Udacity Data Analyst Nanodegree Project 5
This dataset consist of 520 thousand entries which has the features like start station name and id, end station name and id, bike id, member birth date, gender, timeframe etc. This dataset is available at Ford GoBike website. Dataset: (https://s3.amazonaws.com/fordgobike-data/index.html) Dataset has exactly 519700 rows and 15 columns. For the need of analysis I have added more columns like age, age bins etc. User types are customer and subscriber, most of the user age group is 20-40 years, users of over 100 years are also there.
There are some outliers in the age columns, few members are having age greater than 110.
October'17 is the month in which maximum business for the Ford GoBike is done, in this particular data.
Subscriber user type has 80% proportions
Male gender members are about of 75% proportion.
20-40 is the most common age group of the members.
In the month of july member of other gender took mean duration of more than 1500 seconds.
Mean duration for the start hour 3 is highest.
Customer user type use to have mean duration longer than subscriber user type.
260-460seconds is the most common duration time|
https://matplotlib.org/api/_as_gen/matplotlib.pyplot.colorbar.html
https://stackoverflow.com/questions/27019079/move-seaborn-plot-legend-to-a-different-position
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.reset_index.html
https://stackoverflow.com/questions/26163702/how-to-change-figuresize-using-seaborn-factorplot
https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html