![]() ![]() ![]() In order to retrieve the data from the response object, we need to convert the raw response content into a JSON (JavaScript Object Notation) type data structure. The response object could be used to access certain features such as content, headers, etc. # Getting the data and normalizing the JSON to a pandas data frameĭf = pd.json_normalize(json_response) Parsing Nested JSON with Pandas Here, we are connecting to ‘ /users‘ to get a list of users. The GET method is used to retrieve information from a given server using a given url. We will start with importing the modules we need: import requests Amazon RDS for PostgreSQL is yet another free possibility you can play with. ![]() I will use a pre-set PostgreSQL database but you can use any other PostgreSQL instance of your choice. I will cover connection with APIs in a different post. It will be our first attempt to easily get a response from a REST API. We will use the requests module for making HTTP requests and fetch the data in json format. Pushing and pulling data from a database is a process used across many companies and I will try to review its basics.įor practice purposes we will get our data while connecting to JSONPlaceholder, which is a free online practise REST API. We will take advantage of pandas data frames to clean and create a schema, and eventually upload a CSV file to a created table of our choice within PostgreSQL database using the psycopg2 module. In this post we will go through how to upload data from a CSV to a PostgreSQL Database using python. ![]()
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