Group by and find top n value_counts pandas

Question

I have a dataframe of taxi data with two columns that looks like this:

Neighborhood    Borough        Time
Midtown         Manhattan      X
Melrose         B         


        

Answer 1

I think you can use nlargest - you can change 1 to 5:

s = df['Neighborhood'].groupby(df['Borough']).value_counts()
print s
Borough                      
Bronx          Melrose            7
Manhattan      Midtown           12
               Lincoln Square     2
Staten Island  Grant City        11
dtype: int64

print s.groupby(level=[0,1]).nlargest(1)
Bronx          Bronx          Melrose        7
Manhattan      Manhattan      Midtown       12
Staten Island  Staten Island  Grant City    11
dtype: int64

additional columns were getting created, specified level info

Answer 2

You can also try below code to get only top 10 values of value counts

'country_code' and 'raised_amount_usd' is column names.

groupby_country_code=master_frame.groupby('country_code') arr=groupby_country_code['raised_amount_usd'].sum().sort_index()[0:10] print(arr)

[0:10] shows index 0 to 10 from array for slicing. you can choose your slicing option.

Answer 3

df['Neighborhood'].groupby(df['Borough']).value_counts().head(5)

head() gets the top 5 rows in a data frame.

Answer 4

You can do this in a single line by slightly extending your original groupby with 'nlargest':

>>> df.groupby(['Borough', 'Neighborhood']).Neighborhood.value_counts().nlargest(5)
Borough        Neighborhood    Neighborhood  
Bronx          Melrose         Melrose           1
Manhattan      Midtown         Midtown           1
Manhatten      Lincoln Square  Lincoln Square    1
               Midtown         Midtown           1
Staten Island  Grant City      Grant City        1
dtype: int64