Fetching Data from API using Request Library

Step 1: Importing necessary libraries

Here we require two library requests to make API calls, and Pandas to make a DataFrame.

Step 2: Call the API using the requests library 

In this step we are going to call out TMDB API using a requests response then we got a response from it. This line of code is making a GET request to the TMDB API endpoint for top-rated movies.  The response will be a JSON object containing information about the top-rated movies, such as the movie title, overview, release date, popularity, vote average, and vote count. The response object also contains other information such as the status code and headers of the response.

Step 3: Creating a new DataFrame

Here we are going to create a new DataFrame using Pandas in which we store our result fetch from the API.

Step 4:  Putting the Results fetch from our API to the Dataframe

In this step we are  using the requests library to make GET requests to the Movie Database (TMDB) API to retrieve the top rated movies. It starts by checking if the initial request has a status code of 200 (which indicates a successful response), and if it does, it enters a loop that runs 399 times(it means we are going to fetch the data of first 400 pages). In each iteration of the loop, it makes a request to the API for the next page of top-rated movies and appends the relevant data (movie id, title, overview, release date, popularity, vote average, and vote count) to a DataFrame called “temp_df”. After each iteration, it appends the “temp_df” to another DataFrame called “df” using the .append() method. If the initial request has a status code other than 200, then it prints an error message with the status code.

Step 5: Printing first five rows of our DataFrame

The below code prints the shape of our dataset. it means it going to print how many rows and columns have present in our data frame. We are going to print the first five rows of our dataset.

Output:

Step 6: Converting our Dataframe into a CSV file and store it

We are going to save the dataframe df to a CSV file named ‘movies.csv’ and then download it to our computer.

Output:

Complete Code

Output:

In this article, we are going to see how can we fetch data from API and make a CSV file of it, and then we can perform various stuff on it like applying machine learning model data analysis, etc. Sometimes we want to fetch data from our Database Api and train our machine learning model and it was very real-time by applying this method we can train our machine learning model using updated data, so our model’s predictions are accurate. Here we used the requests library in Python to fetch data from our API.