Group Data From A Csv File By Field Value

I have a csv file which has duplicate value in first column . I want to collect all value of second column in a list for one value of first column column1 column2 a 54.2 s

Solution 1:

It seems fairly straightforward using python's CSV reader.

data.csv

a,54.2
s,78.5
k,89.62a,77.2a,65.56

script.py

import csv

result = {}

withopen('data.csv', 'rb') as csvfile:
    csvreader = csv.reader(csvfile, delimiter=',', quotechar='"')
    for row in csvreader:
        if row[0] in result:
            result[row[0]].append(row[1])
        else:
            result[row[0]] = [row[1]]

print result

output

{
    'a': ['54.2', '77.2', '65.56'], 
    's': ['78.5'], 
    'k': ['89.62']
}

---

As @Pete poined out, you can beautify it using defaultdict:

script.py

import csv
from collections import defaultdict

result= defaultdict(list)  # each entry of the dict is, bydefault, an empty list

withopen('data.csv', 'rb') as csvfile:
    csvreader = csv.reader(csvfile, delimiter=',', quotechar='"')
    forrowin csvreader:
        result[row[0]].append(row[1])

print result

Solution 2:

One way of doing this is by using pandas, populate a dataframe, use groupby and then apply list to all the groups:

import pandas as pd

df = pd.DataFrame({'column1':['a','s','k','a','a'],'column2': 
[54.2,78.5,89.62,77.2,65.56]})
print(df.groupby('column1')['column2'].apply(list))

output:

column1a[54.2, 77.2, 65.56]k[89.62]s[78.5]Name: column2, dtype: object

Solution 3:

Similar approach I tried Use groupby with apply and last convert Series to json by Series.to_json

Input

df = pd.DataFrame({'column1':['a','s','k','a','a'],'column2':[54.2,78.5,89.62,77.2,65.56]})

inputData

column1  column2
0a54.201       s    78.502       k    89.623a77.204a65.56

Answer :

jsonData = df.groupby('column1')['column2'].apply(list)
print(jsonData.to_json())
# if you want write a file into json
jsonData.to_json(r"D:/abc/def/xyz.json")

Desired output

{"a":[54.2,77.2,65.56],"k":[89.62],"s":[78.5]}