Options and settings

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 pandasnamespace:

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 optionsthat revert to prior settings after execution.

Note: Developers can check out pandas/core/config.py for more information.

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 calledwith 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, callset_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: 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")

optioncontext context manager has been exposed throughthe top-level API, allowing you to execute code with given option values. Option valuesare 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"))
0

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 walk-through of the more frequently used display options.

display.max_rows and display.max_columns sets the maximum numberof rows and columns displayed when a frame is pretty-printed. Truncatedlines 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')

Once the display.max_rows is exceeded, the display.min_rows optionsdetermines how many rows are shown in the truncated repr.

In [29]: pd.set_option('max_rows', 8)
 
In [30]: pd.set_option('max_rows', 4)
 
# below max_rows -> all rows shown
In [31]: df = pd.DataFrame(np.random.randn(7, 2))
 
In [32]: df
Out[32]: 
           0         1
0  -1.039575  0.271860
1  -0.424972  0.567020
..       ...       ...
5   0.404705  0.577046
6  -1.715002 -1.039268
 
[7 rows x 2 columns]
 
# above max_rows -> only min_rows (4) rows shown
In [33]: df = pd.DataFrame(np.random.randn(9, 2))
 
In [34]: df
Out[34]: 
           0         1
0  -0.370647 -1.157892
1  -1.344312  0.844885
..       ...       ...
7   0.276662 -0.472035
8  -0.013960 -0.362543
 
[9 rows x 2 columns]
 
In [35]: pd.reset_option('max_rows')
 
In [36]: pd.reset_option('min_rows')

display.expand_frame_repr allows for the representation ofdataframes to stretch across pages, wrapped over the full column vs row-wise.

In [37]: df = pd.DataFrame(np.random.randn(5, 10))
 
In [38]: pd.set_option('expand_frame_repr', True)
 
In [39]: df
Out[39]: 
          0         1         2         3         4         5         6         7         8         9
0 -0.006154 -0.923061  0.895717  0.805244 -1.206412  2.565646  1.431256  1.340309 -1.170299 -0.226169
1  0.410835  0.813850  0.132003 -0.827317 -0.076467 -1.187678  1.130127 -1.436737 -1.413681  1.607920
2  1.024180  0.569605  0.875906 -2.211372  0.974466 -2.006747 -0.410001 -0.078638  0.545952 -1.219217
3 -1.226825  0.769804 -1.281247 -0.727707 -0.121306 -0.097883  0.695775  0.341734  0.959726 -1.110336
4 -0.619976  0.149748 -0.732339  0.687738  0.176444  0.403310 -0.154951  0.301624 -2.179861 -1.369849
 
In [40]: pd.set_option('expand_frame_repr', False)
 
In [41]: df
Out[41]: 
          0         1         2         3         4         5         6         7         8         9
0 -0.006154 -0.923061  0.895717  0.805244 -1.206412  2.565646  1.431256  1.340309 -1.170299 -0.226169
1  0.410835  0.813850  0.132003 -0.827317 -0.076467 -1.187678  1.130127 -1.436737 -1.413681  1.607920
2  1.024180  0.569605  0.875906 -2.211372  0.974466 -2.006747 -0.410001 -0.078638  0.545952 -1.219217
3 -1.226825  0.769804 -1.281247 -0.727707 -0.121306 -0.097883  0.695775  0.341734  0.959726 -1.110336
4 -0.619976  0.149748 -0.732339  0.687738  0.176444  0.403310 -0.154951  0.301624 -2.179861 -1.369849
 
In [42]: pd.reset_option('expand_frame_repr')

display.large_repr lets you select whether to display dataframes that exceedmax_columns or max_rows as a truncated frame, or as a summary.

In [43]: df = pd.DataFrame(np.random.randn(10, 10))
 
In [44]: pd.set_option('max_rows', 5)
 
In [45]: pd.set_option('large_repr', 'truncate')
 
In [46]: df
Out[46]: 
           0         1         2         3         4         5         6         7         8         9
0  -0.954208  1.462696 -1.743161 -0.826591 -0.345352  1.314232  0.690579  0.995761  2.396780  0.014871
1   3.357427 -0.317441 -1.236269  0.896171 -0.487602 -0.082240 -2.182937  0.380396  0.084844  0.432390
..       ...       ...       ...       ...       ...       ...       ...       ...       ...       ...
8  -0.303421 -0.858447  0.306996 -0.028665  0.384316  1.574159  1.588931  0.476720  0.473424 -0.242861
9  -0.014805 -0.284319  0.650776 -1.461665 -1.137707 -0.891060 -0.693921  1.613616  0.464000  0.227371
 
[10 rows x 10 columns]
 
In [47]: pd.set_option('large_repr', 'info')
 
In [48]: df
Out[48]: 
<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: 928.0 bytes
 
In [49]: pd.reset_option('large_repr')
 
In [50]: pd.reset_option('max_rows')

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

In [51]: df = pd.DataFrame(np.array([['foo', 'bar', 'bim', 'uncomfortably long string'],
   ....:                             ['horse', 'cow', 'banana', 'apple']]))
   ....: 
 
In [52]: pd.set_option('max_colwidth', 40)
 
In [53]: df
Out[53]: 
       0    1       2                          3
0    foo  bar     bim  uncomfortably long string
1  horse  cow  banana                      apple
 
In [54]: pd.set_option('max_colwidth', 6)
 
In [55]: df
Out[55]: 
       0    1      2      3
0    foo  bar    bim  un...
1  horse  cow  ba...  apple
 
In [56]: pd.reset_option('max_colwidth')

display.max_info_columns sets a threshold for when by-column infowill be given.

In [57]: df = pd.DataFrame(np.random.randn(10, 10))
 
In [58]: pd.set_option('max_info_columns', 11)
 
In [59]: 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: 928.0 bytes
 
In [60]: pd.set_option('max_info_columns', 5)
 
In [61]: df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10 entries, 0 to 9
Columns: 10 entries, 0 to 9
dtypes: float64(10)
memory usage: 928.0 bytes
 
In [62]: 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_colslimit this null check only to frames with smaller dimensions then specified. Note that youcan specify the option df.info(null_counts=True) to override on showing a particular frame.

In [63]: df = pd.DataFrame(np.random.choice([0, 1, np.nan], size=(10, 10)))
 
In [64]: df
Out[64]: 
     0    1    2    3    4    5    6    7    8    9
0  0.0  NaN  1.0  NaN  NaN  0.0  NaN  0.0  NaN  1.0
1  1.0  NaN  1.0  1.0  1.0  1.0  NaN  0.0  0.0  NaN
2  0.0  NaN  1.0  0.0  0.0  NaN  NaN  NaN  NaN  0.0
3  NaN  NaN  NaN  0.0  1.0  1.0  NaN  1.0  NaN  1.0
4  0.0  NaN  NaN  NaN  0.0  NaN  NaN  NaN  1.0  0.0
5  0.0  1.0  1.0  1.0  1.0  0.0  NaN  NaN  1.0  0.0
6  1.0  1.0  1.0  NaN  1.0  NaN  1.0  0.0  NaN  NaN
7  0.0  0.0  1.0  0.0  1.0  0.0  1.0  1.0  0.0  NaN
8  NaN  NaN  NaN  0.0  NaN  NaN  NaN  NaN  1.0  NaN
9  0.0  NaN  0.0  NaN  NaN  0.0  NaN  1.0  1.0  0.0
 
In [65]: pd.set_option('max_info_rows', 11)
 
In [66]: df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10 entries, 0 to 9
Data columns (total 10 columns):
0    8 non-null float64
1    3 non-null float64
2    7 non-null float64
3    6 non-null float64
4    7 non-null float64
5    6 non-null float64
6    2 non-null float64
7    6 non-null float64
8    6 non-null float64
9    6 non-null float64
dtypes: float64(10)
memory usage: 928.0 bytes
 
In [67]: pd.set_option('max_info_rows', 5)
 
In [68]: 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: 928.0 bytes
 
In [69]: pd.reset_option('max_info_rows')

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

In [70]: df = pd.DataFrame(np.random.randn(5, 5))
 
In [71]: pd.set_option('precision', 7)
 
In [72]: df
Out[72]: 
           0          1          2          3          4
0 -1.1506406 -0.7983341 -0.5576966  0.3813531  1.3371217
1 -1.5310949  1.3314582 -0.5713290 -0.0266708 -1.0856630
2 -1.1147378 -0.0582158 -0.4867681  1.6851483  0.1125723
3 -1.4953086  0.8984347 -0.1482168 -1.5960698  0.1596530
4  0.2621358  0.0362196  0.1847350 -0.2550694 -0.2710197
 
In [73]: pd.set_option('precision', 4)
 
In [74]: df
Out[74]: 
        0       1       2       3       4
0 -1.1506 -0.7983 -0.5577  0.3814  1.3371
1 -1.5311  1.3315 -0.5713 -0.0267 -1.0857
2 -1.1147 -0.0582 -0.4868  1.6851  0.1126
3 -1.4953  0.8984 -0.1482 -1.5961  0.1597
4  0.2621  0.0362  0.1847 -0.2551 -0.2710

display.chop_threshold sets at what level pandas rounds to zero whenit displays a Series of DataFrame. This setting does not change theprecision at which the number is stored.

In [75]: df = pd.DataFrame(np.random.randn(6, 6))
 
In [76]: pd.set_option('chop_threshold', 0)
 
In [77]: df
Out[77]: 
        0       1       2       3       4       5
0  1.2884  0.2946 -1.1658  0.8470 -0.6856  0.6091
1 -0.3040  0.6256 -0.0593  0.2497  1.1039 -1.0875
2  1.9980 -0.2445  0.1362  0.8863 -1.3507 -0.8863
3 -1.0133  1.9209 -0.3882 -2.3144  0.6655  0.4026
4  0.3996 -1.7660  0.8504  0.3881  0.9923  0.7441
5 -0.7398 -1.0549 -0.1796  0.6396  1.5850  1.9067
 
In [78]: pd.set_option('chop_threshold', .5)
 
In [79]: df
Out[79]: 
        0       1       2       3       4       5
0  1.2884  0.0000 -1.1658  0.8470 -0.6856  0.6091
1  0.0000  0.6256  0.0000  0.0000  1.1039 -1.0875
2  1.9980  0.0000  0.0000  0.8863 -1.3507 -0.8863
3 -1.0133  1.9209  0.0000 -2.3144  0.6655  0.0000
4  0.0000 -1.7660  0.8504  0.0000  0.9923  0.7441
5 -0.7398 -1.0549  0.0000  0.6396  1.5850  1.9067
 
In [80]: pd.reset_option('chop_threshold')

display.colheader_justify controls the justification of the headers.The options are ‘right’, and ‘left’.

In [81]: 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 [82]: pd.set_option('colheader_justify', 'right')
 
In [83]: df
Out[83]: 
        A    B    C
0  0.1040  0.1  0.0
1  0.1741  0.5  0.0
2 -0.4395  0.4  0.0
3 -0.7413  0.8  0.0
4 -0.0797  0.4  0.0
5 -0.9229  0.3  0.0
 
In [84]: pd.set_option('colheader_justify', 'left')
 
In [85]: df
Out[85]: 
   A       B    C  
0  0.1040  0.1  0.0
1  0.1741  0.5  0.0
2 -0.4395  0.4  0.0
3 -0.7413  0.8  0.0
4 -0.0797  0.4  0.0
5 -0.9229  0.3  0.0
 
In [86]: pd.reset_option('colheader_justify')

Available options

Option	Default	Function
display.chopthreshold	None	If set to a float value, all floatvalues smaller then the giventhreshold will be displayed asexactly 0 by repr and friends.
display.colheader_justify	right	Controls the justification ofcolumn headers. used by DataFrameFormatter.
display.column_space	12	No description available.
display.date_dayfirst	False	When True, prints and parses dateswith the day first, eg 20/01/2005
display.date_yearfirst	False	When True, prints and parses dateswith the year first, eg 2005/01/20
display.encoding	UTF-8	Defaults to the detected encodingof the console. Specifies the encodingto be used for strings returned byto_string, these are generally stringsmeant to be displayed on the console.
display.expand_frame_repr	True	Whether to print out the full DataFramerepr for wide DataFrames acrossmultiple lines, _max_columns isstill respected, but the output willwrap-around across multiple “pages”if its width exceeds display.width.
display.floatformat	None	The callable should accept a floatingpoint number and return a string withthe desired format of the number.This is used in some places likeSeriesFormatter.See core.format.EngFormatter for an example.
display.largerepr	truncate	For DataFrames exceeding maxrows/max_cols,the repr (and HTML repr) can showa truncated table (the default),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 DataFramerepresentation for jupyter frontendsthat support it.
display.latex.escape	True	Escapes special characters in DataFrames, whenusing the to_latex method.
display.latex.longtable	False	Specifies if the to_latex method of a DataFrameuses the longtable format.
display.latex.multicolumn	True	Combines columns when using a MultiIndex
display.latex.multicolumn_format	‘l’	Alignment of multicolumn labels
display.latex.multirow	False	Combines rows when using a MultiIndex.Centered instead of top-aligned,separated by clines.
display.max_columns	0 or 20	max_rows and max_columns are usedin __repr() methods to decide ifto_string() or info() is used torender an object to a string. Incase Python/IPython is running ina terminal this is set to 0 by default andpandas will correctly auto-detectthe width of the terminal and switch toa smaller format in case all columnswould not fit vertically. The IPythonnotebook, IPython qtconsole, or IDLEdo not run in a terminal and henceit is not possible to do correctauto-detection, in which case the defaultis set to 20. ‘None’ value means unlimited.
display.max_colwidth	50	The maximum width in characters ofa column in the repr of a pandasdata structure. When the column overflows,a “…” placeholder is embedded inthe output.
display.max_info_columns	100	max_info_columns is used in DataFrame.infomethod to decide if per column informationwill be printed.
display.max_info_rows	1690785	df.info() will usually show null-countsfor each column. For large framesthis can be quite slow. max_info_rowsand max_info_cols limit this nullcheck only to frames with smallerdimensions then specified.
display.max_rows	60	This sets the maximum number of rowspandas should output when printingout various output. For example,this value determines whether therepr() for a dataframe prints outfully or just a truncated or summary repr.‘None’ value means unlimited.
display.min_rows	10	The numbers of rows to show in a truncatedrepr (when _max_rows is exceeded). Ignoredwhen max_rows is set to None or 0. When setto None, follows the value of max_rows.
display.maxseq_items	100	when pretty-printing a long sequence,no more then _max_seq_items willbe printed. If items are omitted,they will be denoted by the additionof “…” to the resulting string.If set to None, the number of itemsto be printed is unlimited.
display.memory_usage	True	This specifies if the memory usage ofa DataFrame should be displayed when thedf.info() method is invoked.
display.multi_sparse	True	“Sparsify” MultiIndex display (don’tdisplay repeated elements in outerlevels within groups)
display.notebook_repr_html	True	When True, IPython notebook willuse html representation forpandas objects (if it is available).
display.pprint_nest_depth	3	Controls the number of nested levelsto process when pretty-printing
display.precision	6	Floating point output precision interms of number of places after thedecimal, for regular formatting as wellas scientific notation. Similar tonumpy’s `precision` print option
display.show_dimensions	truncate	Whether to print out dimensionsat the end of DataFrame repr.If ‘truncate’ is specified, onlyprint out the dimensions if theframe is truncated (e.g. not displayall rows and/or columns)
display.width	80	Width of the display in characters.In case python/IPython is running ina terminal this can be set to Noneand pandas will correctly auto-detectthe width. Note that the IPython notebook,IPython qtconsole, or IDLE do not run in aterminal and hence it is not possibleto correctly detect the width.
display.html.table_schema	False	Whether to publish a Table Schemarepresentation for frontends thatsupport it.
display.html.border	1	A `border=value` attribute isinserted in the `<table>` tagfor the DataFrame HTML repr.
display.html.use_mathjax	True	When True, Jupyter notebook will processtable contents using MathJax, renderingmathematical expressions enclosed by thedollar symbol.
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, ifNone, then put will default to‘fixed’ and append will default to‘table’
io.hdf.dropna_table	True	drop ALL nan rows when appendingto a table
io.parquet.engine	None	The engine to use as a default forparquet reading and writing. If Nonethen try ‘pyarrow’ and ‘fastparquet’
mode.chained_assignment	warn	Controls `SettingWithCopyWarning`:‘raise’, ‘warn’, or None. Raise anexception, warn, or no action iftrying to use chained assignment.
mode.sim_interactive	False	Whether to simulate interactive modefor purposes of testing.
mode.use_inf_as_na	False	True means treat None, NaN, -INF,INF as NA (old way), False meansNone and NaN are null, but INF, -INFare not NA (new way).
compute.use_bottleneck	True	Use the bottleneck library to acceleratecomputation if it is installed.
compute.use_numexpr	True	Use the numexpr library to acceleratecomputation if it is installed.
plotting.backend	matplotlib	Change the plotting backend to a differentbackend than the current matplotlib one.Backends can be implemented as third-partylibraries implementing the pandas plottingAPI. They can use other plotting librarieslike Bokeh, Altair, etc.
plotting.matplotlib.register_converters	True	Register custom converters withmatplotlib. Set to False to de-register.

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 functionto alter the floating-point formatting of pandas objects to produce a particularformat.

For instance:

In [87]: import numpy as np
 
In [88]: pd.set_eng_float_format(accuracy=3, use_eng_prefix=True)
 
In [89]: s = pd.Series(np.random.randn(5), index=['a', 'b', 'c', 'd', 'e'])
 
In [90]: s / 1.e3
Out[90]: 
a    303.638u
b   -721.084u
c   -622.696u
d    648.250u
e     -1.945m
dtype: float64
 
In [91]: s / 1.e6
Out[91]: 
a    303.638n
b   -721.084n
c   -622.696n
d    648.250n
e     -1.945u
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 [92]: df = pd.DataFrame({'国籍': ['UK', '日本'], '名前': ['Alice', 'しのぶ']})
 
In [93]: df
Out[93]: 
   国籍     名前
0  UK  Alice
1  日本    しのぶ

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 rendertimes than the standard len function.

In [94]: pd.set_option('display.unicode.east_asian_width', True)
 
In [95]: df
Out[95]: 
   国籍    名前
0    UK   Alice
1  日本  しのぶ

In addition, Unicode characters whose width is “Ambiguous” can either be 1 or 2 characters wide depending on theterminal 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 [96]: df = pd.DataFrame({'a': ['xxx', '¡¡'], 'b': ['yyy', '¡¡']})
 
In [97]: df
Out[97]: 
     a    b
0  xxx  yyy
1   ¡¡   ¡¡

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 [98]: pd.set_option('display.unicode.ambiguous_as_wide', True)
 
In [99]: df
Out[99]: 
      a     b
0   xxx   yyy
1  ¡¡  ¡¡

Table schema display

New in version 0.20.0.

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

In [100]: pd.set_option('display.html.table_schema', True)

Only 'display.max_rows' are serialized and published.