How do I get rid of duplicate values in pandas column?

I'll show my csv. I'm using pandas from python and tying to clean up my csv.

Here is my problem

I want my outcome to look like this

I know I just need to get rid of some duplicates I think, but I don't know how with pandas.

I tried some different appoaches like resetting indexes and sorting and using dropna functions, but they didn't seem to work.

You can use a combination of groupby and sum to de-duplicate your rows, using:

df.groupby('Name').sum()

Assuming your pandas dataframe is called df and there are no other columns in the dataframe.

Here's a working example:

import pandas as pd
import numpy as np

points = [np.NaN, 20, np.NaN, 1]
rebounds = [21, np.NaN, np.NaN, 300]
assists = [np.NaN, np.NaN, 3, np.NaN]
name = ['Andrew W', 'Andrew W', 'Andrew W', 'Hello World']

data = {
    'name': name,
    'points': points,
    'rebounds': rebounds,
    'assists': assists
}

df = pd.DataFrame(data)
print(df.to_markdown(index=False))

agg_df = df.groupby('name').sum()
print(agg_df.to_markdown())

DataFrame before aggregation

| name        |   points |   rebounds |   assists |
|:------------|---------:|-----------:|----------:|
| Andrew W    |      nan |         21 |       nan |
| Andrew W    |       20 |        nan |       nan |
| Andrew W    |      nan |        nan |         3 |
| Hello World |        1 |        300 |       nan |

DataFrame after aggregation

| name        |   points |   rebounds |   assists |
|:------------|---------:|-----------:|----------:|
| Andrew W    |       20 |         21 |         3 |
| Hello World |        1 |        300 |         0 |

You can do it with:

df = df.groupby('Name').sum()

Docs here: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html