Python has become the most popular programming language for data analytics. Its ecosystem of libraries makes tasks like data preparation, computation, and visualization simple and efficient. In this lesson, we explore Pandas, NumPy, and Matplotlib.
Pandas is the go-to library for handling structured datasets. It introduces Series (1D data) and DataFrames (2D data like tables).
import pandas as pd
# Load a CSV
df = pd.read_csv("sales.csv")
# Inspect data
print(df.head())
# Group and summarize
monthly_sales = df.groupby("Month")["Revenue"].sum()
print(monthly_sales)
Example: A retail analyst uses Pandas to group sales data by month to track revenue trends.
NumPy provides fast array and matrix computations, essential for numerical analysis. Many analytics and ML libraries (Pandas, Scikit-learn) are built on top of NumPy.
import numpy as np
# Create an array
arr = np.array([10, 20, 30, 40])
# Basic math
print(arr.mean()) # Average
print(arr.std()) # Standard deviation
# Vectorized operations
print(arr * 2)
Example: A financial analyst uses NumPy to calculate standard deviation of daily returns for risk assessment.
Matplotlib is Python’s base plotting library, used for creating line charts, bar charts, scatter plots, and more. It forms the foundation for other libraries like Seaborn.
import matplotlib.pyplot as plt
months = ["Jan", "Feb", "Mar", "Apr"]
revenue = [12000, 15000, 18000, 17000]
plt.plot(months, revenue, marker="o", color="gold")
plt.title("Monthly Revenue")
plt.xlabel("Month")
plt.ylabel("Revenue ($)")
plt.show()
Example: A manager visualizes monthly revenue trends to identify growth and seasonal patterns.
A typical analytics workflow looks like this: