Project - Forecast Bike Rentals

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End to End Project - Bikes Assessment - Basic - Loading the data

Loading the data

To start with, let us load the "Bikes Rental" data set (bikes.csv) and get a first hand look into the same.

The dataset contains the following parameters:

  1. instant: record index

  2. dteday: date

  3. season: season (1: springer, 2: summer, 3: fall, 4: winter)

  4. yr: year (0: 2011, 1:2012)

  5. mnth: month (1 to 12)

  6. hr: hour (0 to 23)

  7. holiday: whether day is holiday or not

  8. weekday: day of the week

  9. workingday: if day is neither weekend nor holiday is 1, otherwise is 0.

  10. weathersit:

    • 1: Clear, Few clouds, Partly cloudy, Partly cloudy

    • 2: Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist

    • 3: Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds

    • 4: Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog

  11. temp: Normalized temperature in Celsius.

  12. atemp: Normalized feeling temperature in Celsius.

  13. hum: Normalized humidity. The values are divided to 100 (max)

  14. windspeed: Normalized wind speed. The values are divided to 67 (max)

  15. casual: count of casual users

  16. registered: count of registered users

  17. cnt: count of total rental bikes including both casual and registered


Please follow the below steps:

  1. Define a variable filePath to store the location path for our dataset file bikes.csv as a string. bikes.csv is located at /cxldata/datasets/project/bikes.csv. This file location is on the Linux box - web console
  2. Use pandas library function read_csv to load this bikes.csv file using the location path defined by filePath variable above and store the loaded file into a dataframe called bikesData.
  3. Have a look at the metadata of this bikesData dataframe by calling an appropriate function of dataframe.

PS - Next couple of question depends on this assessment.

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