Pandas Split Dataframe into two Dataframes at a specific row

Solution 1:

iloc

df1 = datasX.iloc[:, :72]
df2 = datasX.iloc[:, 72:]

(iloc docs)

Solution 2:

use np.split(..., axis=1):

Demo:

In [255]: df = pd.DataFrame(np.random.rand(5, 6), columns=list('abcdef'))

In [256]: df
Out[256]:
          a         b         c         d         e         f
0  0.823638  0.767999  0.460358  0.034578  0.592420  0.776803
1  0.344320  0.754412  0.274944  0.545039  0.031752  0.784564
2  0.238826  0.610893  0.861127  0.189441  0.294646  0.557034
3  0.478562  0.571750  0.116209  0.534039  0.869545  0.855520
4  0.130601  0.678583  0.157052  0.899672  0.093976  0.268974

In [257]: dfs = np.split(df, [4], axis=1)

In [258]: dfs[0]
Out[258]:
          a         b         c         d
0  0.823638  0.767999  0.460358  0.034578
1  0.344320  0.754412  0.274944  0.545039
2  0.238826  0.610893  0.861127  0.189441
3  0.478562  0.571750  0.116209  0.534039
4  0.130601  0.678583  0.157052  0.899672

In [259]: dfs[1]
Out[259]:
          e         f
0  0.592420  0.776803
1  0.031752  0.784564
2  0.294646  0.557034
3  0.869545  0.855520
4  0.093976  0.268974

np.split() is pretty flexible - let's split an original DF into 3 DFs at columns with indexes [2,3]:

In [260]: dfs = np.split(df, [2,3], axis=1)

In [261]: dfs[0]
Out[261]:
          a         b
0  0.823638  0.767999
1  0.344320  0.754412
2  0.238826  0.610893
3  0.478562  0.571750
4  0.130601  0.678583

In [262]: dfs[1]
Out[262]:
          c
0  0.460358
1  0.274944
2  0.861127
3  0.116209
4  0.157052

In [263]: dfs[2]
Out[263]:
          d         e         f
0  0.034578  0.592420  0.776803
1  0.545039  0.031752  0.784564
2  0.189441  0.294646  0.557034
3  0.534039  0.869545  0.855520
4  0.899672  0.093976  0.268974