Matplotlib¶

Install packages¶

You can use both pip or astral/uv.

!uv pip install -q \
    pandas==2.3.2 \
    pandas-stubs==2.3.2.250827 \
    numpy==2.3.2 \
    matplotlib==3.10.6

Import packages¶

import random

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

Usage¶

Plots¶

Basic Plot¶

line_x_values = [1, 2, 3, 4, 5, 6]
line_y_values = list(map(lambda x: x**2, line_x_values))

plt.plot(
    line_x_values,
    line_y_values,
    color="#5B584F",
    linestyle="--",
    linewidth="1.5",
    marker="o",
    markersize="5",
)

plt.xlabel("X axis")
plt.ylabel("Y axis")
plt.title("Line Plot")
plt.grid(True)
plt.show()

Multiple Plots¶

multiplot_x_values = [1, 2, 3, 4, 5, 6]
multiplot_y1_values = list(map(lambda x: x**2, multiplot_x_values))
multyplot_y2_values = list(map(lambda x: x**3, multiplot_x_values))

plt.figure(figsize=(10, 5))

plt.subplot(1, 2, 1)  # rows, columns, plot "slot"
plt.plot(multiplot_x_values, multiplot_y1_values, color="#F79E6B")
plt.plot(multiplot_x_values, multyplot_y2_values, color="#F7CD82")
plt.title("First Plot")

plt.subplot(1, 2, 2)  # rows, columns, plot "slot"
plt.plot(multiplot_x_values, multiplot_y1_values, color="#92A78C")
plt.plot(multiplot_x_values, multyplot_y2_values, color="#E0D5AD")
plt.title("Second Plot")
plt.show()

Bar Plot¶

bar_plot_categories = ["A", "B", "C", "D", "E", "F"]
bar_plot_values = [random.randint(1, 100) for _ in bar_plot_categories]

plt.bar(bar_plot_categories, bar_plot_values, color="#F79E6B")
plt.xlabel("Categories")
plt.ylabel("Values")
plt.title("Line Plot")
plt.show()

Histograms¶

Histogram plots represent the distribution of a dataset. The data is divided into beans that defines X axis, while Y axis show the count of data points in each bin.

histogram_data = [random.randint(1, 5) for _ in range(20)]

plt.hist(histogram_data, bins=5, color="#92A78C", edgecolor="#5B584F")
plt.show()

Scatter Plot¶

scatter_x_values = [random.randint(30, 90) for _ in range(100)]
scatter_y_values = [random.randint(20, 100) for _ in range(100)]

plt.scatter(scatter_x_values, scatter_y_values, color="#F79E6B")
plt.show()

Pie Chart¶

pie_chart_labels = ["A", "B", "C", "D", "E"]
pie_chart_sizes = [random.randint(1, 50) for _ in range(5)]
colors = ["#F79E6B", "#F7CD82", "#5B584F", "#92A78C", "#E0D5AD"]

plt.pie(
    x=pie_chart_sizes,
    labels=pie_chart_labels,
    colors=colors,
    autopct="%1.0f%%",
)
plt.show()

size = 10

start_date = pd.to_datetime("2023-01-01")

data_for_aggregation = {
    "order_id": np.arange(1, size + 1),
    "product": np.random.choice(
        ["Phone", "Laptop", "Keyboard", "Mouse", "Monitor"], size=size
    ),
    "region": np.random.choice(["North", "South", "East", "West"], size=size),
    "price": np.random.randint(100, 2000, size=size),
    "quantity": np.random.randint(1, 10, size=size),
    "order_date": start_date
    + pd.to_timedelta(np.random.randint(0, 365, size=size), unit="D"),
}

dataframe_for_aggregation = pd.DataFrame(data_for_aggregation)

dataframe_for_aggregation.head(10)

	order_id	product	region	price	quantity	order_date
0	1	Mouse	East	1907	2	2023-03-02
1	2	Phone	West	375	4	2023-08-10
2	3	Monitor	West	550	3	2023-07-22
3	4	Keyboard	West	249	5	2023-12-16
4	5	Mouse	South	1829	6	2023-12-27
5	6	Mouse	West	1655	8	2023-05-22
6	7	Keyboard	North	1767	7	2023-05-18
7	8	Monitor	South	659	6	2023-09-16
8	9	Phone	West	1180	4	2023-06-07
9	10	Laptop	South	1660	6	2023-04-15

dataframe_for_aggregation.groupby(["product"]).agg(
    total_price=("price", "sum"),
    total_quantity=("quantity", "sum"),
    revenue=(
        "price",
        lambda price: (
            price * dataframe_for_aggregation.loc[price.index, "quantity"]
        ).sum(),
    ),
).reset_index()[["product", "revenue"]].plot(
    kind="bar",
    x="product",
    y="revenue",
    color="#92A78C",
)
plt.show()