Adventures in Machine Learning

Making Sense of Data: Creating a Scatter Plot with Labeled Points in Python

Creating a Scatter Plot with Text Labels

Visualizing large datasets, particularly in tables, can be challenging and overwhelming. Thus, creating a scatter plot can be a better solution to make sense of the data.

Scatter graphs display the relationship between two variables and show the pattern of data distribution. Pandas, a data manipulation library in Python, offers a handy method for creating scatter plots.

In this article, we will explore how to create a scatter plot with text labels.

Syntax for Adding Text Labels to a Scatter Plot

A scatter plot alone may not be enough to convey the insights we expect from our data. Adding text labels to the scatter plot can help draw attention to particular points or data clusters.

To put a label on a point in a scatter plot, we can use the annotate() method available in the matplotlib library in Python. Here is the syntax for adding text labels to a scatter plot:

“`

import matplotlib.pyplot as plt

import pandas as pd

df = pd.read_csv(‘your_data_file.csv’)

plt.scatter(df.column1, df.column2)

for i, txt in enumerate(df.column3):

plt.annotate(txt, (df.column1[i], df.column2[i]))

plt.show()

“`

This code will create a scatter plot with labels for each point, where column1 and column2 represent the two variables on the x and y-axis, and column3 represents the label for each point.

Example of Using Syntax to Create a Scatter Plot with Labeled Points

As an example, let’s consider the correlation between the number of assists and points scored for each basketball team in an NBA season. We will use the syntax mentioned above to create a scatter plot with labeled points.

“`

import matplotlib.pyplot as plt

import pandas as pd

# reading data from a csv file of a basketball team

df = pd.read_csv(‘nba_season_data.csv’)

# scatter plot with labeled points

plt.scatter(df.assists, df.points)

for i, team in enumerate(df.team):

plt.annotate(team, (df.assists[i], df.points[i]))

# adding labels to the axes

plt.xlabel(‘Number of Assists’)

plt.ylabel(‘Points Scored’)

# showing the plot

plt.show()

“`

This code will produce a scatter plot with labeled points for the NBA season data, where each point represents a team. The x-axis displays the number of assists, and the y-axis displays the points scored.

The label for each team is placed next to its corresponding point.

Modifying Text Labels in a Scatter Plot

Adding text labels to a scatter plot is a great way to enhance data visualization and make it more accessible. However, sometimes, we may want to customize the text labels to highlight specific points or add more details.

The annotate() method in Python provides several arguments to adjust text labels, such as the position and alignment of the text, font size, color, and font style. Here are some arguments to modify text labels:

– ‘s’: This argument determines the font size of the text label.

It accepts an integer or float value representing the font size. – ‘fontfamily’: This argument sets the font family for the text label.

It accepts a string value representing the font family name. – ‘xytext’: This argument moves the text label vertically and horizontally from its original position.

It accepts a tuple of two values that specify the text label’s X and Y coordinate positions. – ‘textcoords’: This argument determines the text coordinates’ reference position.

It accepts a string value representing a coordinate system. Here is an example that illustrates how to utilize these arguments to modify text labels:

“`

import matplotlib.pyplot as plt

import pandas as pd

# reading data from a csv file of a basketball team

df = pd.read_csv(‘nba_season_data.csv’)

# scatter plot with modified text labels

plt.scatter(df.assists, df.points)

for i, team in enumerate(df.team):

plt.annotate(team,

xy=(df.assists[i], df.points[i]),

xytext=(-10, 10),

textcoords=’offset points’,

fontsize=10, fontfamily=’Comic Sans MS’,

ha=’center’, va=’baseline’,

bbox=dict(facecolor=’yellow’, alpha=0.4))

# adding labels to the axes

plt.xlabel(‘Number of Assists’)

plt.ylabel(‘Points Scored’)

# showing the plot

plt.show()

“`

This code will produce a scatter plot with customized text labels, where each point represents an NBA team. The annotate() function uses xytext to adjust the text label’s position, fontsize and fontfamily to change the font size and font family, ha and va to change the horizontal and vertical text alignment, bbox to add a yellow-colored box surrounding the label, and alpha to set the opacity level of the box.

Conclusion

In summary, scatter plots are an excellent tool to interpret patterns and relationships between variables. Adding text labels to a scatter plot makes it more informative and engaging.

Python’s matplotlib library provides the annotate() function to add text labels and customize them with various arguments. With scatter plots and text annotations, we can make sense of our data and share them with others visually.

In conclusion, creating a scatter plot with text labels is an effective way to visualize data and highlight key insights. Python’s pandas library offers a simple syntax for creating scatter plots with annotated points.

The annotate() function can be used to modify the text labels’ size, style, position, and color. By utilizing these features, we can create clear, concise, and visually appealing scatter plots that better represent the data.

The takeaway is that visualizing data through scatter plots is an essential skill for anyone who wants to better understand and present data insights.

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