Mastering Plotly and Dash Data Visualization with Python/

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Subplots

Subplots

Learn what subplots are and how to use them with figure types you have already learned.

We'll cover the following...

Our data

Here we will use two datasets: the nike variable stores a dataset with the high prices and adjusted closing prices for Nike stock, and the dataset nike_log_returns stores the logarithmic returns of the high price and adjusted closing price columns. It is not important that we understand how logarithmic returns work— we just need to understand that it is another way we can interpret our financial time series data.

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Let’s print the first few rows to see what the data looks like:

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# Import libraries
import pandas as pd
import numpy as np
# Import datasets
nike = pd.read_csv('/usr/local/csvfiles/nike.csv')
print(nike.head())

Let’s also look at the second dataset:

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# Import libraries
import pandas as pd
import numpy as np
# Import datasets
nike_log_returns = pd.read_csv('/usr/local/csvfiles/nike_log_returns.csv')
print(nike_log_returns.head())

Column-aligned subplots

To make subplots in Plotly, we first need to import the necessary functionality to do so:

from plotly.subplots import make_subplots

Inside make_subplots, we can pass in rows and columns as keyword arguments, in which we can create a grid of plots that will allow us to explore our datasets from many different angles.

What we will now do is create a grid with a single row and two columns, doing so with the following code:

fig =
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