Introduction to Pandas: How to Read a CSV File in Python

Every data science project starts with the same step: Getting the data. One of the essential tools for this is Pandas, where the Read CSV function plays a crucial role in importing data efficiently. Pandas read CSV files with ease, making data handling seamless.

The most common data format you’ll encounter is the CSV (Comma Separated Values) file. It’s just a text version of a spreadsheet and is frequently read by Pandas, highlighting the importance of it’s features.

Today, you’ll learn how to use the Pandas library to load a CSV file into Python and start analysing it. Understanding how Pandas read CSV files is essential in this process.

Step 1: Install and Import Pandas

Pandas doesn’t come with Python by default. You need to install it. Open your terminal (remember to use your virtual environment!) and run:

pip install pandas

Now, in your Python script, import it. The standard convention is to import it as pd, ready for Pandas data manipulation like reading CSVs.

import pandas as pd

Step 2: Reading the CSV

Let’s say you have a file named sales_data.csv in the same folder as your script. Loading it is one line of code using the function. This is how Pandas reads CSV files effectively:

df = pd.read_csv("sales_data.csv")

We typically call the variable df, short for DataFrame.

Step 3: Inspecting Your Data

Now that it’s loaded, let’s look at it using various Pandas methods once the CSV is read out.

df.head()

This shows you the first 5 rows of your data. It’s the best way to quickly check if it loaded correctly after Pandas has read your CSV file.

print(df.head())

df.info()

This is crucial. It tells you:

• How many rows and columns you have.
• The names of all columns.
• The data type of each column (e.g., int64 for numbers, object for text).
• If you have any missing values (nulls).

print(df.info())

df.describe()

This gives you a quick statistical summary of all your numerical columns (count, mean, min, max, etc.). With the CSV read by Pandas, these statistics become accessible.

print(df.describe())

Full Example Code

import pandas as pd

# 1. Load the data
# (Make sure you actually have a 'sales_data.csv' file!)
try:
    df = pd.read_csv("sales_data.csv")
    print("Data loaded successfully!\n")

    # 2. Inspect the first few rows
    print("--- First 5 Rows ---")
    print(df.head())
    print("\n")

    # 3. Get info about columns and types
    print("--- Data Info ---")
    print(df.info())

except FileNotFoundError:
    print("Error: 'sales_data.csv' was not found. Please check the file name.")

Next Steps

You can now load data! The next step is learning how to filter, sort, and clean it using the power of Pandas Read CSV and beyond.