Adding suffix to variable for every iteration of for loop in python

Question

I have searched for similarly worded questions but haven't found one that answers my question.

I have 2 dataframes showing the results on exam_1 and exam_2 :

exam_1 = pd.DataFrame([['Jim', 87], ['Toby', 44], ['Joe', 71], ['Tom', 59]], columns=['Name', 'Score'])

exam_2 = pd.DataFrame([['Tom', 88], ['Joe', 55], ['Jim', 62], ['Toby', 70]], columns=['Name', 'Score'])

I want to iterate through these dataframes such that we:

• remove subjects with Score less than 60
• create new dataframes ( J and T ) based on the first letter of the subjects name:

for df in (exam_1, exam_2):
    
    # Remove subjects with score less than 60
    df = df[df['Score'] >= 60]

    # Dataframe of names starting with 'J' or 'T'
    J = df[df['Name'].str.startswith('J')]
    T = df[df['Name'].str.startswith('T')]

I want to add a suffix to the dataframes J and T based off the iteration in the for loop

For example, exam_1 is the 1st iteration, so the dataframes would be J_1 and T_1 .

exam_2 is the 2nd iteration, so the dataframes would be J_2 and T_2 .

Is this possible to do?

Answer 1

Use a dict in your case:

J = {}
T = {}

for i, df in enumerate((exam_1, exam_2), 1):
    
    # Remove subjects with score less than 60
    df = df[df['Score'] >= 60]

    # Dataframe of names starting with 'J' or 'T'
    J[i] = df[df['Name'].str.startswith('J')]
    T[i] = df[df['Name'].str.startswith('T')]

Output:

>>> J[1]
  Name  Score
0  Jim     87
2  Joe     71

>>> J[2]
  Name  Score
2  Jim     62

>>> T[1]  # all scores < 60
Empty DataFrame
Columns: [Name, Score]
Index: []

>>> T[2]
   Name  Score
0   Tom     88
3  Toby     70

Answer 2

Maybe with enumerate and globals ?

for idx, df in enumerate((exam_1, exam_2), start=1):
    # Remove subjects with score less than 60
    df = df[df['Score'] >= 60]

    # Dataframe of names starting with 'J' or 'T'
    globals()[f'J_{idx}'] = df[df['Name'].str.startswith('J')]
    globals()[f'T_{idx}'] = df[df['Name'].str.startswith('T')]

NB: This will create the variables (T_1, J_1, T_2 and J_2) as a pandas.core.frame.DataFrame in the global scope.