Introduction to the
Pandas has an option system that controls how Pandas will be displayed. Generally, no changes are required, but we do not rule out the need for changes in particular situations. This article will explain in detail the Option Settings in Pandas.
Commonly used options
Pd.options. display allows you to control display options, such as setting the maximum number of rows to display:
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]: 999In addition, PD has four related methods for modifying options:
- Get_option ()/set_option() – get/set the value of a single option
- Reset_option () – Resets the value of an option to its default value
- Describe_option () – Prints the value for an option
- Option_context () – Performs changes to some option in the snippet
This is as follows:
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]: 102Get/set the options
Pd.get_option and pd.set_option can be used to obtain and modify specific options:
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]: TrueReset using 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]: 60You can reset multiple options using regular expressions:
In [19]: pd.reset_option("^display")Option_context modifies option in the code environment, and when the code completes,option is restored:
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")) 0Frequently used options
Let’s look at some examples of frequently used options:
Maximum number of display rows
Display. max_rows and display.max_columns can set the maximum number of rows and columns to display:
In [23]: df = pd.DataFrame(np.random.randn(7, 2)) In [24]: pd.set_option("max_rows", 7) In [25]: df Out[25]: 0 10 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 rows x 2 columns] Rows x 2 columnsBeyond data presentation
Display.large_repr can select the display behavior for an exceeded row or column, which can be a truncated frame:
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]: 01 23 45 78 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]It can also be statistical information:
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 bytesThe width of the maximum column
Display.max_colWidth is used to set the width of the maximum column.
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... appleDisplay precision
The precision of the display can be set:
In [70]: df = pd.DataFrame(np.random.randn(5, 5)) In [71]: pd.set_option("precision", 7) In [72]: df Out[72]: 0 12 34 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 0.1847350-0.2550694-0.2710197Zero conversion threshold
Display. chop_threshold can be set to display data in a Series or DF as 0:
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", 0.5) 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 1.9067In the above example, anything with an absolute value of less than 0.5 will be shown as 0.
The alignment of the column heads
Display. colheader_justify allows you to change the alignment of column header text:
In [81]: df = pd.DataFrame( .... : np array ([np. The random randn (6), np. Random. The randint (1, 9, 6) * 0.1, np. The 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 2-0.7495 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 0.0Common options table:
| options | The default value | describe |
|---|---|---|
| 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_columnsIs still respected, but the output will wrap-around across multiple “pages” if its width exceedsdisplay.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.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 panda 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. value means unlimited. |
| display.min_rows | 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 | 100 | when pretty-printing a long sequence, no more then max_seq_itemsWill be printed. If items are omitted, they will be stimulative 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 as evidenced 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 precision print option |
| display.show_dimensions | truncate | Whether to print out dimensions at the end of the 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 border=value attribute is inserted in the <table> 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.Deprecated since version 1.2.0: As xlwt package is no longer maintained, the xlwt engine will be removed in a future version of pandas. Since this is the only engine in pandas that supports writing to .xls 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’. |
| mode.chained_assignment | warn | Controls SettingWithCopyWarning: ‘raise’, ‘warn’, or None. Raise an exception, warn, or no action if trying to use chained assignment. |
| 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. |
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