Pandas provides a function called read_json() to read JSON files into a Pandas dataframe. Here’s an example:
import pandas as pd # read the JSON file into a Pandas dataframe df = pd.read_json('data.json') # print the dataframe print(df)
In this example, we use the read_json() function to read a JSON file called ‘data.json’ into a Pandas dataframe. By default, read_json() assumes that the JSON file contains a table with one record per line, and each column in the table corresponds to a key in the JSON object. If the JSON file has a different format, you can specify the orient parameter to indicate how the data is structured. For example, if the JSON file contains a dictionary with keys as the columns and values as the data, you can set orient=’columns’.
Once you have a Pandas dataframe, you can manipulate and analyze the data using the various functions available in Pandas. For example, you can filter rows based on a condition, group the data by one or more columns, aggregate the data using various functions, and more.
Finally, you can write the Pandas dataframe back to a JSON file using the to_json() method. Here’s an example:
# write the dataframe to a JSON file df.to_json('output.json')
In this example, we use the to_json() method to write the Pandas dataframe back to a JSON file called ‘output.json’. By default, to_json() writes the data in a compact format, but you can also specify various parameters to customize the output, such as orient to specify the format of the JSON file or indent to control the indentation level. Check out the pandas documentation for more details on how to handle JSON data in Pandas.