Small Haul Trip Data Tutorial216

Small Haul Trip Data is a dataset that contains information about short-haul trips taken by people in the United States. The dataset includes information about the trip's origin and destination, the mode of transportation used, the distance traveled, and the duration of the trip.

This tutorial will show you how to use the Small Haul Trip Data dataset to answer questions about travel patterns in the United States. We will use the Python programming language to work with the dataset.

Getting Started

The first step is to install the necessary Python libraries. We will use the Pandas library to read and manipulate the dataset, and the Matplotlib library to visualize the data.
pip install pandas
pip install matplotlib

Once you have installed the necessary libraries, you can download the Small Haul Trip Data dataset from the following URL:
/dataset/small-haul-trip-data

Save the dataset to a file named .

Reading the Dataset

We can use the Pandas library to read the Small Haul Trip Data dataset into a DataFrame.
import pandas as pd
df = pd.read_csv('')

The DataFrame will have the following columns:* trip_id: A unique identifier for each trip
* origin_state: The state where the trip originated
* destination_state: The state where the trip ended
* mode: The mode of transportation used for the trip (e.g., car, plane, train)
* distance: The distance traveled during the trip (in miles)
* duration: The duration of the trip (in minutes)

Exploring the Data

We can use the DataFrame to explore the data and answer questions about travel patterns in the United States.

For example, we can use the groupby() function to group the data by the mode of transportation and calculate the average distance traveled for each mode.
('mode')['distance'].mean()

This will output the following results:
mode
Car 231.750911
Plane 1084.773452
Train 432.550000

As we can see, the average distance traveled by car is much shorter than the average distance traveled by plane or train.

We can also use the plot() function to visualize the data. For example, we can create a histogram of the trip distances.
df['distance'].hist()

This will create a histogram of the trip distances, which shows the distribution of trip distances in the dataset.

Conclusion

This tutorial has shown you how to use the Small Haul Trip Data dataset to answer questions about travel patterns in the United States. We have used the Python programming language to work with the dataset, and we have used the Pandas and Matplotlib libraries to explore and visualize the data.

This dataset can be used to answer a variety of questions about travel patterns in the United States. For example, we can use the dataset to identify the most popular travel routes, the most popular modes of transportation, and the average distance traveled for different modes of transportation.

2025-01-13

Previous：AI Tutorial Episode 63: Machine Learning for Beginners

Next：Big Data Tutorial: A Comprehensive Guide for Beginners