Pandas for Data Analytics · Lesson 4 of 20
Reading CSV and Excel Data
Learn how analysts bring real-world CSV and Excel data into Pandas, inspect the imported structure, and connect the data to business questions.
Concept 1
Why Analysts Import Data
Real business data usually already exists outside Python in files such as CSV files and Excel workbooks. Pandas allows an analyst to bring that structured data into a DataFrame so it can be explored, cleaned, transformed, and analyzed.
sales.csv
read_csv()
DataFrame
Your Turn
A sales team gives you a CSV containing 50,000 transactions. Why would you import it into Pandas?
Analyst Workflow
From Business File to Analysis
Receive
CSV or Excel file
Import
Load data into Pandas
Inspect
Understand the structure
Analyze
Connect data to a business question
Reading CSV and Excel Data with Pandas
Reading external data is one of the first practical steps in Pandas for Data Analytics. Business data is commonly stored in CSV files and Excel workbooks. Pandas provides functions that allow analysts to bring these datasets into a DataFrame.
A CSV file can be imported using pd.read_csv(), while Excel data can be imported using pd.read_excel(). Once the data has been loaded, the analyst can inspect its structure and prepare it for further analysis.
Importing data is not the same as understanding data. A strong analytics workflow connects imported columns to a specific business question. This helps the analyst decide what should be explored, cleaned, transformed, grouped, or calculated next.