Get the max value from each group with pandas.DataFrame.groupby

Question

I need to aggregate two columns of my dataframe, count the values of the second columns and then take only the row with the highest value in the "count" column, let me show:

df =
col1|col2
---------
  A | AX
  A | AX
  A | AY
  A | AY
  A | AY
  B | BX
  B | BX
  B | BX
  B | BY
  B | BY
  C | CX
  C | CX
  C | CX
  C | CX
  C | CX
------------

df1 = df.groupby(['col1', 'col2']).agg({'col2': 'count'})
df1.columns = ['count']
df1= df1.reset_index()

out:
col1 col2 count
A    AX   2
A    AY   3
B    BX   3
B    BY   2
C    CX   5

so far so good, but now I need to get only the row of each 'col1' group that has the maximum 'count' value, but keeping the value in 'col2'.

expected output in the end:

col1 col2 count
  A  AY   3
  B  BX   3
  C  CX   5

I have no idea how to do that. My attempts so far of using the max() aggregation always left the 'col2' out.

Answer 1

From your original DataFrame you can .value_counts , which returns a descending count within group, and then given this sorting drop_duplicates will keep the most frequent within group.

df1 = (df.groupby('col1')['col2'].value_counts()
         .rename('counts').reset_index()
         .drop_duplicates('col1'))

  col1 col2  counts
0    A   AY       3
2    B   BX       3
4    C   CX       5

Answer 2

Probably not ideal, but this works:

df1.loc[df1.groupby(level=0).idxmax()['count']]
col1    col2    count
A       AY      3
B       BX      3
C       CX      5

This works because the groupby within the loc will return a list of indices, which loc will then pull up.

Answer 3

I guess you need this: df['qty'] = 1 and then df.groupby([['col1', 'col2']].sum().reset_index(drop=True)

Answer 4

Option 1: Include Ties

In case you have ties and want to show them.

Ties could be, for instance, both (B, BX) and (B, BY) occur 3 times.

# Prepare packages
import pandas as pd

# Create dummy date
df = pd.DataFrame({
    'col1': ['A', 'A', 'A', 'A', 'A', 'B', 'B', 'B', 'B', 'B', 'B', 'C', 'C', 'C', 'C', 'C'],
    'col2': ['AX', 'AX', 'AY', 'AY', 'AY', 'BX', 'BX', 'BX', 'BY', 'BY', 'BY', 'CX', 'CX', 'CX', 'CX', 'CX'],
})

# Get Max Value by Group with Ties
df_count = (df
            .groupby('col1')['col2']
            .value_counts()
            .to_frame('count')
            .reset_index())
m = df_count.groupby(['col1'])['count'].transform(max) == df_count['count']
df1 = df_count[m]

  col1 col2  count
0    A   AY      3
2    B   BX      3
3    B   BY      3
4    C   CX      5

Option 2: Short Code Ignoring Ties

df1 = (df
 .groupby('col1')['col2']
 .value_counts()
 .groupby(level=0)
 .head(1)
 # .to_frame('count').reset_index() # Uncomment to get exact output requested
 )