Python · Advanced level

Advanced level · Module 5: Data Analysis with pandas · Lesson 1 of 4

Loading and Exploring Data with pandas

Read CSV and Excel files into DataFrames and understand their structure.

In this lesson you will

  • Load data and inspect it with head, info and describe
  • Select rows and columns

Key concepts

DataFramedtypeloc/ilocboolean filtering

The lesson

pandas is the standard Python tool for working with tables of data. A DataFrame is like a spreadsheet you control with code; pd.read_csv() and pd.read_excel() load files into one.

Start every analysis by looking: df.head() shows the first rows, df.info() lists columns and types, df.describe() summarises the numbers. Check the number of rows and whether values are missing.

Select columns with df['county'] and filter rows with conditions: df[df['grade'] == 5]. value_counts() quickly counts how often each value appears.

Step by step

  1. Load the enrolment CSV into a DataFrame.
  2. Inspect it with head(), info() and describe().
  3. Filter Grade 5 girls and count them by county.
  4. Answer the five questions and keep the code for each.

Practice activity

Load a school enrolment dataset and answer five questions such as 'how many girls enrolled in Grade 5 by county?'.

Expected result: Correct answers to all five questions with the code used.

Check your understanding

  1. Which method shows column types and missing-value counts?

    Show answer

    DataFrame.info()

Further learning

Taught live by an instructor — online or in person. Free course lessons are for self-study and do not lead to a certificate — see registration options.