0.370647 -1.157892 -1.344312  0.844885  1.075770 -0.109050
    2  1.643563 -1.469388  0.357021 -0.674600 -1.776904 -0.968914 -1.294524  0.413738  0.276662 -0.472035
    3 -0.013960 -0.362543 -0.006154 -0.923061  0.895717  0.805244 -1.206412  2.565646  1.431256  1.340309
    4 -1.170299 -0.226169  0.410835  0.813850  0.132003 -0.827317 -0.076467 -1.187678  1.130127 -1.436737

    In [34]: 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.

    In [35]: df = pd.DataFrame(np.random.randn(10,10))

    In [36]: pd.set_option('max_rows', 5)

    In [37]: pd.set_option('large_repr', 'truncate')

    In [38]: df
    Out[38]:
               0         1         2         3         4         5         6  \
    0  -1.413681  1.607920  1.024180  0.569605  0.875906 -2.211372  0.974466
    1   0.545952 -1.219217 -1.226825  0.769804 -1.281247 -0.727707 -0.121306
    ..       ...       ...       ...       ...       ...       ...       ...
    8  -2.484478 -0.281461  0.030711  0.109121  1.126203 -0.977349  1.474071
    9  -1.071357  0.441153  2.353925  0.583787  0.221471 -0.744471  0.758527

               7         8         9
    0  -2.006747 -0.410001 -0.078638
    1  -0.097883  0.695775  0.341734
    ..       ...       ...       ...
    8  -0.064034 -1.282782  0.781836
    9   1.729689 -0.964980 -0.845696

    [10 rows x 10 columns]

    In [39]: pd.set_option('large_repr', 'info')

    In [40]: df
    Out[40]:
    <class 'pandas.core.frame.DataFrame'>
    Int64Index: 10 entries, 0 to 9
    Data columns (total 10 columns):
    0    10 non-null float64
    1    10 non-null float64
    2    10 non-null float64
    3    10 non-null float64
    4    10 non-null float64
    5    10 non-null float64
    6    10 non-null float64
    7    10 non-null float64
    8    10 non-null float64
    9    10 non-null float64
    dtypes: float64(10)
    memory usage: 880.0 bytes

    In [41]: pd.reset_option('large_repr')

    In [42]: pd.reset_option('max_rows')

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

    In [43]: df = pd.DataFrame(np.array([['foo', 'bar', 'bim', 'uncomfortably long string'],
       ....:                             ['horse', 'cow', 'banana', 'apple']]))
       ....:

    In [44]: pd.set_option('max_colwidth',40)

    In [45]: df
    Out[45]:
           0    1       2                          3
    0    foo  bar     bim  uncomfortably long string
    1  horse  cow  banana                      apple

    In [46]: pd.set_option('max_colwidth', 6)

    In [47]: df
    Out[47]:
           0    1      2      3
    0    foo  bar    bim  un...
    1  horse  cow  ba...  apple

    In [48]: pd.reset_option('max_colwidth')

`display.max_info_columns` sets a threshold for when by-column info will be given.

    In [49]: df = pd.DataFrame(np.random.randn(10,10))

    In [50]: pd.set_option('max_info_columns', 11)

    In [51]: df.info()
    <class 'pandas.core.frame.DataFrame'>
    Int64Index: 10 entries, 0 to 9
    Data columns (total 10 columns):
    0    10 non-null float64
    1    10 non-null float64
    2    10 non-null float64
    3    10 non-null float64
    4    10 non-null float64
    5    10 non-null float64
    6    10 non-null float64
    7    10 non-null float64
    8    10 non-null float64
    9    10 non-null float64
    dtypes: float64(10)
    memory usage: 880.0 bytes

    In [52]: pd.set_option('max_info_columns', 5)

    In [53]: df.info()
    <class 'pandas.core.frame.DataFrame'>
    Int64Index: 10 entries, 0 to 9
    Columns: 10 entries, 0 to 9
    dtypes: float64(10)
    memory usage: 880.0 bytes

    In [54]: 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. Note that you can specify the option `df.info(null_counts=True)` to override on showing a particular frame.

    In [55]: df  =pd.DataFrame(np.random.choice([0,1,np.nan], size=(10,10)))

    In [56]: df
    Out[56]:
        0   1   2   3   4   5   6   7   8   9
    0   0   1   1   0   1   1   0 NaN   1 NaN
    1   1 NaN   0   0   1   1 NaN   1   0   1
    2 NaN NaN NaN   1   1   0 NaN   0   1 NaN
    3   0   1   1 NaN   0 NaN   1 NaN NaN   0
    4   0   1   0   0   1   0   0 NaN   0   0
    5   0 NaN   1 NaN NaN NaN NaN   0   1 NaN
    6   0   1   0   0 NaN   1 NaN NaN   0 NaN
    7   0 NaN   1   1 NaN   1   1   1   1 NaN
    8   0   0 NaN   0 NaN   1   0   0 NaN NaN
    9 NaN NaN   0 NaN NaN NaN   0   1   1 NaN

    In [57]: pd.set_option('max_info_rows', 11)

    In [58]: df.info()
    <class 'pandas.core.frame.DataFrame'>
    Int64Index: 10 entries, 0 to 9
    Data columns (total 10 columns):
    0    8 non-null float64
    1    5 non-null float64
    2    8 non-null float64
    3    7 non-null float64
    4    5 non-null float64
    5    7 non-null float64
    6    6 non-null float64
    7    6 non-null float64
    8    8 non-null float64
    9    3 non-null float64
    dtypes: float64(10)
    memory usage: 880.0 bytes

    In [59]: pd.set_option('max_info_rows', 5)

    In [60]: df.info()
    <class 'pandas.core.frame.DataFrame'>
    Int64Index: 10 entries, 0 to 9
    Data columns (total 10 columns):
    0    float64
    1    float64
    2    float64
    3    float64
    4    float64
    5    float64
    6    float64
    7    float64
    8    float64
    9    float64
    dtypes: float64(10)
    memory usage: 880.0 bytes

    In [61]: pd.reset_option('max_info_rows')

`display.precision` sets the output display precision in terms of decimal places. This is only a suggestion.

    In [62]: df = pd.DataFrame(np.random.randn(5,5))

    In [63]: pd.set_option('precision',7)

    In [64]: df
    Out[64]:
               0          1          2          3          4
    0 -2.0490276  2.8466122 -1.2080493 -0.4503923  2.4239054
    1  0.1211080  0.2669165  0.8438259 -0.2225400  2.0219807
    2 -0.7167894 -2.2244851 -1.0611370 -0.2328247  0.4307933
    3 -0.6654779  1.8298075 -1.4065093  1.0782481  0.3227741
    4  0.2003243  0.8900241  0.1948132  0.3516326  0.4488815

    In [65]: pd.set_option('precision',4)

    In [66]: df
    Out[66]:
            0       1       2       3       4
    0 -2.0490  2.8466 -1.2080 -0.4504  2.4239
    1  0.1211  0.2669  0.8438 -0.2225  2.0220
    2 -0.7168 -2.2245 -1.0611 -0.2328  0.4308
    3 -0.6655  1.8298 -1.4065  1.0782  0.3228
    4  0.2003  0.8900  0.1948  0.3516  0.4489

`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.

    In [67]: df = pd.DataFrame(np.random.randn(6,6))

    In [68]: pd.set_option('chop_threshold', 0)

    In [69]: df
    Out[69]:
            0       1       2       3       4       5
    0 -0.1979  0.9657 -1.5229 -0.1166  0.2956 -1.0477
    1  1.6406  1.9058  2.7721  0.0888 -1.1442 -0.6334
    2  0.9254 -0.0064 -0.8204 -0.6009 -1.0393  0.8248
    3 -0.8241 -0.3377 -0.9278 -0.8401  0.2485 -0.1093
    4  0.4320 -0.4607  0.3365 -3.2076 -1.5359  0.4098
    5 -0.6731 -0.7411 -0.1109 -2.6729  0.8645  0.0609

    In [70]: pd.set_option('chop_threshold', .5)

    In [71]: df
    Out[71]:
            0       1       2       3       4       5
    0  0.0000  0.9657 -1.5229  0.0000  0.0000 -1.0477
    1  1.6406  1.9058  2.7721  0.0000 -1.1442 -0.6334
    2  0.9254  0.0000 -0.8204 -0.6009 -1.0393  0.8248
    3 -0.8241  0.0000 -0.9278 -0.8401  0.0000  0.0000
    4  0.0000  0.0000  0.0000 -3.2076 -1.5359  0.0000
    5 -0.6731 -0.7411  0.0000 -2.6729  0.8645  0.0000

    In [72]: pd.reset_option('chop_threshold')

`display.colheader_justify` controls the justification of the headers. Options are ‘right’, and ‘left’.

    In [73]: 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')
       ....:

    In [74]: pd.set_option('colheader_justify', 'right')

    In [75]: df
    Out[75]:
            A    B  C
    0  0.9331  0.3  0
    1  0.2888  0.2  0
    2  1.3250  0.2  0
    3  0.5892  0.7  0
    4  0.5314  0.1  0
    5 -1.1987  0.7  0

    In [76]: pd.set_option('colheader_justify', 'left')

    In [77]: df
    Out[77]:
       A       B    C
    0  0.9331  0.3  0
    1  0.2888  0.2  0
    2  1.3250  0.2  0
    3  0.5892  0.7  0
    4  0.5314  0.1  0
    5 -1.1987  0.7  0

    In [78]: pd.reset_option('colheader_justify')

## List of Options

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 its 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.memory_usage | True | This specifies if the memory usage of a DataFrame should be displayed when the df.info() method is invoked.
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 | 6 | Floating point output precision in terms of number of places after the decimal, for regular formatting as well as scientific notation. Similar to numpy’s `precision` print option
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).

## 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.

For instance:

    In [79]: import numpy as np

    In [80]: pd.set_eng_float_format(accuracy=3, use_eng_prefix=True)

    In [81]: s = pd.Series(np.random.randn(5), index=['a', 'b', 'c', 'd', 'e'])

    In [82]: s/1.e3
    Out[82]:
    a   -236.866u
    b    846.974u
    c   -685.597u
    d    609.099u
    e   -303.961u
    dtype: float64

    In [83]: s/1.e6
    Out[83]:
    a   -236.866n
    b    846.974n
    c   -685.597n
    d    609.099n
    e   -303.961n
    dtype: float64

To round floats on a case-by-case basis, you can also use `round()` and `round()`.

## 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 its width is corresponding to 2 alphabets. If DataFrame or Series contains these characters, default output cannot be aligned properly.

Note

Screen captures are attached for each outputs to show the actual results.

    In [84]: df = pd.DataFrame({u'国籍': ['UK', u'日本'], u'名前': ['Alice', u'しのぶ']})

    In [85]: df;

Enable `display.unicode.east_asian_width` allows pandas to check each character’s “East Asian Width” property. These characters can be aligned properly by checking this property, but it takes longer time than standard `len` function.

    In [86]: pd.set_option('display.unicode.east_asian_width', True)

    In [87]: df;

In addition, Unicode contains characters which width is “Ambiguous”. These character’s width should be either 1 or 2 depending on terminal setting or encoding. Because this cannot be distinguished from Python, `display.unicode.ambiguous_as_wide` option is added to handle this.

By default, “Ambiguous” character’s width, “¡” (inverted exclamation) in below example, is regarded as 1.

    In [88]: df = pd.DataFrame({'a': ['xxx', u'¡¡'], 'b': ['yyy', u'¡¡']})

    In [89]: df;

Enabling `display.unicode.ambiguous_as_wide` lets pandas to figure these character’s width as 2. Note that this option will be effective only when `display.unicode.east_asian_width` is enabled. Confirm starting position has been changed, but is not aligned properly because the setting is mismatched with this environment.

    In [90]: pd.set_option('display.unicode.ambiguous_as_wide', True)

    In [91]: df;