How to convert JSON to Pandas DataFrame in Python?

In data processing and analysis, JSON is a common data format, while Pandas DataFrame is a widely used data structure in Python. Converting JSON data to Pandas DataFrame can facilitate data analysis and processing. In this article, we will explore how to convert JSON to Pandas DataFrame, and introduce related steps and examples.

Read JSON files with Pandas

Before we get started, let's understand how to read_json()read data from a JSON file using Pandas' functions. Here are the steps to read a JSON file:

1. Import the required libraries:

import pandas as pd

2. Read the JSON file using read_json()the function:

df = pd.read_json('data.json')

In the above code, data.jsonis the path to the JSON file to read and dfis the Pandas DataFrame object to load the data into.

Create a DataFrame from a JSON string using Pandas

In addition to reading data from JSON files, we can also use Pandas DataFrame()functions to create DataFrames from JSON strings. Here are the steps to create a DataFrame from a JSON string:

1. Import the required libraries:

import pandas as pd
import json

2. Parse a JSON string into a Python object:

data = json.loads(json_string)

In the above code, json_stringis a string containing JSON data, and datais a parsed Python object.

3. Create a DataFrame using DataFrame()a function:

df = pd.DataFrame(data)

In the above code, dfa Pandas DataFrame object is created containing the data converted from the JSON string.

Parsing nested JSON data

When dealing with JSON data, we often encounter nested JSON structures. To properly parse and expand nested JSON data, we can use Pandas json_normalize()functions. Here are the steps to parse nested JSON data:

1. Import the required libraries:

import pandas as pd
from pandas.io.json import json_normalize

2. Use json_normalize()functions to parse nested JSON data:

df = json_normalize(data, 'nested_key')

In the above code, datais the Python object containing the nested JSON data, and nested_keyis the nested key to parse.

Case Study: Fetch JSON Data from Public API and Convert to DataFrame

Let's provide a practical example of how to use the public API to take JSON data and convert it into a Pandas DataFrame.

1. Import the required libraries:

import pandas as pd
import requests

2. Call API and get JSON data:

response = requests.get('https://api.example.com/data')
data = response.json()

In the above code, we use requeststhe library to send a request to the API and use .json()the method to convert the returned response into JSON data.

3. Convert JSON data to DataFrame:

df = pd.DataFrame(data)

In the above code, dfis the converted Pandas DataFrame object containing the JSON data fetched from the API.

JSON data cleaning and transformation

After converting JSON data to DataFrame, we may need to perform some data cleaning and transformation operations. This includes handling missing values, data type conversions, renaming columns, and more. Here are some examples of common operations:

1. Handle missing values:

df = df.fillna(0)  # 将缺失值填充为0

2. Data type conversion:

df['column_name'] = df['column_name'].astype(int)  # 将列的数据类型转换为整数

3. Multiple named columns:

df = df.rename(columns={
    
    'old_name': 'new_name'})  # 将列名从"old_name"改为"new_name"

Through these operations, we can clean and transform the JSON data to suit the needs of the DataFrame.

in conclusion

In this article, we discussed how to convert JSON to Pandas DataFrame. We covered using Pandas read_json()functions to read data from JSON files, and DataFrame()functions to create DataFrames from JSON strings. We also explored how to parse nested JSON data, and provided an example of getting JSON data from a public API and converting it to a DataFrame. Finally, we provide some common JSON data cleaning and transformation operations.

By converting JSON to Pandas DataFrame, we can conduct data analysis and processing more conveniently. Remember, before doing anything, make sure you have properly imported the required libraries and understand the structure of your data.