How to create a column that measures the number of items that exits in another string column?

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I have the dataframe that has employees, and their level.

import pandas as pd
d = {'employees': ["John", "Jamie", "Ann", "Jane", "Kim", "Steve"],  'Level': ["A/Ba", "C/A", "A", "C", "Ba/C", "D"]}
df = pd.DataFrame(data=d)

How do I add a new column that measures the number of employees with the same levels. For example, John would have 3 as there are 2 A's (Jamie and Ann) and one other Ba (Kim). Note it does not count the employee in this case John level(s) to that count.

My goal is for the end dataframe to be this.

enter image description here

pandas-groupby pandas dataframe group-by python
2 Answers

Try this:

df['Number of levels'] = df['Level'].str.split('/').explode().map(df['Level'].str.split('/').explode().value_counts()).sub(1).groupby(level=0).sum()

Output:

>>> df
  employees Level  Number of levels
0      John  A/Ba                 3
1     Jamie   C/A                 4
2       Ann     A                 2
3      Jane     C                 2
4       Kim  Ba/C                 3
5     Steve     D                 0
Admin1 
exploded = df.Level.str.split("/").explode()
counts = exploded.groupby(exploded).transform("count").sub(1)
df["Num Levels"] = counts.groupby(level=0).sum()

We first explode the "Level" column by splitting over "/" so we can reach each level:

>>> exploded = df.Level.str.split("/").explode()
>>> exploded

0     A
0    Ba
1     C
1     A
2     A
3     C
4    Ba
4     C
5     D
Name: Level, dtype: object

We now need counts of each element in this series so we group by itself and transform by counts:

>>> exploded.groupby(exploded).transform("count")
0    3
0    2
1    3
1    3
2    3
3    3
4    2
4    3
5    1
Name: Level, dtype: int64

Since it counts elements themselves but you look at other places, we subtract 1 to get counts:

>>> counts = exploded.groupby(exploded).transform("count").sub(1)
>>> counts
0    2
0    1
1    2
1    2
2    2
3    2
4    1
4    2
5    0
Name: Level, dtype: int64

Now, we need to "come back", and the index is our helper for that; we group by it (level=0 means that) and sum the counts thereof:

>>> counts.groupby(level=0).sum()
0    3
1    4
2    2
3    2
4    3
5    0
Name: Level, dtype: int64

This is the end result and is assigned to df["Num Levels"].

to get

  employees Level  Num Levels
0      John  A/Ba           3
1     Jamie   C/A           4
2       Ann     A           2
3      Jane     C           2
4       Kim  Ba/C           3
5     Steve     D           0

This is all writable in "1 line" but it may hinder readability and further debuggings!

df["Num Levels"] = (df.Level
                      .str.split("/")
                      .explode()
                      .pipe(lambda ex: ex.groupby(ex))
                      .transform("count")
                      .sub(1)
                      .groupby(level=0)
                      .sum())
user_93321870
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