LM Creating

In Python, there are several libraries you can use to visualize charts and graphs. Here are some of the most popular options:

Different Types of Plots in Matplotlib

1. Line Plot

A library built on top of Matplotlib that makes it easier to create attractive statistical graphics.

import matplotlib.pyplot as plt

x = [0, 1, 2, 3, 4]
y = [0, 1, 4, 9, 16]

plt.plot(x, y, marker='o', color="green", linestyle="--", label="Series A")
plt.title("Line Plot Example")
plt.xlabel("X-Axis")
plt.ylabel("Y-Axis")
plt.grid(True)
plt.legend()
plt.show()

Code	Shape	Description
.	Point	Small point marker
o	Circle	Circle marker
v	Triangle down	Downward triangle
^	Triangle up	Upward triangle
<	Triangle left	Left-pointing triangle
>	Triangle right	Right-pointing triangle
s	Square	Square marker
p	Pentagon	Pentagon marker
*	Star	Star marker
h	Hexagon1	Hexagon marker
H	Hexagon2	Rotated hexagon marker
+	Plus	Plus marker
x	Cross	Cross marker
D	Diamond	Diamond marker
d	Thin diamond	Thin diamond marker
|	Vertical line	Vertical line marker
_	Horizontal line	Horizontal line marker

2. Bar Plot

A library for interactive graphics that allows you to create more complex visualizations.

import matplotlib.pyplot as plt

categories = ["A", "B", "C", "D"]
values = [4, 7, 1, 8]

plt.bar(categories, values, color="green")
plt.title("Bar Plot Example")
plt.xlabel("Categories")
plt.ylabel("Values")
plt.show()

3. Scatter Plot

A scatter plot shows individual data points. It is used to observe relationships or patterns between two numeric variables.

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [5, 4, 3, 2, 1]

plt.scatter(x, y, color="red")
plt.title("Scatter Plot Example")
plt.xlabel("X-Axis")
plt.ylabel("Y-Axis")
plt.show()

4. Histogram

A histogram displays the distribution of numerical data by dividing it into bins. It helps to understand how data is spread or grouped.

import matplotlib.pyplot as plt

# Custom data points
data = [1, 2, 2, 3, 3, 3, 4, 4, 5]

# Create histogram
plt.hist(data, bins=5, color="skyblue", edgecolor="black")
plt.title("Histogram with Custom Data")
plt.xlabel("Value")
plt.ylabel("Frequency")
plt.show()

The bins parameter controls how many intervals (bars or “bins”) the data is divided into. bins=30 means: Divide the data into 30 equal-width intervals, and then count how many values fall into each interval. Each “bar” you see represents one bin.

5. Pie Chart

A pie chart shows percentages or proportional data as slices of a circle.

import matplotlib.pyplot as plt

labels = ["Apples", "Bananas", "Cherries", "Dates"]
sizes = [15, 30, 45, 10]

plt.pie(sizes, labels=labels, autopct="%1.1f%%", startangle=90)
plt.title("Pie Chart Example")
plt.show()

autopct stands for “automatic percentage”.

It defines how to format the labels showing the percentage on each slice. “%1.1f%%” means: 1.1f → show the number with one decimal place (e.g., 23.5%). %% → the percent sign.

startangle=90

Sets the starting angle of the pie chart (where the first slice begins).

import matplotlib.pyplot as plt


labels = ["Apples", "Bananas", "Cherries", "Dates"]
sizes = [15, 30, 45, 10]
colors = ["red", "yellow", "pink", "brown"]


plt.pie(
    sizes,
    labels=labels,
    colors=colors,
    autopct="%1.1f%%",   
    startangle=30       
)

plt.title("Pie Chart Example 30")
plt.show()