Loop Through Pandas Dataframe And Split Into Multiple Dataframes Based On Unique Column Values

I have a dataframe saved in a list. Is there a way to loop through the list to create separate dataframes based of a column value? ex: Turn this df To this: df1 df2 I have searc

Solution 1:

Try this:

df1 = df.loc[df['ID'] == 0902]
df2 = df.loc[df['ID'] == 0105]

Or this:

df1, df2 = [groupfor _, groupin df.groupby('ID')]

Or if you want it dynamically:

dct = {f'df{idx}': groupfor _, groupin df.groupby('ID')]}
print(dct)

Or:

dct = {}
for idx, v in enumerate(df['ID'].unique()):
    dct[f'df{idx}'] = df.loc[df['ID'] == v]

print(dct)

And print like this for specific dataframe:

print(dct['df1'])

Solution 2:

You can create variables df1 and df2 dynamically using locals():

for i, (ID, subdf) inenumerate(df.groupby('ID'), 1):
    locals()[f'df{i}'] = subdf

Output:

>>>df1IDColourTransport20105     redcar30105  yellowcar40105  orangeboat>>>df2IDColourTransport00902    redcar10902   bluecar

Or you can create a dictionary indexed by the group ID:

dfs = dict(list(df.groupby('ID')))

Output:

>>>dfs['0105']
     ID  Colour Transport
2  0105     red       car
3  0105  yellow       car
4  0105  orange      boat

>>>dfs['0902']
     ID Colour Transport
0  0902    red       car
1  0902   blue       car