Visualization in Python — Creating Dynamic Time Series using matplotlib

Learn how to create a dynamic time series using matplotlib

Photo by Jiyeon Park on Unsplash

Most of the time when comes to matplotlib, you probably use it to create static visualization such as line charts, histograms, pie charts, scatter plots, etc. However, what if you want to create a dynamic chart that updates itself as time goes by?

In this article, I will show you how to use matplotlib to create a dynamic time series chart. In particular, you will use it to display the prices of a stock and update it dynamically every second.

Creating the Stocks REST API

Instead of connecting to a live stocks API, it is easier to write your own simulated API for retrieving stock prices. For this, I will use the flask framework to create a simple REST API with a single endpoint to fetch the prices of stocks.

Here is the entire program for the REST API (which I have named stocksdata.py):

import pickle
from flask import Flask, request, jsonify    
import numpy as np
app = Flask(__name__)
@app.route('/stocks/v1/fetch', methods=['POST'])
def fetch():
    #---get the features to predict---
    details = request.json
    #---create the features list for prediction---
    symbol = details["Symbol"] 
    #---formulate the response to return to client---
    response = {}
    response['symbol'] = symbol
    if symbol == 'AAPL':
        response['price'] = np.random.uniform(168,172)
    if symbol == 'AMD':
        response['price'] = np.random.uniform(140,145)
    if symbol == 'AMZN':
        response['price'] = np.random.uniform(2900,3200)    
    return  jsonify(response)
if __name__ == '__main__':
    app.run(host='0.0.0.0', port=5000)

In the above program, there is a single endpoint — /stocks/v1/fetch, which you can call to fetch the stock prices of a specific stock. All you need is to send it a JSON string containing the stock symbol using the POST method. For simulation purposes the API only supports three stocks — AAPL, AMD, and AMZN. I have also randomized the prices of each stock based on their recent price ranges…