A script written in Python to explore bike sharing data in Chicago, as part of Data Science I - Foundation Nanodegree Program.
In this project, you will make use of Python to explore data related to bike share systems for Chicago. You will write code to import the data and answer interesting questions about it by computing descriptive statistics. You will also create some important functions and plot charts.
In this project, you will use data for the first six months of 2017, provided by Motivate, a bike share system provider for many major cities in the United States, to uncover bike share usage patterns. You will use the data from one of the largest cities of United States: Chicago. The data file contain six (6) columns:
Start Time (e.g. 2017-01-01 00:07:57) End Time (e.g. 2017-01-01 00:20:53) Trip Duration (in seconds, e.g., 776) Start Station (e.g. Broadway & Barry Ave) End Station (e.g. Sedgwick St & North Ave) User Type (Subscriber or Customer) Gender (Male or Female) Birth Year (e.g., 1980)
The script do the following tasks:
- 1: Print the first 20 samples(rows) from the database
- 2: Print the gender(column) of the first 20 samples
- 3: Create a function to get the columns as a list
- 4: Count how many of each gender do we have
- 5: Create a function to count the genders
- 6: Show the most popular gender
- 7: Plot a a chart using the previous data
- 8: Answer why summing the number of Males and Females doesn't match the number of samples
- 9: Find the minimum, maximum, mean and median duration of the trips
- 10: Get all the start stations of the dataset
- 11: Create a function count the occurrence of any given column (optional)