I have a dataframe that looks like this:
Company Name Organisation Name Amount
10118 Vifor Pharma UK Ltd Welsh Assoc for Gastro
df.groupby('Company Name').agg({'Organisation name':'count','Amount':'sum'})\
.apply(lambda x: x.sort_values(['count','sum'], ascending=False))
Just in case you were wondering how to rename columns during aggregation, here's how for
df.groupby('Company Name')['Amount'].agg(MySum='sum', MyCount='count')
Or,
df.groupby('Company Name').agg(MySum=('Amount', 'sum'), MyCount=('Amount', 'count'))
MySum MyCount
Company Name
Vifor Pharma UK Ltd 4207.93 5
If you have lots of columns and only one is different you could do:
In[1]: grouper = df.groupby('Company Name')
In[2]: res = grouper.count()
In[3]: res['Amount'] = grouper.Amount.sum()
In[4]: res
Out[4]:
Organisation Name Amount
Company Name
Vifor Pharma UK Ltd 5 4207.93
Note you can then rename the Organisation Name column as you wish.
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'])
or
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