Introduction

In this section, we will learn how to plot data using Python. We will use the matplotlib library to create plots.

Plotting is a useful tool for visualizing data. It allows us to see trends and patterns in the data that may not be immediately apparent from the raw data.

Plotting a Line Graph

To plot a line graph, we can use the plot function from the matplotlib.pyplot module. The plot function takes two arguments: the x-coordinates and the y-coordinates of the data points.

import matplotlib.pyplot as plt

# Data
x = [1, 2, 3, 4, 5]
y = [2, 3, 5, 7, 11]

# Plot
plt.plot(x, y)
plt.show()

Customizing the Plot

We can customize the plot by adding labels, titles, and legends. We can also change the color, line style, and marker style of the plot.

import matplotlib.pyplot as plt

# Data
x = [1, 2, 3, 4, 5]
y = [2, 3, 5, 7, 11]

# Plot
plt.plot(x, y, color='red', linestyle='dashed', marker='o', label='data')
plt.xlabel('x')
plt.ylabel('y')
plt.title('Line Graph')
plt.legend()
plt.show()

Plotting

Introduction

In this section, we will learn how to plot data using Python. We will use the matplotlib library to create plots.

Plotting is a useful tool for visualizing data. It allows us to see trends and patterns in the data that may not be immediately apparent from the raw data.

Plotting a Line Graph

To plot a line graph, we can use the plot function from the matplotlib.pyplot module. The plot function takes two arguments: the x-coordinates and the y-coordinates of the data points.

import matplotlib.pyplot as plt

# Data
x = [1, 2, 3, 4, 5]
y = [2, 3, 5, 7, 11]

# Plot
plt.plot(x, y)
plt.show()

Boxplot

Visualisiert 5 Werte:

• Minimum
• 1. Quartil
• Median
• 3. Quartil
• Maximum

import matplotlib.pyplot as plt

# Data
data = [1, 2, 3, 4, 5]

# Plot
plt.boxplot(data)
plt.show()