Working with Data

Python is widely used for working with data because it has simple syntax and powerful libraries for analysis, visualization, and data processing.

In this guide you will learn how to:

• Load data
• Store data in Python structures
• Work with tabular data
• Filter and transform data
• Save results

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Reading Data From a CSV File

CSV (Comma Separated Values) files are one of the most common data formats.

Example CSV:

name,age,city
Alice,30,Denver
Bob,25,Boulder

This example shows a CSV (Comma-Separated Values) file, which is a simple format used to store tabular data.

The first line is the header row. It defines the column names for the data. In this case, the columns are:

• name
• age
• city

Commas separate each column value. Each comma marks the boundary between columns.

Each line after the header represents a row of data. Every row must follow the same column order and structure defined in the header.

Create a plain text file on your computer and save it as ‘people.csv’

Using Python’s built-in csv module:

import csv

with open("people.csv") as file:
    reader = csv.DictReader(file)

    for row in reader:
        print(row["name"], row["age"])

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Using Pandas for Data Analysis

The pandas library is the most popular tool for working with tabular data in Python.

Install pandas:

pip install pandas

Import pandas:

import pandas as pd

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Loading Data With Pandas

Load a CSV file:

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

View the first rows:

data.head()

Example output:

   name  age    city
0 Alice   30  Denver
1 Bob     25 Boulder

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Exploring Data

Check column names:

data.columns

Basic summary statistics:

data.describe()

View data types:

data.dtypes

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Selecting Columns

Select a single column:

data["age"]

Select multiple columns:

data[["name", "city"]]

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Filtering Data

Select rows matching conditions.

Example: people older than 25

filtered = data[data["age"] > 25]

Example: select a specific city

denver_people = data[data["city"] == "Denver"]

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Creating New Columns

You can calculate new data from existing columns.

Example:

data["age_next_year"] = data["age"] + 1

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Sorting Data

Sort by a column:

data.sort_values("age")

Sort descending:

data.sort_values("age", ascending=False)

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Grouping Data

Grouping summarizes data by categories.

Example dataset:

city,temperature
Denver,70
Denver,72
Boulder,68

Calculate average temperature by city:

data.groupby("city")["temperature"].mean()

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Handling Missing Data

Check missing values:

data.isnull()

Remove missing rows:

data.dropna()

Fill missing values:

data.fillna(0)

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Saving Data

Save a DataFrame to CSV:

data.to_csv("output.csv", index=False)

Save to Excel:

data.to_excel("output.xlsx")

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Working With JSON Data

JSON is commonly used in APIs.

Example JSON:

{
  "name": "Alice",
  "age": 30
}

Load JSON in Python:

import json

with open("data.json") as f:
    data = json.load(f)

Access values:

print(data["name"])

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Example Data Workflow

Example complete workflow:

import pandas as pd

# load data
data = pd.read_csv("sales.csv")

# filter rows
recent_sales = data[data["year"] == 2024]

# calculate totals
total = recent_sales["revenue"].sum()

print("Total revenue:", total)

# save results
recent_sales.to_csv("filtered_sales.csv", index=False)

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Summary

Python provides powerful tools for working with data:

• Lists and dictionaries store structured data
• CSV and JSON allow data exchange
• Pandas simplifies tabular analysis
• Filtering, grouping, and sorting reveal insights

These tools make Python one of the most widely used languages for data science, research, and analytics.