Restaurant Tips Analysis with Pandas
Budget: $30 – $250 USD
Create a Jupyter notebook that analyzes restaurant tip data using Pandas. Use the famous "tips" dataset to help a restaurant owner understand tipping patterns and customer behavior.
Key Requirements:
- Jupyter Notebook: Create a well-organized notebook with markdown cells explaining each step.
- Data Loading: Load the built-in tips dataset using seaborn:
1. import seaborn as sns
2. tips = sns.load_dataset('tips')
- Basic Data Exploration: Use Pandas methods to:
1. Display basic info about the dataset (shape, columns, data types)
2. Show the first and last few rows using .head() and .tail()
3. Check for any missing values using .isnull()
- Simple Analysis: Calculate and display:
1. Average tip amount and total bill
2. Tips by day of the week (which day gets the highest tips?)
3. Tips by meal time (lunch vs dinner)
4. Average tip percentage (tip/total_bill * 100)
- Basic Visualization: Create 2-3 simple plots using pandas plotting:
1. Bar chart showing average tips by day
2. Histogram of tip amounts
- Summary: Include markdown cells with insights about tipping patterns.
Deliverables:
A Jupyter notebook (.ipynb file) that:
- Loads the tips dataset using seaborn
- Uses proper markdown documentation between code sections
- Performs basic data analysis with Pandas
- Creates simple visualizations
- Includes clear explanations and business insights
Estimate budget: $50
Key Requirements:
- Jupyter Notebook: Create a well-organized notebook with markdown cells explaining each step.
- Data Loading: Load the built-in tips dataset using seaborn:
1. import seaborn as sns
2. tips = sns.load_dataset('tips')
- Basic Data Exploration: Use Pandas methods to:
1. Display basic info about the dataset (shape, columns, data types)
2. Show the first and last few rows using .head() and .tail()
3. Check for any missing values using .isnull()
- Simple Analysis: Calculate and display:
1. Average tip amount and total bill
2. Tips by day of the week (which day gets the highest tips?)
3. Tips by meal time (lunch vs dinner)
4. Average tip percentage (tip/total_bill * 100)
- Basic Visualization: Create 2-3 simple plots using pandas plotting:
1. Bar chart showing average tips by day
2. Histogram of tip amounts
- Summary: Include markdown cells with insights about tipping patterns.
Deliverables:
A Jupyter notebook (.ipynb file) that:
- Loads the tips dataset using seaborn
- Uses proper markdown documentation between code sections
- Performs basic data analysis with Pandas
- Creates simple visualizations
- Includes clear explanations and business insights
Estimate budget: $50