遍历CSV文件并创建表

Question

I'm trying to read in a .csv file and extract specific columns so that I can output a single table that essentially performs a 'GROUP BY' on a particular column and aggregates certain other columns of interest (similar to how you would in SQL) but I'm not too familiar how to do this easily in Python.

The csv file is in the following form:

age,education,balance,approved
30,primary,1850,yes
54,secondary,800,no
24,tertiary,240,yes

I've tried to import and read in the csv files to parse the three columns I care about and iterate through them to put them into three separate array lists. I'm not too familiar with packages and how to get these into a data frame or matrix with 3 columns so that I can then iterate through them mutate or perform all of the aggregated output field (see below expected results).

with open('loans.csv') as csvfile:
    readCSV = csv.reader(csvfile, delimiter = ',')

    next(readCSV)  ##skips header row

    education = []
    balance = []
    loan_approved = []

    for row in readCSV:
        educat = row[1]
        bal = row[2]
        approve = row[3]

        education.append(educat)
        balance.append(bal)
        loan_approved.append(approve)

    print(education)
    print(balance)
    print(loan_approved)

The output would be a 4x7 table of four rows (grouped by education level) and the following headers:

Education|#Applicants|Min Bal|Max Bal|#Approved|#Rejected|%Apps Approved
Primary  ...
Secondary  ...
Terciary ...

Answer 1

It seems to be much simpler by using Pandas instead. For instance, you can read only the columns that you care for instead of all of them:

import Pandas as pd

df = pd.read_csv(usecols=['education', 'balance', 'loan_approved'])

Now, to group by education level, you can find all the unique entries for that column and group them:

groupby_education = {}
for level in list(set(df['education'])):
    groupby_education[level] = df.loc[df['education'] == level]

print(groupby_education)

I hope this helped. Let me know if you still need help. Cheers!