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

The API is 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")

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: 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'>
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: 880.0 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: 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  0.0  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: 880.0 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: 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  0.0
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

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 whose width corresponds to two Latin characters. If a DataFrame or Series contains these characters, the default output mode may not align them properly.

Note

Screen captures are attached for each output 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

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

In [87]: df;
_images/option_unicode02.png

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

In [89]: df;
_images/option_unicode03.png

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

In [91]: df;
_images/option_unicode04.png

Table Schema Display

New in version 0.20.0.

DataFrame and Series will publish a Table Schema representation by default. False by default, this can be enabled globally with the display.html.table_schema option:

In [92]: pd.set_option('display.html.table_schema', True)

Only 'display.max_rows' are serialized and published.

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