Pandas Split Dataframe into two Dataframes at a specific row

iloc

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

(iloc docs)

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

Demo:

In [255]: df = pd.DataFrame(np.random.rand(5, 6), columns=list('abcdef'))In [256]: dfOut[256]:          a         b         c         d         e         f0  0.823638  0.767999  0.460358  0.034578  0.592420  0.7768031  0.344320  0.754412  0.274944  0.545039  0.031752  0.7845642  0.238826  0.610893  0.861127  0.189441  0.294646  0.5570343  0.478562  0.571750  0.116209  0.534039  0.869545  0.8555204  0.130601  0.678583  0.157052  0.899672  0.093976  0.268974In [257]: dfs = np.split(df, [4], axis=1)In [258]: dfs[0]Out[258]:          a         b         c         d0  0.823638  0.767999  0.460358  0.0345781  0.344320  0.754412  0.274944  0.5450392  0.238826  0.610893  0.861127  0.1894413  0.478562  0.571750  0.116209  0.5340394  0.130601  0.678583  0.157052  0.899672In [259]: dfs[1]Out[259]:          e         f0  0.592420  0.7768031  0.031752  0.7845642  0.294646  0.5570343  0.869545  0.8555204  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         b0  0.823638  0.7679991  0.344320  0.7544122  0.238826  0.6108933  0.478562  0.5717504  0.130601  0.678583In [262]: dfs[1]Out[262]:          c0  0.4603581  0.2749442  0.8611273  0.1162094  0.157052In [263]: dfs[2]Out[263]:          d         e         f0  0.034578  0.592420  0.7768031  0.545039  0.031752  0.7845642  0.189441  0.294646  0.5570343  0.534039  0.869545  0.8555204  0.899672  0.093976  0.268974

I generally use array split because it's easier simple syntax and scales better with more than 2 partitions.

import numpy as nppartitions = 2dfs = np.array_split(df, partitions)

np.split(df, [100,200,300], axis=0] wants explicit index numbers which may or may not be desirable.