I'm very lost with a problem and some help or tips will be appreciated.
The problem: I've a csv file with a column with the possibility of multiple values like:
Fruit;Color;The_evil_column
Apple;Red;something1
Apple;Green;something1
Orange;Orange;something1
Orange;Green;something2
Apple;Red;something2
Apple;Red;something3
I've loaded the data into a dataframe and i need to split that dataframe into multiple dataframes based on the value of the column "The_evil_column":
df1
Fruit;Color;The_evil_column
Apple;Red;something1
Apple;Green;something1
Orange;Orange;something1
df2
Fruit;Color;The_evil_column
Orange;Green;something2
Apple;Red;something2
df3
Fruit;Color;The_evil_column
Apple;Red;something3
After reading some posts i'm even more confused and i need some tip about this please.
split() Pandas provide a method to split string around a passed separator/delimiter. After that, the string can be stored as a list in a series or it can also be used to create multiple column data frames from a single separated string.
you can generate a dictionary of DataFrames:
d = {g:x for g,x in df.groupby('The_evil_column')}
In [95]: d.keys()
Out[95]: dict_keys(['something1', 'something2', 'something3'])
In [96]: d['something1']
Out[96]:
Fruit Color The_evil_column
0 Apple Red something1
1 Apple Green something1
2 Orange Orange something1
or a list of DataFrames:
In [103]: l = [x for _,x in df.groupby('The_evil_column')]
In [104]: l[0]
Out[104]:
Fruit Color The_evil_column
0 Apple Red something1
1 Apple Green something1
2 Orange Orange something1
In [105]: l[1]
Out[105]:
Fruit Color The_evil_column
3 Orange Green something2
4 Apple Red something2
In [106]: l[2]
Out[106]:
Fruit Color The_evil_column
5 Apple Red something3
UPDATE:
In [111]: g = pd.read_csv(filename, sep=';').groupby('The_evil_column')
In [112]: g.ngroups # number of unique values in the `The_evil_column` column
Out[112]: 3
In [113]: g.apply(lambda x: x.to_csv(r'c:\temp\{}.csv'.format(x.name)))
Out[113]:
Empty DataFrame
Columns: []
Index: []
will produce 3 files:
In [115]: glob.glob(r'c:\temp\something*.csv')
Out[115]:
['c:\\temp\\something1.csv',
'c:\\temp\\something2.csv',
'c:\\temp\\something3.csv']
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