Most of the data that you will work with will be available in the CSV or Excel formats and thus you will inevitably convert them into a pandas DataFrame in order to work with them effectively. Bokeh extends its functionality to help us build interactive yet meaningful plots using a pandas DataFrame in Python. In this section, we will construct scatter plots and time series plots using a pandas DataFrame.
For this section, we will be using a popular dataset about the stock market found on Kaggle that can be accessed via this link: Kaggle S&P 500 stock data (https://www.kaggle.com/camnugent/sandp500/data).
As a first step, let's load the dataset into Jupyter Notebook. We can do this using the code shown here:
#Importing the required packages
import pandas as pd
#Read in the data
df = pd.read_csv('all_stocks_5yr.csv')
#Filtering...