Creating a new column after every iteration of For loop in Python

Question

I want to create a column and assign it to a dataframe after every iteration of the for loop in python.

df_xyz = pd.DataFrame()
for j in range(0,3):
    for k in range(j+1,4):
        print(j,k)

So, in this case it should create 6 new columns in the dataframe with the name as "ABC1","ABC2"..."ABC6". And the columns will get the values from a numpy array which is generated by running the code present in the loop. My actual code involves some algorithm but here I am just placing the relevant code on which I need help.

Edit 1:

Updated the code:

z= np.array([1,2,4])
df_xyz = pd.DataFrame()
for j in range(0,3):
    for k in range(j+1,4):
        print(j,k)
        df_xyz = pd.DataFrame(z)

This creates a new column only once.

Many sources on line, including on SO (Stack Overflow) show you how to add a new column to a data frame, using existing data. The column name is just keeping a counter and concatenating its string equivalent to ABC. Where are you stuck with this? — Prune
– Prune, Commented Aug 21, 2019 at 19:09
Possible duplicate of Adding new column to existing DataFrame in Python pandas — Tom Wojcik
– Tom Wojcik, Commented Aug 21, 2019 at 19:10
I did check the old posts, but in my case it is just showing me the last column. Meaning it is overriding the old columns. — Akash
– Akash, Commented Aug 21, 2019 at 19:11
You need to update your explanations. The code you provided has an empty DafaFrame and prints numbers for 2 loops that are never related to a dataframe and adding a column — Benoit Drogou
– Benoit Drogou, Commented Aug 21, 2019 at 19:15
@TomWojcik Hi Tom, Can you please tell me where exactly my scenario is covered in the given link ? I want to create columns within for loop. I know how to create a new column but not sure how to keep adding the columns in the dataframe. — Akash
– Akash, Commented Aug 21, 2019 at 19:15

Benoit Drogou · Accepted Answer · 2019-08-21 21:00:13Z

3

It really looks awful, but I think you are trying to do this:

In [1]:
import pandas as pd 
import numpy as np

z= np.array([1,2,4])
df_xyz = pd.DataFrame()
iterator = 1
for j in range(0,3):
    for k in range(j+1,4):
        print(j,k)
        col_name = 'ABC' + str(iterator)
        df_xyz.loc[:, col_name] = z  
        iterator += 1 
df

Out [1]:
    ABC1    ABC2    ABC3    ABC4    ABC5    ABC6
0   1       1       1       1       1       1
1   2       2       2       2       2       2
2   4       4       4       4       4       4

You can as well do something like this :

In [2]:
import pandas as pd 
import numpy as np

my_cols = ['ABC1', 'ABC2', 'ABC3', 'ABC4', 'ABC5', 'ABC6']

z= np.array([1,2,4])
df_xyz = pd.DataFrame()
for j in range(0,3):
    for k in range(j+1,4):
        print(j,k)
        col_name = my_cols[0]
        my_cols.pop(0)
        df_xyz.loc[:, col_name] = z  
df

Out [2]:
    ABC1    ABC2    ABC3    ABC4    ABC5    ABC6
0   1       1       1       1       1       1
1   2       2       2       2       2       2
2   4       4       4       4       4       4

edited Aug 21, 2019 at 21:00

answered Aug 21, 2019 at 19:25

Benoit Drogou

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3 Comments

Akash Over a year ago

The above code creates only 3 columns. However I want this to create 6 columns because j value also changes from 0 to 3

Benoit Drogou Over a year ago

Just a little edit then. Add both j an k values in columns name

Benoit Drogou Over a year ago

It only created 3 columns because there was only 3 distinct column names. When the column already exist, it updates the column and do not create another one. You cannot have 2 columns with the same name

nick · Accepted Answer · 2021-11-24 07:40:44Z

1

# importing pandas
import pandas as pd

# Creating new dataframe
initial_data = {'First_name': ['Ram', 'Mohan', 'Tina', 'Jeetu', 'Meera'], 
            'Last_name': ['Kumar', 'Sharma', 'Ali', 'Gandhi', 'Kumari'], 
            'Marks': [12, 52, 36, 85, 23] }

df = pd.DataFrame(initial_data, columns = ['First_name', 'Last_name', 
'Marks'])

# Generate result using pandas
result = []
for value in df["Marks"]:
if value >= 33:
    result.append("Pass")
elif value < 0 and value > 100:
    result.append("Invalid")
else:
    result.append("Fail")
   
df["Result"] = result   
print(df)

answered Nov 24, 2021 at 7:40

nick

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