# Chapter 10: Visual Exploratory Data Analysis

## 10.2 Bivariate Analysis

Now that you have a good grasp of univariate analysis, which focuses on the study of a single variable, it's time to delve into the world of bivariate analysis. This method involves the examination of two variables to better comprehend the relationship that exists between them.

This is a vital process in data science, as it allows you to identify more complex patterns, correlations, and interdependencies in a multi-dimensional space. To put it simply, while univariate analysis provides insights about individual characters in a story, bivariate analysis helps to unveil the interactions and relationships between them, thus giving you a more complete picture of the narrative.

### 10.2.1 Scatter Plots

A scatter plot is an incredibly useful tool in your data visualization toolkit. It enables you to visually display the relationship between two variables in a clear and concise manner. By plotting data points against two axes, a scatter plot provides a quick and easy way to see patterns and trends.

Furthermore, scatter plots can be used for a wide range of applications, from analyzing market trends to examining scientific data. In addition, scatter plots can be customized to highlight specific data points or to compare multiple sets of data. Overall, mastering the use of scatter plots is an essential skill for anyone working with data analysis or visualization.

Let's generate a simple scatter plot using Matplotlib:

`import matplotlib.pyplot as plt`

import numpy as np

# Generate some data

x = np.random.rand(50)

y = 2 * x + 1 + 0.1 * np.random.randn(50) # y is somewhat linearly dependent on x

# Create scatter plot

plt.scatter(x, y)

plt.xlabel('X-values')

plt.ylabel('Y-values')

plt.title('Scatter Plot of X vs Y')

plt.show()

### 10.2.2 Correlation Coefficient

Understanding the correlation between two variables is a crucial aspect of data analysis. It is important to know how strongly one variable is related to the other. This knowledge can help us to draw meaningful insights from the data.

The Pearson's correlation coefficient is a statistical measure that is often used to quantify the correlation between two variables. It ranges from -1 to 1, where -1 indicates a strong negative correlation, 0 indicates no correlation, and 1 indicates a strong positive correlation. By analyzing the correlation coefficient, we can determine the strength and direction of the relationship between the two variables.

In addition, it is worth noting that there are other types of correlation coefficients, such as Spearman's rank correlation and Kendall's tau correlation, which are used for non-linear relationships or non-normal data. Therefore, understanding the different types of correlation coefficients and their applications is essential for accurate data interpretation and analysis.

Example:

`import numpy as np`

# Calculate correlation

correlation_coefficient = np.corrcoef(x, y)[0, 1]

print(f'Correlation Coefficient: {correlation_coefficient}')

### 10.2.3 Line Plots

Line plots, also known as line graphs, are a popular way of displaying data when both variables are continuous. They are particularly useful when you want to observe trends over a range or period. When creating a line plot, it is important to choose the appropriate scale for your axes to ensure that your data is accurately represented.

In addition to stock prices, line plots can be used to show changes in temperature over time, the growth of a population, or the number of website visitors per day. By using a line plot to visualize your data, you can easily identify patterns and trends that might not be as apparent in a table or spreadsheet.

Example:

`# Create line plot`

plt.plot(x, y)

plt.xlabel('Time')

plt.ylabel('Stock Price')

plt.title('Stock Price Over Time')

plt.show()

### 10.2.4 Heatmaps

Heatmaps are an excellent tool for data visualization and analysis, especially when dealing with multiple variables or complex data sets. By using color-coded cells to represent different values, heatmaps allow the user to quickly identify patterns and trends in the data.

In addition to studying the correlation of each pair of variables, heatmaps can also be used to identify outliers, detect clusters, and highlight areas of interest. This makes them a valuable tool for researchers, analysts, and data scientists across a wide range of fields, from biology and medicine to finance and marketing.

Seaborn makes it simple:

`import seaborn as sns`

import pandas as pd

# Create DataFrame

df = pd.DataFrame({'A': x, 'B': y})

# Create heatmap

sns.heatmap(df.corr(), annot=True)

plt.show()

### 10.2.5 Pairplots

When dealing with a dataset that has multiple numerical features, it's often helpful to use pairplots (also called scatterplot matrices) to visualize pairwise bivariate distributions. Pairplots allow for quick and easy comparison of the relationships between each pair of features, making it easier to identify trends and patterns in the data.

By examining the scatterplots within the pairplot, it becomes possible to see how different numerical features are related to one another and whether any correlations exist between them. Additionally, pairplots can also be used to identify any outliers or anomalies in the dataset that may require further investigation. Overall, the use of pairplots can greatly enhance the understanding of complex datasets and aid in the analysis and interpretation of data.

Example:

`# Create pairplot`

sns.pairplot(df)

plt.show()

Bivariate analysis is a crucial component in data analysis as it provides a deeper understanding of how variables can affect each other. This statistical method allows you to investigate the relationship between two variables and determine if there is a correlation or causation between them. By examining the interaction between variables, you can gain a better understanding of the underlying patterns and trends in your data.

Bivariate analysis can also help you to identify any outliers or anomalies that may be present in your data, which can be further investigated to gain a more comprehensive understanding of the data. By utilizing bivariate analysis, you can construct more meaningful and insightful narratives from your data, allowing you to tell the story that your data is waiting to reveal.

So, it is important to give due attention to bivariate analysis, as this can help you to extract the best possible insights from your data and make informed decisions based on those insights.

### 10.2.6 Statistical Significance in Bivariate Analysis

While it is important to visually observe the relationship between two variables, this is just the beginning of the process. It is important to statistically validate these findings to ensure they are not simply random patterns. This step is crucial in order to obtain reliable and accurate results. There are different statistical tests that can be used for this purpose, depending on the nature of the variables involved.

For example, the Pearson correlation test can be used to measure the strength and direction of the relationship between two numerical variables. Similarly, the Chi-square test is a useful tool to analyze the relationship between categorical variables. By using these tests, we can gain a deeper understanding of the relationship between different variables and create a more comprehensive analysis of the data at hand.

Here's a quick Python example using `scipy.stats`

to check the Pearson correlation for significance:

`from scipy import stats`

# Generate some example data

x = [10, 20, 30, 40, 50]

y = [15, 25, 35, 45, 55]

# Perform Pearson correlation test

correlation, p_value = stats.pearsonr(x, y)

print(f'Correlation: {correlation}, P-value: {p_value}')

The P-value will tell you if the correlation is statistically significant. Generally, a P-value less than 0.05 is considered to indicate statistical significance.

### 10.2.7 Handling Categorical Variables in Bivariate Analysis

When one variable is numerical and the other is categorical, box plots and violin plots can offer valuable insights. For instance, by grouping the numerical variable by the categorical variable and creating a box plot or violin plot for each group, we can visually compare the distribution of the numerical variable across different categories.

Additionally, we can add statistical measures such as the median, quartiles, and range to the plot to provide a more complete view of the data. Furthermore, we can customize the plot by changing the color, size, or shape of the plot elements to highlight specific patterns or trends that we want to emphasize. Overall, box plots and violin plots are powerful tools that can help us to better understand the relationship between numerical and categorical variables in our data.

Here's an example using Seaborn to generate a box plot:

`import seaborn as sns`

import matplotlib.pyplot as plt

# Generate example data

data = sns.load_dataset("tips")

# Create a boxplot

sns.boxplot(x='day', y='total_bill', data=data)

plt.show()

This box plot provides a good summary of how the `total_bill`

varies across different days of the week.

### 10.2.8 Real-world Applications of Bivariate Analysis

In today's data-driven world, the ability to analyze the relationship between two variables is crucial for anyone working with data. By examining how two variables are related to each other, we can gain valuable insights that can help us make more informed decisions. For example, in the field of healthcare, we could use bivariate analysis to understand the relationship between patient age and recovery time post-surgery. By doing so, we could identify any trends or patterns that could help us develop more effective treatment plans.

Similarly, in marketing, understanding the relationship between advertising spend and customer acquisition can be extremely valuable. By analyzing this relationship, we can determine how much money we need to spend on advertising in order to acquire a certain number of customers. This information can help us optimize our marketing campaigns and allocate our resources more effectively.

While bivariate analysis is a powerful tool for data scientists, its applications are not limited to just one industry. In fact, this analytical technique has wide-ranging applications across industries, from finance to retail to sports. By leveraging the power of bivariate analysis, we can uncover hidden insights that can help us make better decisions and drive better outcomes.

## 10.2 Bivariate Analysis

Now that you have a good grasp of univariate analysis, which focuses on the study of a single variable, it's time to delve into the world of bivariate analysis. This method involves the examination of two variables to better comprehend the relationship that exists between them.

This is a vital process in data science, as it allows you to identify more complex patterns, correlations, and interdependencies in a multi-dimensional space. To put it simply, while univariate analysis provides insights about individual characters in a story, bivariate analysis helps to unveil the interactions and relationships between them, thus giving you a more complete picture of the narrative.

### 10.2.1 Scatter Plots

A scatter plot is an incredibly useful tool in your data visualization toolkit. It enables you to visually display the relationship between two variables in a clear and concise manner. By plotting data points against two axes, a scatter plot provides a quick and easy way to see patterns and trends.

Furthermore, scatter plots can be used for a wide range of applications, from analyzing market trends to examining scientific data. In addition, scatter plots can be customized to highlight specific data points or to compare multiple sets of data. Overall, mastering the use of scatter plots is an essential skill for anyone working with data analysis or visualization.

Let's generate a simple scatter plot using Matplotlib:

`import matplotlib.pyplot as plt`

import numpy as np

# Generate some data

x = np.random.rand(50)

y = 2 * x + 1 + 0.1 * np.random.randn(50) # y is somewhat linearly dependent on x

# Create scatter plot

plt.scatter(x, y)

plt.xlabel('X-values')

plt.ylabel('Y-values')

plt.title('Scatter Plot of X vs Y')

plt.show()

### 10.2.2 Correlation Coefficient

Understanding the correlation between two variables is a crucial aspect of data analysis. It is important to know how strongly one variable is related to the other. This knowledge can help us to draw meaningful insights from the data.

The Pearson's correlation coefficient is a statistical measure that is often used to quantify the correlation between two variables. It ranges from -1 to 1, where -1 indicates a strong negative correlation, 0 indicates no correlation, and 1 indicates a strong positive correlation. By analyzing the correlation coefficient, we can determine the strength and direction of the relationship between the two variables.

In addition, it is worth noting that there are other types of correlation coefficients, such as Spearman's rank correlation and Kendall's tau correlation, which are used for non-linear relationships or non-normal data. Therefore, understanding the different types of correlation coefficients and their applications is essential for accurate data interpretation and analysis.

Example:

`import numpy as np`

# Calculate correlation

correlation_coefficient = np.corrcoef(x, y)[0, 1]

print(f'Correlation Coefficient: {correlation_coefficient}')

### 10.2.3 Line Plots

Line plots, also known as line graphs, are a popular way of displaying data when both variables are continuous. They are particularly useful when you want to observe trends over a range or period. When creating a line plot, it is important to choose the appropriate scale for your axes to ensure that your data is accurately represented.

In addition to stock prices, line plots can be used to show changes in temperature over time, the growth of a population, or the number of website visitors per day. By using a line plot to visualize your data, you can easily identify patterns and trends that might not be as apparent in a table or spreadsheet.

Example:

`# Create line plot`

plt.plot(x, y)

plt.xlabel('Time')

plt.ylabel('Stock Price')

plt.title('Stock Price Over Time')

plt.show()

### 10.2.4 Heatmaps

Heatmaps are an excellent tool for data visualization and analysis, especially when dealing with multiple variables or complex data sets. By using color-coded cells to represent different values, heatmaps allow the user to quickly identify patterns and trends in the data.

In addition to studying the correlation of each pair of variables, heatmaps can also be used to identify outliers, detect clusters, and highlight areas of interest. This makes them a valuable tool for researchers, analysts, and data scientists across a wide range of fields, from biology and medicine to finance and marketing.

Seaborn makes it simple:

`import seaborn as sns`

import pandas as pd

# Create DataFrame

df = pd.DataFrame({'A': x, 'B': y})

# Create heatmap

sns.heatmap(df.corr(), annot=True)

plt.show()

### 10.2.5 Pairplots

When dealing with a dataset that has multiple numerical features, it's often helpful to use pairplots (also called scatterplot matrices) to visualize pairwise bivariate distributions. Pairplots allow for quick and easy comparison of the relationships between each pair of features, making it easier to identify trends and patterns in the data.

By examining the scatterplots within the pairplot, it becomes possible to see how different numerical features are related to one another and whether any correlations exist between them. Additionally, pairplots can also be used to identify any outliers or anomalies in the dataset that may require further investigation. Overall, the use of pairplots can greatly enhance the understanding of complex datasets and aid in the analysis and interpretation of data.

Example:

`# Create pairplot`

sns.pairplot(df)

plt.show()

Bivariate analysis is a crucial component in data analysis as it provides a deeper understanding of how variables can affect each other. This statistical method allows you to investigate the relationship between two variables and determine if there is a correlation or causation between them. By examining the interaction between variables, you can gain a better understanding of the underlying patterns and trends in your data.

Bivariate analysis can also help you to identify any outliers or anomalies that may be present in your data, which can be further investigated to gain a more comprehensive understanding of the data. By utilizing bivariate analysis, you can construct more meaningful and insightful narratives from your data, allowing you to tell the story that your data is waiting to reveal.

So, it is important to give due attention to bivariate analysis, as this can help you to extract the best possible insights from your data and make informed decisions based on those insights.

### 10.2.6 Statistical Significance in Bivariate Analysis

While it is important to visually observe the relationship between two variables, this is just the beginning of the process. It is important to statistically validate these findings to ensure they are not simply random patterns. This step is crucial in order to obtain reliable and accurate results. There are different statistical tests that can be used for this purpose, depending on the nature of the variables involved.

For example, the Pearson correlation test can be used to measure the strength and direction of the relationship between two numerical variables. Similarly, the Chi-square test is a useful tool to analyze the relationship between categorical variables. By using these tests, we can gain a deeper understanding of the relationship between different variables and create a more comprehensive analysis of the data at hand.

Here's a quick Python example using `scipy.stats`

to check the Pearson correlation for significance:

`from scipy import stats`

# Generate some example data

x = [10, 20, 30, 40, 50]

y = [15, 25, 35, 45, 55]

# Perform Pearson correlation test

correlation, p_value = stats.pearsonr(x, y)

print(f'Correlation: {correlation}, P-value: {p_value}')

The P-value will tell you if the correlation is statistically significant. Generally, a P-value less than 0.05 is considered to indicate statistical significance.

### 10.2.7 Handling Categorical Variables in Bivariate Analysis

When one variable is numerical and the other is categorical, box plots and violin plots can offer valuable insights. For instance, by grouping the numerical variable by the categorical variable and creating a box plot or violin plot for each group, we can visually compare the distribution of the numerical variable across different categories.

Additionally, we can add statistical measures such as the median, quartiles, and range to the plot to provide a more complete view of the data. Furthermore, we can customize the plot by changing the color, size, or shape of the plot elements to highlight specific patterns or trends that we want to emphasize. Overall, box plots and violin plots are powerful tools that can help us to better understand the relationship between numerical and categorical variables in our data.

Here's an example using Seaborn to generate a box plot:

`import seaborn as sns`

import matplotlib.pyplot as plt

# Generate example data

data = sns.load_dataset("tips")

# Create a boxplot

sns.boxplot(x='day', y='total_bill', data=data)

plt.show()

This box plot provides a good summary of how the `total_bill`

varies across different days of the week.

### 10.2.8 Real-world Applications of Bivariate Analysis

In today's data-driven world, the ability to analyze the relationship between two variables is crucial for anyone working with data. By examining how two variables are related to each other, we can gain valuable insights that can help us make more informed decisions. For example, in the field of healthcare, we could use bivariate analysis to understand the relationship between patient age and recovery time post-surgery. By doing so, we could identify any trends or patterns that could help us develop more effective treatment plans.

Similarly, in marketing, understanding the relationship between advertising spend and customer acquisition can be extremely valuable. By analyzing this relationship, we can determine how much money we need to spend on advertising in order to acquire a certain number of customers. This information can help us optimize our marketing campaigns and allocate our resources more effectively.

While bivariate analysis is a powerful tool for data scientists, its applications are not limited to just one industry. In fact, this analytical technique has wide-ranging applications across industries, from finance to retail to sports. By leveraging the power of bivariate analysis, we can uncover hidden insights that can help us make better decisions and drive better outcomes.

## 10.2 Bivariate Analysis

Now that you have a good grasp of univariate analysis, which focuses on the study of a single variable, it's time to delve into the world of bivariate analysis. This method involves the examination of two variables to better comprehend the relationship that exists between them.

This is a vital process in data science, as it allows you to identify more complex patterns, correlations, and interdependencies in a multi-dimensional space. To put it simply, while univariate analysis provides insights about individual characters in a story, bivariate analysis helps to unveil the interactions and relationships between them, thus giving you a more complete picture of the narrative.

### 10.2.1 Scatter Plots

A scatter plot is an incredibly useful tool in your data visualization toolkit. It enables you to visually display the relationship between two variables in a clear and concise manner. By plotting data points against two axes, a scatter plot provides a quick and easy way to see patterns and trends.

Furthermore, scatter plots can be used for a wide range of applications, from analyzing market trends to examining scientific data. In addition, scatter plots can be customized to highlight specific data points or to compare multiple sets of data. Overall, mastering the use of scatter plots is an essential skill for anyone working with data analysis or visualization.

Let's generate a simple scatter plot using Matplotlib:

`import matplotlib.pyplot as plt`

import numpy as np

# Generate some data

x = np.random.rand(50)

y = 2 * x + 1 + 0.1 * np.random.randn(50) # y is somewhat linearly dependent on x

# Create scatter plot

plt.scatter(x, y)

plt.xlabel('X-values')

plt.ylabel('Y-values')

plt.title('Scatter Plot of X vs Y')

plt.show()

### 10.2.2 Correlation Coefficient

Understanding the correlation between two variables is a crucial aspect of data analysis. It is important to know how strongly one variable is related to the other. This knowledge can help us to draw meaningful insights from the data.

The Pearson's correlation coefficient is a statistical measure that is often used to quantify the correlation between two variables. It ranges from -1 to 1, where -1 indicates a strong negative correlation, 0 indicates no correlation, and 1 indicates a strong positive correlation. By analyzing the correlation coefficient, we can determine the strength and direction of the relationship between the two variables.

In addition, it is worth noting that there are other types of correlation coefficients, such as Spearman's rank correlation and Kendall's tau correlation, which are used for non-linear relationships or non-normal data. Therefore, understanding the different types of correlation coefficients and their applications is essential for accurate data interpretation and analysis.

Example:

`import numpy as np`

# Calculate correlation

correlation_coefficient = np.corrcoef(x, y)[0, 1]

print(f'Correlation Coefficient: {correlation_coefficient}')

### 10.2.3 Line Plots

Line plots, also known as line graphs, are a popular way of displaying data when both variables are continuous. They are particularly useful when you want to observe trends over a range or period. When creating a line plot, it is important to choose the appropriate scale for your axes to ensure that your data is accurately represented.

In addition to stock prices, line plots can be used to show changes in temperature over time, the growth of a population, or the number of website visitors per day. By using a line plot to visualize your data, you can easily identify patterns and trends that might not be as apparent in a table or spreadsheet.

Example:

`# Create line plot`

plt.plot(x, y)

plt.xlabel('Time')

plt.ylabel('Stock Price')

plt.title('Stock Price Over Time')

plt.show()

### 10.2.4 Heatmaps

Heatmaps are an excellent tool for data visualization and analysis, especially when dealing with multiple variables or complex data sets. By using color-coded cells to represent different values, heatmaps allow the user to quickly identify patterns and trends in the data.

In addition to studying the correlation of each pair of variables, heatmaps can also be used to identify outliers, detect clusters, and highlight areas of interest. This makes them a valuable tool for researchers, analysts, and data scientists across a wide range of fields, from biology and medicine to finance and marketing.

Seaborn makes it simple:

`import seaborn as sns`

import pandas as pd

# Create DataFrame

df = pd.DataFrame({'A': x, 'B': y})

# Create heatmap

sns.heatmap(df.corr(), annot=True)

plt.show()

### 10.2.5 Pairplots

When dealing with a dataset that has multiple numerical features, it's often helpful to use pairplots (also called scatterplot matrices) to visualize pairwise bivariate distributions. Pairplots allow for quick and easy comparison of the relationships between each pair of features, making it easier to identify trends and patterns in the data.

By examining the scatterplots within the pairplot, it becomes possible to see how different numerical features are related to one another and whether any correlations exist between them. Additionally, pairplots can also be used to identify any outliers or anomalies in the dataset that may require further investigation. Overall, the use of pairplots can greatly enhance the understanding of complex datasets and aid in the analysis and interpretation of data.

Example:

`# Create pairplot`

sns.pairplot(df)

plt.show()

Bivariate analysis is a crucial component in data analysis as it provides a deeper understanding of how variables can affect each other. This statistical method allows you to investigate the relationship between two variables and determine if there is a correlation or causation between them. By examining the interaction between variables, you can gain a better understanding of the underlying patterns and trends in your data.

Bivariate analysis can also help you to identify any outliers or anomalies that may be present in your data, which can be further investigated to gain a more comprehensive understanding of the data. By utilizing bivariate analysis, you can construct more meaningful and insightful narratives from your data, allowing you to tell the story that your data is waiting to reveal.

So, it is important to give due attention to bivariate analysis, as this can help you to extract the best possible insights from your data and make informed decisions based on those insights.

### 10.2.6 Statistical Significance in Bivariate Analysis

While it is important to visually observe the relationship between two variables, this is just the beginning of the process. It is important to statistically validate these findings to ensure they are not simply random patterns. This step is crucial in order to obtain reliable and accurate results. There are different statistical tests that can be used for this purpose, depending on the nature of the variables involved.

For example, the Pearson correlation test can be used to measure the strength and direction of the relationship between two numerical variables. Similarly, the Chi-square test is a useful tool to analyze the relationship between categorical variables. By using these tests, we can gain a deeper understanding of the relationship between different variables and create a more comprehensive analysis of the data at hand.

Here's a quick Python example using `scipy.stats`

to check the Pearson correlation for significance:

`from scipy import stats`

# Generate some example data

x = [10, 20, 30, 40, 50]

y = [15, 25, 35, 45, 55]

# Perform Pearson correlation test

correlation, p_value = stats.pearsonr(x, y)

print(f'Correlation: {correlation}, P-value: {p_value}')

The P-value will tell you if the correlation is statistically significant. Generally, a P-value less than 0.05 is considered to indicate statistical significance.

### 10.2.7 Handling Categorical Variables in Bivariate Analysis

When one variable is numerical and the other is categorical, box plots and violin plots can offer valuable insights. For instance, by grouping the numerical variable by the categorical variable and creating a box plot or violin plot for each group, we can visually compare the distribution of the numerical variable across different categories.

Additionally, we can add statistical measures such as the median, quartiles, and range to the plot to provide a more complete view of the data. Furthermore, we can customize the plot by changing the color, size, or shape of the plot elements to highlight specific patterns or trends that we want to emphasize. Overall, box plots and violin plots are powerful tools that can help us to better understand the relationship between numerical and categorical variables in our data.

Here's an example using Seaborn to generate a box plot:

`import seaborn as sns`

import matplotlib.pyplot as plt

# Generate example data

data = sns.load_dataset("tips")

# Create a boxplot

sns.boxplot(x='day', y='total_bill', data=data)

plt.show()

This box plot provides a good summary of how the `total_bill`

varies across different days of the week.

### 10.2.8 Real-world Applications of Bivariate Analysis

In today's data-driven world, the ability to analyze the relationship between two variables is crucial for anyone working with data. By examining how two variables are related to each other, we can gain valuable insights that can help us make more informed decisions. For example, in the field of healthcare, we could use bivariate analysis to understand the relationship between patient age and recovery time post-surgery. By doing so, we could identify any trends or patterns that could help us develop more effective treatment plans.

Similarly, in marketing, understanding the relationship between advertising spend and customer acquisition can be extremely valuable. By analyzing this relationship, we can determine how much money we need to spend on advertising in order to acquire a certain number of customers. This information can help us optimize our marketing campaigns and allocate our resources more effectively.

While bivariate analysis is a powerful tool for data scientists, its applications are not limited to just one industry. In fact, this analytical technique has wide-ranging applications across industries, from finance to retail to sports. By leveraging the power of bivariate analysis, we can uncover hidden insights that can help us make better decisions and drive better outcomes.

## 10.2 Bivariate Analysis

### 10.2.1 Scatter Plots

Let's generate a simple scatter plot using Matplotlib:

`import matplotlib.pyplot as plt`

import numpy as np

# Generate some data

x = np.random.rand(50)

y = 2 * x + 1 + 0.1 * np.random.randn(50) # y is somewhat linearly dependent on x

# Create scatter plot

plt.scatter(x, y)

plt.xlabel('X-values')

plt.ylabel('Y-values')

plt.title('Scatter Plot of X vs Y')

plt.show()

### 10.2.2 Correlation Coefficient

Example:

`import numpy as np`

# Calculate correlation

correlation_coefficient = np.corrcoef(x, y)[0, 1]

print(f'Correlation Coefficient: {correlation_coefficient}')

### 10.2.3 Line Plots

Example:

`# Create line plot`

plt.plot(x, y)

plt.xlabel('Time')

plt.ylabel('Stock Price')

plt.title('Stock Price Over Time')

plt.show()

### 10.2.4 Heatmaps

Seaborn makes it simple:

`import seaborn as sns`

import pandas as pd

# Create DataFrame

df = pd.DataFrame({'A': x, 'B': y})

# Create heatmap

sns.heatmap(df.corr(), annot=True)

plt.show()

### 10.2.5 Pairplots

Example:

`# Create pairplot`

sns.pairplot(df)

plt.show()

### 10.2.6 Statistical Significance in Bivariate Analysis

Here's a quick Python example using `scipy.stats`

to check the Pearson correlation for significance:

`from scipy import stats`

# Generate some example data

x = [10, 20, 30, 40, 50]

y = [15, 25, 35, 45, 55]

# Perform Pearson correlation test

correlation, p_value = stats.pearsonr(x, y)

print(f'Correlation: {correlation}, P-value: {p_value}')

### 10.2.7 Handling Categorical Variables in Bivariate Analysis

Here's an example using Seaborn to generate a box plot:

`import seaborn as sns`

import matplotlib.pyplot as plt

# Generate example data

data = sns.load_dataset("tips")

# Create a boxplot

sns.boxplot(x='day', y='total_bill', data=data)

plt.show()

`total_bill`

varies across different days of the week.