Introduction to Creating Data Visualizations with Python matplotlib

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As a data professional, you must be able to extract, curate, analyze, and visualize your data. How do you approach data visualization if your dataset is ready to be visualized?


In this tutorial, we will examine the matplotlib data visualization library for Python. There are other libraries, too, e.g., Plotly, seaborn, yt among others. Each has some strengths, but matplotlib is a preferred choice for static visualizations - plots that do not offer interaction.

Python matplotlib Installation

If you have a fresh conda environment, install the module by running pip install matplotlib or conda install matplotlib, depending on your package manager.

Basic Line Plot

As a first example, let us see how to plot a variable x vs. a variable y.

1: import matplotlib.pyplot as plt
2: import numpy as np
4: x = np.linspace(0, 10, 100)
5: y = np.sqrt(x)
7: fig, ax = plt.subplots()
8: ax.plot(x, y)

First, we create a one-dimensional array, x, consisting of 100 data points between 1 and 10. On the other hand, Y returned the non-negative square root of the array, elementwise. Plotting these variables results in the following:

matplotlib basic plot


Let us examine lines 7 to 9 more closely:

  • 7: A figure on which matplotlib charts our data and a single axis. By providing additional parameters to subplots, we can create a grid of axes, e.g., a 2x2.
  • 8: On the single axes ax, we create a plot. Specific plots can be called, e.g., scatter. Here, plot automatically determines the resulting plot based on the input variables.
  • 9: Finally, we show the plot. Alternatively, we can save it to the current working directory by calling plt.savefig('foo.png')

There are many properties available to the axes object, and a good knowledge of them is required to develop impactful visuals. I recommend checking out the anatomy of a matplotlib figure for additional information.


Generally, the expected input to the variables being plotted should be numpy.array or a data object than can be parsed to it by using numpy.asarray. In practice, you may find yourself using Pandas dataframes directly as x and y arguments, but they may not always work as expected. On such occasions, try parsing your dataframe to a numpy array.


There are two coding styles available:

  • The OO-style (object-oriented): Explicitly create your figure and axes and call the related methods as needed. This is what we did in the example above.
  • Pyplot-style: Relies on pyplot internals to implicitly create and manage figure and axes objects. The same example would look like this:
1: plt.figure() 
2: plt.plot(x,y) 

In this tutorial, I will stick to the OO style.

Scatter Plot

Let us expand the first example by specifically creating a scatter plot. The data I will use represent a comparison of the nutritional values of apples and oranges. We start with a dictionary that we convert to a Pandas dataframe with this syntax:

01: d = {'magnesium':[7,13],
02:     'calcium':[9.5,52],
03:     'phosphorus':[9.5,18],
04:     'vit_c':[9,70],
05:     'fat':[0,0.2],
06:     'fiber':[4,3.1],
07:     'calories':[77,62],
08:     'carbs': [20, 15.4]
09:     }
10: df = pd.DataFrame(data=d)

Here is how we can compare the nutritional content of the two fruits with a matplotlib scatter plot:

01: fig, ax = plt.subplots(figsize=(8, 4), dpi=100)
03: x = np.array(df.iloc[0])
04: y = np.array(df.iloc[1])
06: ax.scatter(x, y, size=np.int16(y*4), color='g')
08: ax.set(xlim=(0,np.max(x)+3),
09:         ylim=(0,np.max(y)+3),
10:         xlabel='Apples',
11:         ylabel='Oranges',
12:         title='Comparison of Apples vs Oranges')
14: ax.grid(True)    
16: for i,n in enumerate(d):
17:         ax.annotate(n, (d[n][0], d[n][1]), xytext=(x[i]+1, y[i]+1))

First, we create a Figure and an Axes (line 1). Then, we create x and y variables by parsing the rows of the dataframe to numpy arrays (lines 3 and 4). Next, we create the scatter plot (6) by passing x and y as variables to the plot. Additionally, we pass the y array converted to integer values and multiply them by 4 to serve as the size for the dots. We also pass 'g' as a single color, denoting green. Next (8), using the set method, we pass several properties to the plot, such as axis limits, labels, and plot title. On line 14, we instruct matplotlib to display the grid lines. On lines 16 and 17, we iterate over the dictionary and pass the keys along with the data points to the annotated method, so we can see which fruit property we are analyzing. Finally (19), we show the plot.

The result is:

matplotlib scatter plot

Numeric and Categorical Data

In the examples so far in this Python tutorial, we plotted only two numeric variables. What if we had to plot two categorical variables against one numerical? One way to achieve a good result is a parallel bar plot. Let us first create a small dataset representing sales of apples and oranges:

1: d = {
2:     'date':['2022-12-01','2022-12-01','2022-12-02','2022-12-02','2022-12-03','2022-12-03','2022-12-04','2022-12-04','2022-12-05','2022-12-05'],
3:     'fruit':['apples','oranges','apples','oranges','apples','oranges','apples','oranges','apples','oranges'],
4:     'sales_kg':[5,12,3,10,8,6,7,9,10,5]
5: }
6: df = pd.DataFrame(data=d, index=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
7: df
pandas dataframe sales of apples and oranges

The first categorical variable is "date," which we will use as the x-axis. Against it, on the y-axis, we will plot the sales in kilograms (kg). The fruit variables will be used for a legend.

01: fig, ax = plt.subplots(figsize=(8, 4), dpi=100)
03: labels = np.array(df[df['fruit']=='apples']['date'])
04: y1 = np.array(df[df['fruit']=='apples']['sales_kg'])
05: y2 = np.array(df[df['fruit']=='oranges']['sales_kg'])
06: width = 0.15
07: target_kg = 7
09: x = np.arange(len(labels))
11: p1 = + width/2, y1, width, label='Apples', color='#6AB547')
12: p2 = - width/2, y2, width, label='Oranges', color='#F2BB05')
14: ax.axhline(target_kg, color='grey', linewidth=0.8)
15: ax.bar_label(p1, label_type='center')
16: ax.bar_label(p2, label_type='center')
17: ax.set_xticks(x, labels)
18: ax.set(xlabel='date',
19:         ylabel='kg',
20:         title='Apples and Oranges Sales in KG')
21: ax.annotate('target', (0.5,target_kg),xytext =(0.5, target_kg+2),arrowprops = dict(facecolor ='#7EBC89',
22:                                shrink = 0.05))
23: ax.legend()

Let us break this down, line by line:

  • 1: Creating a figure and a single axis with a size of 8x4 inches and a DPI of 100.
  • 3: Casting to numpy array the labels we need for the x-axis.
  • 4 & 5: Creating y variables. There are two because we have two types of fruits.
  • 6: Setting the width of the bar.
  • 7: Setting a sales target which will be used to display a constant horizontal line on the plot.
  • 9: Create an array of length equal to the number of labels we have, so 5.
  • 11 & 12: Create two bar plots, passing values for x, then corresponding y, the width, a label, and a desired color. The results are assigned to variables p1 and p2 of type matplotlib.container.BarContainer.
  • 14:Add a constant horizontal line with the value target_kg.
  • 15 & 16: Using the bar_label method, we can display the data point's value inside the bar. We pass p1 and p2, and the bar label automatically uses their datavalue property.
  • 17: Need to explicitly set the ticks of the x-axis. X is an array [0,1,2,3,5], but we need the dates. set_xticks allows to pass an array and the corresponding labels.
  • 18 - 20: Use the set method to set some additional properties.
  • 21 & 22: Using annotate, we can add text and point to a specific area on the plot. Here we want to explain what the horizontal line is.
  • 23: display the legend.
  • 24: show the plot.

The result is the following bar chart:

matplotlib sales of apples vs oranges plot


Using some synthetic data, we showcased how to get started with two basic types of plots available in the matplotlib python package: a scatter plot and a parallel bar plot. You can reuse these examples and plug your data from a database or an API. Matplotlib supports many other types of plots, such as statistics plots and 3D plots.

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About the author
MSSQLTips author Hristo Hristov Hristo Hristov is a Data Scientist and Power Platform engineer with more than 12 years of experience. Between 2009 and 2016 he was a web engineering consultant working on projects for local and international clients. Since 2017, he has been working for Atlas Copco Airpower in Flanders, Belgium where he has tackled successfully multiple end-to-end digital transformation challenges. His focus is delivering advanced solutions in the analytics domain with predominantly Azure cloud technologies and Python. Hristoís real passion is predictive analytics and statistical analysis. He holds a masterís degree in Data Science and multiple Microsoft certifications covering SQL Server, Power BI, Azure Data Factory and related technologies.

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Article Last Updated: 2023-02-22

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