Using Python to Import API Data into Excel

Some things never change. For years, Excel has been one of the main tools that data analysts use. The issue is that, traditionally, this meant manually updating spreadsheets with fresh data. This is repetitive, tedious, and bound to introduce errors. Python flips the script (pun intended) and lets us grab data straight from APIs and drop it right into our Excel files.

When you use Python with Excel, you get a refreshing balance between programmatic data retrieval and a user-friendly spreadsheet app. Using them together gives you the best of both worlds. Python has incredible capabilities that let you pull data from almost anywhere, and Excel's tried and tested interface is perfect for organizing and visualizing that information. This setup is really flexible, and works for everything from financial reporting to marketing analytics.

This guide shows you how to write Python code that pulls API data into Excel. You'll learn the basics of setting up your environment, making API calls, and automating the whole process to save yourself hours of manual work.

Getting Started with Using Python with Excel

Before you jump into coding, you need to set up some tools on your computer first.

Installing Python and Specific Libraries

First, download and install Python from python.org. During installation, make sure to check the box that adds Python to your PATH. This lets you execute Python from anywhere in your command line interface without needing to change to the Python executable first.

Once you’ve installed Python, you'll need these libraries:

• Requests: for making API calls

• Pandas: for data manipulation

• Openpyxl:  for working with Excel files

Open a command prompt or terminal and install them with pip:

pip install requests pandas openpyxl

Setting Up Your Environment for Excel Integration

The next step is to create a Python script that can talk to both APIs and Excel. In a code editor like VS Code, create a new file named api_to_excel.py.

A script normally has:

• Import statements for your libraries

• API connection details

• Functions to fetch and process data

• An Excel output configuration

Here's a starter template:

import requests
import pandas as pd

# API connection settings
api_url = "https://api.example.com/data"
headers = {"Authorization": "Bearer YOUR_API_KEY"}

# Function to fetch data
def get_api_data():
    response = requests.get(api_url, headers=headers)
    return response.json()

# Main process
if __name__ == "__main__":
    data = get_api_data()
    # We'll add code to process and export to Excel later

Fetching Data from an API

Making API calls with Python is easier than it sounds. You'll end up with raw data from the web that you can turn into something useful for your Excel sheets.

Understanding APIs and Endpoints

APIs are access points that let you grab data from other systems. Each API has specific endpoints, which are just URLs that deliver different chunks of information when you request them.

Before writing any code, you’ll need to first figure out:

• The base URL of the API (like https://api.example.com)

• The specific endpoint for your data (like /stocks/daily)

• What authentication the API wants (API keys, OAuth tokens, etc.)

• Any parameters needed to filter the data (date ranges, limits, etc.)

You can practice with public APIs, such as GitHub, weather services, financial data providers, and social media platforms.

Making Your First API Call

The requests library makes grabbing data straightforward. Here's how to make a simple call:

import requests

# API details
api_url = "https://api.coingecko.com/api/v3/coins/markets"
params = {
    "vs_currency": "usd",
    "order": "market_cap_desc",
    "per_page": 10,
    "page": 1
}

# Make the request
response = requests.get(api_url, params=params)

# Check if the request worked
if response.status_code == 200:
    # Convert the response to JSON
    data = response.json()
    print(f"Successfully retrieved data for {len(data)} cryptocurrencies")
else:
    print(f"Error: {response.status_code}")
    print(response.text)

This example pulls cryptocurrency data without needing an API key. When you run it, you'll get market data for the top 10 cryptocurrencies by market cap.

For APIs that need authentication, you’ll have to add your credentials:

headers = {"X-API-Key": "your_api_key_here"}
response = requests.get(api_url, params=params, headers=headers)

The data comes back in JSON format, which is a structured way to organize information that Python can easily work with. Getting familiar with JSON is a good idea because so many APIs use it to send and receive requests, and understanding how it is structured will speed up your scripting.

Extracting API Data and Formatting for Excel

Once you've got your API response, you need to pick out the useful bits and shape them into a format that works for Excel.

Parsing JSON Responses

API data usually comes as JSON. JSON has nested data in it that looks like dictionaries and lists in Python. The hard part is pulling out just what you need while leaving the junk behind.

Here's how you might extract needed fields from our cryptocurrency example:

# Extract only the fields we want
crypto_data = []
for coin in data:
    crypto_data.append({
        "name": coin["name"],
        "symbol": coin["symbol"],
        "current_price": coin["current_price"],
        "market_cap": coin["market_cap"],
        "price_change_24h": coin["price_change_percentage_24h"]
    })

This creates a cleaner list of dictionaries with just the fields you care about.

Converting to DataFrame Format

Pandas makes it easy to convert your extracted data into a table format that Excel understands:

import pandas as pd

# Convert list of dictionaries to DataFrame
df = pd.DataFrame(crypto_data)

# Preview the data
print(df.head())

# Clean up column names
df.columns = [col.replace("_", " ").title() for col in df.columns]
DataFrames are great  because they let you manipulate your data before you export it:

# Add a calculated column
df["Price In Euros"] = df["Current Price"] * 0.93  # Rough USD to EUR conversion

# Sort by market cap
df = df.sort_values("Market Cap", ascending=False)

# Format numeric columns
df["Current Price"] = df["Current Price"].map("${:.2f}".format)
df["Market Cap"] = df["Market Cap"].map("${:,.0f}".format)

These transformations turn your raw API data into clean, formatted information ready for Excel. Think about what format will make the most sense in your spreadsheet before you export it. This will save you time afterward when the data is in Excel. The more you can do on the scripting front, the less manual work you’ll have to do on your spreadsheet.

Saving API Data to Excel Using Python

Finally, we need to import our data into Excel. Python offers options for basic exports or more customized spreadsheets.

Writing Data to Excel with Pandas

The easiest way to create an Excel file is by using pandas' built-in export function:

# Simple export in only one line of code
df.to_excel("crypto_data.xlsx", index=False)

This creates a basic Excel file with your data. Setting index=False stops pandas from adding an extra column of row numbers, which you won't normally need in Excel.

You can even be specific about the sheet name and start position you want the data to go into:

# More specific export with more details if you need them
with pd.ExcelWriter("crypto_data.xlsx") as writer:
    df.to_excel(writer, sheet_name="Market Data", startrow=1, index=False)

Formatting Excel Sheets with Openpyxl

If you want better-looking spreadsheets, then openpyxl is a good option because it lets you add formatting:

import pandas as pd
from openpyxl import load_workbook
from openpyxl.styles import Font, Alignment, PatternFill

# First export the data
df.to_excel("crypto_data.xlsx", index=False)

# Then open the file for formatting
wb = load_workbook("crypto_data.xlsx")
ws = wb.active

# Add a title
ws.insert_rows(1)
ws['A1'] = "Cryptocurrency Market Data"
ws['A1'].font = Font(bold=True, size=14)
ws.merge_cells('A1:E1')
ws['A1'].alignment = Alignment(horizontal='center')

# Format headers
header_fill = PatternFill(start_color="D3D3D3", end_color="D3D3D3", fill_type="solid")
for cell in ws[2]:
    cell.font = Font(bold=True)
    cell.fill = header_fill

# Adjust column widths
for column in ws.columns:
    max_length = 0
    column_letter = column[0].column_letter
    for cell in column:
        if cell.value:
            max_length = max(max_length, len(str(cell.value)))
    ws.column_dimensions[column_letter].width = max_length + 2

# Save the formatted workbook
wb.save("crypto_data.xlsx")

This code adds a title, formats headers with a gray background, makes text bold, and adjusts column widths to fit the data.

We end up with an Excel file created entirely with Python, and the best part is that no manual formatting was needed!

Automate Excel Tasks with Python

The real magic happens when you set up Python to update your Excel files automatically. If you ever have repetitive data entry in your job, then this could be a massive time saver.

Scheduling API Calls to Update Excel

Once your script works, you can schedule it to run on a timer using your operating system's task scheduler:

For Windows, you can use Task Scheduler:

1. Create a batch file (update_excel.bat) with this command: python C:\path\to\your\api_to_excel.py

2. Open Task Scheduler and create a new task

3. Set the trigger (daily, weekly, etc.)

4. Add an action to start the batch file

For Mac or Linux, there’s crontab:

# Run every day at 9 AM
0 9 * * * python /path/to/your/api_to_excel.py

This setup pulls fresh data automatically without you ever needing to lift a finger.

Building Data Pipelines for Reporting

If you have really complicated requirements, then you can build a full reporting pipeline:

def main():
    # Get data from multiple APIs
    stock_data = get_stock_data()
    weather_data = get_weather_data()
    sales_data = get_sales_data()

    # Process and combine data
    combined_df = process_data(stock_data, weather_data, sales_data)

    # Create Excel report with multiple sheets
    with pd.ExcelWriter("daily_report.xlsx") as writer:
        combined_df.to_excel(writer, sheet_name="Summary", index=False)
        pd.DataFrame(stock_data).to_excel(writer, sheet_name="Stocks", index=False)
        pd.DataFrame(weather_data).to_excel(writer, sheet_name="Weather", index=False)
        pd.DataFrame(sales_data).to_excel(writer, sheet_name="Sales", index=False)

    # Add formatting
    format_excel_report("daily_report.xlsx")

    # Send email with the report
    send_report_email("daily_report.xlsx", "team@company.com")

if __name__ == "__main__":
    main()

This setup works for sales dashboards, financial reporting, marketing analytics, or any data that needs regular updating. The Python for Excel-Driven Business Analysts (PCED) course dives deeper into these techniques.

These Excel reports run silently in the background and will keep bringing in the data without needing any manual effort from you. Your colleagues and bosses will wonder how you get such consistent reporting out on time without missing a beat!

Key Takeaways: Python + Excel + APIs = Efficiency

Combining Python with Excel to grab API data makes your workflow much faster and more reliable. You get accurate reporting delivered on time, every time.

The Certified Entry-Level Data Analyst with Python (PCED) course covers all these topics more deeply, with hands-on examples that you can start applying for yourself.

And don’t worry: you don't need to be a programming expert to get started. Basic Python skills can automate hours of Excel work without a developer. Start with a small project that solves a real problem for you, then build from there.

Conclusion

Python gives you a direct pipeline from web APIs to Excel without the copy-paste headache that so many people are stuck with. The code examples in this guide can help you understand how the data is structured, but you can create your own for whatever API you want.

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