Basic Plotting with Matplotlib

In today's lesson, we'll immerse ourselves in the world of Basic Plotting with Matplotlib. You'll learn how to create basic line plots to visualize Tesla's ($TSLA) stock data. This lesson is essential as visualizing financial data is crucial for identifying trends and making informed trading decisions. By the end of this lesson, you'll be proficient in plotting data using Matplotlib, customizing plots, and interpreting the results.

Matplotlib is a powerful plotting library in Python that allows developers to create a wide variety of static, animated, and interactive plots. It's widely used in data visualization because of its simplicity and versatility.

Why Matplotlib?

• Versatility: Matplotlib can be used to create plots ranging from simple line charts to complex 3D plots.
• Customization: Almost every aspect of a Matplotlib plot can be customized.
• Integration: It works well with Pandas, making it easy to visualize DataFrame data.

To get started with Matplotlib, you need to import the pyplot module:

import matplotlib.pyplot as plt

Before plotting, we need to prepare our Tesla stock data.

Loading the Tesla Dataset: We'll use the load_dataset function from the datasets library to load Tesla's historic price data:

import pandas as pd
from datasets import load_dataset

# Load TSLA dataset
tesla_data = load_dataset('codesignal/tsla-historic-prices')
tesla_df = pd.DataFrame(tesla_data['train'])

Converting Dates to Datetime Objects: To handle the time series data correctly, we need to convert the 'Date' column to datetime format:

tesla_df['Date'] = pd.to_datetime(tesla_df['Date'])

Setting Date as Index: Next, we'll set the 'Date' column as the index of our DataFrame and sort it:

tesla_df.set_index('Date', inplace=True)
tesla_df.sort_index(inplace=True)

At this point, our DataFrame is ready for plotting.

Now that our data is prepared - let's create a basic line plot.

Using Matplotlib, we can plot the 'Close' prices against dates:

plt.plot(tesla_df.index, tesla_df['Close'])

In the plt.plot() function:

• tesla_df.index: Represents the x-axis data, which, in this case, is the dates from the DataFrame index.
• tesla_df['Close']: Represents the y-axis data, which are the closing prices from the 'Close' column of the DataFrame.

It's also essential to add titles and labels to make our plot more informative:

plt.title('TSLA Closing Price Over Time')
plt.xlabel('Date')
plt.ylabel('Price (USD)')

On top of that, adding a legend helps identify what the plotted line represents:

plt.legend(['Close Price'])