This project aims to answer specific questions related to shared electric car usage in Paris during April 2018. We will perform data analysis and cleaning to derive meaningful insights from the dataset. Below is an overview of the key steps in our analysis.
We begin by importing the necessary libraries, particularly numpy and pandas, to facilitate data preparation and analysis. We will load the dataset from the provided CSV file and inspect the data's initial structure.
In this section, we perform data cleaning tasks to ensure the dataset is ready for analysis. We drop irrelevant columns, check for missing values, remove duplicates, and detect outliers. Our goal is to have a clean and reliable dataset for analysis.
We make necessary transformations to the dataset, including changing column names to lowercase, stripping whitespaces, and replacing spaces with underscores. This step enhances the dataset's query-friendlines.
We address specific research questions using the cleaned and transformed data. For instance, we identify the most popular hour of the day for picking up a shared electric car in Paris during April 2018. We also determine the most popular hour for returning cars and explore other relevant queries.
To engage with this project, you will need:
- Text editor- Visual Studio Code or any text editor that supports python.
- Install Python 3.9.0.
- Jupyter Notebook for the data preparation and analysis online.
There are no known bugs in the application.
This project was implimeted using python. Python libraries used incluse: Pandas , Numpy and Matplotlib .
In conclusion, this project provides valuable insights into shared electric car usage in Paris, April 2018. Our analysis answers key questions and helps us understand usage patterns and trends.
For detailed code and findings, refer to the Jupyter Notebook associated with this project.