Dataframe: create new columns and assign its values from existing column's values

I have a Dataset downloaded from Kaggle for my project, I would like to seek help in creating new columns and assigning its values based on an existing column.

My actual Dataset is complicated, I will give a similar but simpler dataset for easy discussion.

Input:

Month  | Fruit  | Weight
------- -------- --------
1-2020 | Orange | 0.2
1-2020 | Kiwi   | 0.9
2-2020 | Orange | 2.1
2-2020 | Kiwi   | 1.4
...... | .....  | ...

To be able to create a required line chart, I need to change this Dataset structure, making "Orange", "Kiwi" new columns with Weight values, so that "Month' is not repeated.

Desired output:

    Month  | Orange| Kiwi
    ------- -------- --------
    1-2020 | 0.2   | 0.9
    2-2020 | 2.1   | 1.4

Any help would be greatly appreciated

>Solution :

The basic thing you’re looking for is a pivot:

import pandas as pd

data = {
    'Month': ['1-2020', '1-2020', '2-2020', '2-2020'],
    'Fruit': ['Orange', 'Kiwi', 'Orange', 'Kiwi'],
    'Weight': [0.2, 0.9, 2.1, 1.4]
}
df = pd.DataFrame(data)

pivot_df = df.pivot_table(index='Month', columns='Fruit', values='Weight')

print(pivot_df)

Output:

Fruit   Kiwi  Orange
Month               
1-2020   0.9     0.2
2-2020   1.4     2.1

If you want Month as a column instead of the index:

pivot_df.reset_index(inplace=True)
pivot_df.columns.name = None

print(pivot_df)

Output:

    Month  Kiwi  Orange
0  1-2020   0.9     0.2
1  2-2020   1.4     2.1

If there are duplicate combinations of Month and Fruit in the original data, and you want to sum them (for example):

data = {
    'Month': ['1-2020', '1-2020', '1-2020', '2-2020', '2-2020'],
    'Fruit': ['Orange', 'Kiwi', 'Kiwi', 'Orange', 'Kiwi'],
    'Weight': [0.2, 0.9, 1.1, 2.1, 1.4]
}
df = pd.DataFrame(data)

pivot_df = df.pivot_table(index='Month', columns='Fruit', values='Weight', aggfunc='sum')
print(pivot_df)

Output:

Fruit   Kiwi  Orange
Month               
1-2020   2.0     0.2
2-2020   1.4     2.1