pandas
python
import pandas as pd import numpy as np np.random.seed(123456)
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:
python
import pandas as pd pd.options.display.max_rows pd.options.display.max_rows = 999 pd.options.display.max_rows
There is also an API composed of 5 relevant functions, available directly from the pandas namespace:
~pandas.get_option/~pandas.set_option- get/set the value of a single option.~pandas.reset_option- reset one or more options to their default value.~pandas.describe_option- print the descriptions of one or more options.~pandas.option_context- execute a codeblock with a set of options that revert to prior settings after execution.
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 :
python
pd.get_option("display.max_rows") pd.set_option("display.max_rows",101) pd.get_option("display.max_rows") pd.set_option("max_r",102) pd.get_option("display.max_rows")
The following will not work because it matches multiple option names, e.g. display.max_colwidth, display.max_rows, display.max_columns:
python
- try:
pd.get_option("column")
- except KeyError as e:
print(e)
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.
python
pd.reset_option("all")
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)
python
pd.get_option('mode.sim_interactive') pd.set_option('mode.sim_interactive', True) pd.get_option('mode.sim_interactive')
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:
python
pd.reset_option("display.max_rows")
python
pd.get_option("display.max_rows") pd.set_option("display.max_rows",999) pd.get_option("display.max_rows") pd.reset_option("display.max_rows") pd.get_option("display.max_rows")
It's also possible to reset multiple options at once (using a regex):
python
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:
python
- 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"))
print(pd.get_option("display.max_rows")) print(pd.get_option("display.max_columns"))
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/startupMore 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 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.
python
df = pd.DataFrame(np.random.randn(7,2)) pd.set_option('max_rows', 7) df pd.set_option('max_rows', 5) df 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.
python
df = pd.DataFrame(np.random.randn(5,10)) pd.set_option('expand_frame_repr', True) df pd.set_option('expand_frame_repr', False) df 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.
python
df = pd.DataFrame(np.random.randn(10,10)) pd.set_option('max_rows', 5) pd.set_option('large_repr', 'truncate') df pd.set_option('large_repr', 'info') df pd.reset_option('large_repr') 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.
python
- df = pd.DataFrame(np.array([['foo', 'bar', 'bim', 'uncomfortably long string'],
['horse', 'cow', 'banana', 'apple']]))
pd.set_option('max_colwidth',40) df pd.set_option('max_colwidth', 6) df pd.reset_option('max_colwidth')
display.max_info_columns sets a threshold for when by-column info will be given.
python
df = pd.DataFrame(np.random.randn(10,10)) pd.set_option('max_info_columns', 11) df.info() pd.set_option('max_info_columns', 5) df.info() 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.
python
df =pd.DataFrame(np.random.choice([0,1,np.nan], size=(10,10))) df pd.set_option('max_info_rows', 11) df.info() pd.set_option('max_info_rows', 5) df.info() pd.reset_option('max_info_rows')
display.precision sets the output display precision in terms of decimal places. This is only a suggestion.
python
df = pd.DataFrame(np.random.randn(5,5)) pd.set_option('precision',7) df pd.set_option('precision',4) df
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.
python
df = pd.DataFrame(np.random.randn(6,6)) pd.set_option('chop_threshold', 0) df pd.set_option('chop_threshold', .5) df pd.reset_option('chop_threshold')
display.colheader_justify controls the justification of the headers. Options are 'right', and 'left'.
python
- 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')
pd.set_option('colheader_justify', 'right') df pd.set_option('colheader_justify', 'left') df pd.reset_option('colheader_justify')
| 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.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.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 |
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. |
display.html.table_schema |
False |
Whether to publish a Table Schema representation for frontends that support it. |
html.border |
1 |
A |
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). |
compute.use_bottleneck |
True |
Use the bottleneck library to accelerate computation if it is installed |
compute.use_numexpr |
True |
Use the numexpr library to accelerate computation if it is installed |
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:
python
import numpy as np
pd.set_eng_float_format(accuracy=3, use_eng_prefix=True) s = pd.Series(np.random.randn(5), index=['a', 'b', 'c', 'd', 'e']) s/1.e3 s/1.e6
python
pd.reset_option('^display.')
To round floats on a case-by-case basis, you can also use ~pandas.Series.round and ~pandas.DataFrame.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 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.
python
df = pd.DataFrame({u'国籍': ['UK', u'日本'], u'名前': ['Alice', u'しのぶ']}) df;
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.
python
pd.set_option('display.unicode.east_asian_width', True) df;
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.
python
df = pd.DataFrame({'a': ['xxx', u'¡¡'], 'b': ['yyy', u'¡¡']}) df;
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.
python
pd.set_option('display.unicode.ambiguous_as_wide', True) df;
python
pd.set_option('display.unicode.east_asian_width', False) pd.set_option('display.unicode.ambiguous_as_wide', False)
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:
python
pd.set_option('display.html.table_schema', True)
Only 'display.max_rows' are serialized and published.
python
pd.reset_option('display.html.table_schema')