Data Analytics with Pandas 🐼
The Pandas package is the most imperative tool in Data Science and Analysis working in Python nowadays.
In this notebook, we would analyze Supermarket Data Across the Country; Company XYZ.
Alright, let’s start!
Step 1 - Loading the Dataset
Step 2 - Data Exploration
It is an important pillar of data science, a critical step required to complete every project regardless of the domain or the type of data you are working with.
The dataset was explored using some built-in pandas function like; Numpy for carrying out numerical computations, pandas for making a dataframe object seaborn and matplotlib for visualizations. The .head() to view the first few observations of the dataframe and .info() for information about the dataframe.
Step 3 - Dealing with DateTime Features
Step 4 - Unique Values in Columns
Step 5 - Aggregation with GroupBy
Step 6 - Data Visualization
In this tutorial, we have learnt how to perform Exploratory Data Analysis (EDA) in Python and how to use external Python packages such as Pandas, Numpy, Matplotlib, Seaborn etc. to conduct univariate analysis, bivariate analysis and data visualization.