Day 1: Introduction to Python for Data
Science
Objective:
● Learn Python basics, NumPy, and Pandas for Data Science.
● Perform hands-on exercises using Google Colab.
● Understand the real-world application of each concept.
1️⃣Google Colab Setup
�� Scenario:
You have joined a data science team that uses Google Colab for analysis. Your first task is
to set up the environment and test a simple script.
1.1 Open Google Colab and Create a Notebook
● Go to Google Colab
● Click File → New Notebook
● Rename the notebook (e.g., Day1_Python_for_DS).
1.2 Print a Simple Statement
�� Activity: Writing and executing a basic Python script.
print("Hello, Excited to start Data Science.")
�� Explanation: This prints a simple message to verify that our Python environment is
working. �� Output:
Hello, Excited to start Data Science.
2️⃣Python Basics
�� Scenario:
You're building a customer profile system for an e-commerce company. You need to
store customer details, calculate discounts, and analyze data.
2.1 Variable Assignment (Storing Customer Data)
�� Activity: Creating and storing customer information using Python variables.
customer_name = "John Doe"
customer_age = 28
customer_balance = 120.75
print("Customer:", customer_name)
print("Age:", customer_age)
print("Balance:", customer_balance)
�� Explanation:
● customer_name holds a string value.
● customer_age holds an integer.
● customer_balance holds a floating-point number.
�� Output:
Customer: John Doe
Age: 28
Balance: 120.75
2.2 Arithmetic Operations (Applying Discount)
�� Activity: Performing basic mathematical operations to calculate a product discount.
product_price = 200
discount = product_price * 0.10 # 10% discount
final_price = product_price - discount
print("Final Price after Discount:", final_price)
�� Explanation:
● product_price stores the original price.
● discount calculates 10% of the price.
● final_price computes the new price after discount.
�� Output:
Final Price after Discount: 180.0
3️⃣NumPy Basics
�� Scenario:
You are analyzing the daily sales of an online store over a week using
NumPy. 3.1 Create NumPy Arrays (Sales Data)
�� Activity: Storing daily sales data in a NumPy array.
import numpy as np
sales = np.array([150, 200, 250, 300, 400, 350, 500]) # Sales for
each day
print("Sales Data:", sales)
�� Explanation:
● The np.array() function creates an array that holds daily sales figures.
�� Output:
Sales Data: [150 200 250 300 400 350 500]
3.2 Statistical Analysis (Sales Performance)
�� Activity: Calculating mean, maximum, and minimum sales.
print("Average Sales:", np.mean(sales))
print("Highest Sale:", np.max(sales))
print("Lowest Sale:", np.min(sales))
�� Explanation:
● np.mean(sales): Calculates the average sales.
● np.max(sales): Finds the highest sales figure.
● np.min(sales): Identifies the lowest sales figure.
�� Output:
Average Sales: 307.14
Highest Sale: 500
Lowest Sale: 150
4️⃣Pandas Basics
�� Scenario:
Your company stores customer purchases in a CSV file, and you need to analyze it using
Pandas.
4.1 Create a DataFrame (Customer Transactions)
�� Activity: Creating a table-like structure using Pandas.
import pandas as pd
data = {
"Customer": ["Alice", "Bob", "Charlie"],
"Age": [25, 30, 35],
"Amount Spent": [120, 200, 150]
}
df = pd.DataFrame(data)
print(df)
�� Explanation:
● data dictionary holds customer details.
● pd.DataFrame(data) converts the dictionary into a structured table.
�� Output:
Customer Age Amount Spent
0 Alice 25 120
1 Bob 30 200
2 Charlie 35 150
4.2 Load and View a CSV File
�� Activity: Uploading and reading a CSV file in Pandas.
from google.colab import files
uploaded = files.upload() # Upload your CSV file
df = pd.read_csv("customer_data.csv") # Replace with your file name
df.head()
�� Explanation:
● files.upload() opens a file picker to upload a CSV.
● pd.read_csv() loads the file into a Pandas DataFrame.
5️⃣Data Manipulation with Pandas
�� Scenario:
You need to analyze customer spending habits.
5.1 Filter High-Spending Customers
�� Activity: Selecting customers who spent more than $150.
high_spenders = df[df["Amount Spent"] > 150]
print(high_spenders)
�� Explanation:
● df[df["Amount Spent"] > 150] filters rows where spending is over $150.
5.2 Sorting Customers by Spending
�� Activity: Sorting customers from highest to lowest spending.
df_sorted = df.sort_values(by="Amount Spent", ascending=False)
print(df_sorted)
�� Explanation:
● sort_values(by="Amount Spent", ascending=False) arranges data from
highest to lowest spender.
5.3 Add a New Column (Loyalty Points Calculation)
�� Activity: Adding Loyalty Points based on spending.
df["Loyalty Points"] = df["Amount Spent"] // 10
print(df)
�� Explanation:
● df["Loyalty Points"] = df["Amount Spent"] // 10 assigns 1 point per $10
spent.
6️⃣Saving Processed Data
�� Scenario:
Your processed data must be saved and shared with the marketing
team. �� Activity: Exporting cleaned data as a CSV file.
df.to_csv("cleaned_customer_data.csv", index=False)
files.download("cleaned_customer_data.csv") # Download the file
�� Explanation:
● to_csv() saves the DataFrame as a CSV file.
● files.download() lets you download the file to your computer.
Summary
✔ Set up Google Colab
✔ Used Python for basic operations
✔ Analyzed sales data with NumPy
✔ Processed customer data using Pandas
✔ Filtered and saved insights
