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

Neighborhood    Borough        Time
Midtown         Manhattan      X
Melrose         Bronx          Y
Grant City      Staten Island  Z
Midtown         Manhattan      A
Lincoln Square  Manhattan      B

Basically, each row represents a taxi pickup in that neighborhood in that borough. Now, I want to find the top 5 neighborhoods in each borough with the most number of pickups. I tried this:

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

Which gives me something like this:

borough                          
Bronx          High  Bridge          3424
               Mott Haven            2515
               Concourse Village     1443
               Port Morris           1153
               Melrose                492
               North Riverdale        463
               Eastchester            434
               Concourse              395
               Fordham                252
               Wakefield              214
               Kingsbridge            212
               Mount Hope             200
               Parkchester            191
......

Staten Island  Castleton Corners        4
               Dongan Hills             4
               Eltingville              4
               Graniteville             4
               Great Kills              4
               Castleton                3
               Woodrow                  1

How do I filter it so that I get only the top 5 from each? I know there are a few questions with a similar title but they weren’t helpful to my case.

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

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

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

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

Solution:

df.groupby(['Borough']).Neighborhood.value_counts().groupby(level=0, group_keys=False).head(5)
  1. .value_counts().nlargest(5) only give you one group top 5
  2. group_keys=False to avoid duplicated index
  3. because value_counts() has already sorted, just need head(5)

Try this one (just change the number in head() to your choice):

# top 3 : total counts of 'Neighborhood' in each Borough
Z = df.groupby('Borough')['Neighborhood'].value_counts().groupby(level=0).head(3).sort_values(ascending=False).to_frame('counts').reset_index()

Z

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.