Getting Started with Pandas: The Power Tool for Data Analysis in Python

Introduction If you work with data, pandas is your best friend. It lets you manipulate, analyze, and visualize data with just a few lines of code. Installation pip install pandas Basic Objects Series – 1D array with labels DataFrame – 2D table with rows & columns Creating DataFrames import pandas as pd data = { 'Name': ['AF4', 'Shadow', 'Wolf'], 'Score': [90, 85, 95] } df = pd.DataFrame(data) print(df) Reading & Writing Files df = pd.read_csv('file.csv') df.to_csv('output.csv', index=False) DataFrame Operations print(df.describe()) # Stats summary print(df.head()) # First few rows df['Score'] = df['Score'] + 5 Filtering & Selection high_scores = df[df['Score'] > 90] print(high_scores) Grouping and Aggregation df.groupby('Name').mean() Conclusion pandas is the heart of Python data analysis. Once you get comfortable, you'll fly through any dataset like a pro.

Apr 5, 2025 - 07:52

If you work with data, pandas is your best friend. It lets you manipulate, analyze, and visualize data with just a few lines of code.

pip install pandas

import pandas as pd

data = {
    'Name': ['AF4', 'Shadow', 'Wolf'],
    'Score': [90, 85, 95]
}

df = pd.DataFrame(data)
print(df)

df = pd.read_csv('file.csv')
df.to_csv('output.csv', index=False)

print(df.describe())  # Stats summary
print(df.head())      # First few rows

df['Score'] = df['Score'] + 5

high_scores = df[df['Score'] > 90]
print(high_scores)

df.groupby('Name').mean()

pandas is the heart of Python data analysis. Once you get comfortable, you'll fly through any dataset like a pro.