具有唯一 groupby 的新列在数据框中产生

Question

I have a data frame with duplicate rows ('id').

I want to aggregate the data, but first need to sum unique sessions per id.

id     session
123      X
123      X 
123      Y
123      Z
234      T
234      T

This code works well, but not when I want to add this new column 'ncount' to my data frame.

df['ncount'] = df.groupby('id')['session'].nunique().reset_index()

I tried using transform and it didn't work.

df['ncount'] = df.groupby('id')['session'].transform('nunique')

This is the result from the transform code (my data as duplicates id):

id     session    ncount
123      X          1
123      X          1
123      Y          1
123      Z          1
234      T          1
234      T          1

This is the result I'm interested in:

id     session    ncount
123      X          3
123      X          3
123      Y          3
123      Z          3
234      T          1
234      T          1

Answer 1

Use the following steps:

1.Group data and store in separate variable.

2.Then merge back to original data frame.

Code:

import pandas as pd

df = pd.DataFrame({"id":[123,123,123,123,234,234],"session":["X","X","Y","Z","T","T"]})

x = df.groupby(["id"])['session'].nunique().reset_index() 

res = pd.merge(df,x,how="left",on="id")

print(res)

You can rename the column names if required.

Answer 2

using .count()

Steps:

1: Group the data by "id" and count the values of id values then

2: Decrease the Count by one for index format and Merge to two DataFrames

import pandas as pd

df = pd.DataFrame({"id":[123,123,123,123,234,234],"session":["X","X","Y","Z","T","T"]})

uniq_df = df.groupby(["id"])["session"].count().reset_index()
uniq_df["session"] = uniq_df["session"] - 1

result = pd.merge(df,uniq_df,how="left",on="id")

print(result)