Plot Time Series data in Python using Matplotlib

In this tutorial we will learn to create a scatter plot of time series data in Python using matplotlib.pyplot.plot_date(). We will use Pandas Dataframe to extract the time series data from a CSV file using pandas.read_csv().

The syntax and the parameters of matplotlib.pyplot.plot_date()

The syntax for plt.plot_date() is :-

matplotlib.pyplot.plot_date(x, y, fmt='o', tz=None, xdate=True, ydate=False, *, data=None, **kwargs)

and it returns a list of Line2D objects representing the plotted data.

The parameters of matplotlib.pyplot.plot_date() are shown in the table below:-

Creating a scatter plot from time series data in Python Matplotlib

First of all, we will create a scatter plot of dates and values in Matplotlib using plt.plot_date(). We will be using Python’s built-in module called datetime(datetime, timedelta) for parsing the dates. So, let us create a python file called ‘plot_time_series.py’ and make necessary imports.

We will be using seaborn style to create scatter plot of the time series data. Finally, we will be passing dates and values to plt.plot_date() method and call plt.show() to plot.

# plot_time_series.py

import matplotlib.pyplot as plt
from datetime import datetime, timedelta
plt.style.use('seaborn')

dates = [
    datetime(2019, 8, 21),
    datetime(2019, 8, 22),
    datetime(2019, 8, 23),
    datetime(2019, 8, 24),
    datetime(2019, 8, 25),
    datetime(2019, 8, 26),
    datetime(2019, 8, 27),
]

y = [0, 1, 2, 3, 4, 5, 6]

plt.plot_date(dates, y)
plt.tight_layout()
plt.show()

This will create a simple scatter plot for the time series data.

Creating a line plot from time series data in Python Matplotlib

If we want to create a line plot instead of the scatter plot, we will have to set linestyle=’solid’ in plt.plot_date(). We can also change the markers.

# plot_time_series.py

plt.plot_date(dates, y, linestyle ='solid')

Aligning date ticks labels in Matplotlib

Sometimes, we are working with a lot of dates and showing them horizontally won’t be a good idea in that case. So, we can also change the alignment of the dates on x-axis of time series plot by using autofmt_xdate() on plt.gcf().

# plot_time_series.py

plt.gcf().autofmt_xdate

Formatting dates in Time Series plots in Matplotlib using Python

We will be formatting the date in our time series plot by using dates from matplotlib. We will be passing a python format string , as we would have passed to strftime to format the date in our time series plot.

So, I will be using matplotlib.dates.DateFormatter to format the date in DD-MM-YYYY and then pass it to set_major_formatter() method of matplotlib.pyplot.gca().

# plot_time_series.py

date_format = mpl_dates.DateFormatter('%d-%m-%Y')
plt.gca().xaxis.set_major_formatter(date_format)

Plotting time series data in Python from a CSV File

Currently, we were using hard-fed example data to plot the time series. Now we will be grabbing a real csv file of bitcoin prices from here and then create a time series plot from that CSV file in Python using Matplotlib. So, now we have the time series data in CSV file called ‘plot_time_series.csv’. Let us plot this time series data. We will be using pandas’ read_csv method to plot the time series data:-

# plot_time_series.py

data = pd.read_csv('plot_time_series.csv')
price_date = data['Date']
price_close = data['Close']
plt.plot_date(price_date, price_close, linestyle='solid')

Converting the dates to datetime format

One thing to note here is that the dates on x-axis are shown as strings and not the dates so DateFormatter won’t work on that. To make them date objects, we will be using pandas.to_datetime.

# plot_time_series.py

import matplotlib.pyplot as plt
from datetime import datetime, timedelta
from matplotlib import dates as mpl_dates
import pandas as pd
plt.style.use('seaborn')

data = pd.read_csv('plot_time_series.csv')
data['Date'] = pd.to_datetime(data['Date'])
data.sort_values('Date', inplace=True)
price_date = data['Date']
price_close = data['Close']
plt.plot_date(price_date, price_close, linestyle='solid')
plt.gcf().autofmt_xdate()
date_format = mpl_dates.DateFormatter('%d-%m-%Y')
plt.gca().xaxis.set_major_formatter(date_format)
plt.tight_layout()
plt.title('Bitcoin Prices')
plt.xlabel('Date')
plt.ylabel('Closing')
plt.show()

So, in this tutorial we have learned to plot time series data in python from raw data as well as csv using pandas. We also learned how to change the scatter time series plot to line time series plot and much more.

Matplotlib Video Tutorial Series

We are glad to inform you that we are coming up with the Video Tutorial Series of Matplotlib on Youtube. Check it out below.

Table of Contents of Matplotlib Tutorial in Python

Matplotlib Tutorial in Python | Chapter 1 | Introduction

Matplotlib Tutorial in Python | Chapter 2 | Extracting Data from CSVs and plotting Bar Charts

Pie Charts in Python | Matplotlib Tutorial in Python | Chapter 3

Matplotlib Stack Plots/Bars | Matplotlib Tutorial in Python | Chapter 4

Filling Area on Line Plots | Matplotlib Tutorial in Python | Chapter 5

Python Histograms | Matplotlib Tutorial in Python | Chapter 6

Scatter Plotting in Python | Matplotlib Tutorial | Chapter 7

Plot Time Series in Python | Matplotlib Tutorial | Chapter 8

Python Realtime Plotting | Matplotlib Tutorial | Chapter 9

Matplotlib Subplot in Python | Matplotlib Tutorial | Chapter 10

Python Candlestick Chart | Matplotlib Tutorial | Chapter 11

In the next tutorial we will be learning to plot live data in real-time. Stay tuned.

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