1.4 Frequently Used Options
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.
In [1]: df = pd.DataFrame(np.random.randn(7,2))
In [2]: pd.set_option('max_rows', 7)
In [3]: df
Out[3]:
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 [4]: pd.set_option('max_rows', 5)
In [5]: df
Out[5]:
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 [6]: 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.
In [7]: df = pd.DataFrame(np.random.randn(5,10))
In [8]: pd.set_option('expand_frame_repr', True)
In [9]: df
Out[9]:
0 1 2 3 4 5 6 \
0 -1.039575 0.271860 -0.424972 0.567020 0.276232 -1.087401 -0.673690
1 0.404705 0.577046 -1.715002 -1.039268 -0.370647 -1.157892 -1.344312
2 1.643563 -1.469388 0.357021 -0.674600 -1.776904 -0.968914 -1.294524
3 -0.013960 -0.362543 -0.006154 -0.923061 0.895717 0.805244 -1.206412
4 -1.170299 -0.226169 0.410835 0.813850 0.132003 -0.827317 -0.076467
7 8 9
0 0.113648 -1.478427 0.524988
1 0.844885 1.075770 -0.109050
2 0.413738 0.276662 -0.472035
3 2.565646 1.431256 1.340309
4 -1.187678 1.130127 -1.436737
In [10]: pd.set_option('expand_frame_repr', False)
In [11]: df
Out[11]:
0 1 2 3 4 5 6 7 8 9
0 -1.039575 0.271860 -0.424972 0.567020 0.276232 -1.087401 -0.673690 0.113648 -1.478427 0.524988
1 0.404705 0.577046 -1.715002 -1.039268 -0.370647 -1.157892 -1.344312 0.844885 1.075770 -0.109050
2 1.643563 -1.469388 0.357021 -0.674600 -1.776904 -0.968914 -1.294524 0.413738 0.276662 -0.472035
3 -0.013960 -0.362543 -0.006154 -0.923061 0.895717 0.805244 -1.206412 2.565646 1.431256 1.340309
4 -1.170299 -0.226169 0.410835 0.813850 0.132003 -0.827317 -0.076467 -1.187678 1.130127 -1.436737
In [12]: 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.
In [13]: df = pd.DataFrame(np.random.randn(10,10))
In [14]: pd.set_option('max_rows', 5)
In [15]: pd.set_option('large_repr', 'truncate')
In [16]: df
Out[16]:
0 1 2 3 4 5 6 \
0 -1.413681 1.607920 1.024180 0.569605 0.875906 -2.211372 0.974466
1 0.545952 -1.219217 -1.226825 0.769804 -1.281247 -0.727707 -0.121306
.. ... ... ... ... ... ... ...
8 -2.484478 -0.281461 0.030711 0.109121 1.126203 -0.977349 1.474071
9 -1.071357 0.441153 2.353925 0.583787 0.221471 -0.744471 0.758527
7 8 9
0 -2.006747 -0.410001 -0.078638
1 -0.097883 0.695775 0.341734
.. ... ... ...
8 -0.064034 -1.282782 0.781836
9 1.729689 -0.964980 -0.845696
[10 rows x 10 columns]
In [17]: pd.set_option('large_repr', 'info')
In [18]: df
Out[18]:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10 entries, 0 to 9
Data columns (total 10 columns):
0 10 non-null float64
1 10 non-null float64
2 10 non-null float64
3 10 non-null float64
4 10 non-null float64
5 10 non-null float64
6 10 non-null float64
7 10 non-null float64
8 10 non-null float64
9 10 non-null float64
dtypes: float64(10)
memory usage: 872.0 bytes
In [19]: pd.reset_option('large_repr')
In [20]: 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 [21]: df = pd.DataFrame(np.array([['foo', 'bar', 'bim', 'uncomfortably long string'],
....: ['horse', 'cow', 'banana', 'apple']]))
....:
In [22]: pd.set_option('max_colwidth',40)
In [23]: df
Out[23]:
0 1 2 3
0 foo bar bim uncomfortably long string
1 horse cow banana apple
In [24]: pd.set_option('max_colwidth', 6)
In [25]: df
Out[25]:
0 1 2 3
0 foo bar bim un...
1 horse cow ba... apple
In [26]: pd.reset_option('max_colwidth')
display.max_info_columns sets a threshold for when by-column info
will be given.
In [27]: df = pd.DataFrame(np.random.randn(10,10))
In [28]: pd.set_option('max_info_columns', 11)
In [29]: df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10 entries, 0 to 9
Data columns (total 10 columns):
0 10 non-null float64
1 10 non-null float64
2 10 non-null float64
3 10 non-null float64
4 10 non-null float64
5 10 non-null float64
6 10 non-null float64
7 10 non-null float64
8 10 non-null float64
9 10 non-null float64
dtypes: float64(10)
memory usage: 872.0 bytes
In [30]: pd.set_option('max_info_columns', 5)
In [31]: df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10 entries, 0 to 9
Columns: 10 entries, 0 to 9
dtypes: float64(10)
memory usage: 872.0 bytes
In [32]: 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.
In [33]: df =pd.DataFrame(np.random.choice([0,1,np.nan], size=(10,10)))
In [34]: df
Out[34]:
0 1 2 3 4 5 6 7 8 9
0 0.0 1.0 1.0 0.0 1.0 1.0 0.0 NaN 1.0 NaN
1 1.0 NaN 0.0 0.0 1.0 1.0 NaN 1.0 0.0 1.0
2 NaN NaN NaN 1.0 1.0 0.0 NaN 0.0 1.0 NaN
3 0.0 1.0 1.0 NaN 0.0 NaN 1.0 NaN NaN 0.0
4 0.0 1.0 0.0 0.0 1.0 0.0 0.0 NaN 0.0 0.0
5 0.0 NaN 1.0 NaN NaN NaN NaN 0.0 1.0 NaN
6 0.0 1.0 0.0 0.0 NaN 1.0 NaN NaN 0.0 NaN
7 0.0 NaN 1.0 1.0 NaN 1.0 1.0 1.0 1.0 NaN
8 0.0 0.0 NaN 0.0 NaN 1.0 0.0 0.0 NaN NaN
9 NaN NaN 0.0 NaN NaN NaN 0.0 1.0 1.0 NaN
In [35]: pd.set_option('max_info_rows', 11)
In [36]: df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10 entries, 0 to 9
Data columns (total 10 columns):
0 8 non-null float64
1 5 non-null float64
2 8 non-null float64
3 7 non-null float64
4 5 non-null float64
5 7 non-null float64
6 6 non-null float64
7 6 non-null float64
8 8 non-null float64
9 3 non-null float64
dtypes: float64(10)
memory usage: 872.0 bytes
In [37]: pd.set_option('max_info_rows', 5)
In [38]: df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10 entries, 0 to 9
Data columns (total 10 columns):
0 float64
1 float64
2 float64
3 float64
4 float64
5 float64
6 float64
7 float64
8 float64
9 float64
dtypes: float64(10)
memory usage: 872.0 bytes
In [39]: pd.reset_option('max_info_rows')
display.precision sets the output display precision in terms of decimal places. This is only a
suggestion.
In [40]: df = pd.DataFrame(np.random.randn(5,5))
In [41]: pd.set_option('precision',7)
In [42]: df
Out[42]:
0 1 2 3 4
0 -2.0490276 2.8466122 -1.2080493 -0.4503923 2.4239054
1 0.1211080 0.2669165 0.8438259 -0.2225400 2.0219807
2 -0.7167894 -2.2244851 -1.0611370 -0.2328247 0.4307933
3 -0.6654779 1.8298075 -1.4065093 1.0782481 0.3227741
4 0.2003243 0.8900241 0.1948132 0.3516326 0.4488815
In [43]: pd.set_option('precision',4)
In [44]: df
Out[44]:
0 1 2 3 4
0 -2.0490 2.8466 -1.2080 -0.4504 2.4239
1 0.1211 0.2669 0.8438 -0.2225 2.0220
2 -0.7168 -2.2245 -1.0611 -0.2328 0.4308
3 -0.6655 1.8298 -1.4065 1.0782 0.3228
4 0.2003 0.8900 0.1948 0.3516 0.4489
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.
In [45]: df = pd.DataFrame(np.random.randn(6,6))
In [46]: pd.set_option('chop_threshold', 0)
In [47]: df
Out[47]:
0 1 2 3 4 5
0 -0.1979 0.9657 -1.5229 -0.1166 0.2956 -1.0477
1 1.6406 1.9058 2.7721 0.0888 -1.1442 -0.6334
2 0.9254 -0.0064 -0.8204 -0.6009 -1.0393 0.8248
3 -0.8241 -0.3377 -0.9278 -0.8401 0.2485 -0.1093
4 0.4320 -0.4607 0.3365 -3.2076 -1.5359 0.4098
5 -0.6731 -0.7411 -0.1109 -2.6729 0.8645 0.0609
In [48]: pd.set_option('chop_threshold', .5)
In [49]: df
Out[49]:
0 1 2 3 4 5
0 0.0000 0.9657 -1.5229 0.0000 0.0000 -1.0477
1 1.6406 1.9058 2.7721 0.0000 -1.1442 -0.6334
2 0.9254 0.0000 -0.8204 -0.6009 -1.0393 0.8248
3 -0.8241 0.0000 -0.9278 -0.8401 0.0000 0.0000
4 0.0000 0.0000 0.0000 -3.2076 -1.5359 0.0000
5 -0.6731 -0.7411 0.0000 -2.6729 0.8645 0.0000
In [50]: pd.reset_option('chop_threshold')
display.colheader_justify controls the justification of the headers.
Options are ‘right’, and ‘left’.
In [51]: 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 [52]: pd.set_option('colheader_justify', 'right')
In [53]: df
Out[53]:
A B C
0 0.9331 0.3 0.0
1 0.2888 0.2 0.0
2 1.3250 0.2 0.0
3 0.5892 0.7 0.0
4 0.5314 0.1 0.0
5 -1.1987 0.7 0.0
In [54]: pd.set_option('colheader_justify', 'left')
In [55]: df
Out[55]:
A B C
0 0.9331 0.3 0.0
1 0.2888 0.2 0.0
2 1.3250 0.2 0.0
3 0.5892 0.7 0.0
4 0.5314 0.1 0.0
5 -1.1987 0.7 0.0
In [56]: pd.reset_option('colheader_justify')