Pandas custom option settings

Time:2021-10-14
catalogue
  • brief introduction
  • Common options
  • Get / set options
  • Frequently used options
    • Maximum number of display lines
    • Beyond data presentation
    • Maximum column width
    • Display accuracy
    • Threshold of zero conversion
    • Alignment direction of column head

brief introduction

Pandas has an option system to control the display of pandas. Generally speaking, we don’t need to modify it, but we don’t rule out the modification requirements in special cases. This article will explain the option setting in pandas in detail.

Common options

Pd.options.display can control display options, such as setting the maximum number of display lines:


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

In addition, PD has four related methods to modify the option:

  • get_ option() / set_ Option () – get / set the value of a single option
  • reset_ Option () – reset the value of an option to the default value
  • describe_ Option () – print the value of an option
  • option_ Context () – perform some option changes in the code snippet

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]: 102

Get / set 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]: True

use   reset_ option   To reset:


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

Multiple options can be reset using regular expressions:


In [19]: pd.reset_option("^display")

option_ Context modify the option in the code environment. After the code ends, the option will be 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"))
0

Frequently used options

Here are some examples of frequently used options:

Maximum number of display lines

display.max_ Rows and display.max_ Columns can set the maximum number of display rows and columns:


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]

Beyond data presentation

display.large_ Repr can select the display behavior for the exceeded rows or columns, which can be 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]: 
           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]

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 bytes

Maximum column width

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...  apple

Display accuracy

Display.precision you can set the 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

Threshold of zero conversion

display.chop_ threshold   You can set the threshold for displaying data in 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  1.9067

In the above example, the absolute value < 0.5 will be displayed as 0.

Alignment direction of column head

display.colheader_ Justify can change the alignment direction of column header text:


In [81]: df = pd.DataFrame(
   ....:     np.array([np.random.randn(6), np.random.randint(1, 9, 6) * 0.1, 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

Common options table:

option 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_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.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 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_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 precision 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 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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