Pandas provides several functions and tools to work with APIs (Application Programming Interfaces) and import data from them into a Pandas dataframe. Here is an example:
import pandas as pd import requests # send a GET request to the API and get the response response = requests.get('https://api.example.com/data') # convert the JSON response to a Pandas dataframe df = pd.read_json(response.text) # print the dataframe print(df)
In this example, we use the requests library to send a GET request to an API endpoint and get the response. We then use the read_json() function to convert the JSON response to a Pandas dataframe. The response.text attribute contains the JSON response as a string. If the API response is in a different format, such as XML or CSV, you can use other Pandas functions to read the response into a dataframe.
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 to a file or database using various Pandas functions such as to_csv(), to_excel(), to_sql(), and more. Here’s an example:
# write the dataframe to a CSV file df.to_csv('output.csv', index=False) # write the dataframe to a SQL database import sqlite3 conn = sqlite3.connect('example.db') df.to_sql('data', conn, if_exists='replace', index=False)
In this example, we use the to_csv() function to write the Pandas dataframe to a CSV file called ‘output.csv’. The index=False parameter is used to prevent writing the Pandas dataframe index to the CSV file. We also use the to_sql() function to write the Pandas dataframe to a SQLite database. The if_exists=’replace’ parameter is used to replace any existing data in the ‘data’ table in the database. Check out the pandas documentation for more details on working with APIs in Pandas.