Python CSV & Data Analysis

In this page:

Reading CSV Files
Writing CSV Files
CSV Data Filtering
Aggregating CSV Metrics
Reading CSV with Pandas

Reading CSV Files

The built-in csv module reads comma-separated data row by row without needing pandas as a dependency. This tutorial uses io.StringIO to simulate an in-memory CSV file, so the examples run without needing an actual file on disk -- in real code you'd typically open a real file and pass its handle instead.

Example: Reading CSV Files

python

import csv
import io

text = "name,age\nAlex,30\nSam,25\n"
reader = csv.reader(io.StringIO(text))
for row in reader:
    print(row)

Writing CSV Files

csv.writer writes individual rows as plain lists of values, while csv.DictWriter writes rows from dictionaries and requires you to specify the column headers (fieldnames) up front. DictWriter is usually clearer when your data already exists as a list of dictionaries rather than parallel lists.

Example: Writing CSV Files

python

import csv
import io

buffer = io.StringIO()
writer = csv.DictWriter(buffer, fieldnames=["name", "age"])
writer.writeheader()
writer.writerow({"name": "Alex", "age": 30})
print(buffer.getvalue())

CSV Data Filtering

Filtering CSV data means iterating over the parsed rows and checking a condition on each one, keeping only the rows that pass -- the same pattern as filtering any other list of records, just applied to rows read from a file rather than data already in memory.

Example: CSV Data Filtering

python

import csv
import io

text = "name,age\nAlex,30\nSam,17\n"
reader = csv.DictReader(io.StringIO(text))
adults = [row for row in reader if int(row["age"]) >= 18]
print(adults)

Aggregating CSV Metrics

Once rows are parsed into numbers (remember: csv values arrive as strings and need explicit conversion), you can compute sums, averages, or other aggregate metrics with ordinary Python loops or the statistics module, the same way you would for any other numeric dataset.

Example: Aggregating CSV Metrics

python

import csv
import io

text = "name,score\nAlex,80\nSam,90\n"
reader = csv.DictReader(io.StringIO(text))
scores = [int(row["score"]) for row in reader]
print(sum(scores) / len(scores))

Reading CSV with Pandas

pandas.read_csv() loads an entire CSV file directly into a DataFrame in one call, handling type inference, header detection, and missing values automatically. For anything beyond very simple parsing, this is far less code than manually working with the csv module row by row.

Example: Reading CSV with Pandas

python

import pandas as pd
import io

text = "name,age\nAlex,30\nSam,25\n"
df = pd.read_csv(io.StringIO(text))
print(df)

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Python CSV & Data Analysis

Reading CSV Files

Writing CSV Files

CSV Data Filtering

Aggregating CSV Metrics

Reading CSV with Pandas

Chapter Quiz — Complete all 14 topics to unlock

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