Python for Data Science

NumPy, Pandas, data analysis, and visualization basics.

Overview

Learn data science fundamentals with Python.

NumPy Basics

import numpy as np

# Create arrays
arr = np.array([1, 2, 3, 4, 5])
print(arr)  # [1 2 3 4 5]

# 2D array
matrix = np.array([[1, 2, 3], [4, 5, 6]])
print(matrix)

# Array operations
print(arr * 2)        # [2 4 6 8 10]
print(arr + 10)       # [11 12 13 14 15]
print(np.mean(arr))   # 3.0
print(np.sum(arr))    # 15

# Random numbers
random_arr = np.random.rand(5)
print(random_arr)

Pandas Basics

import pandas as pd

# Create DataFrame
data = {
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Age': [25, 30, 35],
    'City': ['New York', 'London', 'Paris']
}
df = pd.DataFrame(data)
print(df)

# Read CSV
df = pd.read_csv('data.csv')

# Basic operations
print(df.head())      # First 5 rows
print(df.describe())  # Statistics
print(df.info())      # Column info

# Filtering
adults = df[df['Age'] > 25]
print(adults)

# Grouping
city_counts = df.groupby('City').size()
print(city_counts)

Practice

Analyze a dataset using Pandas and create visualizations.