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Below is an example DataFrame.

 0 1 2 3 4 0 0.0 13.00 4.50 30.0 0.0,13.0 1 0.0 13.00 4.75 30.0 0.0,13.0 2 0.0 13.00 5.00 30.0 0.0,13.0 3 0.0 13.00 5.25 30.0 0.0,13.0 4 0.0 13.00 5.50 30.0 0.0,13.0 5 0.0 13.00 5.75 0.0 0.0,13.0 6 0.0 13.00 6.00 30.0 0.0,13.0 7 1.0 13.25 0.00 30.0 0.0,13.25 8 1.0 13.25 0.25 0.0 0.0,13.25 9 1.0 13.25 0.50 30.0 0.0,13.25 10 1.0 13.25 0.75 30.0 0.0,13.25 11 2.0 13.25 1.00 30.0 0.0,13.25 12 2.0 13.25 1.25 30.0 0.0,13.25 13 2.0 13.25 1.50 30.0 0.0,13.25 14 2.0 13.25 1.75 30.0 0.0,13.25 15 2.0 13.25 2.00 30.0 0.0,13.25 16 2.0 13.25 2.25 30.0 0.0,13.25

I want to split this into new dataframes when the row in column 0 changes. 

 0 1 2 3 4 0 0.0 13.00 4.50 30.0 0.0,13.0 1 0.0 13.00 4.75 30.0 0.0,13.0 2 0.0 13.00 5.00 30.0 0.0,13.0 3 0.0 13.00 5.25 30.0 0.0,13.0 4 0.0 13.00 5.50 30.0 0.0,13.0 5 0.0 13.00 5.75 0.0 0.0,13.0 6 0.0 13.00 6.00 30.0 0.0,13.0 7 1.0 13.25 0.00 30.0 0.0,13.25 8 1.0 13.25 0.25 0.0 0.0,13.25 9 1.0 13.25 0.50 30.0 0.0,13.25 10 1.0 13.25 0.75 30.0 0.0,13.25 11 2.0 13.25 1.00 30.0 0.0,13.25 12 2.0 13.25 1.25 30.0 0.0,13.25 13 2.0 13.25 1.50 30.0 0.0,13.25 14 2.0 13.25 1.75 30.0 0.0,13.25 15 2.0 13.25 2.00 30.0 0.0,13.25 16 2.0 13.25 2.25 30.0 0.0,13.25

I've tried adapting the following solutions without any luck so far. Split array at value in numpy Split a large pandas dataframe

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10

Looks like you want to groupby the first colum. You could create a dictionary from the groupby object, and have the groupby keys be the dictionary keys:

out = dict(tuple(df.groupby(0)))

Or we could also build a list from the groupby object. This becomes more useful when we only want positional indexing rather than based on the grouping key:

out = [sub_df for _, sub_df in df.groupby(0)]

We could then index the dict based on the grouping key, or the list based on the group's position:

print(out[0]) 0 1 2 3 4 0 0.0 13.0 4.50 30.0 0.0,13.0 1 0.0 13.0 4.75 30.0 0.0,13.0 2 0.0 13.0 5.00 30.0 0.0,13.0 3 0.0 13.0 5.25 30.0 0.0,13.0 4 0.0 13.0 5.50 30.0 0.0,13.0 5 0.0 13.0 5.75 0.0 0.0,13.0 6 0.0 13.0 6.00 30.0 0.0,13.0
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Based on

I want to split this into new dataframes when the row in column 0 changes.

If you only want to group when value in column 0 changes , You can try:

d=dict([*df.groupby(df['0'].ne(df['0'].shift()).cumsum())]) print(d[1]) print(d[2]) 
 0 1 2 3 4 0 0.0 13.0 4.50 30.0 0.0,13.0 1 0.0 13.0 4.75 30.0 0.0,13.0 2 0.0 13.0 5.00 30.0 0.0,13.0 3 0.0 13.0 5.25 30.0 0.0,13.0 4 0.0 13.0 5.50 30.0 0.0,13.0 5 0.0 13.0 5.75 0.0 0.0,13.0 6 0.0 13.0 6.00 30.0 0.0,13.0 0 1 2 3 4 7 1.0 13.25 0.00 30.0 0.0,13.25 8 1.0 13.25 0.25 0.0 0.0,13.25 9 1.0 13.25 0.50 30.0 0.0,13.25 10 1.0 13.25 0.75 30.0 0.0,13.25
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2

I will use GroupBy.__iter__:

d = dict(df.groupby(df['0'].diff().ne(0).cumsum()).__iter__()) #d = dict(df.groupby(df[0].diff().ne(0).cumsum()).__iter__())

Note that if there are repeated non-consecutive values ​​different groups will be created, if you only use groupby(0) they will be grouped in the same group

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