advanced-pandas-for-data-analytics's Introduction

Advanced Pandas for Data Analytics

This repository provides the data in support of the course Advanced Pandas for Data Analytics, provided by Cloud Academy.

I suggest to follow the following steps in order you to be able to replicate the course steps in your local host. Open your favourite terminal emulator, and then:

1. Clone the repo

git clone https://github.com/cloudacademy/advanced-pandas-for-data-analytics.git

2. Create a python virtualenv

mkvirtualenv - p python3 <NAME_ENV>

3. Install the necessary requirements:

pip install -r requirements.txt

4. Open a jupyter notebook by running:

jupyter notebook

You are now ready to get your hands dirty: enjoy!

Data

We have used the Bike Sharing Dataset in this course, which all are available in the data folder. You can find more information on the data here.

Dataset Characteristics

Among many fields, it is worth to map the following variables:

workingday: if day is neither weekend nor holiday is 1, otherwise is 0.
weathersit:
- Clear, Few clouds, Partly cloudy, Partly cloudy mapped in value 1;
- Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist mapped in value 2;
- Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds mapped in 3;
- Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog mapped in 4.
temp: Normalized temperature in Celsius. The values are divided to 41 (max).
atemp: Normalized feeling temperature in Celsius. The values are divided to 50 (max).
hum: Normalized humidity. The values are divided to 100 (max).
windspeed: Normalized wind speed. The values are divided to 67 (max).
casual: count of casual users.
registered: count of registered users.
cnt: count of total rental bikes including both casual and registered.

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