Day-20/90| Loading & Exploring Data in Pandas🐻❄️ | AI, DS and ML complete course in English
Day 20 of your Complete AI Course in English focuses on one of the most important early steps in any AI, Machine Learning, or Data Science project: loading and exploring data with Pandas. In this lesson, you will learn how to read CSV and Excel files into Pandas, and how to use info(), describe(), and data types (dtypes) to quickly understand a dataset.
You start with reading data from files using pd.read_csv() and pd.read_excel(). You’ll see how to provide the file path, what the resulting DataFrame looks like, and how this step connects data from the outside world (Excel sheets, CSV exports, downloaded datasets) into your Python environment. This is the first step of every AI/ML pipeline: “Get the data into a DataFrame.”
Next, you’ll use DataFrame.info() to get a high-level summary of your dataset: number of rows and columns, column names, number of non-null values, and data types of each column. This helps you quickly spot potential problems such as missing values, unexpected column types, or very wide tables. Then you’ll use DataFrame.describe() to compute summary statistics (count, mean, standard deviation, minimum, maximum, quartiles) for numeric columns, giving you a first feel for the scale and spread of your features.
A key part of this lesson is understanding data types in Pandas using the dtypes attribute. You’ll learn how columns might be integers, floats, objects (strings), booleans, or datetimes, and why using the correct data type is crucial for performance and for applying the right operations (e.g., mathematical operations on numeric columns, string methods on text columns, date operations on datetime columns).
By the end of Day 20, you will be able to load CSV/Excel files into Pandas, inspect your data using info() and describe(), and interpret column data types. This gives you a solid starting point for all later stages: data cleaning, feature engineering, visualization, and model building.