Matplotlib Basics: Creating Charts with Python

Matplotlib is one of the most widely used Python libraries for data visualization. It can create line charts, bar charts, scatter plots, histograms, box plots, and many other figures directly from code.

This guide takes you from installation to your first chart, then covers styling, saving images, common problems, and the practical differences between Matplotlib, Excel, and the no-code Sakura Chart tool.

Contents

1. What Matplotlib is
2. Installation
3. Your first line chart
4. Figure and Axes
5. Common chart types
6. CSV and Excel data
7. Styling a chart
8. Saving an image
9. Font problems
10. Common problems
11. Choosing the right tool

1. What Is Matplotlib?

Matplotlib is a Python visualization library commonly used with NumPy and pandas. It is useful for data analysis, research, production monitoring, and automated report generation.

• Repeat the same chart design with new datasets
• Control colors, lines, axes, legends, annotations, and layouts
• Export figures as PNG, SVG, PDF, and other formats
• Arrange several charts in a single Figure

Its main tradeoff is that you generally create and edit charts through Python code rather than direct on-screen controls.

2. Install Matplotlib

With Python installed, run the following command in a terminal or command prompt:

python -m pip install matplotlib

In an Anaconda environment, you can use:

conda install matplotlib

Then import pyplot with its conventional alias:

import matplotlib.pyplot as plt

3. Create Your First Line Chart

import matplotlib.pyplot as plt

month = [1, 2, 3, 4, 5]
sales = [120, 150, 135, 180, 210]

fig, ax = plt.subplots()
ax.plot(month, sales, marker="o")
ax.set_xlabel("Month")
ax.set_ylabel("Sales")
ax.set_title("Monthly Sales")

plt.show()

ax.plot() draws the line, the setter methods add labels and a title, and plt.show() displays the completed figure.

4. Understand Figure and Axes

fig, ax = plt.subplots(figsize=(7, 4))

For several charts, specify rows and columns, such as plt.subplots(2, 2). The explicit fig and ax style is especially helpful for reusable code and multi-chart layouts.

5. Common Chart Types and Methods

import matplotlib.pyplot as plt
import numpy as np

x = np.arange(1, 6)
y = np.array([12, 19, 15, 24, 28])
group_a = np.array([11, 13, 12, 16, 15, 18])
group_b = np.array([8, 10, 9, 11, 12, 13])

fig, axes = plt.subplots(2, 2, figsize=(10, 6), layout="constrained")

axes[0, 0].plot(x, y, marker="o", color="#be3455")
axes[0, 0].set_title("Line")

axes[0, 1].bar(x, y, color="#e78aa0")
axes[0, 1].set_title("Bar")

axes[1, 0].scatter(x, y, color="#2563eb", s=55)
axes[1, 0].set_title("Scatter")

axes[1, 1].boxplot(
    [group_a, group_b],
    tick_labels=["A", "B"],
    patch_artist=True,
    boxprops={"facecolor": "#f8d7df", "edgecolor": "#9f1239"},
    medianprops={"color": "#9f1239", "linewidth": 2}
)
axes[1, 1].set_title("Box plot")

for ax in axes.flat:
    ax.grid(axis="y", linestyle="--", alpha=0.25)
    ax.spines[["top", "right"]].set_visible(False)

fig.savefig("matplotlib_basics_example.png", dpi=150)
plt.show()

6. Plot Data from CSV or Excel

In practical work, pandas can load a table and pass its columns to Matplotlib.

import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv("sales.csv")

fig, ax = plt.subplots(figsize=(7, 4))
ax.plot(df["month"], df["sales"], marker="o")
ax.set_xlabel("Month")
ax.set_ylabel("Sales")
ax.set_title("Monthly Sales")

fig.savefig("sales.png", dpi=150, bbox_inches="tight")
plt.show()

Use pd.read_excel("sales.xlsx") for an Excel workbook.

python -m pip install pandas openpyxl

7. Customize Titles, Colors, Legends, and Grids

fig, ax = plt.subplots(figsize=(8, 4.5))

ax.plot(
    month,
    sales,
    color="#be3455",
    linewidth=2,
    marker="o",
    label="Sales"
)
ax.set_title("Monthly Sales", fontsize=16)
ax.set_xlabel("Month")
ax.set_ylabel("Sales")
ax.set_ylim(0, 250)
ax.grid(axis="y", linestyle="--", alpha=0.35)
ax.legend()

fig.tight_layout()

For repeated designs, style sheets and rcParams can collect shared defaults instead of repeating every option in each script.

8. Save a Chart as PNG, SVG, or PDF

fig.savefig("chart.png", dpi=300, bbox_inches="tight")
fig.savefig("chart.svg", bbox_inches="tight")
fig.savefig("chart.pdf", bbox_inches="tight")

• PNG: convenient for websites, email, and ordinary documents
• SVG: scalable and useful for web or later editing
• PDF: useful for print, papers, and reports

dpi controls raster resolution, while bbox_inches="tight" helps prevent labels from being clipped. Saving before plt.show() also avoids blank output in some environments.

9. Fix Missing or Square Font Characters

If a label appears as empty squares, the required font may be missing or Matplotlib may not be selecting it.

import matplotlib.pyplot as plt

plt.rcParams["font.family"] = "Noto Sans CJK JP"
plt.rcParams["axes.unicode_minus"] = False

10. Common Problems

11. Matplotlib, Excel, or Sakura Chart?

Matplotlib is the strongest option when automation and reproducibility matter. If you simply need to turn an existing table into a chart without installing Python, a table-first web tool can be faster.

12. Summary

• Matplotlib creates many types of charts from Python code
• fig, ax = plt.subplots() provides a clear structure for reusable plots
• Choose plot, bar, scatter, hist, or boxplot based on the question
• savefig exports a Figure as PNG, SVG, PDF, and other formats
• Use Matplotlib for automation and flexibility; use Excel or Sakura Chart for a table-first workflow

Official Resources

• Matplotlib Installation
• Matplotlib Quick start guide
• Matplotlib Examples
• Customizing Matplotlib