I am trying to make a loop that will output into multiple dataframes from one large dataframe.
raw_df['names'] = [joe, joe, bob, john, john]
raw_df['order_id'] = [10, 12, 5, 20, 25]
raw_df['amount'] = [100, 1000, 200, 20 25]
for name in raw_df['name'].unique():
names = pd.DataFrame(raw_df.loc[raw_df['name'] == name])
name['cummulative_sum'] = owner_names['amount'].cumsum()
Expected outcome for all names: joe.head()
name id sum
joe 10 100
joe 12 110
Instead of checking for each unique item, it's possible to do .groupby on the variable of interest:
for group_name, group_df in raw_df.groupby("name"):
print("Processing name:", group_name)
names = group_df # this is the same as "names" in your snippet
names["cum_sum"] = names["amount"].cumsum()
The group_df is the same df one would get with raw_df.loc[raw_df['name'] == name].
You could do
variables = locals()
for name, data in raw_df.groupby('names'):
variables[name] = data
joe
Out[607]:
names order_id amount
0 joe 10 100
1 joe 12 1000
