Split pandas dataframe into blocks based on column values

You can use cumsum to create groups then query the datafame for each group:

df = pd.DataFrame({'data':['MODS start 12/12/202','Some data', 'Some data', 'MODS starts 30/12/2020', 'Some data', 'Some data'],
                  'flag':[True, False, False, True, False, False]})

df['grp'] = df['flag'].cumsum()

print(df)

Output:

                     data   flag  grp
0    MODS start 12/12/202   True    1
1               Some data  False    1
2               Some data  False    1
3  MODS starts 30/12/2020   True    2
4               Some data  False    2
5               Some data  False    2

The use:

df.query('grp == 1')

                   data   flag  grp
0  MODS start 12/12/202   True    1
1             Some data  False    1
2             Some data  False    1

and

df.query('grp == 2')

                     data   flag  grp
3  MODS starts 30/12/2020   True    2
4               Some data  False    2
5               Some data  False    2

You can use numpy.split:

np.split(df, df.index[df.flag])[1:]

Here, I used [1:] because numpy.split also consider the groups before the first index, even if it's empty.

That said, you can also use a simple list comprehension:

idx = df.index[df.flag].tolist() + [df.shape[0]]
[df.iloc[idx[i]:idx[i+1]] for i in range(len(idx)-1)]

Output (both approaches):

                    data   flag
0  MODS start 12/12/2020   True
1              Some data  False
2              Some data  False 

                    data   flag
3  MODS start 30/12/2020   True
4              Some data  False
5              Some data  False 

Get a list of indices of rows with flag = True

true_idx = df[df['flag']==True].index
n = len(true_idx)

Loop over true_idx and create a list of dataframes from each true index to next

new_dfs_list = [df.iloc[ true_idx[i]:true_idx[i+1], :] for i in range(n-1)]

append last df from last true index to the tail of df

new_dfs_list.append(df.iloc[ true_idx[n-1]:, :])

access any of your new_dfs by index

print(new_dfs_list[-1])