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:
display.max_rows
options
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:
pandas
get_option() / set_option() - get/set the value of a single option.
get_option()
set_option()
reset_option() - reset one or more options to their default value.
reset_option()
describe_option() - print the descriptions of one or more options.
describe_option()
option_context() - execute a codeblock with a set of options that revert to prior settings after execution.
option_context()
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:
re.search
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:
display.max_colwidth
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.
describe_option
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).
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:
reset_option
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:
option_context
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
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)
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.
display.min_rows
In [29]: pd.set_option('max_rows', 8) In [30]: pd.set_option('min_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 2 0.276232 -1.087401 3 -0.673690 0.113648 4 -1.478427 0.524988 5 0.404705 0.577046 6 -1.715002 -1.039268 # 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.
display.expand_frame_repr
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.
display.large_repr
max_columns
max_rows
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): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 0 10 non-null float64 1 1 10 non-null float64 2 2 10 non-null float64 3 3 10 non-null float64 4 4 10 non-null float64 5 5 10 non-null float64 6 6 10 non-null float64 7 7 10 non-null float64 8 8 10 non-null float64 9 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.
display.max_info_columns
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): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 0 10 non-null float64 1 1 10 non-null float64 2 2 10 non-null float64 3 3 10 non-null float64 4 4 10 non-null float64 5 5 10 non-null float64 6 6 10 non-null float64 7 7 10 non-null float64 8 8 10 non-null float64 9 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_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.
display.max_info_rows
df.info()
max_info_rows
max_info_cols
df.info(null_counts=True)
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): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 0 8 non-null float64 1 1 3 non-null float64 2 2 7 non-null float64 3 3 6 non-null float64 4 4 7 non-null float64 5 5 6 non-null float64 6 6 2 non-null float64 7 7 6 non-null float64 8 8 6 non-null float64 9 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): # Column Dtype --- ------ ----- 0 0 float64 1 1 float64 2 2 float64 3 3 float64 4 4 float64 5 5 float64 6 6 float64 7 7 float64 8 8 float64 9 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.
display.precision
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.
display.chop_threshold
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’.
display.colheader_justify
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')
Option
Default
Function
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.
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
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.
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
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.
truncate
For 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.repr
Whether to produce a latex DataFrame representation for jupyter frontends that support it.
display.latex.escape
Escapes special characters in DataFrames, when using the to_latex method.
display.latex.longtable
Specifies if the to_latex method of a DataFrame uses the longtable format.
display.latex.multicolumn
Combines columns when using a MultiIndex
display.latex.multicolumn_format
‘l’
Alignment of multicolumn labels
display.latex.multirow
Combines rows when using a MultiIndex. Centered instead of top-aligned, separated by clines.
0 or 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 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.
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. ‘None’ value means unlimited.
100
max_info_columns is used in DataFrame.info method to decide if per column information will be printed.
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.
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 truncated or summary repr. ‘None’ value means unlimited.
10
The 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_items
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
This specifies if the memory usage of a DataFrame should be displayed when the df.info() method is invoked.
display.multi_sparse
“Sparsify” MultiIndex display (don’t display repeated elements in outer levels within groups)
display.notebook_repr_html
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
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
precision
display.show_dimensions
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.
display.html.table_schema
Whether to publish a Table Schema representation for frontends that support it.
display.html.border
1
A border=value attribute is inserted in the <table> tag for the DataFrame HTML repr.
border=value
<table>
display.html.use_mathjax
When True, Jupyter notebook will process table contents using MathJax, rendering mathematical expressions enclosed by the dollar 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
The default Excel writer engine for ‘xlsx’ files.
io.hdf.default_format
default format writing format, if None, then put will default to ‘fixed’ and append will default to ‘table’
io.hdf.dropna_table
drop ALL nan rows when appending to a table
io.parquet.engine
The engine to use as a default for parquet reading and writing. If None then try ‘pyarrow’ and ‘fastparquet’
mode.chained_assignment
warn
Controls SettingWithCopyWarning: ‘raise’, ‘warn’, or None. Raise an exception, warn, or no action if trying to use chained assignment.
SettingWithCopyWarning
mode.sim_interactive
Whether to simulate interactive mode for purposes of testing.
mode.use_inf_as_na
True 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_bottleneck
Use the bottleneck library to accelerate computation if it is installed.
compute.use_numexpr
Use the numexpr library to accelerate computation if it is installed.
plotting.backend
matplotlib
Change 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_converters
Register custom converters with matplotlib. Set to False to de-register.
pandas also allows you to set how numbers are displayed in the console. This option is not set through the set_options API.
set_options
Use the set_eng_float_format function to alter the floating-point formatting of pandas objects to produce a particular format.
set_eng_float_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: float64
To round floats on a case-by-case basis, you can also use round() and round().
round()
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 render times than the standard len function.
display.unicode.east_asian_width
len
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 the terminal setting or encoding. The option display.unicode.ambiguous_as_wide can be used to handle the ambiguity.
display.unicode.ambiguous_as_wide
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 ¡¡ ¡¡
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:
DataFrame
Series
In [100]: pd.set_option('display.html.table_schema', True)
Only 'display.max_rows' are serialized and published.
'display.max_rows'