Converting JSON Data to Pandas DataFrame

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In this article, we will explore the process of converting data from a JSON format to a pandas DataFrame. The conversion involves several steps including parsing the JSON data, extracting the required fields, and constructing a DataFrame with the desired structure.

Introduction

JSON (JavaScript Object Notation) is a popular data interchange format that provides a lightweight and easy-to-read way of representing data structures. Pandas DataFrames are a powerful tool for data manipulation and analysis in Python, providing an efficient way to store, manipulate, and analyze structured data.

The goal of this article is to demonstrate how to convert JSON data to a pandas DataFrame, highlighting the key concepts and techniques involved in the process.

Assumptions

For this tutorial, we assume that you have:

Python installed on your system.
The pandas library installed (pip install pandas).
Basic knowledge of JSON and pandas DataFrames.

Background

In the given Stack Overflow question, a user is trying to extract data from a JSON object containing market information for various currencies. The desired output format is a pandas DataFrame with columns representing the exchange symbol, price, and last updated timestamp.

The provided code attempts to parse the JSON data but encounters errors due to incorrect indexing. To resolve this issue, we will focus on converting the JSON data to a pandas DataFrame using a step-by-step approach.

Step 1: Parsing the JSON Data

To start, we need to parse the JSON data into a Python object that can be easily manipulated.

import json

# Define the JSON data as a string
json_data = '''
{
    "status": {
        "timestamp": "2019-10-04T02:57:39.238Z",
        "error_code": 0,
        "error_message": None,
        "elapsed": 14,
        "credit_count": 1,
        "notice": None
    },
    "data": {
        "id": 112,
        "name": "Liquid",
        "slug": "liquid",
        "num_market_pairs": 190,
        "market_pairs": [
            {
                "market_pair_base": {
                    "exchange_symbol": "ETH",
                    "currency_id": 1027,
                    "currency_symbol": "ETH",
                    "currency_type": "cryptocurrency"
                },
                "market_pair_quote": {
                    "exchange_symbol": "JPY",
                    "currency_id": 2797,
                    "currency_symbol": "JPY",
                    "currency_type": "fiat"
                },
                "quote": {
                    "exchange_reported": {
                        "price": 18522.8757,
                        "volume_24h_base": 12901.4143123,
                        "volume_24h_quote": 238971293.660934,
                        "last_updated": "2019-10-04T02:57:04.000Z"
                    },
                    "USD": {
                        "price": 173.503768779353,
                        "volume_24h": 2238444.00576794,
                        "last_updated": "2019-10-04T02:57:04.000Z"
                    }
                },
                "market_id": 4431,
                "market_pair": "ETH/JPY",
                "category": "spot",
                "fee_type": "percentage"
            },
            {
                "market_pair_base": {
                    "exchange_symbol": "XRP",
                    "currency_id": 52,
                    "currency_symbol": "XRP",
                    "currency_type": "cryptocurrency"
                },
                "market_pair_quote": {
                    "exchange_symbol": "JPY",
                    "currency_id": 2797,
                    "currency_symbol": "JPY",
                    "currency_type": "fiat"
                },
                "quote": {
                    "exchange_reported": {
                        "price": 26.55199,
                        "volume_24h_base": 8223150.63965144,
                        "volume_24h_quote": 218341013.552519,
                        "last_updated": "2019-10-04T02:56:04.000Z"
                    },
                    "USD": {
                        "price": 0.248712479001935,
                        "volume_24h": 2045200.18079406,
                        "last_updated": "2019-10-04T02:56:04.000Z"
                    }
                },
                "market_id": 16254,
                "market_pair": "XRP/JPY",
                "category": "spot",
                "fee_type": "percentage"
            }
        ]
    }
}
'''

# Parse the JSON data into a Python object
data = json.loads(json_data)

Step 2: Extracting the Required Fields

Next, we need to extract the required fields from the parsed JSON data. The desired output format includes exchange symbol, price, and last updated timestamp.

# Define the target DataFrame structure
target_df = pd.DataFrame(columns=['market_pair_base', 'market_pair_quote', 'price', 'last_updated'])

# Initialize an empty dictionary to store the extracted data
target_dict = {}

# Iterate over the market pairs in the JSON data
for i, market_pair in enumerate(data['data']['market_pairs']):
    # Extract the exchange symbol, price, and last updated timestamp
    target_dict['market_pair_base'] = [market_pair['market_pair_base']['exchange_symbol']]
    target_dict['market_pair_quote'] = [market_pair['market_pair_quote']['exchange_symbol']]
    target_dict['price'] = [market_pair['quote']['exchange_reported']['price']]
    target_dict['last_updated'] = [market_pair['quote']['exchange_reported']['last_updated']]

Step 3: Constructing the DataFrame

Now that we have extracted the required fields, we can construct the desired DataFrame structure.

# Create a new DataFrame from the extracted data
target_df = pd.concat([target_df, pd.DataFrame(target_dict)], ignore_index=True)

# Print the resulting DataFrame
print(target_df)

Conclusion

In this article, we demonstrated how to convert JSON data to a pandas DataFrame. The process involves parsing the JSON data into a Python object, extracting the required fields, and constructing the desired DataFrame structure.

The code snippets provided highlight the key concepts and techniques involved in the conversion process, including handling errors and achieving the desired output format.

By following these steps and understanding the underlying principles, you can effectively convert JSON data to pandas DataFrames for efficient data manipulation and analysis.

Last modified on 2023-09-17