Day 4: Data Analysis with Pandas | Python for AI Course

What You'll Build Today

Load a real-world dataset (a public sales CSV), perform filtering and grouping analysis on it, build two visualizations with matplotlib, and write out 3 genuine insights as a summary. The workflow data analysts do manually every day — automated.

Setup

Installing pandas and matplotlib

bash

$ pip install pandas matplotlib

Then grab a dataset to work with. The Superstore sales dataset is a classic — it has orders, regions, categories, and revenue, which makes it ideal for practicing analysis.

pythonload_data.py

import pandas as pd

# Load a CSV into a DataFrame
df = pd.read_csv("superstore.csv")

# First look at the data
print(df.head())       # first 5 rows
print(df.shape)        # (rows, columns)
print(df.columns.tolist())  # column names
print(df.dtypes)       # data type of each column
print(df.describe())   # stats: mean, std, min, max

# Check for missing values
print(df.isnull().sum())

Filter & Group

Filtering, Grouping, and Aggregating

pythonanalysis.py

import pandas as pd

df = pd.read_csv("superstore.csv")

# Filter rows
tech_orders = df[df["Category"] == "Technology"]
big_orders = df[df["Sales"] > 1000]

# Multiple conditions
west_tech = df[(df["Region"] == "West") & (df["Category"] == "Technology")]

# GroupBy: total sales by region
regional_sales = df.groupby("Region")["Sales"].sum().sort_values(ascending=False)
print(regional_sales)

# Multiple aggregations
summary = df.groupby("Category").agg({
    "Sales": ["sum", "mean"],
    "Profit": "sum",
    "Order ID": "count"
})
print(summary)

# Top 10 most profitable products
top_products = df.groupby("Product Name")["Profit"].sum().nlargest(10)
print(top_products)

# Profit margin by segment
df["profit_margin"] = df["Profit"] / df["Sales"]
print(df.groupby("Segment")["profit_margin"].mean())

Visualize

Basic Visualization with Matplotlib

pythoncharts.py

import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv("superstore.csv")

# Bar chart: sales by category
category_sales = df.groupby("Category")["Sales"].sum()
plt.figure(figsize=(8, 5))
category_sales.plot(kind="bar", color="#1e3a5f")
plt.title("Total Sales by Category")
plt.xlabel("Category")
plt.ylabel("Sales ($)")
plt.xticks(rotation=0)
plt.tight_layout()
plt.savefig("sales_by_category.png")
plt.show()

# Scatter: sales vs profit
plt.figure(figsize=(8, 5))
plt.scatter(df["Sales"], df["Profit"], alpha=0.3, color="#c4873e")
plt.title("Sales vs Profit")
plt.xlabel("Sales ($)")
plt.ylabel("Profit ($)")
plt.tight_layout()
plt.savefig("sales_vs_profit.png")
plt.show()

Tomorrow: You'll replace the manual "find insights" step with Claude. Instead of staring at charts, you'll feed the numbers to the API and get written analysis back automatically.

Today's Exercise

Analyze a Real Dataset — Find 3 Insights

Download the Superstore CSV from Kaggle (free account needed) or use any CSV you have
Load it with pandas and run .describe() to understand the shape
Write 3 groupby queries that each answer a specific business question
Build 2 charts using matplotlib and save them as PNG files
Write your 3 insights as Python comments next to the code that revealed them

What You Learned Today

Loading CSVs into pandas DataFrames and inspecting their structure
Filtering rows with boolean conditions and multiple conditions
Groupby + aggregate: sum, mean, count, nlargest — the core analysis toolkit
Building bar charts and scatter plots with matplotlib
Creating new computed columns from existing ones

Course Progress

Day 4 of 5 — 80%

Day 4 Complete

Day 5: Build an AI-Powered Data Pipeline

Tomorrow you combine everything — files, APIs, pandas, and Claude — into one end-to-end pipeline that runs itself.

Start Day 5

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