Group Dataframe and Get Sum and Count

Group dataframe and get sum AND count?

try this:

In [110]: (df.groupby('Company Name')
   .....:    .agg({'Organisation Name':'count', 'Amount': 'sum'})
   .....:    .reset_index()
   .....:    .rename(columns={'Organisation Name':'Organisation Count'})
   .....: )
Out[110]:
          Company Name   Amount  Organisation Count
0  Vifor Pharma UK Ltd  4207.93                   5

or if you don't want to reset index:

df.groupby('Company Name')['Amount'].agg(['sum','count'])

df.groupby('Company Name').agg({'Amount': ['sum','count']})

Demo:

In [98]: df.groupby('Company Name')['Amount'].agg(['sum','count'])
Out[98]:
                         sum  count
Company Name
Vifor Pharma UK Ltd  4207.93      5

In [99]: df.groupby('Company Name').agg({'Amount': ['sum','count']})
Out[99]:
                      Amount
                         sum count
Company Name
Vifor Pharma UK Ltd  4207.93     5

How do I Pandas group-by to get sum?

Use GroupBy.sum:

df.groupby(['Fruit','Name']).sum()

Out[31]: 
               Number
Fruit   Name         
Apples  Bob        16
        Mike        9
        Steve      10
Grapes  Bob        35
        Tom        87
        Tony       15
Oranges Bob        67
        Mike       57
        Tom        15
        Tony        1

To specify the column to sum, use this: df.groupby(['Name', 'Fruit'])['Number'].sum()

Get statistics for each group (such as count, mean, etc) using pandas GroupBy?

On groupby object, the agg function can take a list to apply several aggregation methods at once. This should give you the result you need:

df[['col1', 'col2', 'col3', 'col4']].groupby(['col1', 'col2']).agg(['mean', 'count'])

Groupby sum and count on multiple columns in python

It can be done using pivot_table this way:

>>> df1=pd.pivot_table(df, index=['country','month'],values=['revenue','profit','ebit'],aggfunc=np.sum)
>>> df1 
                ebit  profit  revenue
country month                        
Canada  201411     5      10       15
UK      201410     5      10       20
USA     201409     5      12       19

>>> df2=pd.pivot_table(df, index=['country','month'], values='ID',aggfunc=len).rename('count')
>>> df2

country  month 
Canada   201411    1
UK       201410    1
USA      201409    2

>>> pd.concat([df1,df2],axis=1)

                ebit  profit  revenue  count
country month                               
Canada  201411     5      10       15      1
UK      201410     5      10       20      1
USA     201409     5      12       19      2

UPDATE

It can be done in one-line using pivot_table and providing a dict of functions to apply to each column in the aggfunc argument:

pd.pivot_table(
   df,
   index=['country','month'],
   aggfunc={'revenue': np.sum, 'profit': np.sum, 'ebit': np.sum, 'ID': len}
).rename(columns={'ID': 'count'})

                count  ebit  profit  revenue
country month                               
Canada  201411      1     5      10       15
UK      201410      1     5      10       20
USA     201409      2     5      12       19

Pandas Groupby and Sum Only One Column

The only way to do this would be to include C in your groupby (the groupby function can accept a list).

Give this a try:

df.groupby(['A','C'])['B'].sum()

One other thing to note, if you need to work with df after the aggregation you can also use the as_index=False option to return a dataframe object. This one gave me problems when I was first working with Pandas. Example:

df.groupby(['A','C'], as_index=False)['B'].sum()

groupby, sum and count to one table

One option is to count the size and sum the columns for each group separately and then join them by index:

df.groupby("C")['A'].agg({"number": 'size'}).join(df.groupby('C').sum())

    number  A   B
# C         
# a     2   11  8
# b     2   14  12
# c     2   8   5
# d     2   11  12
# e     1   7   2

You can also do df.groupby('C').agg(["sum", "size"]) which gives an extra duplicated size column, but if you are fine with that, it should also work.