2020 New York Taxi Trips Analysis with Python

Welcome to the world of data-driven exploration of New York City's iconic yellow taxis! In this project, we will dive into the vast dataset of 2020 New York taxi trips using Python to gain valuable insights into the city's transportation patterns, peak hours, popular routes, and much more.

The yellow taxis are an integral part of the city's daily life, ferrying millions of passengers across the bustling streets of New York. By harnessing the power of Python and its versatile data analysis libraries, we will uncover hidden patterns and trends buried within this massive dataset.

Our analysis:

1. Calculate Average Fare Amount: This query calculates the average fare amount for all taxi trips in the dataset.

2. Calculate Total Trip Distance: This query calculates the total trip distance covered by all taxi trips.

3. Identify Longest and Shortest Trips: Find the longest and shortest trip durations among all the taxi trips.

4. Check Store and Forward Flags: Count the number of trips where the store and forward flag is set to true.

5. Calculate Total Tip Amount and Average Tip Percentage: This query calculates the total tip amount and the average tip percentage for all trips where fare amount is greater than zero.

6. Passenger Count per Hour: Calculate the number of trips made during each hour of the day and present the results in descending order.

7. Average Tip Percentage by Payment Type: Calculate the average tip percentage for different payment types, considering only trips with positive fare amounts.

8. Common Payment Type: Find the most common payment types used for taxi trips, excluding trips with zero or negative fare amounts.

9. Common Trip Type Count: Determine the most common trip types based on fare amounts greater than zero.

10. Trip Count by Month: Calculate the number of trips made in each month of the year and present the results in descending order.

11. Passenger Count Analysis: Explore the distribution of trips across different passenger counts.

12. Average Fare Analysis: Calculate the average fare for trips with varying passenger counts.

13. Best and Worst Pickup Locations: Identify the top 10 and bottom 10 pickup locations based on the frequency of trips.