Advanced Scatter Plot Customization | CodeSignal Learn

Advanced Scatter Plot Customization | CodeSignal Learn

Introduction to Advanced Scatter Plot Customization

Hello and welcome! In today’s lesson, we’re diving into advanced scatter plot customization using the diamonds dataset in Python. By the end of this lesson, you will have learned how to customize scatter plots to reveal complex patterns and provide better insights into your data. We'll do this by adjusting aesthetics like colors, markers, and transparency, as well as incorporating additional details using size and hue.

Marker Size Adjustment

Changing the marker size with the s parameter can help in making the scatter plot more readable, especially when dealing with overlapping points. Python import seaborn as sns import matplotlib.pyplot as plt import pandas as pd # Load the diamonds dataset diamonds = sns.load_dataset('diamonds') # Creating a new feature 'volume' (x * y * z) diamonds['volume'] = diamonds['x'] * diamonds['y'] * diamonds['z'] # Filter out unrealistic volumes diamonds = diamonds[(diamonds['volume'] > 0) & (diamonds['volume'] < 300)] # Scatter plot with adjusted marker size plt.figure(figsize=(10,6)) sns.scatterplot(x='volume', y='price', data=diamonds, s=100, alpha=0.4) plt.title('Scatter Plot of Volume vs. Price (Adjusted Marker Size)') plt.xlabel('Volume') plt.ylabel('Price') plt.show()

Changing Marker Style

Customizing marker styles with the marker parameter helps in distinguishing between different types of data points. Python import seaborn as sns import matplotlib.pyplot as plt import pandas as pd # Load the diamonds dataset diamonds = sns.load_dataset('diamonds') # Creating a new feature 'volume' (x * y * z) diamonds['volume'] = diamonds['x'] * diamonds['y'] * diamonds['z'] # Filter out unrealistic volumes diamonds = diamonds[(diamonds['volume'] > 0) & (diamonds['volume'] < 300)] # Scatter plot with adjusted marker style plt.figure(figsize=(10,6)) sns.scatterplot(x='volume', y='price', data=diamonds, marker='x', alpha=0.6, s=100) plt.title('Scatter Plot of Volume vs. Price (Marker Style X)') plt.xlabel('Volume') plt.ylabel('Price') plt.show()

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Beyond basic aesthetics, leveraging the hue parameter can add more layers of information to your scatter plot. This adds color differentiation to the data points, representing another dimension by using distinct colors for different categories of a variable. Here is an example that uses the'cut' feature with the hue parameter to differentiate between the cut categories: import seaborn as sns import matplotlib.pyplot as plt import pandas as pd # Load the diamonds dataset diamonds = sns.load_dataset('diamonds') # Creating a new feature 'volume' (x * y * z) diamonds['volume'] = diamonds['x'] * diamonds['y'] * diamonds['z'] # Filter out unrealistic volumes diamonds = diamonds[(diamonds['volume'] > 0) & (diamonds['volume'] < 300)] # Scatter plot using hue to differentiate cut categories plt.figure(figsize=(10,6)) sns.scatterplot(x='volume', y='price', hue='cut', data=diamonds, alpha=0.6, s=100) plt.title('Scatter Plot of Volume vs. Price (Hue by Cut)') plt.xlabel('Volume') plt.ylabel('Price') plt.legend(title='Cut', bbox_to_anchor=(1.05, 1), loc='upper left') plt.show()

Using Size to Represent Another Variable

Adjusting marker sizes size to represent the'carat' variable results in the following plot. Note that this is different to the s parameter, which sets a constant marker size for all the points in the plot. The sizes parameter is used to set the range. import seaborn as sns import matplotlib.pyplot as plt import pandas as pd # Load the diamonds dataset diamonds = sns.load_dataset('diamonds') # Creating a new feature 'volume' (x * y * z) diamonds['volume'] = diamonds['x'] * diamonds['y'] * diamonds['z'] # Filter out unrealistic volumes diamonds = diamonds[(diamonds['volume'] > 0) & (diamonds['volume'] < 300)] # Scatter plot with marker sizes representing carat values plt.figure(figsize=(10,6)) sns.scatterplot(x='volume', y='price', hue='cut', size='carat', sizes=(20, 200), data=diamonds, alpha=0.6) plt.title('Scatter Plot of Volume vs. Price (Size by Carat)') plt.xlabel('Volume') plt.ylabel('Price') plt.legend(title='Cut', bbox_to_anchor=(1.05, 1), loc='upper left') plt.show()

Adding Regression Lines

Adding regression lines can help to identify trends. This is done using the regplot function, which uses the same primary parametes as the other plotting functions. import seaborn as sns import matplotlib.pyplot as plt import pandas as pd # Load the diamonds dataset diamonds = sns.load_dataset('diamonds') # Creating a new feature 'volume' (x * y * z) diamonds['volume'] = diamonds['x'] * diamonds['y'] * diamonds['z'] # Filter out unrealistic volumes diamonds = diamonds[(diamonds['volume'] > 0) & (diamonds['volume'] < 300)] plt.figure(figsize=(10,6)) sns.scatterplot(x='volume', y='price', hue='cut', size='carat', sizes=(20, 200), data=diamonds, alpha=0.6) sns.regplot(x='volume', y='price', data=diamonds, scatter=False, color='gray') plt.title('Scatter Plot with Regression Line') plt.xlabel('Volume') plt.ylabel('Price') plt.legend(title='Cut', bbox_to_anchor=(1.05, 1), loc='upper left') plt.show()

Python

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd

# Load the diamonds dataset
diamonds = sns.load_dataset('diamonds')

# Creating a new feature 'volume' (x * y * z)
diamonds['volume'] = diamonds['x'] * diamonds['y'] * diamonds['z']

# Filter out unrealistic volumes
diamonds = diamonds[(diamonds['volume'] > 0) & (diamonds['volume'] < 300)]

# Scatter plot using hue to differentiate cut categories
plt.figure(figsize=(10,6))
sns.scatterplot(x='volume', y='price', hue='cut', data=diamonds, alpha=0.6, s=100)
plt.title('Scatter Plot of Volume vs. Price (Hue by Cut)')
plt.xlabel('Volume')
plt.ylabel('Price')
plt.legend(title='Cut', bbox_to_anchor=(1.05, 1), loc='upper left')
plt.show()

Python

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd

# Load the diamonds dataset
diamonds = sns.load_dataset('diamonds')

# Creating a new feature 'volume' (x * y * z)
diamonds['volume'] = diamonds['x'] * diamonds['y'] * diamonds['z']

# Filter out unrealistic volumes
diamonds = diamonds[(diamonds['volume'] > 0) & (diamonds['volume'] < 300)]

# Scatter plot with marker sizes representing carat values
plt.figure(figsize=(10,6))
sns.scatterplot(x='volume', y='price', hue='cut', size='carat', sizes=(20, 200), data=diamonds, alpha=0.6)
plt.title('Scatter Plot of Volume vs. Price (Size by Carat)')
plt.xlabel('Volume')
plt.ylabel('Price')
plt.legend(title='Cut', bbox_to_anchor=(1.05, 1), loc='upper left')
plt.show()

Python

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd

# Load the diamonds dataset
diamonds = sns.load_dataset('diamonds')

# Creating a new feature 'volume' (x * y * z)
diamonds['volume'] = diamonds['x'] * diamonds['y'] * diamonds['z']

# Filter out unrealistic volumes
diamonds = diamonds[(diamonds['volume'] > 0) & (diamonds['volume'] < 300)]

plt.figure(figsize=(10,6))
sns.scatterplot(x='volume', y='price', hue='cut', size='carat', sizes=(20, 200), data=diamonds, alpha=0.6)
sns.regplot(x='volume', y='price', data=diamonds, scatter=False, color='gray')
plt.title('Scatter Plot with Regression Line')
plt.xlabel('Volume')
plt.ylabel('Price')
plt.legend(title='Cut', bbox_to_anchor=(1.05, 1), loc='upper left')
plt.show()