Python Pandas Read/Write JSON Data
Pandas can read and write JSON data using the read_json and to_json functions.
These functions make it easy to convert JSON data to Pandas dataframes and vice versa.
This article will explore the various ways in which you can read JSON data in Pandas, as well as some useful tips and tricks on how to do so.
Pandas JSON Benefits
The benefits of using Pandas for working with JSON data are numerous.
Some of the key benefits include:
- Pandas provides a simple and intuitive interface for working with JSON data. With just a few lines of code, you can load JSON data into a Pandas dataframe, manipulate the data, and save it back to JSON format.
- It is designed to handle large datasets efficiently. It uses optimized data structures and algorithms to provide fast and efficient data handling, which is particularly important when working with large JSON datasets.
- It provides a wide range of data manipulation and analysis tools, making it possible to handle complex JSON data in a variety of ways. For example, you can easily filter, group, and aggregate data in a Pandas dataframe.
- It integrates seamlessly with other popular Python libraries, such as NumPy and Matplotlib. This makes it possible to use Pandas as part of a larger data analysis workflow, and to visualize and communicate your findings using Matplotlib.
- Pandas JSON data is often nested, meaning that a key in a JSON object can have another JSON object as its value. Pandas can handle nested JSON data by flattening the data into a dataframe, making it easier to work with and analyze.
Reading JSON Data with Pandas
Pandas JSON is often utilized as a means of saving, or retrieving, large data sets that are in JSON format.
In the world of programming, such as Pandas, JSON is commonly known as plain text with the format of an object.
‘student_data.json’ is the name of the JSON file that we will be working with in our examples.
Into a DataFrame load the student_data.json file:
Display the entire data by Implementing the read_json() function then set the title of the index:
Dictionary as JSON
JSON = Python Dictionary
There is no difference between Python dictionaries and JSON objects according to Pandas JSON since JSON objects follow the same format.
In a DataFrame, load a student_data Python dictionary as follows:
Assign “Name” an index title utilizing set_index() function:
The given code defines a dictionary student_data with keys as the column names of a data frame and values as a list of data points for each column. It includes data for six students, their gender, and their scores in three courses – Data Science, Artificial Intelligence, and Machine Learning.
Next, the pandas library is imported, and a data frame is created from the student_data dictionary using the DataFrame method of pandas. The set_index() method is used to set the “Name” column as the index of the data frame. Finally, the resulting data frame is printed using the print() function.
The resulting data frame has six rows (one for each student) and four columns – “Gender”, “Data Science”, “Artificial Intelligence”, and “Machine Learning”. The index of the data frame is the “Name” column. This data frame can be used to analyze the performance of students in the three courses and compare their scores with their gender.
Writing JSON Data with Pandas
To write JSON data with Pandas, we can use the to_json function.
This function takes a Pandas dataframe as input and returns a JSON string.
Here is an example of how to use “to_json” to save a Pandas dataframe as JSON data:
In above example, we create a Pandas dataframe containing some sample data. We then use the to_json function to save the dataframe as JSON data to a file called data.json.
We then load the JSON data back into a new dataframe using the read_json function, and display the first few rows of the resulting dataframe using the “head” function.