Read in a specific way from a csv file with pandas python

March 21, 2022

I have a data in a csv file here is a sample:

firstnb,secondnb,distance
901,19011,459.73618164837535
901,19017,492.5540450352788
901,19018,458.489289271722
903,13019,167.46632044684435
903,13020,353.16001204909657

the desired output:

901,19011,19017,19018
903,13019,13020

As you can see in the output I want to take firstnb column (901/903)

and put beside each one the secondnb I believe you can understand from the desired output better than my explanation 😀

What I tried so far is the following:

import pandas as pd
import csv
df = pd.read_csv('test.csv')
    with open('neighborList.csv','w',newline='') as file:
        writer = csv.writer(file)
        secondStation = []
        for row in range(len(df)):
            firstStation = df['firstnb'][row]
            for x in range(len(df)):
                if firstStation == df['firstnb'][x]:
                    secondStation.append(df['secondnb'][x])
                    # line = firstStation ,secondStation
                    # writer.writerow(line)        
            print(firstStation,secondStation)
            secondStation = []

my code output this :

901 [19011, 19017, 19018]
901 [19011, 19017, 19018]
901 [19011, 19017, 19018]
903 [13019, 13020]
903 [13019, 13020]

>Solution :

Pandas has a built in function to do this, called groupby:

df = pd.read_csv(YOUR_CSV_FILE) 
df_grouped = list(df.groupby(df['firstnb'])) # group by first column

# chain keys and values into merged list
for key, values in df_grouped:
    print([key] + values['secondnb'].tolist())

Here I just print the sublists; you can save them into a new csv in any format you’d like (strings, ints, etc)