pandas dataframe indexing by columns

Question

I would like to choose a dataframe by df[:,(1,5:)] i.e. all rows and column number 1 and columns 5 to the end.

I have tried:

df.loc[1:3,('col_1','col_5':)]

Any easy way to do it by column number or column name (both)?

Answer 1

I think you need numpy.r_ for concanecate indices and len(df.columns) for dynamically get position of last column:

df.iloc[1:3,np.r_[1, 5:len(df.columns)]]

Sample:

np.random.seed(23)
df = pd.DataFrame(np.random.randint(10, size=(5,11)), columns=list('ABCDEFGHIJK'))
print (df)
   A  B  C  D  E  F  G  H  I  J  K
0  3  6  8  9  6  8  7  9  3  6  1
1  2  5  5  0  5  0  9  9  3  1  7
2  4  1  1  4  6  7  3  6  9  2  3
3  0  8  6  6  0  5  6  0  2  0  3
4  0  6  2  2  5  7  9  4  7  7  3

df = df.iloc[1:3,np.r_[1, 5:len(df.columns)]]
print (df)
   B  F  G  H  I  J  K
1  5  0  9  9  3  1  7
2  1  7  3  6  9  2  3

If need index from 1 to end:

df = df.iloc[1:,np.r_[1, 5:len(df.columns)]]
print (df)
   B  F  G  H  I  J  K
1  5  0  9  9  3  1  7
2  1  7  3  6  9  2  3
3  8  5  6  0  2  0  3
4  6  7  9  4  7  7  3

And also is possible use np.r_ for index (select first index values and from 3 to end):

df = df.iloc[np.r_[1, 3:len(df.index)],np.r_[1, 5:len(df.columns)]]
print (df)
   B  F  G  H  I  J  K
1  5  0  9  9  3  1  7
3  8  5  6  0  2  0  3
4  6  7  9  4  7  7  3

Answer 2

we can use np.r_[] method, which:

translates slice objects to concatenation along the first axis.

In [86]: df
Out[86]:
      col_0     col_1     col_2     col_3     col_4     col_5     col_6     col_7     col_8     col_9
0  0.167483  0.104568  0.636430  0.706476  0.031586  0.936212  0.051971  0.541296  0.709061  0.870969
1  0.714087  0.801728  0.339450  0.814825  0.080115  0.894817  0.547592  0.817298  0.452318  0.643578
2  0.526403  0.731590  0.081630  0.060352  0.247103  0.159545  0.871784  0.219214  0.975865  0.336896
3  0.182118  0.789699  0.658708  0.498196  0.555364  0.719202  0.228455  0.996334  0.974793  0.650326
4  0.199542  0.680228  0.072198  0.030653  0.257683  0.462623  0.868273  0.727169  0.742707  0.425493
5  0.345935  0.371039  0.987650  0.040109  0.867031  0.578675  0.438615  0.725258  0.486669  0.873423
6  0.900702  0.421721  0.276828  0.592350  0.912363  0.210662  0.622967  0.631560  0.733113  0.131568
7  0.715825  0.909033  0.179683  0.237543  0.971395  0.180977  0.854385  0.492278  0.247231  0.870750
8  0.445305  0.514817  0.359233  0.592951  0.163524  0.391082  0.969412  0.258133  0.656737  0.325190
9  0.773473  0.130874  0.969821  0.453790  0.236050  0.073497  0.169758  0.519774  0.337003  0.828883

In [87]: df.iloc[:, np.r_[1, 5:df.shape[1]]]
Out[87]:
      col_1     col_5     col_6     col_7     col_8     col_9
0  0.104568  0.936212  0.051971  0.541296  0.709061  0.870969
1  0.801728  0.894817  0.547592  0.817298  0.452318  0.643578
2  0.731590  0.159545  0.871784  0.219214  0.975865  0.336896
3  0.789699  0.719202  0.228455  0.996334  0.974793  0.650326
4  0.680228  0.462623  0.868273  0.727169  0.742707  0.425493
5  0.371039  0.578675  0.438615  0.725258  0.486669  0.873423
6  0.421721  0.210662  0.622967  0.631560  0.733113  0.131568
7  0.909033  0.180977  0.854385  0.492278  0.247231  0.870750
8  0.514817  0.391082  0.969412  0.258133  0.656737  0.325190
9  0.130874  0.073497  0.169758  0.519774  0.337003  0.828883