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psweep.psweep.df_print

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psweep.psweep.df_print#

psweep.psweep.df_print(df, index=False, special_cols=None, prefix_cols=False, cols=[], skip_cols=[])[source]#

Print DataFrame, by default without the index and prefix columns such as _pset_id.

Similar logic as in bin/psweep-db2table, w/o tabulate support but more features (skip_cols for instance).

Column names are always sorted, so the order of names in e.g. cols doesn’t matter.

Parameters:

  • df (DataFrame)

  • index (bool) – include DataFrame index

  • prefix_cols (bool) – include all prefix columns (_pset_id etc.), we don’t support skipping user-added postfix columns (e.g. result_)

  • cols (Sequence[str]) – explicit sequence of columns, overrides prefix_cols when prefix columns are specified

  • skip_cols (Sequence[str]) – skip those columns instead of selecting them (like cols would), use either this or cols; overrides prefix_cols when prefix columns are specified

Examples

>>> import pandas as pd
>>> df=pd.DataFrame(dict(a=rand(3), b=rand(3), _c=rand(3)))

>>> df
          a         b        _c
0  0.373534  0.304302  0.161799
1  0.698738  0.589642  0.557172
2  0.343316  0.186595  0.822023

>>> ps.df_print(df)
       a        b
0.373534 0.304302
0.698738 0.589642
0.343316 0.186595

>>> ps.df_print(df, prefix_cols=True)
       a        b       _c
0.373534 0.304302 0.161799
0.698738 0.589642 0.557172
0.343316 0.186595 0.822023

>>> ps.df_print(df, index=True)
          a        b
0  0.373534 0.304302
1  0.698738 0.589642
2  0.343316 0.186595

>>> ps.df_print(df, cols=["a"])
       a
0.373534
0.698738
0.343316

>>> ps.df_print(df, cols=["a"], prefix_cols=True)
       a       _c
0.373534 0.161799
0.698738 0.557172
0.343316 0.822023

>>> ps.df_print(df, cols=["a", "_c"])
       a       _c
0.373534 0.161799
0.698738 0.557172
0.343316 0.822023

>>> ps.df_print(df, skip_cols=["a"])
       b
0.304302
0.589642
0.186595
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