Create multiple pandas dataframes based on column value

Question

I have a df I'd like to split into 5 (named df1 - df5) based on the value of one column (origin). I've tried groupby, and a few other things (like this and this) with no success.

My df looks like this

     origin t_id    Group   ids            ...
0    g2     300     group2  23, 54, 24     ...
1    g      300     group2  1, 89          ...
2    g3     300     group10 155, 4, 90     ...
3    g5     300     group11 38, 13, 45.    ...
4    g4     300     group2  2.             ...

Right now I have it broken up into multiple .loc statements for each unique value of origin, but there must be a cleaner, more concise way to do this.

It's hard to help with no illustration. Have a look at How to make good reproducible pandas examples and then provide a sample of your data with the expected output — Alexandre B.
– Alexandre B., Commented Aug 15, 2019 at 22:55

Parijat Bhatt · Accepted Answer · 2019-08-16 16:50:34Z

2

This should do


a = []

for value in df['origin'].unique():
    a.append(df[df['origin']==value])

The array will contain the dataframes corresponding to the unique values.Let me know if I misunderstood anything.

edited Aug 16, 2019 at 16:50

answered Aug 16, 2019 at 0:22

Parijat Bhatt

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6 Comments

LMGagne Over a year ago

This returns TypeError: 'method' object is not iterable

Parijat Bhatt Over a year ago

Please try again. unique was supposed to be called as a function

LMGagne Over a year ago

that seems to run, but then a just prints all the data, not a list of df names.

Parijat Bhatt Over a year ago

To access one particular dataframe, you will have to do a[i]

LMGagne Over a year ago

is there a way to automatically name them in the same block of code. For example, df1-df5?

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