Pandas Lesson 3 – Reading Data | Dataplexa

Reading Data into Pandas

In real-world data analysis, data rarely starts inside your code. Most of the time, data comes from external files such as CSV, Excel, or databases.

In this lesson, you will learn how to load external data files into Pandas so you can begin analyzing them.

Why Reading Data Is Important

Almost every data project follows this flow:

If you cannot read data correctly, all later analysis becomes unreliable.

Pandas supports many data formats. The most commonly used are:

In this course, we will primarily work with CSV files because they are simple, fast, and widely used in industry.

To follow along with this course, you should download the official Pandas practice dataset provided by Dataplexa.

This dataset will be used across multiple lessons so you can see how Pandas operations work on real data.

Download the dataset file and place it in your working directory before proceeding.

Once the dataset is downloaded, you can load it using the read_csv() function.

import pandas as pd

df = pd.read_csv("dataplexa_pandas_sales.csv")
print(df)

This command reads the CSV file and stores it as a DataFrame.

When Pandas reads a CSV file:

This automatic behavior makes Pandas powerful but also requires careful inspection.

Large datasets should never be printed entirely. Instead, you should preview a few rows using head().

df.head()

By default, this shows the first 5 rows of the dataset.

To inspect the end of the dataset, use tail().

df.tail()

This is useful for checking recent records or data completeness.

Knowing how large your dataset is helps plan analysis and performance.

df.shape

This returns:

While reading CSV files, you may encounter:

Pandas provides additional parameters to handle these cases, which you will learn in later lessons.

Download the course dataset and load it into Pandas.

You should be comfortable loading external data files and previewing their contents.

In the next lesson, you will learn how to explore DataFrames using built-in functions to understand columns, data types, and summary statistics.