Google Colab as alternative in Data Analysis Python - Mini Project: Build & Apply

Using Google Colab as an Alternative for Data Analysis

📖 Scenario: You want to analyze sales data but do not have Python installed on your computer. Google Colab is a free online tool that lets you write and run Python code in your browser without installation.In this project, you will create a simple sales data dictionary, set a sales threshold, filter the sales above that threshold, and print the filtered results using Google Colab.

🎯 Goal: Build a small Python program in Google Colab that stores sales data, sets a sales threshold, filters sales above the threshold, and prints the filtered sales.

📋 What You'll Learn

Create a dictionary called sales with exact product names and sales numbers

Create a variable called threshold with a specific sales number

Use a dictionary comprehension to filter sales for products with sales above threshold

Print the filtered dictionary

💡 Why This Matters

🌍 Real World

Filtering sales data helps businesses focus on products that sell well. Google Colab allows you to do this analysis anywhere without installing software.

💼 Career

Data analysts and scientists often use online tools like Google Colab to quickly explore and analyze data before sharing results with teams.

1

Create the sales data dictionary

Create a dictionary called sales with these exact entries: 'Apples': 150, 'Bananas': 90, 'Cherries': 120, 'Dates': 60, 'Elderberries': 30.

Data Analysis Python

# Create the sales dictionary with exact product names and sales numbers
# Your code here

Hint

Use curly braces {} to create a dictionary. Separate keys and values with a colon :. Separate items with commas.

2

Set the sales threshold

Create a variable called threshold and set it to 100.

Data Analysis Python

sales = {'Apples': 150, 'Bananas': 90, 'Cherries': 120, 'Dates': 60, 'Elderberries': 30}
# Create the threshold variable and set it to 100
# Your code here

Hint

Use a simple assignment statement to create the variable threshold.

3

Filter sales above the threshold

Use a dictionary comprehension to create a new dictionary called filtered_sales that contains only products from sales with sales greater than threshold. Use for product, amount in sales.items() in your comprehension.

Data Analysis Python

sales = {'Apples': 150, 'Bananas': 90, 'Cherries': 120, 'Dates': 60, 'Elderberries': 30}
threshold = 100
# Create filtered_sales dictionary using dictionary comprehension
# Your code here

Hint

Dictionary comprehension syntax: {key: value for key, value in dict.items() if condition}.

4

Print the filtered sales

Print the filtered_sales dictionary.

Data Analysis Python

sales = {'Apples': 150, 'Bananas': 90, 'Cherries': 120, 'Dates': 60, 'Elderberries': 30}
threshold = 100
filtered_sales = {product: amount for product, amount in sales.items() if amount > threshold}
# Print the filtered_sales dictionary
# Your code here

Hint

Use print(filtered_sales) to display the filtered dictionary.

Practice

1.

What is the main advantage of using Google Colab for data analysis?

easy

A. It requires expensive software licenses.

B. It only works with JavaScript code.

C. It allows running Python code without installing anything on your computer.

D. It can only be used offline.

Solution

1. Step 1: Understand what Google Colab offers

   Google Colab is a cloud-based tool that lets you write and run Python code online.

2. Step 2: Identify the key benefit for beginners

   Since it runs in the cloud, you don't need to install Python or any software on your computer.

3. Final Answer:

   It allows running Python code without installing anything on your computer. -> Option C

4. Quick Check:

   Cloud-based Python execution = It allows running Python code without installing anything on your computer. [OK]

Hint: Remember: Colab runs code online, no install needed [OK]

Common Mistakes:

• Thinking Colab only runs JavaScript
• Assuming Colab requires paid licenses
• Believing Colab works offline only

2.

Which of the following is the correct way to start a new Python notebook in Google Colab?

1. Open colab.research.google.com
2. Click on 'File' menu
3. Select which option?

easy

A. Open Notebook

B. New Python 3 Notebook

C. Download Notebook

D. Upload Notebook

Solution

1. Step 1: Navigate to Google Colab website

   Open the URL colab.research.google.com in your browser.

2. Step 2: Use the File menu to create a new notebook

   Under the 'File' menu, the option to start a new Python notebook is 'New Python 3 Notebook'.

3. Final Answer:

   New Python 3 Notebook -> Option B

4. Quick Check:

   New notebook creation = New Python 3 Notebook [OK]

Hint: Look for 'New Python 3 Notebook' under File menu [OK]

Common Mistakes:

• Choosing 'Open Notebook' which opens existing files
• Selecting 'Download Notebook' which saves locally
• Picking 'Upload Notebook' which uploads files

3.

What will be the output of the following code run in Google Colab?

import pandas as pd

data = {'Name': ['Anna', 'Bob'], 'Age': [28, 34]}
df = pd.DataFrame(data)
df.head()

medium

A. A list of names only.

B. An error because pandas is not installed in Colab.

C. An empty DataFrame with no rows.

D. A table showing the first two rows with columns 'Name' and 'Age'.

Solution

1. Step 1: Understand pandas DataFrame creation

   The code creates a DataFrame with two columns: 'Name' and 'Age', and two rows with data.

2. Step 2: Use df.head() to display the first rows

   Calling df.head() shows the first 5 rows, but since there are only 2 rows, it shows both.

3. Final Answer:

   A table showing the first two rows with columns 'Name' and 'Age'. -> Option D

4. Quick Check:

   DataFrame head shows data rows = A table showing the first two rows with columns 'Name' and 'Age'. [OK]

Hint: df.head() shows first rows of your DataFrame [OK]

Common Mistakes:

• Thinking pandas is not pre-installed in Colab
• Expecting an error from this simple code
• Assuming df.head() returns empty or partial data

4.

Identify the error in this Google Colab code snippet:

import matplotlib.pyplot as plt

plt.plot([1, 2, 3], [4, 5])
plt.show()

medium

A. The lists have different lengths causing a ValueError.

B. matplotlib is not supported in Google Colab.

C. plt.show() is missing parentheses.

D. No error; the plot will display correctly.

Solution

1. Step 1: Check the input lists to plt.plot()

   The x-values list has 3 elements, but the y-values list has only 2 elements.

2. Step 2: Understand matplotlib's requirement

   Both lists must be the same length to plot points; otherwise, a ValueError occurs.

3. Final Answer:

   The lists have different lengths causing a ValueError. -> Option A

4. Quick Check:

   List length mismatch = ValueError = The lists have different lengths causing a ValueError. [OK]

Hint: Check if x and y lists have same length before plotting [OK]

Common Mistakes:

• Assuming matplotlib is unsupported in Colab
• Forgetting plt.show() needs parentheses
• Thinking plot works with unequal list lengths

5.

You want to share your data analysis project with a friend who does not have Python installed. How can Google Colab help you?

hard

A. By letting you save your notebook in Google Drive and share the link.

B. By requiring your friend to install Python first.

C. By exporting the notebook only as a PDF without code.

D. By sending the raw Python files via email.

Solution

1. Step 1: Understand sharing options in Google Colab

   Colab notebooks are saved in Google Drive and can be shared via links easily.

2. Step 2: Compare with other sharing methods

   Sharing a link allows your friend to open and run the notebook online without installing Python.

3. Final Answer:

   By letting you save your notebook in Google Drive and share the link. -> Option A

4. Quick Check:

   Sharing via Drive link = By letting you save your notebook in Google Drive and share the link. [OK]

Hint: Share your notebook link from Google Drive for easy access [OK]

Common Mistakes:

• Thinking friend must install Python to run notebook
• Believing PDF export shares runnable code
• Assuming raw files emailed are easiest way