Python

Data Loading & Saving

Stop manually entering data. Learn to read and write CSV, Excel, and JSON files with Pandas.

By TechCoder TeamLast updated: 2026-06-02
In a Nutshell

Stop manually entering data. Learn to read and write CSV, Excel, and JSON files with Pandas. This hands-on tutorial focuses on practical implementation of data loading & saving concepts.

Module 2: Data Loading & Saving

Real-world data comes from files: CSVs, Excel spreadsheets, SQL databases, and JSON APIs. Pandas allows you to load this data with a single line of code.

Lesson 3: Reading Data

Pandas supports a huge number of input formats using functions that start with read_.

Common Readers:

  • pd.read_csv("file.csv"): The most common format.
  • pd.read_excel("file.xlsx"): Reads Excel sheets.
  • pd.read_json("file.json"): Reads JSON strings or files.
  • pd.read_html("url"): Scrapes all <table> tags from a webpage!

Key Parameters:

  • header=0: Takes the first row as column names (default).
  • index_col=0: Uses the first column as the index.
  • sep=",": Specifies the delimiter (useful for TSV files).
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Lesson 4: Writing Data

Saving data is just as easy using methods that start with to_.

Common Writers:

  • df.to_csv("new_file.csv", index=False): Saves to CSV. index=False prevents writing the row numbers.
  • df.to_excel("report.xlsx"): Requires openpyxl.
  • df.to_json("data.json"): Useful for web apps.
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Mini Project: Data Converter

Challenge: Imagine you received data in JSON format but need to send it to your boss as a CSV.

  1. Create a DataFrame from a list of dictionaries (JSON-like).
  2. "Save" it to a CSV string without the index numbers.

Quiz

Question 1 of 5

Which parameter should you use in to_csv() to avoid saving the row numbers (0, 1, 2...)?

no_rows=True
header=False
index=False
skip_rows=True

Key Takeaways

✅ read_csv is your bread and butter.
✅ Use index=False when saving CSVs to keep them clean.
✅ Pandas can handle JSON, Excel, SQL, and even HTML tables out of the box.