Pandas 选项和设置

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:


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_colwidthdisplay.max_rowsdisplay.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: 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"))
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 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')

Once the display.max_rows is exceeded, the display.min_rows options determines 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 of dataframes 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 exceed max_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. Cells of 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 info will 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_rowsdf.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 [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 when it displays a Series of DataFrame. This setting does not change the precision 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

OptionDefaultFunction
display.chop_thresholdNoneIf 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_justifyrightControls the justification of column headers. used by DataFrameFormatter.
display.column_space12No description available.
display.date_dayfirstFalseWhen True, prints and parses dates with the day first, eg 20/01/2005
display.date_yearfirstFalseWhen True, prints and parses dates with the year first, eg 2005/01/20
display.encodingUTF-8Defaults 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_reprTrueWhether 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_formatNoneThe 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.large_reprtruncateFor DataFrames exceeding max_rows/max_cols, the repr (and HTML repr) can show a 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.reprFalseWhether to produce a latex DataFrame representation for jupyter frontends that support it.
display.latex.escapeTrueEscapes special characters in DataFrames, when using the to_latex method.
display.latex.longtableFalseSpecifies if the to_latex method of a DataFrame uses the longtable format.
display.latex.multicolumnTrueCombines columns when using a MultiIndex
display.latex.multicolumn_format‘l’Alignment of multicolumn labels
display.latex.multirowFalseCombines rows when using a MultiIndex. Centered instead of top-aligned, separated by clines.
display.max_columns0 or 20max_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 is set to 0 by default and pandas will correctly auto-detect the width of the terminal and switch 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, in which case the default is set to 20. ‘None’ value means unlimited.
display.max_colwidth50The 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_columns100max_info_columns is used in DataFrame.info method to decide if per column information will be printed.
display.max_info_rows1690785df.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_rows60This 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 truncated or summary repr. ‘None’ value means unlimited.
display.min_rows10The numbers of rows to show in a truncated repr (when max_rows is exceeded). Ignored when max_rows is set to None or 0. When set to None, follows the value of max_rows.
display.max_seq_items100when 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_usageTrueThis specifies if the memory usage of a DataFrame should be displayed when the df.info() method is invoked.
display.multi_sparseTrue“Sparsify” MultiIndex display (don’t display repeated elements in outer levels within groups)
display.notebook_repr_htmlTrueWhen True, IPython notebook will use html representation for pandas objects (if it is available).
display.pprint_nest_depth3Controls the number of nested levels to process when pretty-printing
display.precision6Floating 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_dimensionstruncateWhether 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.width80Width 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.
display.html.table_schemaFalseWhether to publish a Table Schema representation for frontends that support it.
display.html.border1A border=value attribute is inserted in the <table> tag for the DataFrame HTML repr.
display.html.use_mathjaxTrueWhen True, Jupyter notebook will process table contents using MathJax, rendering mathematical expressions enclosed by the dollar symbol.
io.excel.xls.writerxlwtThe default Excel writer engine for ‘xls’ files.
io.excel.xlsm.writeropenpyxlThe default Excel writer engine for ‘xlsm’ files. Available options: ‘openpyxl’ (the default).
io.excel.xlsx.writeropenpyxlThe default Excel writer engine for ‘xlsx’ files.
io.hdf.default_formatNonedefault format writing format, if None, then put will default to ‘fixed’ and append will default to ‘table’
io.hdf.dropna_tableTruedrop ALL nan rows when appending to a table
io.parquet.engineNoneThe engine to use as a default for parquet reading and writing. If None then try ‘pyarrow’ and ‘fastparquet’
mode.chained_assignmentwarnControls SettingWithCopyWarning: ‘raise’, ‘warn’, or None. Raise an exception, warn, or no action if trying to use chained assignment.
mode.sim_interactiveFalseWhether to simulate interactive mode for purposes of testing.
mode.use_inf_as_naFalseTrue means treat None, NaN, -INF, INF as NA (old way), False means None and NaN are null, but INF, -INF are not NA (new way).
compute.use_bottleneckTrueUse the bottleneck library to accelerate computation if it is installed.
compute.use_numexprTrueUse the numexpr library to accelerate computation if it is installed.
plotting.backendmatplotlibChange the plotting backend to a different backend than the current matplotlib one. Backends can be implemented as third-party libraries implementing the pandas plotting API. They can use other plotting libraries like Bokeh, Altair, etc.
plotting.matplotlib.register_convertersTrueRegister custom converters with matplotlib. 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 function to alter the floating-point formatting of pandas objects to produce a particular format.

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: float64To 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

option_unicode01

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 [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

option_unicode02

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

In [97]: df
Out[97]: 
     a    b
0  xxx  yyy
1   ¡¡   ¡¡

option_unicode03

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  ¡¡  ¡¡

option_unicode04

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

Only 'display.max_rows' are serialized and published.

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