pandas.DataFrame.groupby leaving out columns

Question

I have a Pandas DataFrame that contains some values and I want to sum up those values according to the date column.

The DataFrame looks like the following:

and when I run pandas.DataFrame.groupby(['date']).sum() I get

As you can see, this isn't the result that I want because I want all of the columns summed up, not just polarity and subjectivity .

Does anybody know why it's only summing up these two, and how might I get the desired result?

Thank you.

Answer 1

We need numeric columns to be able to do calculation on them, in this case sum :

#Example dataframe
df = pd.DataFrame({'date':['2019-01-04', '2019-01-04', '2019-01-03', '2018-12-22', '2018-08-31'],
                   'replies_count':['46', '143', '64', '154', '50'],
                   'polarity':[10, 20, 30, 40, 50]})

print(df)
         date replies_count  polarity
0  2019-01-04            46        10
1  2019-01-04           143        20
2  2019-01-03            64        30
3  2018-12-22           154        40
4  2018-08-31            50        50

Check types of columns

print(df.dtypes)

date             object
replies_count    object
polarity          int64
dtype: object

Apply groupby with sum

print(df.groupby('date').sum())

            polarity
date                
2018-08-31        50
2018-12-22        40
2019-01-03        30
2019-01-04        30

Now change type of replies_count column to int and do the same groupby with sum

df['replies_count'] = df['replies_count'].astype(int)

print(df.groupby('date').sum())
            replies_count  polarity
date                               
2018-08-31             50        50
2018-12-22            154        40
2019-01-03             64        30
2019-01-04            189        30

As we can see, the column is included now.