2019-12-31

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bikeshare_tw.py

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Description

The program let's you analyse bikeshare data for the following cities:

1. Chicago
2. New York City
3. Washington
4. Random mode (program chooses a random city, month and day)

To use the program, a filter requests the user to input filter data for the city, month and day. To analyse the bikeshare data provided by .csv files, the code is grouped in the following functions.

1. time_stats

• This function analyses frequentation during the filtered time (month + day) for the city selected.
• It states what the most frequent travels times are.

2. station_stats

• This function states the most popular stations to start or end a trip.
• Furhtermore, it gives the most popular combination of start and end station.

3. trip_duration_stats

• This function calculates the total usage time for the timeframe selected.
• In addition, the mean travel time is calculated.

4. user_stats

• This function gives insight into the distribution of customer groups in terms of subscription model.
• Secondly, this section analyses the gender distribution of the customers.
• Third, this function checks for the oldest and youngest customers as well as the most common birth year.
• Lastly, this function investigates a correlation between user age and travel distance.

At the end, the user is able to evaluate the data sets for the query chosen.

Files used

Data is provided in form of .csv files. chicago.csv new_york_city.csv washington.csv

Credits

It's important to give proper credit. Add links to any repo that inspired you or blogposts you consulted.