pandas: best way to select all columns whose names start with X

Question

I have a DataFrame:

import pandas as pd
import numpy as np

df = pd.DataFrame({\'foo.aa\': [1, 2.1, np.nan, 4.7, 5.6, 6.8],
                   \'foo.fighters         


        

Answer 1

Now that pandas' indexes support string operations, arguably the simplest and best way to select columns beginning with 'foo' is just:

df.loc[:, df.columns.str.startswith('foo')]

---

Alternatively, you can filter column (or row) labels with df.filter(). To specify a regular expression to match the names beginning with foo.:

>>> df.filter(regex=r'^foo\.', axis=1)
   foo.aa  foo.bars  foo.fighters  foo.fox foo.manchu
0     1.0         0             0        2         NA
1     2.1         0             1        4          0
2     NaN         0           NaN        1          0
3     4.7         0             0        0          0
4     5.6         0             0        0          0
5     6.8         1             0        5          0

To select only the required rows (containing a 1) and the columns, you can use loc, selecting the columns using filter (or any other method) and the rows using any:

>>> df.loc[(df == 1).any(axis=1), df.filter(regex=r'^foo\.', axis=1).columns]
   foo.aa  foo.bars  foo.fighters  foo.fox foo.manchu
0     1.0         0             0        2         NA
1     2.1         0             1        4          0
2     NaN         0           NaN        1          0
5     6.8         1             0        5          0