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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:

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'>
RangeIndex: 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: 872.0 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'>
RangeIndex: 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: 872.0 bytes

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

In [53]: df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10 entries, 0 to 9
Columns: 10 entries, 0 to 9
dtypes: float64(10)
memory usage: 872.0 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  0.0  1.0  1.0  0.0  1.0  1.0  0.0  NaN  1.0  NaN
1  1.0  NaN  0.0  0.0  1.0  1.0  NaN  1.0  0.0  1.0
2  NaN  NaN  NaN  1.0  1.0  0.0  NaN  0.0  1.0  NaN
3  0.0  1.0  1.0  NaN  0.0  NaN  1.0  NaN  NaN  0.0
4  0.0  1.0  0.0  0.0  1.0  0.0  0.0  NaN  0.0  0.0
5  0.0  NaN  1.0  NaN  NaN  NaN  NaN  0.0  1.0  NaN
6  0.0  1.0  0.0  0.0  NaN  1.0  NaN  NaN  0.0  NaN
7  0.0  NaN  1.0  1.0  NaN  1.0  1.0  1.0  1.0  NaN
8  0.0  0.0  NaN  0.0  NaN  1.0  0.0  0.0  NaN  NaN
9  NaN  NaN  0.0  NaN  NaN  NaN  0.0  1.0  1.0  NaN

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

In [58]: df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 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: 872.0 bytes

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

In [60]: df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 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: 872.0 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  0.0
1  0.2888  0.2  0.0
2  1.3250  0.2  0.0
3  0.5892  0.7  0.0
4  0.5314  0.1  0.0
5 -1.1987  0.7  0.0

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

In [77]: df
Out[77]: 
   A       B    C  
0  0.9331  0.3  0.0
1  0.2888  0.2  0.0
2  1.3250  0.2  0.0
3  0.5892  0.7  0.0
4  0.5314  0.1  0.0
5 -1.1987  0.7  0.0

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

Available 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.latex.repr False Whether to produce a latex DataFrame representation for jupyter frontends that support it.
display.latex.escape True Escapes special caracters in Dataframes, when using the to_latex method.
display.latex.longtable False Specifies if the to_latex method of a Dataframe uses the longtable format.
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.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.
html.border 1 A border=value attribute is inserted in the <table> tag for the DataFrame HTML repr.
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;
_images/option_unicode01.png

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;
_images/option_unicode02.png

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;
_images/option_unicode03.png

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;
_images/option_unicode04.png

© 2011–2012 Lambda Foundry, Inc. and PyData Development Team
© 2008–2011 AQR Capital Management, LLC
© 2008–2014 the pandas development team
Licensed under the 3-clause BSD License.
http://pandas.pydata.org/pandas-docs/version/0.19.2/options.html