matplotlib cheat sheet

You haven't touched matplotlib for awhile and feeling a bit rusty?

Here you go!

Various Visualization Type

Box Plot

The box plot is an excellent tool to visually represent descriptive statistics of a given dataset. It can show the range, interquartile range (IQR), median, mode, outliers, and all quartiles.

np.random.seed(seed=0)
x = np.random.randn(1000)
y = np.random.randn(100)
z = np.random.randn(10)

fig, ax = plt.subplots()
ax.boxplot((x, y, z),
  vert=False,
  showmeans=True,
  meanline=True,
  tick_labels=('x', 'y', 'z'),
  patch_artist=True,
  medianprops={'linewidth': 2, 'color': 'purple'},
  meanprops={'linewidth': 2, 'color': 'red'})

plt.show()

The black line is called "whiskers". It is defined a q1 - k*IQR to the left. Where k is 1.5 by default. The right it's q3 + k * IQR. All points outside the whisker are considered outliers.

The mean is the dashed red line.
The median is the purple blue line.
The first quartile is the left edge of the blue rectangle.
The third quartile is the right edge of the blue rectangle.
The interquartile range is the length of the blue rectangle.
The range contains everything from left to right.
The outliers are the dots to the left and right.

Histogram

Define a bunch of bins and add elements to it.

x = np.random.randn(1000)
hist, bin_edges = np.histogram(x, bins=20)

fig, ax = plt.subplots()
ax.hist(x, bin_edges, cumulative=False)
ax.set_xlabel('x')
ax.set_ylabel('Frequency')

plt.savefig(blog_post_path+"histogram", bbox_inches="tight", dpi=200)
plt.show()

print(bin_edges)

You can almost see the bell curve.

Pie Chart

x, y, z = 128, 256, 1024
fig, ax = plt.subplots()
ax.pie((x, y, z), labels=('x', 'y', 'z'), autopct='%1.1f%%')

plt.show()

Bar chart

x = np.arange(21)
y = np.random.randint(21, size=21)

_, ax = plt.subplots()
ax.bar(x, y)
ax.set_xlabel('x')
ax.set_ylabel('y')

plt.show()

X-Y (scatter) plot

x = np.arange(21)
y = 5 + 2 * x + 2 * np.random.randn(21)
slope, intercept, r, *__ = stats.linregress(x, y)
line = f'Regression line: y={intercept:.2f}+{slope:.2f}x, r={r:.2f}'

fig, ax = plt.subplots()
ax.plot(x, y, linewidth=0, marker='s', label='Data points')
ax.plot(x, intercept + slope * x, label=line)
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.legend(facecolor='white')

plt.savefig(blog_post_path+"scatter_plot", bbox_inches="tight", dpi=100)
plt.show()

Heat Map

np.random.seed(42)

data = np.random.randn(500, 20)
matrix = np.cov(data, rowvar=False)

fig, ax = plt.subplots()
im = ax.imshow(matrix, cmap="coolwarm")
plt.colorbar(im, ax=ax)
plt.title("20×20 Covariance Heatmap")

plt.savefig(blog_post_path+"heat_map", bbox_inches="tight", dpi=100)
plt.show()

Time Series

data = [
    ("18.46%", "2025-12-19 15:09:03.820743+00"),
    ("23.76%", "2025-12-20 00:44:20.406011+00"),
    ("32.92%", "2025-12-20 15:44:12.245323+00"),
    ("48.07%", "2025-12-21 22:23:57.511577+00"),
    ("55.79%", "2025-12-22 15:16:56.125265+00"),
    ("68.42%", "2025-12-23 13:47:07.883421+00"),
]

df = pd.DataFrame(data, columns=["value", "timestamp"])
df["value"] = df["value"].str.rstrip("%").astype(float)
df["timestamp"] = pd.to_datetime(df["timestamp"])

plt.figure(figsize=(8, 4))
plt.plot(df["timestamp"], df["value"], marker="o")
plt.xlabel("Time")
plt.ylabel("Percentage")
plt.title("Time Series")
plt.grid(True)
plt.tight_layout()
plt.show()