Concatenate strings from several rows using Pandas groupby

Question

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

data = StringIO("""
"name1","hej","2014-11-01"
"name1","du","2014-11-02"
"name1","aj","2014-12-01"
"name1","oj","2014-12-02"
"name2","fin","2014-11-01"
"name2","katt","2014-11-02"
"name2","mycket","2014-12-01"
"name2","lite","2014-12-01"
""")

# load string as stream into dataframe
df = pd.read_csv(data,header=0, names=["name","text","date"],parse_dates=[2])

# add column with month
df["month"] = df["date"].apply(lambda x: x.month)

I want the end result to look like this:

I don't get how I can use groupby and apply some sort of concatenation of the strings in the column "text". Any help appreciated!

Solution 1

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

Solution 2

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})

Solution 3

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)

Solution 4

If you want to concatenate your "text" in a list:

df.groupby(['name', 'month'], as_index = False).agg({'text': list})

Solution 5

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()

View more solutions

Comments

[Matthias Fripp]

In pandas < 1.0, .drop_duplicates() ignores the index, which may give unexpected results. You can avoid this by using .agg(lambda x: ','.join(x)) instead of .transform().drop_duplicates().

[Alex Fedotov]

Man, you've just saved me a lot of time. Thank you. This is the best way to assemble the chronological lists of registrations/user ids into 'cohorts' that I know of. Thank you once again.

[Raghavan vmvs]

Neat and uncomplicated. Eminently fleixible also

[IAmBotmaker]

drop_duplicates() might not work if you do not include parameter drop_duplicates(inplace=True) or just rewrite the line of code as df = df[['name','text','month']].drop_duplicates()

[Thomas]

What ensures that the text e.g. in the first column is actually "hej du" and not "du hej"? Is there an implicit sort somewhere? How can I make this explicit, e.g. sort by the date column?

[kağan hazal koçdemir]

hi, any ideas for dropping duplicates with agg function ?

[Nicolas78]

@kağanhazalkoçdemir agg({'text': lambda x: ' '.join(set(x))})

[constantstranger]

Why did 'hej,du' change to just 'du' in the "update" section?

[whydoesntwork]

This solution worked for me very well for getting the unique appearances too. I just used “set” instead of “list” and then daisy chained a join and presto. Note that it doesn’t work if there are nan values, so I had to use fillna() on the text field first. In my case the command ended: df.groupby(['doc_id'])['author'].apply(set).apply(", ".join).reset_index()

[Bill]

I don't think this adds spaces between the strings does it?