Updated On : Mar-05,2023 Time Investment : ~15 mins

# Bullet Charts using Matplotlib | Python¶

Data visualization is an essential aspect of data analysis and communication. It is crucial to present data in a format that is easy to comprehend, and bullet charts have proven to be an excellent way to visualize data.

Bullet charts are a variation of bar charts that display a single measure along with qualitative ranges that help in understanding the data better.

Matplotlib is a powerful data visualization library in Python that provides a simple and effective way to create bullet charts.

Below, we have included sample images showing how bullet chart looks.

As a part of this tutorial, we will explore how to create bullet charts using Python data viz library "Matplotlib" and see some examples of how they can be used to represent different types of data. First, we have created a simple bullet chart from sample image above and then added a theme to improve its look further.

### Video Tutorial¶

Please feel free to check below video tutorial if feel comfortable learning through videos.

First, we have imported matplotlib and printed the version that we have used in our tutorial.

```import matplotlib

print("Matplotlib Version : {}".format(matplotlib.__version__))
```
```Matplotlib Version : 3.5.3
```

Below we have created our bullet chart using matplotlib.

First, it defines a list of colors to use for the chart.

Then, it creates a new figure. The figsize parameter specifies the size of the figure.

Next, it adds a subplot to the figure with ax = fig.add_subplot(1,1,1) line. The "1,1,1" parameter specifies that the subplot should be a single plot.

The next line creates two horizontal bar charts using the plt.barh() method.

The first bar chart has five bars with a width of 2 and starting at positions 0, 2, 4, 6, and 8, respectively. The y parameter specifies the y-axis positions for the bars, and the color parameter specifies the color for each bar. It is used to create a long horizontal bar of various colors specifying stages.

The second bar chart is a single bar with a width of 6.5 and a height of 0.4, colored black. It is an inner bar chart used to represent current value of the metric.

The next line creates a vertical line using the plt.vlines() method. This is used to represent some kind of threshold for metric value.

The next two lines set the x and y tick labels using the plt.xticks() and plt.yticks() methods. The fontsize and fontweight parameters specify the font size and weight of the tick labels.

The next line hides the spines (the lines that frame the chart).

Finally, the last two lines set the x-axis label and the title of the chart using the plt.xlabel() and plt.title() methods. The loc parameter specifies the location of the title, and the pad parameter specifies the padding around the title.

```import matplotlib.pyplot as plt

colors = ["#19d228", "#b4dd1e", "#f4fb16", "#f6d32b", "#fb7116"]

fig = plt.figure(figsize=(20,4))

plt.barh(y=[0,0,0,0,0], width=[2,2,2,2,2], left=[0,2,4,6,8], color=colors);
plt.barh(y=, width=[6.5], color="black", height=0.4);

plt.vlines(x=9.0, ymin=-0.2, ymax=0.2, color="black", linewidth=5);

plt.xticks(range(0,11), ["{} %".format(i) for i in range(0,101,10)]);
plt.yticks(, ["Evaluation"], fontsize=16, fontweight="bold");

ax.spines[["bottom", "top", "left", "right"]].set_visible(False);

plt.xlabel("Percent (%)", fontsize=16, fontweight="bold");
plt.title("Bullet Chart", loc="left", pad=15, fontsize=25, fontweight="bold");
```

Below, we have recreated our previous example bullet chart but we have introduced a theme to the chart to improve its look. We have wrapped chart code in call to plt.style.context("fivethirtyeight") method. This will apply theme fivethirtyeight to chart.

```import matplotlib.pyplot as plt

with plt.style.context("fivethirtyeight"):
fig = plt.figure(figsize=(20,4))

plt.barh(y=[0,0,0,0,0], width=[2,2,2,2,2], left=[0,2,4,6,8], color=colors);
plt.barh(y=, width=[6.5], color="black", height=0.4);

plt.vlines(x=9.0, ymin=-0.2, ymax=0.2, color="black", linewidth=5);

plt.xticks(range(0,11), ["{} %".format(i) for i in range(0,101,10)]);
plt.yticks(, ["Evaluation"], fontsize=16, fontweight="bold");

ax.spines[["bottom", "top", "left", "right"]].set_visible(False);

plt.grid(False);

plt.xlabel("Percent (%)", fontsize=16, fontweight="bold");
plt.title("Bullet Chart", loc="left", pad=15, fontsize=25, fontweight="bold");
```

This ends our small tutorial explaining how to create bullet charts using the Matplotlib library in Python. Bullet charts are a type of bar chart that is useful for visualizing a single measure along with qualitative ranges.

The author provided step-by-step instructions for creating a bullet chart using Matplotlib, including setting the figure size, adding subplots, creating horizontal and vertical lines, setting the tick labels, hiding the spines, and adding a title and axis label.

The author also included a sample chart and provided tips for customizing the chart to meet different data visualization needs.

Overall, the article provides a helpful introduction to bullet charts and how to create them using Matplotlib in Python.

Sunny Solanki

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