I want to merge several strings in a dataframe based on a groupedby in Pandas.
This is my code so far:
import pandas as pd
from io import StringIO
You can groupby the 'name'
and 'month'
columns, then call transform
which will return data aligned to the original df and apply a lambda where we join
the text entries:
In [119]:
df['text'] = df[['name','text','month']].groupby(['name','month'])['text'].transform(lambda x: ','.join(x))
df[['name','text','month']].drop_duplicates()
Out[119]:
name text month
0 name1 hej,du 11
2 name1 aj,oj 12
4 name2 fin,katt 11
6 name2 mycket,lite 12
I sub the original df by passing a list of the columns of interest df[['name','text','month']]
here and then call drop_duplicates
EDIT actually I can just call apply
and then reset_index
:
In [124]:
df.groupby(['name','month'])['text'].apply(lambda x: ','.join(x)).reset_index()
Out[124]:
name month text
0 name1 11 hej,du
1 name1 12 aj,oj
2 name2 11 fin,katt
3 name2 12 mycket,lite
update
the lambda
is unnecessary here:
In[38]:
df.groupby(['name','month'])['text'].apply(','.join).reset_index()
Out[38]:
name month text
0 name1 11 du
1 name1 12 aj,oj
2 name2 11 fin,katt
3 name2 12 mycket,lite
For me the above solutions were close but added some unwanted /n's and dtype:object, so here's a modified version:
df.groupby(['name', 'month'])['text'].apply(lambda text: ''.join(text.to_string(index=False))).str.replace('(\\n)', '').reset_index()
we can groupby the 'name' and 'month' columns, then call agg() functions of Panda’s DataFrame objects.
The aggregation functionality provided by the agg() function allows multiple statistics to be calculated per group in one calculation.
df.groupby(['name', 'month'], as_index = False).agg({'text': ' '.join})
The answer by EdChum provides you with a lot of flexibility but if you just want to concateate strings into a column of list objects you can also:
output_series = df.groupby(['name','month'])['text'].apply(list)