How to split a dataframe based on consecutive index?

groupby

You can make a perfectly "consecutive" array with

np.arange(10)

array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

If I were to subtract this from an index that is monotonically increasing, only those index members that were "consecutive" would show up as equal. This is a clever way to establish a key to group by.

list_of_df = [d for _, d in df.groupby(df.index - np.arange(len(df)))]

And print each one to prove it

print(*list_of_df, sep='\n\n')

       Column1  Column2
Index                  
4464      10.5     12.7
4465      11.3     12.8
4466      10.3     22.8

       Column1  Column2
Index                  
5123      11.3     21.8
5124      10.6     22.4

       Column1  Column2
Index                  
5323      18.6     23.5

---

np.split

You can use np.flatnonzero to identify where the differences are not equal to 1 and avoid using cumsum and groupby

list_of_df = np.split(df, np.flatnonzero(np.diff(df.index) != 1) + 1)

Proof

print(*list_of_df, sep='\n\n')

       Column1  Column2
Index                  
4464      10.5     12.7
4465      11.3     12.8
4466      10.3     22.8

       Column1  Column2
Index                  
5123      11.3     21.8
5124      10.6     22.4

       Column1  Column2
Index                  
5323      18.6     23.5

Here is an alternative:

grouper = (~(pd.Series(df.index).diff() == 1)).cumsum().values  
dfs = [dfx for _ , dfx in df.groupby(grouper)]

We use the fact that a continuous difference of 1 equals a sequence (diff == 1).

Full example:

import pandas as pd

data = '''\
Index Column1 Column2
4464  10.5    12.7
4465  11.3    12.8
4466  10.3    22.8
5123  11.3    21.8
5124  10.6    22.4
5323  18.6    23.5
'''

fileobj = pd.compat.StringIO(data)
df = pd.read_csv(fileobj, sep='\s+', index_col='Index')

non_sequence = pd.Series(df.index).diff() != 1
grouper = non_sequence.cumsum().values
dfs = [dfx for _ , dfx in df.groupby(grouper)]

print(dfs[0])

#       Column1  Column2
#Index                  
#4464      10.5     12.7
#4465      11.3     12.8
#4466      10.3     22.8

---

Another way of seeing it is that we look for non-sequence to groupby, might be more readable:

non_sequence = pd.Series(df.index).diff() != 1
grouper = non_sequence.cumsum().values
dfs = [dfx for _ , dfx in df.groupby(grouper)]