select columns based on columns names containing a specific string in pandas

In case @Pedro answer doesn't work here is official way of doing it for pandas 0.25

Sample dataframe:

>>> df = pd.DataFrame(np.array(([1, 2, 3], [4, 5, 6])),
...                   index=['mouse', 'rabbit'],
...                   columns=['one', 'two', 'three'])

         one two three
mouse     1   2   3
rabbit    4   5   6

Select columns by name

df.filter(items=['one', 'three'])
         one  three
mouse     1      3
rabbit    4      6

Select columns by regular expression

df.filter(regex='e$', axis=1) #ending with *e*, for checking containing just use it without *$* in the end
         one  three
mouse     1      3
rabbit    4      6

Select rows containing 'bbi'

df.filter(like='bbi', axis=0)
         one  two  three
rabbit    4    5      6

You've several options, here's a couple:

1 - filter with like:

df.filter(like='alp')

2 - filter with regex:

df.filter(regex='alp')

alternative methods:

In [13]: df.loc[:, df.columns.str.startswith('alp')]
Out[13]:
       alp1      alp2
0  0.357564  0.108907
1  0.341087  0.198098
2  0.416215  0.644166
3  0.814056  0.121044
4  0.382681  0.110829
5  0.130343  0.219829
6  0.110049  0.681618
7  0.949599  0.089632
8  0.047945  0.855116
9  0.561441  0.291182

In [14]: df.loc[:, df.columns.str.contains('alp')]
Out[14]:
       alp1      alp2
0  0.357564  0.108907
1  0.341087  0.198098
2  0.416215  0.644166
3  0.814056  0.121044
4  0.382681  0.110829
5  0.130343  0.219829
6  0.110049  0.681618
7  0.949599  0.089632
8  0.047945  0.855116
9  0.561441  0.291182

option 1
Full numpy + pd.DataFrame

m = np.core.defchararray.find(df.columns.values.astype(str), 'alp') >= 0
pd.DataFrame(df.values[:, m], df.index, df.columns[m])

       alp1      alp2
0  0.819189  0.356867
1  0.900406  0.968947
2  0.201382  0.658768
3  0.700727  0.946509
4  0.176423  0.290426
5  0.132773  0.378251
6  0.749374  0.983251
7  0.768689  0.415869
8  0.292140  0.457596
9  0.214937  0.976780

option 2
numpy + loc

m = np.core.defchararray.find(df.columns.values.astype(str), 'alp') >= 0
df.loc[:, m]

       alp1      alp2
0  0.819189  0.356867
1  0.900406  0.968947
2  0.201382  0.658768
3  0.700727  0.946509
4  0.176423  0.290426
5  0.132773  0.378251
6  0.749374  0.983251
7  0.768689  0.415869
8  0.292140  0.457596
9  0.214937  0.976780

---

timing
numpy is faster