A Scatter plot is a type of data visualization technique that shows the relationship between two numerical variables. In Pandas, we can create a scatter plot using the DataFrame.plot.scatter() method. This method helps in visualizing how one variable correlates with another. Example:

import pandas as pd
import matplotlib.pyplot as plt

data = {'Height': [150, 160, 170, 180, 190],
        'Weight': [50, 65, 75, 85, 95]}

df = pd.DataFrame(data)

# Creating a scatter plot
df.plot.scatter(x='Height', y='Weight')

plt.show()

Output

Explanation: This scatter plot shows how Weight changes with Height. As height increases, weight also tends to increase, indicating a positive correlation

Syntax of DataFrame.plot.scatter()

DataFrame.plot.scatter(x, y, s=None, c=None, colormap=None, alpha=None, figsize=None, grid=False, **kwargs)

Parameters:

Returns: It returns a Matplotlib AxesSubplot object with the scatter plot.

Examples of scatter plot

Example 1: In this example, we visualize Age distribution among individuals. The size of each point is determined by the Age and the color of all points is set to red.

import pandas as pd
import matplotlib.pyplot as plt

data = {'Name': ['Dhanashri', 'Smita', 'Rutuja', 'Sunita', 'Poonam', 'Srushti'],
        'Age': [20, 18, 27, 50, 12, 15]}

df = pd.DataFrame(data)

# scatter plot with size determined by age
df.plot.scatter(x='Name', y='Age', s=df['Age']*10, c='red')

plt.show()

Output

Explanation: A scatter plot where each person's name is plotted on the x-axis, and their age on the y-axis. The marker size is proportional to the age, making older individuals more prominent in the plot.

Example 2: In this example, we analyze how the population of different countries correlates with their CO₂ emissions. The size of the markers is determined by the country's population, making larger countries more prominent.

import pandas as pd
import matplotlib.pyplot as plt

data = {'Country': ['USA', 'China', 'India', 'Germany', 'Brazil', 'Australia'],
        'Population': [331, 1441, 1393, 83, 213, 26],  # in millions
        'CO2_Emissions': [5000, 12000, 2500, 800, 1300, 400]}  # in megatonnes

df = pd.DataFrame(data)

# Creating scatter plot
df.plot.scatter(x='Population', y='CO2_Emissions', s=df['Population'] * 2, c='blue')

plt.xlabel("Population (in millions)")
plt.ylabel("CO₂ Emissions (megatonnes)")
plt.grid(True)
plt.show()

Output

Explanation: A scatter plot showing the relationship between a country's population and its CO₂ emissions. Larger populations tend to have higher emissions, which is reflected in the marker size.

Example 3: In this example, we analyze how years of experience affect salary while using job level to size the markers. The size of each marker is determined by the Job Level (higher job levels result in larger markers).

import pandas as pd
import matplotlib.pyplot as plt

data = {'Experience': [1, 3, 5, 7, 10, 12, 15],
        'Salary': [40000, 60000, 80000, 110000, 140000, 180000, 220000],  # in $
        'Job_Level': [1, 2, 3, 4, 5, 6, 7]}  # Job level (higher = senior)

df = pd.DataFrame(data)

# Creating scatter plot
df.plot.scatter(x='Experience', y='Salary', s=df['Job_Level'] * 50, c='green')

plt.xlabel("Years of Experience")
plt.ylabel("Salary ($)")
plt.grid(True)
plt.show()

Output

Explanation: A scatter plot where salary increases as experience grows. Higher job levels are represented with larger markers, making it easy to see how senior positions impact salary.