2/22/2026 · 3 min read

Getting Started with Pandas

A Minimal, Practical Guide for Python Developers

Pandas is the go-to Python library for working with structured, tabular data — think spreadsheets, CSVs, or SQL tables, but in code. It sits at the heart of most data science and machine learning workflows.

Installation

pip install pandas

Core Data Structures

Pandas has two primary building blocks:

Series

import pandas as pd

s = pd.Series([10, 20, 30], index=['a', 'b', 'c'])
print(s['b'])  # 20

DataFrame

df = pd.DataFrame({
    'name':  ['Alice', 'Bob', 'Charlie'],
    'age':   [25, 30, 35],
    'score': [88.5, 92.0, 79.5]
})

Selecting Data

df['name']            # single column → Series
df[['name', 'age']]  # multiple columns → DataFrame

df.loc[0]             # row by label / index
df.iloc[1]            # row by integer position

df[df['age'] > 28]    # filter rows by condition

Exploring a DataFrame

df.head(2)       # first N rows
df.tail(2)       # last N rows
df.shape         # (rows, cols)
df.dtypes        # column data types
df.describe()    # statistical summary

Common Operations

df['age'].mean()
df['score'].max()

# Group and aggregate
df.groupby('name')['score'].mean()

Adding & Modifying Columns

# Derive a new column
df['grade'] = df['score'].apply(lambda x: 'A' if x >= 90 else 'B')

# Modify in place
df['age'] = df['age'] + 1

Handling Missing Data

df.isnull().sum()   # count NaNs per column
df.dropna()         # drop rows containing any NaN
df.fillna(0)        # replace NaN with a default value

Reading & Writing Files

# Reading
df = pd.read_csv('data.csv')
df = pd.read_json('data.json')
df = pd.read_excel('data.xlsx')

# Writing
df.to_csv('output.csv', index=False)

Sorting & Merging

# Sort
df.sort_values('score', ascending=False)

# SQL-style JOIN
merged = pd.merge(df1, df2, on='id', how='inner')  # also: left, right, outer

# Stack rows
combined = pd.concat([df1, df2], ignore_index=True)

Mental Model (coming from C / plain Python)

Key Takeaway

Think column-first and vectorized. Avoid Python for loops over rows — instead, let pandas operate on entire columns at once. It’s faster, more readable, and more idiomatic.

Next step: pair pandas with NumPy for numerical computing, or feed your cleaned DataFrames into PyTorch / scikit-learn for machine learning.