Reading CSV files in Python

1. Basic CSV Reading with csv.reader

import csv

with open("data.csv", "r") as file:
    reader = csv.reader(file)
    for row in reader:
        print(row)

Reads each row as a list of string values.

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2. Skipping Header Row

import csv

with open("data.csv", "r") as file:
    reader = csv.reader(file)
    header = next(reader)
    print("Header:", header)

    for row in reader:
        print(row)

Useful when the first row contains column names.

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3. Reading CSV as Dictionary (DictReader)

import csv

with open("data.csv", "r") as file:
    reader = csv.DictReader(file)
    for row in reader:
        print(row)

Each row is returned as an ordered dictionary mapped to column headers.

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4. Accessing Specific Columns

import csv

with open("data.csv", "r") as file:
    reader = csv.DictReader(file)
    for row in reader:
        print(row["name"], row["age"])

Enables column-based data processing.

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5. Reading CSV with Custom Delimiter

import csv

with open("data_pipe.csv", "r") as file:
    reader = csv.reader(file, delimiter="|")
    for row in reader:
        print(row)

Handles CSV files that use non-standard separators.

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6. Reading Large CSV Files Efficiently

import csv

with open("large_data.csv", "r") as file:
    reader = csv.reader(file)
    for row in reader:
        process = row  # Lazy iteration prevents memory overload

Iterative reading avoids loading the entire file into memory.

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7. Converting CSV Data Types

import csv

with open("data.csv", "r") as file:
    reader = csv.DictReader(file)
    for row in reader:
        age = int(row["age"])
        salary = float(row["salary"])
        print(age, salary)

CSV values are strings by default and often require type casting.

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8. Reading CSV with Error Handling

import csv

try:
    with open("data.csv", "r") as file:
        reader = csv.reader(file)
        for row in reader:
            print(row)
except FileNotFoundError:
    print("CSV file not found.")

Ensures robustness in production code.

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9. Reading CSV into a List

import csv

with open("data.csv", "r") as file:
    reader = csv.reader(file)
    data = list(reader)

print(data)

Loads the entire CSV into memory (use cautiously for large files).

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10. Reading CSV Using pandas (Structured Approach)

import pandas as pd

df = pd.read_csv("data.csv")

print(df)
print(df["name"])

Pandas offers a high-level interface for complex data analysis and transformations.

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