[FIXED] Sum value in specific combinations of rows

Issue

I have the following dataframe:

import pandas as pd
import numpy as np
df1 = pd.DataFrame({'Name' : ['Jake', 'Nate', '', 'Alex', '', 'Max', 'Nate', 'Jake'],
                    'Color' : ['', 'red;blue', 'blue;pink', 'green;blue;red', '', '', 'blue', 'red;yellow'],
                    'Value_1' : [1211233.419, 4007489.726, 953474.6894, np.NaN, 1761987.704, 222600361, 404419.2243, 606066.067 ],
                    'Value_2' : [np.NaN, 1509907.457, 4792269.911, 43486.59312, np.NaN, np.NaN, 2066645.251, 60988660.37],
                    'Value_3' : [1175299.998, np.NaN, 1888559.459, np.NaN, 444689.0177, 405513.0572, 343704.0269, 2948494.383]})
---
   Name           Color       Value_1       Value_2       Value_3
0  Jake                  1.211233e+06           NaN  1.175300e+06
1  Nate        red;blue  4.007490e+06  1.509907e+06           NaN
2             blue;pink  9.534747e+05  4.792270e+06  1.888559e+06
3  Alex  green;blue;red           NaN  4.348659e+04           NaN
4                        1.761988e+06           NaN  4.446890e+05
5   Max                  2.226004e+08           NaN  4.055131e+05
6  Nate            blue  4.044192e+05  2.066645e+06  3.437040e+05
7  Jake      red;yellow  6.060661e+05  6.098866e+07  2.948494e+06

I need two things:

1)In the first case I need to add all the values (Value_1, Value_2, Value_3) where I have the same name and get for example:

   Name       Value_1       Value_2       Value_3
0  Jake  1.817299e+06  6.098866e+07  4.123794e+06
1  Nate  4.411909e+06  3.576553e+06  3.437040e+05
2  Alex           NaN  4.348659e+04           NaN
3   Max  2.226004e+08           NaN  4.055131e+05

2)I need the same thing but with the values of the name column plus the splits of the color column (only if there is at least one name and one color in the same row):

   Name           Color       Value_1       Value_2       Value_3
0  Alex           green           NaN  4.348659e+04           NaN
1  Alex            blue           NaN  4.348659e+04           NaN
3  Alex             red           NaN  4.348659e+04           NaN
4  Jake             red  6.060661e+05  6.098866e+07  2.948494e+06
5  Jake          yellow  6.060661e+05  6.098866e+07  2.948494e+06
6  Nate             red  4.007490e+06  1.509907e+06           NaN
7  Nate            blue  4.411909e+06  3.576553e+06  3.437040e+05

(Note that in this case the only line present twice is Nate-Blue)

Solution

First replace empty strings in first 2 columns to mising values:

df1[['Name','Color']] = df1[['Name','Color']].replace('', np.nan)

Then aggregate sum with min_count=1 for missing values instead 0:

df2 = df1.groupby('Name', as_index=False).sum(min_count=1)
print (df2)
   Name       Value_1       Value_2       Value_3
0  Alex           NaN  4.348659e+04           NaN
1  Jake  1.817299e+06  6.098866e+07  4.123794e+06
2   Max  2.226004e+08           NaN  4.055131e+05
3  Nate  4.411909e+06  3.576553e+06  3.437040e+05

For second ouput first use Series.str.split with DataFrame.explode and then aggregate sum:

df3 = (df1.assign(Color=df1['Color'].str.split(';'))
          .explode('Color')
          .groupby(['Name', 'Color'], as_index=False)
          .sum(min_count=1))
print (df3)
   Name   Color       Value_1       Value_2       Value_3
0  Alex    blue           NaN  4.348659e+04           NaN
1  Alex   green           NaN  4.348659e+04           NaN
2  Alex     red           NaN  4.348659e+04           NaN
3  Jake     red  6.060661e+05  6.098866e+07  2.948494e+06
4  Jake  yellow  6.060661e+05  6.098866e+07  2.948494e+06
5  Nate    blue  4.411909e+06  3.576553e+06  3.437040e+05
6  Nate     red  4.007490e+06  1.509907e+06           NaN

Answered By - jezrael