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In this #Python data exploration and visualization (EDA) case study for data science project for beginners in anaconda Jupyter notebook where I have shown various techniques of python pandas library and matplotlib library which are helpful answering the business or interview questions related to data exploration on parking dataset. This will help you understand the basic as well as some advance topics related to data manipulation in python. Things like how to create table from main data source, visualization on entire or custom table, filtering data to answering questions, creating pivot table, sorting the data etc are part of this case study.

I hope you’ll enjoy following and answering this case study to build your data science skill and become a great data scientist.

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Thank you
Abhishek Agarrwal

[karanmthevar]

Can you share the original data source link

[mayanktripathiu]

Below is the code for question # 10. What are those states which has Maximum and Minimum parking spaces.
Please let know how accurate it is.. and is there any other optimized way, if so please share.
Solution :
parking_states = pd.pivot_table(parking, index = 'Bldg State', values = 'Total Parking Space')
display(parking_states['Total Parking Space'].max(), parking_states['Total Parking Space'].min())
Parking Space'] == parking_states['Total Parking Space'].max()]
Parking Space'] == parking_states['Total Parking Space'].min()]

[mayanktripathiu]

Below is the code for question # 9. List the Bldg states which has 0 parking spaces.
Please let know how accurate it is.. and is there any other optimized way, if so please share.
Solution :
zero_parking_state = Parking Space'] == 1 ], index = 'Bldg State', values = 'Total Parking Space')
zero_parking_state

[mayanktripathiu]

Below is the code for question # 7. Which property type has more active parking.
Please let know how accurate it is.. and is there any other optimized way, if so please share.
Solution :
property_active_parking = Status'] == 'ACTIVE'], index = 'Property Type', values = 'Total Parking Space')
property_active_parking

[mayanktripathiu]

Please share the data-set or the link from where we can download it.

[mayanktripathiu]

Below is the code for question # 8. How's the distribution of parking in city by property type.
Please let know how accurate it is.. and is there any other optimized way, if so please share.
Solution :
parking_distr = pd.pivot_table(parking, index = 'Bldg City', columns = 'Property Type', values = 'Total Parking Space')
parking_distr