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I have a dataframe that looks like this:
Company Name Organisation Name Amount 10118 Vifor Pharma UK Ltd Welsh Assoc for Gastro & Endo 2700.00 10119 Vifor Pharma UK Ltd Welsh IBD Specialist Group, 169.00 10120 Vifor Pharma UK Ltd West Midlands AHSN 1200.00 10121 Vifor Pharma UK Ltd Whittington Hospital 63.00 10122 Vifor Pharma UK Ltd Ysbyty Gwynedd 75.93 How do I sum the Amount and count the Organisation Name, to get a new dataframe that looks like this?
Company Name Organisation Count Amount 10118 Vifor Pharma UK Ltd 5 11000.00 I know how to sum or count:
df.groupby('Company Name').sum() df.groupby('Company Name').count() But not how to do both!
770
To sum given or list of columns then create a list with all columns you wanted and slice the DataFrame with the selected list of columns and use the sum() function. Use df['Sum']=df[col_list]. sum(axis=1) to get the total sum.
Groupby sum in pandas dataframe python Groupby sum in pandas python can be accomplished by groupby() function. Groupby sum of multiple column and single column in pandas is accomplished by multiple ways some among them are groupby() function and aggregate() function.
Groupby count in pandas dataframe python Groupby count in pandas python can be accomplished by groupby() function. Groupby count of multiple column and single column in pandas is accomplished by multiple ways some among them are groupby() function and aggregate() function.
We will groupby count with “Product” and “State” columns along with the reset_index () will give a proper table structure , so the result will be agg () function takes ‘count’ as input which performs groupby count, reset_index () assigns the new index to the grouped by dataframe and makes them a proper dataframe structure
You can use pandas DataFrame.groupby ().count () to group columns and compute the count or size aggregate, this calculates a rows count for each group combination.
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 If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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