How to Parse and Analyze CSV Data in Python

Introduction

CSV (Comma-Separated Values) files are common in data science. Using simple string operations and lists/dictionaries, I processed .csv files like iris.csv and mpg.csv to extract useful insights.

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Reading and Parsing a CSV File

file = open("iris.csv", "r")
lines = file.readlines()
file.close()

for line in lines[1:]:
    values = line.strip().split(",")
    print(values)

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Converting to Dictionary for Easier Use

irisDict = {
    "sepal_length": values[0],
    "sepal_width": values[1],
    "petal_length": values[2],
    "petal_width": values[3],
    "species": values[4]
}

This structure makes it easier to calculate averages or filter data.

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What I learned

• .readlines() + .strip().split(",") is a basic but powerful way to parse CSVs.
• Dictionaries are useful for organizing row data by column names.
• Loops help calculate statistics like averages or category counts.

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What I want to do next

• Try using the built-in csv module for more robust handling.
• Apply filtering and grouping logic to larger datasets.