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, is still respected, but the output will wrap-around across multiple “pages” if its width exceeds .
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.large_repr
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
False
Whether to produce a latex DataFrame representation for Jupyter frontends that support it.
display.latex.escape
True
Escapes special characters 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.max_columns
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.
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. ‘None’ value means unlimited.
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 truncated or summary repr. ‘None’ value means unlimited.
display.min_rows
10
The numbers of rows to show in a truncated repr (when is exceeded). Ignored when is set to None or 0. When set to None, follows the value of .
display.max_seq_items
100
when pretty-printing a long sequence, no more then 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 print option
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.
display.html.border
1
A attribute is inserted in the tag for the DataFrame HTML repr.
display.html.use_mathjax
True
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.
As package is no longer maintained, the engine will be removed in a future version of pandas. Since this is the only engine in pandas that supports writing to files, this option will also be removed.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
io.parquet.engine
None
The engine to use as a default for parquet reading and writing. If None then try ‘pyarrow’ and ‘fastparquet’
io.sql.engine
None
The engine to use as a default for sql reading and writing, with SQLAlchemy as a higher level interface. If None then try ‘sqlalchemy’
mode.chained_assignment
warn
Controls : ‘raise’, ‘warn’, or None. Raise an exception, warn, or no action if trying to use .
mode.sim_interactive
False
Whether to simulate interactive mode for purposes of testing.
mode.use_inf_as_na
False
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
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.
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
True
Register custom converters with matplotlib. Set to False to de-register.
styler.sparse.index
True
“Sparsify” MultiIndex display for rows in Styler output (don’t display repeated elements in outer levels within groups).
styler.sparse.columns
True
“Sparsify” MultiIndex display for columns in Styler output.
styler.render.max_elements
262144
Maximum number of datapoints that Styler will render trimming either rows, columns or both to fit.
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:
To round floats on a case-by-case basis, you can also use round() and round() .
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.
Screen captures are attached for each output to show the actual results.
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 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.
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:
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:
Only 'display.max_rows' are serialized and published.
Time deltas
Enhancing performance
IMAGES
VIDEO
COMMENTS
pd.set_option('chained_assignment',None) Pandas runs with the entire test suite with this set to raise (so we know if chaining is happening) on, FYI. Share. Improve this answer. Follow edited Jan 30, 2014 at 19:49. answered Jan 30, 2014 at 17:49. Jeff Jeff. 128k 21 21 ...
The default sql reader/writer engine. Available options: 'auto', 'sqlalchemy', the default is 'auto' [default: auto] [currently: auto] mode.chained_assignment string. Raise an exception, warn, or no action if trying to use chained assignment, The default is warn [default: warn] [currently: warn] mode.copy_on_write bool
pd.set_option('mode.chained_assignment', "raise") Doing this forces chained assignment to be dealt with, rather than allowing warnings to accumulate. In my experience, cleaning up notebooks with stderr clogged by SettingWithCopyWarnings is its special kind of zen. I wholeheartedly recommend it.
We can go ahead and suppress the warning by changing the default behavior as follows, pd.set_option ('mode.chained_assignment', None) - suppresses and turn off the warning. pd.set_option ('mode.chained_assignment', 'Warn') - issues a warning. pd.set_option ('mode.chained_assignment', 'Raise') - raises an exception.
If we'd like to prevent the warning message from ever showing, we can use the following bit of code: #prevent SettingWithCopyWarning message from appearing. pd.options.mode.chained_assignment = None. For an in-depth explanation for why chained assignment should be avoided, refer to the online pandas documentation.
Copy-on-Write will be the default and only mode in pandas 3.0. This means that users need to migrate their code to be compliant with CoW rules. The default mode in pandas will raise warnings for certain cases that will actively change behavior and thus change user intended behavior. We added another mode, e.g.
You can safely disable this warning with the following assignment. pd.options.mode.chained_assignment = None. The real problem behind the warning is that it is generally difficult to predict whether a view or a copy is returned. When filtering Pandas DataFrames , it is possible slice/index a frame to return either a view or a copy.
The default sql reader/writer engine. Available options: 'auto', 'sqlalchemy', the default is 'auto' [default: auto] [currently: auto] mode.chained_assignment string. Raise an exception, warn, or no action if trying to use chained assignment, The default is warn [default: warn] [currently: warn] mode.data_manager string
Chained assignment is essentially when you are assigning a value to something which will be returning something then further indexed upon. For example, the following code: a b. is a chained assignment. First, a reference to the pd.Series object df['a'] is gotten, then is further indexed upon with [0]. This will work in some cases (like this one ...
Chained indexing is a godsend of convenience for selecting the right data, but chained assignment is a minefield for assigning the correct values. The TowardsDataScience article in 2 has a nice example where inverting the order of chained indexing alone is the difference between whether an assignment to the original dataframe occurs or not:
Similar to an observation on reddit I noticed that there is a huge performance difference between the default pandas pd.options.mode.chained_assignment = 'warn' over setting it to None. Code Sample import time import pandas as pd import ...
The API is composed of 5 relevant functions, available directly from the pandas namespace:. 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 options that revert to prior settings after ...
I have a dataframe with multiple columns and I simply want to update a column with new values df['Z'] = df['A'] % df['C']/2.However, I keep getting SettingWithCopyWarning message even when I use the .loc[] method or when I drop() the column and add it again.:75: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame.
pandas.set_option. ¶. Sets the value of the specified option. Regexp which should match a single option. Note: partial matches are supported for convenience, but unless you use the full option name (e.g. x.y.z.option_name), your code may break in future versions if new options with similar names are introduced.
I was using pandas for some processing and was getting the SettingWithCopyWarning. I followed some advice on from SO and used the. pd.options.mode.chained_assignment = None. statement to remove the warning. I knew it was a false positive. The warning went away and my code runs faster. This is what is puzzling me.
The updated solution to suppress the SettingWithCopyWarning is: import warnings. import pandas as pd. from pandas.errors import SettingWithCopyWarning. warnings.simplefilter(action='ignore', category=(SettingWithCopyWarning)) answered Oct 20, 2023 at 13:04. syd. 97 1 5. Earn 10 reputation (not counting the ) in order to answer this question.
Warning raised when trying to set using chained assignment. When the mode.copy_on_write option is enabled, chained assignment can never work. In such a situation, we are always setting into a temporary object that is the result of an indexing operation (getitem), which under Copy-on-Write always behaves as a copy.
The API is composed of 5 relevant functions, available directly from the pandas namespace:. 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 options that revert to prior settings after ...