Data-Visualization

Details various Python Data Visualization libraries like matplotlib, seaborn, folium. The repository will help any aspiring data analyst, to understand Python libraries like Pandas, Numpy, Seaborn, Matplotlib,seaborn to make attractive visualization. The repository contains multiple jupyter notebook , explaining various python libraries used in data analysis

Dataset :

https://www.un.org/en/development/desa/population/migration/data/empirical2/migrationflows.asp The dataset contains annual data on the flows of international migrants as recorded by the countries of destination. The data presents both inflows and outflows according to the place of birth, citizenship or place of previous / next residence both for foreigners and nationals. Here we will focus on the Canadian Immigration data.

The first notebook(Introduction-to-Matplotlib-and-Line-Plots.ipynb) covers:

• Data visualization and some of the best practices to keep in mind when creating plots and visuals.
• Basic plotting with Matplotlib.
• Introduction to dataset on immigration to Canada, which will be used extensively throughout the repo.
• How to read csv files into a pandas dataframe and process and manipulate the data in the dataframe.
• How to generate line plots using Matplotlib.

The second notebook(Area-Plots-Histograms-and-Bar-Chart.ipynb) covers:

• Area plots, and how to create them with Matplotlib.
• Histograms, and how to create them with Matplotlib.
• Bar charts, and how to create them with Matplotlib.

The third notebook(Pie-charts-Box-plots-Scatter-plots.ipynb) covers:

• Pie charts, and how to create them with Matplotlib.
• Box plots, and how to create them with Matplotlib.
• Scatter plots and bubble plots, and how to create them with Matplotlib.