We can use the pandas.read_json() to read JSON data into a DataFrame, followed by a method called DataFrame.to_csv() to write the DataFrame to a CSV file. Luckily, the pandas library can also help us with that. Sometimes we might want to convert JSON data into a CSV format. It allows us to easily manipulate and analyze the data using the DataFrame object, which offers a rich set of functionality for working with tabular data. Using pandas to parse and work with JSON data in Python can be a convenient and powerful alternative to using the built-in json package. In this example, we select only the name and age columns from the DataFrame, and filter out any rows where the age is less than or equal to 30. One benefit of using pandas to parse JSON data is that we can easily manipulate the resulting DataFrame, for example by selecting columns, filtering rows, or grouping data. Finally, we print the resulting DataFrame. We then use pandas.read_json() to read the JSON string into a DataFrame. In this example, we define a Python dictionary representing JSON data, and use json.dumps() to convert it to a JSON string. Here is an example of how to parse JSON data with pandas: import pandas as pd pandas provides a method called pandas.read_json() that can read JSON data into a DataFrame.Ĭompared to using the built-in json package, working with pandas can be easier and more convenient when we want to analyze and manipulate the data further, as it allows us to use the powerful and flexible DataFrame object. In addition to the built-in json package, we can also use pandas to parse and work with JSON data in Python. If indent is not specified, the JSON data will be printed without any indentation. Note that indent is an optional argument to json.dumps() that specifies the number of spaces to use for indentation. We then print the resulting pretty printed JSON string. In this example, we define a Python dictionary representing JSON data, and then use json.dumps() with the indent argument set to 4 to pretty print the data. Here is an example of how to pretty print JSON data in Python: import json The json module provides a method called json.dumps() that can be used to pretty print JSON data. When working with JSON data in Python, it can often be helpful to pretty print the data, which means to format it in a more human-readable way. Note that if the JSON file is not valid JSON, json.load() will raise a exception. We then print the resulting Python object. We then pass the file object to json.load(), which parses the JSON data and returns a Python object. In this example, we use the open() function to open a JSON target file called data.json in read mode. The only difference is that instead of passing a JSON string to json.loads(), we pass the contents of a JSON file.įor example, assume we have a file named **data.json** that we would like to parse and read. To parse a JSON file in Python, we can use the same json module we used in the previous section. How to read and parse JSON files in Python JSON data is represented as a collection of key-value pairs, where the keys are strings and the values can be any valid JSON data type, such as a string, number, boolean, null, array, or object. It is widely used for transmitting data between a client and a server, as an alternative to XML. JSON (JavaScript Object Notation) is a lightweight data-interchange format that is easy for humans to read and write while also being easy for machines to parse and generate.
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