Got it, let's break this down into clear, actionable steps with code examples for every part:

First, we'll create a simple Flask endpoint that accepts POST requests with JSON data, processes it, and sends a confirmation response.

Create a file named app.py with this code:

from flask import Flask, request, jsonify
import pandas as pd

app = Flask(__name__)

@app.route('/receive-data', methods=['POST'])
def receive_data():
    # Parse incoming JSON data from the request
    json_data = request.get_json()
    
    if not json_data:
        return jsonify({"error": "No data provided in the request"}), 400
    
    # Convert JSON back to a DataFrame (optional, for server-side data manipulation)
    df = pd.DataFrame(json_data)
    
    # Example processing: print the DataFrame to server console
    print("Received DataFrame:")
    print(df)
    
    # Send a success response with metadata about the received data
    return jsonify({
        "status": "success",
        "message": "Data received successfully",
        "number_of_rows": len(df),
        "columns": list(df.columns),
        "sample_rows": df.head(2).to_dict('records')
    }), 200

if __name__ == '__main__':
    app.run(debug=True)

Quick Notes: #

• request.get_json() handles parsing the JSON payload automatically.
• Converting back to a DataFrame is optional but useful if you need to clean, analyze, or store the data server-side.
• Debug mode is great for testing, but remember to disable it in production.

Next, let's create a sample 2-column DataFrame and convert it to a JSON format optimized for API requests.

Create a script named generate_data.py (or run this in a Python shell/Jupyter notebook):

import pandas as pd

# Create a sample 2-column DataFrame
data = {
    "Product": ["Laptop", "Phone", "Tablet", "Headphones"],
    "Price": [999, 699, 299, 149]
}
df = pd.DataFrame(data)

# Convert DataFrame to JSON (using 'records' orient for a clean, API-friendly structure)
json_output = df.to_json(orient='records', indent=2)
print("Generated JSON to send:")
print(json_output)

Key Detail: #

Using orient='records' gives us a JSON array of objects (each object represents a row from the DataFrame), which is the most widely accepted format for API payloads. The output will look like this:

[
  {
    "Product": "Laptop",
    "Price": 999
  },
  {
    "Product": "Phone",
    "Price": 699
  },
  {
    "Product": "Tablet",
    "Price": 299
  },
  {
    "Product": "Headphones",
    "Price": 149
  }
]

Now let's send the JSON data to our Flask server:

1. Install Dependencies First:
   Run these commands in your terminal to set up required packages:

   pip install flask pandas
   

2. Start the Flask Server:
   Launch app.py with:

   python app.py
   

   You'll see output like Running on http://127.0.0.1:5000/—that's our server's base URL.

3. Configure Postman:

   • Open Postman, click New > Request, name it (e.g., "Send DataFrame JSON"), and save it to a collection.
   • Set the request method to POST.
   • Enter the full endpoint URL: http://127.0.0.1:5000/receive-data
   • Go to the Body tab, select raw, and set the format dropdown to JSON.
   • Paste the JSON output you generated from the DataFrame into the text area.
   • Click Send.

4. Verify the Response:
   You'll get a JSON response like this:

   {
     "status": "success",
     "message": "Data received successfully",
     "number_of_rows": 4,
     "columns": ["Product", "Price"],
     "sample_rows": [
       {"Product": "Laptop", "Price": 999},
       {"Product": "Phone", "Price": 699}
     ]
   }
   

   Also, check your Flask server terminal—you'll see the received DataFrame printed there.

内容的提问来源于stack exchange，提问作者Mohan Ram