How to Plot Time Series with Matplotlib

What will we cover in this tutorial?

In this tutorial we will show how to visualize time series with Matplotlib. We will do that using Jupyter notebook and you can download the resources (the notebook and data used) from here.

Step 1: What is a time series?

I am happy you asked.

The easiest way to understand it, is to show it. If you downloaded the resources and started the Jupyter notebook execute the following lines.

import pandas as pd
data = pd.read_csv("stock_data.csv", index_col=0, parse_dates=True)

This will produce the following output.

	High	Low	Open	Close	Volume	Adj Close
2020-01-02	86.139999	84.342003	84.900002	86.052002	47660500.0	86.052002
2020-01-03	90.800003	87.384003	88.099998	88.601997	88892500.0	88.601997
2020-01-06	90.311996	88.000000	88.094002	90.307999	50665000.0	90.307999
2020-01-07	94.325996	90.671997	92.279999	93.811996	89410500.0	93.811996
2020-01-08	99.697998	93.646004	94.739998	98.428001	155721500.0	98.428001

You notice the the far left column is called Date and that is the index. This index has a time value, in this case, a date.

Time series data is data “stamped” by a time. In this case, it is time indexed by dates.

The data you see is historic stock prices.

Step 2: How to visualize data with Matplotlib

The above data is kept in a DataFrame (Pandas data object), this makes it straight forward to visualize it.

import matplotlib.pyplot as plt
%matplotlib notebook

Which will result in a chart similar to this one.


This is not impressive. It seems like something is wrong.

Actually, there is not. It just does what you ask for. It plots all the 6 columns all together in one chart. Because the Volume is such a high number, all the other columns are in the same brown line (the one that looks straight).

Step 3: Matplotlib has a functional and object oriented interface

This is often a bit confusing at first.

But Matplotlib has a functional and object oriented interface. We used the functional.

If you try to execute the following in your Jupyter notebook.

data['My col'] = data['Volume']*0.5
data['My col'].plot()

It would seem like nothing happened.

But then investigate your previous plot.

Previous plot

It got updated with a new line. Hence, instead of creating a new chart (or figure) it just added it to the existing one.

If you want to learn more about functional and object oriented way of using Matplotlib we recommend this tutorial.

Step 4: How to make a new figure

What to do?

Well, you need to use the object oriented interface of Matplotlib.

You can do that as follows.

fig1, ax1 = plt.subplots()
data['My col'].plot(ax=ax1)

Which will produce what you are looking for. A new figure.

The new figure

Step 5: Make multiple plots in one figure

This is getting fun.

How can you create multiple plots in one figure?

On creating you actually do that.

fig2, ax2 = plt.subplots(2, 2)
data['Open'].plot(ax=ax2[0, 0])
data['High'].plot(ax=ax2[0, 1])
data['Low'].plot(ax=ax2[1, 0])
data['Close'].plot(ax=ax2[1, 1])

Notice that subplots(2, 2) creates a 2 times 2 array of axis you can use to create a plot.

This should result in this chart.


Step 6: Make a histogram

This can be done as follows.

fig3, ax3 = plt.subplots()
data.loc[:'2020-01-31', 'Volume']

Notice that we only take the first month of the Volume data here (data.loc[:’2020-01-31′, ‘Volume’]).

This should result in this figure.

Step 7: Save the figures

This is straight forward.


And the above figures should be available in the same location you are running your Jupyter notebook.

Want to learn more?

If you are serious about learning Python for Finance check out this course.

  • Learn Python for Finance with pandas and NumPy.
  • 21 hours of video in over 180 lectures.
  • “Excellent course for anyone trying to learn to code and invest.” – Lorenzo B.

Get Python for Finance HERE.

Python for Finance

Learn Python


  • 70 pages to get you started on your journey to master Python.
  • How to install your setup with Anaconda.
  • Written description and introduction to all concepts.
  • Jupyter Notebooks prepared for 17 projects.

Python 101: A CRASH COURSE

  1. How to get started with this 8 hours Python 101: A CRASH COURSE.
  2. Best practices for learning Python.
  3. How to download the material to follow along and create projects.
  4. A chapter for each lesson with a descriptioncode snippets for easy reference, and links to a lesson video.

Expert Data Science Blueprint

Expert Data Science Blueprint

  • Master the Data Science Workflow for actionable data insights.
  • How to download the material to follow along and create projects.
  • A chapter to each lesson with a Description, Learning Objective, and link to the lesson video.

Machine Learning

Machine Learning – The Simple Path to Mastery

  • How to get started with Machine Learning.
  • How to download the material to follow along and make the projects.
  • One chapter for each lesson with a Description, Learning Objectives, and link to the lesson video.

Leave a Comment