Read a CSV in Python without losing track of your data

Before computing anything, inspect what the file actually contains. A CSV is plain text with rows and separators, but the separator and missing-value conventions can vary.

Start with a tiny example

Save this as scores.csv next to your Python script:

student,score
Ada,82
Ben,91
Cora,

Python's built-in csv module can read it without installing a package.

import csv

with open('scores.csv', newline='', encoding='utf-8') as file:
    reader = csv.DictReader(file)
    rows = list(reader)

print(reader.fieldnames)  # ['student', 'score']
print(rows[0])           # {'student': 'Ada', 'score': '82'}

CSV values arrive as strings. Convert the score only after checking for blanks:

for row in rows:
    score = int(row['score']) if row['score'] else None
    print(row['student'], score)

Check the separator

If the headers appear as one combined field, the file may use semicolons. Pass delimiter=';' to csv.DictReader. Inspect the first few lines in a text editor before assuming a format.

When the file grows

For larger analysis tasks, pandas provides useful inspection tools. Its read_csv function infers common missing values and types, but you should still check the result.

import pandas as pd

df = pd.read_csv('scores.csv')
print(df.head())
print(df.info())
print(df.isna().sum())

If you do not have pandas installed, use the standard-library example above. For your own file, verify the header names, row count, types, and missing values before plotting or calculating an average.