# Chapter 8: Understanding EDA

## 8.2 Types of Data

### 8.2.1 Numerical Data

Numerical data is an essential element of scientific research and represents quantitative measurements of various phenomena. It is divided into two main types: discrete and continuous data. Discrete data refers to data that can only take certain specific values and is often obtained by counting.

For example, the number of cars in a parking lot can be counted, and the result is a discrete number. On the other hand, continuous data refers to data that can take any value within a specific range and can be measured using a scale. For example, the weight of an object can be measured using a scale, and the result is continuous data. Both types of data are important in scientific research and can provide valuable insights into various phenomena.

**Discrete Data **

This type of data consists of distinct and separate values that cannot be subdivided into smaller units. It is often composed of counts of things that are readily measurable. A good example of discrete data is the number of employees in a company.

However, it is important to note that discrete data can also include other types of information such as age groups, shoe sizes, and the number of students in a classroom. The analysis of discrete data involves determining the frequency of occurrence of each value and identifying patterns and trends that emerge.

This type of data is extremely useful in various fields such as statistics, finance, and marketing, where it is used to derive meaningful insights and make informed decisions.

**Continuous Data**

These are data points that can take any value within a range. Continuous data can be expressed in decimal or fractional values. Continuous data can be measured to a very high degree of accuracy, which is why they are frequently used in scientific research. Height, weight, and temperature are all examples of continuous data.

Additionally, other examples of continuous data include distance, time, and age. Continuous data can be further subdivided into two types: interval data and ratio data. Interval data refers to data that has no true zero point, while ratio data refers to data that does have a true zero point.

Example:

`# Example code to plot discrete and continuous data`

import matplotlib.pyplot as plt

import numpy as np

# Discrete Data

discrete_data = np.random.choice([1, 2, 3, 4, 5], 50)

plt.subplot(1, 2, 1)

plt.hist(discrete_data, bins=5)

plt.title('Discrete Data')

# Continuous Data

continuous_data = np.random.normal(5, 2, 50)

plt.subplot(1, 2, 2)

plt.hist(continuous_data, bins=5)

plt.title('Continuous Data')

plt.tight_layout()

plt.show()

### 8.2.2 Categorical Data

Categorical data is a type of data that is used to represent different characteristics or labels. Categorical data can be divided into two categories, namely nominal and ordinal categories. Nominal categories are used to represent data that has no inherent order, such as the colors of a rainbow or the different breeds of dogs.

On the other hand, ordinal categories are used to represent data that has a natural order, such as the different sizes of t-shirts (small, medium, large). It is important to note that categorical data can be useful in many different fields, such as marketing, social sciences, and data analysis.

**Nominal Data**

These have no natural order or ranking. Examples include colors, gender, and types of fruits. Nominal data is a type of data that has no natural order or ranking. This means that there is no inherent hierarchy or order in the data, and each value is considered to be equal. For example, when we collect data on colors, gender, or types of fruits, we are dealing with nominal data.

One way to think about nominal data is to consider the categories that the data represents. Each category is considered to be distinct and separate from the others, which means that there is no way to compare or rank them. For instance, when we collect data on the different colors of cars, we do not rank one color as being better or worse than another. Rather, each color is simply a separate category.

It is important to note that nominal data is not the only type of data that we can collect. Other types of data include ordinal, interval, and ratio data. Each of these types of data has its own unique properties and characteristics, which make them useful for different types of analysis.

In summary, nominal data is a type of data that has no natural order or ranking. It consists of categories that are distinct and separate from one another, and each value is considered to be equal. Examples of nominal data include colors, gender, and types of fruits.

**Ordinal Data**:

This type of data has a natural order in which the categories are arranged, but the intervals between the categories are not equal. It is used to represent data that involves subjective judgments, such as customer satisfaction ratings.

In this case, the data can be classified into categories such as 'Poor,' 'Average,' and 'Excellent.' Ordinal data can also be used to represent data from surveys that ask respondents to rate their level of agreement with a statement using categories such as 'Strongly Disagree,' 'Disagree,' 'Neutral,' 'Agree,' and 'Strongly Agree.' Since the categories are ranked, but the intervals between them are not uniform, ordinal data can be tricky to analyze.

Therefore, it is important to choose an appropriate statistical method to analyze this type of data, such as non-parametric tests like the Wilcoxon rank-sum test or the Kruskal-Wallis test.

Example:

`# Example code to plot nominal and ordinal data using bar plots`

import seaborn as sns

# Nominal Data

sns.countplot(x=["Apple", "Banana", "Apple", "Orange", "Banana", "Apple", "Orange"])

plt.title('Nominal Data')

plt.show()

# Ordinal Data

sns.countplot(x=["Poor", "Average", "Excellent", "Poor", "Average"])

plt.title('Ordinal Data')

plt.show()

### 8.2.3 Textual Data

Textual data refers to any kind of unstructured data, such as social media posts, comments, and news articles. These types of data were not traditionally analyzed with EDA, but with the advancements in Natural Language Processing (NLP), it is now possible to extract meaningful insights from textual data.

NLP techniques can be used to identify patterns and trends in large amounts of text data. Moreover, sentiment analysis can be conducted to understand the emotional tone of the text and to categorize it into positive, negative, or neutral.

This allows businesses and organizations to gain a better understanding of customer feedback and overall public sentiment towards their brand or product. Additionally, text data can be used to detect emerging topics and issues, which can help businesses stay ahead of the curve and respond proactively to changing trends.

Example:

`# Simple example using word frequency`

from collections import Counter

text_data = "Exploratory Data Analysis is important for data science."

word_count = Counter(text_data.split())

print("Word Frequency:", word_count)

### 8.2.4 Time-Series Data

Time-series data refers to a particular type of data that is collected or recorded at successive points in time. These data points can be captured at regular or irregular intervals and are often used to analyze patterns or trends over time.

One practical application of time-series data is in the stock market, where the prices of stocks and other financial instruments are tracked over time to inform investment decisions. Another example is weather data, which is collected at regular intervals to monitor changes in temperature, precipitation, and other meteorological phenomena.

In recent years, the explosive growth of social media has also led to the creation of vast amounts of time-series data. For instance, Twitter activity data can be analyzed to track changes in public opinion or to identify emerging trends and topics.

Overall, the use of time-series data in a variety of fields has become increasingly important, as it provides a valuable tool for understanding and predicting patterns over time.

Example:

`# Simple time-series plot`

import pandas as pd

time_series_data = pd.DataFrame({

'Date': pd.date_range(start='1/1/2022', periods=10, freq='D'),

'Stock_Price': [1, 2, 3, 4, 3, 4, 5, 6, 7, 8]

})

time_series_data.plot(x='Date', y='Stock_Price', kind='line')

plt.title('Time-Series Data')

plt.show()

Understanding different types of data is an essential aspect of exploratory data analysis (EDA). It involves learning how to visualize and handle data effectively, which is crucial for your data journey. In the upcoming sections, we will provide you with detailed insights into how each type of data requires a unique approach for effective analysis.

By gaining proficiency in these techniques, you will be well-equipped to handle complex data sets and draw meaningful conclusions from them. This, in turn, will help you derive valuable insights and make informed decisions in various fields, including business, finance, healthcare, and more.

### 8.2.5 Multivariate Data

Multivariate data analysis is a technique that involves examining multiple variables simultaneously to uncover patterns, trends or correlations that may be missed by analyzing variables independently. For instance, when making a decision about purchasing a car, you may consider factors such as mileage, price, year of manufacture, and brand. By examining how these variables are related, you can make a more informed decision.

One popular way of visualizing multivariate data is using a `pairplot`

. A pairplot is a matrix of scatter plots for each pair of variables, which provides a bird's-eye view of the relationships between all the variables involved. Through the use of a pairplot, one can easily identify correlations and outliers within the data. Moreover, this plot can be used to determine which variables are most influential in a given outcome.

In addition to pair plots, multivariate data analysis techniques can be used to develop models that can predict outcomes based on the relationship between multiple variables. These models can be used to forecast trends, identify patterns, and make informed decisions. By using multivariate data analysis, one can gain a more comprehensive understanding of complex data sets and make informed decisions based on the relationships between multiple variables.

Here's a Python example that uses Seaborn to create a `pairplot`

:

`import seaborn as sns`

import matplotlib.pyplot as plt

import pandas as pd

# Create a DataFrame

df = pd.DataFrame({

'Height': [5.9, 5.8, 5.6, 6.1, 5.7],

'Weight': [75, 80, 77, 89, 94],

'Age': [21, 22, 20, 19, 18]

})

# Create a pairplot

sns.pairplot(df)

plt.suptitle('Multivariate Data Visualization', y=1.02)

plt.show()

In the pairplot above, you can visually examine how `Height`

, `Weight`

, and `Age`

interact with each other. This can be very useful for identifying patterns or anomalies in the data.

### 8.2.6 Geospatial Data

Geospatial data is a type of data that contains information about the geographical location of objects or events. This type of data is highly valuable since it provides a wide range of information that can be used in various fields.

For instance, it can provide detailed information about the weather patterns of a particular region, the location of natural resources, and the population density of an area. This data can also be used to study the impact of human activities on the environment, and to develop strategies to mitigate them.

The complexity of geospatial data can vary widely, ranging from simple latitude and longitude coordinates of a city to a multi-layer map containing a wide range of information. Overall, geospatial data is an essential tool in many industries and plays a crucial role in our understanding of the world around us.

Here's a simple example that plots the geographical coordinates (latitude and longitude) of three cities: New York, Los Angeles, and Chicago.

`import matplotlib.pyplot as plt`

# Sample coordinates: [latitude, longitude]

locations = [

[40.7128, -74.0060], # New York

[34.0522, -118.2437], # Los Angeles

[41.8781, -87.6298], # Chicago

]

# Unzip the coordinates

latitudes, longitudes = zip(*locations)

# Create a scatter plot

plt.scatter(longitudes, latitudes)

plt.xlabel('Longitude')

plt.ylabel('Latitude')

plt.title('Geospatial Data Visualization')

plt.show()

This is a basic example that can be expanded in several ways to enhance its functionality and usefulness. For instance, you can include additional layers such as roads, landmarks, or other relevant data that might be useful to your specific application.

By introducing these additional types of data, you can gain a more comprehensive understanding of the types of data that you might encounter in real-world data analysis scenarios. This can help you to better prepare for such scenarios and to develop more accurate and reliable data analysis models. Additionally, by incorporating more data layers into your analysis, you can also increase the depth and complexity of your analysis, allowing you to uncover more insights and trends that might not be apparent from a more basic analysis.

## 8.2 Types of Data

### 8.2.1 Numerical Data

Numerical data is an essential element of scientific research and represents quantitative measurements of various phenomena. It is divided into two main types: discrete and continuous data. Discrete data refers to data that can only take certain specific values and is often obtained by counting.

For example, the number of cars in a parking lot can be counted, and the result is a discrete number. On the other hand, continuous data refers to data that can take any value within a specific range and can be measured using a scale. For example, the weight of an object can be measured using a scale, and the result is continuous data. Both types of data are important in scientific research and can provide valuable insights into various phenomena.

**Discrete Data **

This type of data consists of distinct and separate values that cannot be subdivided into smaller units. It is often composed of counts of things that are readily measurable. A good example of discrete data is the number of employees in a company.

However, it is important to note that discrete data can also include other types of information such as age groups, shoe sizes, and the number of students in a classroom. The analysis of discrete data involves determining the frequency of occurrence of each value and identifying patterns and trends that emerge.

This type of data is extremely useful in various fields such as statistics, finance, and marketing, where it is used to derive meaningful insights and make informed decisions.

**Continuous Data**

These are data points that can take any value within a range. Continuous data can be expressed in decimal or fractional values. Continuous data can be measured to a very high degree of accuracy, which is why they are frequently used in scientific research. Height, weight, and temperature are all examples of continuous data.

Additionally, other examples of continuous data include distance, time, and age. Continuous data can be further subdivided into two types: interval data and ratio data. Interval data refers to data that has no true zero point, while ratio data refers to data that does have a true zero point.

Example:

`# Example code to plot discrete and continuous data`

import matplotlib.pyplot as plt

import numpy as np

# Discrete Data

discrete_data = np.random.choice([1, 2, 3, 4, 5], 50)

plt.subplot(1, 2, 1)

plt.hist(discrete_data, bins=5)

plt.title('Discrete Data')

# Continuous Data

continuous_data = np.random.normal(5, 2, 50)

plt.subplot(1, 2, 2)

plt.hist(continuous_data, bins=5)

plt.title('Continuous Data')

plt.tight_layout()

plt.show()

### 8.2.2 Categorical Data

Categorical data is a type of data that is used to represent different characteristics or labels. Categorical data can be divided into two categories, namely nominal and ordinal categories. Nominal categories are used to represent data that has no inherent order, such as the colors of a rainbow or the different breeds of dogs.

On the other hand, ordinal categories are used to represent data that has a natural order, such as the different sizes of t-shirts (small, medium, large). It is important to note that categorical data can be useful in many different fields, such as marketing, social sciences, and data analysis.

**Nominal Data**

These have no natural order or ranking. Examples include colors, gender, and types of fruits. Nominal data is a type of data that has no natural order or ranking. This means that there is no inherent hierarchy or order in the data, and each value is considered to be equal. For example, when we collect data on colors, gender, or types of fruits, we are dealing with nominal data.

One way to think about nominal data is to consider the categories that the data represents. Each category is considered to be distinct and separate from the others, which means that there is no way to compare or rank them. For instance, when we collect data on the different colors of cars, we do not rank one color as being better or worse than another. Rather, each color is simply a separate category.

It is important to note that nominal data is not the only type of data that we can collect. Other types of data include ordinal, interval, and ratio data. Each of these types of data has its own unique properties and characteristics, which make them useful for different types of analysis.

In summary, nominal data is a type of data that has no natural order or ranking. It consists of categories that are distinct and separate from one another, and each value is considered to be equal. Examples of nominal data include colors, gender, and types of fruits.

**Ordinal Data**:

This type of data has a natural order in which the categories are arranged, but the intervals between the categories are not equal. It is used to represent data that involves subjective judgments, such as customer satisfaction ratings.

In this case, the data can be classified into categories such as 'Poor,' 'Average,' and 'Excellent.' Ordinal data can also be used to represent data from surveys that ask respondents to rate their level of agreement with a statement using categories such as 'Strongly Disagree,' 'Disagree,' 'Neutral,' 'Agree,' and 'Strongly Agree.' Since the categories are ranked, but the intervals between them are not uniform, ordinal data can be tricky to analyze.

Therefore, it is important to choose an appropriate statistical method to analyze this type of data, such as non-parametric tests like the Wilcoxon rank-sum test or the Kruskal-Wallis test.

Example:

`# Example code to plot nominal and ordinal data using bar plots`

import seaborn as sns

# Nominal Data

sns.countplot(x=["Apple", "Banana", "Apple", "Orange", "Banana", "Apple", "Orange"])

plt.title('Nominal Data')

plt.show()

# Ordinal Data

sns.countplot(x=["Poor", "Average", "Excellent", "Poor", "Average"])

plt.title('Ordinal Data')

plt.show()

### 8.2.3 Textual Data

Textual data refers to any kind of unstructured data, such as social media posts, comments, and news articles. These types of data were not traditionally analyzed with EDA, but with the advancements in Natural Language Processing (NLP), it is now possible to extract meaningful insights from textual data.

NLP techniques can be used to identify patterns and trends in large amounts of text data. Moreover, sentiment analysis can be conducted to understand the emotional tone of the text and to categorize it into positive, negative, or neutral.

This allows businesses and organizations to gain a better understanding of customer feedback and overall public sentiment towards their brand or product. Additionally, text data can be used to detect emerging topics and issues, which can help businesses stay ahead of the curve and respond proactively to changing trends.

Example:

`# Simple example using word frequency`

from collections import Counter

text_data = "Exploratory Data Analysis is important for data science."

word_count = Counter(text_data.split())

print("Word Frequency:", word_count)

### 8.2.4 Time-Series Data

Time-series data refers to a particular type of data that is collected or recorded at successive points in time. These data points can be captured at regular or irregular intervals and are often used to analyze patterns or trends over time.

One practical application of time-series data is in the stock market, where the prices of stocks and other financial instruments are tracked over time to inform investment decisions. Another example is weather data, which is collected at regular intervals to monitor changes in temperature, precipitation, and other meteorological phenomena.

In recent years, the explosive growth of social media has also led to the creation of vast amounts of time-series data. For instance, Twitter activity data can be analyzed to track changes in public opinion or to identify emerging trends and topics.

Overall, the use of time-series data in a variety of fields has become increasingly important, as it provides a valuable tool for understanding and predicting patterns over time.

Example:

`# Simple time-series plot`

import pandas as pd

time_series_data = pd.DataFrame({

'Date': pd.date_range(start='1/1/2022', periods=10, freq='D'),

'Stock_Price': [1, 2, 3, 4, 3, 4, 5, 6, 7, 8]

})

time_series_data.plot(x='Date', y='Stock_Price', kind='line')

plt.title('Time-Series Data')

plt.show()

Understanding different types of data is an essential aspect of exploratory data analysis (EDA). It involves learning how to visualize and handle data effectively, which is crucial for your data journey. In the upcoming sections, we will provide you with detailed insights into how each type of data requires a unique approach for effective analysis.

By gaining proficiency in these techniques, you will be well-equipped to handle complex data sets and draw meaningful conclusions from them. This, in turn, will help you derive valuable insights and make informed decisions in various fields, including business, finance, healthcare, and more.

### 8.2.5 Multivariate Data

Multivariate data analysis is a technique that involves examining multiple variables simultaneously to uncover patterns, trends or correlations that may be missed by analyzing variables independently. For instance, when making a decision about purchasing a car, you may consider factors such as mileage, price, year of manufacture, and brand. By examining how these variables are related, you can make a more informed decision.

One popular way of visualizing multivariate data is using a `pairplot`

. A pairplot is a matrix of scatter plots for each pair of variables, which provides a bird's-eye view of the relationships between all the variables involved. Through the use of a pairplot, one can easily identify correlations and outliers within the data. Moreover, this plot can be used to determine which variables are most influential in a given outcome.

In addition to pair plots, multivariate data analysis techniques can be used to develop models that can predict outcomes based on the relationship between multiple variables. These models can be used to forecast trends, identify patterns, and make informed decisions. By using multivariate data analysis, one can gain a more comprehensive understanding of complex data sets and make informed decisions based on the relationships between multiple variables.

Here's a Python example that uses Seaborn to create a `pairplot`

:

`import seaborn as sns`

import matplotlib.pyplot as plt

import pandas as pd

# Create a DataFrame

df = pd.DataFrame({

'Height': [5.9, 5.8, 5.6, 6.1, 5.7],

'Weight': [75, 80, 77, 89, 94],

'Age': [21, 22, 20, 19, 18]

})

# Create a pairplot

sns.pairplot(df)

plt.suptitle('Multivariate Data Visualization', y=1.02)

plt.show()

In the pairplot above, you can visually examine how `Height`

, `Weight`

, and `Age`

interact with each other. This can be very useful for identifying patterns or anomalies in the data.

### 8.2.6 Geospatial Data

Geospatial data is a type of data that contains information about the geographical location of objects or events. This type of data is highly valuable since it provides a wide range of information that can be used in various fields.

For instance, it can provide detailed information about the weather patterns of a particular region, the location of natural resources, and the population density of an area. This data can also be used to study the impact of human activities on the environment, and to develop strategies to mitigate them.

The complexity of geospatial data can vary widely, ranging from simple latitude and longitude coordinates of a city to a multi-layer map containing a wide range of information. Overall, geospatial data is an essential tool in many industries and plays a crucial role in our understanding of the world around us.

Here's a simple example that plots the geographical coordinates (latitude and longitude) of three cities: New York, Los Angeles, and Chicago.

`import matplotlib.pyplot as plt`

# Sample coordinates: [latitude, longitude]

locations = [

[40.7128, -74.0060], # New York

[34.0522, -118.2437], # Los Angeles

[41.8781, -87.6298], # Chicago

]

# Unzip the coordinates

latitudes, longitudes = zip(*locations)

# Create a scatter plot

plt.scatter(longitudes, latitudes)

plt.xlabel('Longitude')

plt.ylabel('Latitude')

plt.title('Geospatial Data Visualization')

plt.show()

This is a basic example that can be expanded in several ways to enhance its functionality and usefulness. For instance, you can include additional layers such as roads, landmarks, or other relevant data that might be useful to your specific application.

By introducing these additional types of data, you can gain a more comprehensive understanding of the types of data that you might encounter in real-world data analysis scenarios. This can help you to better prepare for such scenarios and to develop more accurate and reliable data analysis models. Additionally, by incorporating more data layers into your analysis, you can also increase the depth and complexity of your analysis, allowing you to uncover more insights and trends that might not be apparent from a more basic analysis.

## 8.2 Types of Data

### 8.2.1 Numerical Data

Numerical data is an essential element of scientific research and represents quantitative measurements of various phenomena. It is divided into two main types: discrete and continuous data. Discrete data refers to data that can only take certain specific values and is often obtained by counting.

For example, the number of cars in a parking lot can be counted, and the result is a discrete number. On the other hand, continuous data refers to data that can take any value within a specific range and can be measured using a scale. For example, the weight of an object can be measured using a scale, and the result is continuous data. Both types of data are important in scientific research and can provide valuable insights into various phenomena.

**Discrete Data **

This type of data consists of distinct and separate values that cannot be subdivided into smaller units. It is often composed of counts of things that are readily measurable. A good example of discrete data is the number of employees in a company.

However, it is important to note that discrete data can also include other types of information such as age groups, shoe sizes, and the number of students in a classroom. The analysis of discrete data involves determining the frequency of occurrence of each value and identifying patterns and trends that emerge.

This type of data is extremely useful in various fields such as statistics, finance, and marketing, where it is used to derive meaningful insights and make informed decisions.

**Continuous Data**

These are data points that can take any value within a range. Continuous data can be expressed in decimal or fractional values. Continuous data can be measured to a very high degree of accuracy, which is why they are frequently used in scientific research. Height, weight, and temperature are all examples of continuous data.

Additionally, other examples of continuous data include distance, time, and age. Continuous data can be further subdivided into two types: interval data and ratio data. Interval data refers to data that has no true zero point, while ratio data refers to data that does have a true zero point.

Example:

`# Example code to plot discrete and continuous data`

import matplotlib.pyplot as plt

import numpy as np

# Discrete Data

discrete_data = np.random.choice([1, 2, 3, 4, 5], 50)

plt.subplot(1, 2, 1)

plt.hist(discrete_data, bins=5)

plt.title('Discrete Data')

# Continuous Data

continuous_data = np.random.normal(5, 2, 50)

plt.subplot(1, 2, 2)

plt.hist(continuous_data, bins=5)

plt.title('Continuous Data')

plt.tight_layout()

plt.show()

### 8.2.2 Categorical Data

Categorical data is a type of data that is used to represent different characteristics or labels. Categorical data can be divided into two categories, namely nominal and ordinal categories. Nominal categories are used to represent data that has no inherent order, such as the colors of a rainbow or the different breeds of dogs.

On the other hand, ordinal categories are used to represent data that has a natural order, such as the different sizes of t-shirts (small, medium, large). It is important to note that categorical data can be useful in many different fields, such as marketing, social sciences, and data analysis.

**Nominal Data**

These have no natural order or ranking. Examples include colors, gender, and types of fruits. Nominal data is a type of data that has no natural order or ranking. This means that there is no inherent hierarchy or order in the data, and each value is considered to be equal. For example, when we collect data on colors, gender, or types of fruits, we are dealing with nominal data.

One way to think about nominal data is to consider the categories that the data represents. Each category is considered to be distinct and separate from the others, which means that there is no way to compare or rank them. For instance, when we collect data on the different colors of cars, we do not rank one color as being better or worse than another. Rather, each color is simply a separate category.

It is important to note that nominal data is not the only type of data that we can collect. Other types of data include ordinal, interval, and ratio data. Each of these types of data has its own unique properties and characteristics, which make them useful for different types of analysis.

In summary, nominal data is a type of data that has no natural order or ranking. It consists of categories that are distinct and separate from one another, and each value is considered to be equal. Examples of nominal data include colors, gender, and types of fruits.

**Ordinal Data**:

This type of data has a natural order in which the categories are arranged, but the intervals between the categories are not equal. It is used to represent data that involves subjective judgments, such as customer satisfaction ratings.

In this case, the data can be classified into categories such as 'Poor,' 'Average,' and 'Excellent.' Ordinal data can also be used to represent data from surveys that ask respondents to rate their level of agreement with a statement using categories such as 'Strongly Disagree,' 'Disagree,' 'Neutral,' 'Agree,' and 'Strongly Agree.' Since the categories are ranked, but the intervals between them are not uniform, ordinal data can be tricky to analyze.

Therefore, it is important to choose an appropriate statistical method to analyze this type of data, such as non-parametric tests like the Wilcoxon rank-sum test or the Kruskal-Wallis test.

Example:

`# Example code to plot nominal and ordinal data using bar plots`

import seaborn as sns

# Nominal Data

sns.countplot(x=["Apple", "Banana", "Apple", "Orange", "Banana", "Apple", "Orange"])

plt.title('Nominal Data')

plt.show()

# Ordinal Data

sns.countplot(x=["Poor", "Average", "Excellent", "Poor", "Average"])

plt.title('Ordinal Data')

plt.show()

### 8.2.3 Textual Data

Textual data refers to any kind of unstructured data, such as social media posts, comments, and news articles. These types of data were not traditionally analyzed with EDA, but with the advancements in Natural Language Processing (NLP), it is now possible to extract meaningful insights from textual data.

NLP techniques can be used to identify patterns and trends in large amounts of text data. Moreover, sentiment analysis can be conducted to understand the emotional tone of the text and to categorize it into positive, negative, or neutral.

This allows businesses and organizations to gain a better understanding of customer feedback and overall public sentiment towards their brand or product. Additionally, text data can be used to detect emerging topics and issues, which can help businesses stay ahead of the curve and respond proactively to changing trends.

Example:

`# Simple example using word frequency`

from collections import Counter

text_data = "Exploratory Data Analysis is important for data science."

word_count = Counter(text_data.split())

print("Word Frequency:", word_count)

### 8.2.4 Time-Series Data

Time-series data refers to a particular type of data that is collected or recorded at successive points in time. These data points can be captured at regular or irregular intervals and are often used to analyze patterns or trends over time.

One practical application of time-series data is in the stock market, where the prices of stocks and other financial instruments are tracked over time to inform investment decisions. Another example is weather data, which is collected at regular intervals to monitor changes in temperature, precipitation, and other meteorological phenomena.

In recent years, the explosive growth of social media has also led to the creation of vast amounts of time-series data. For instance, Twitter activity data can be analyzed to track changes in public opinion or to identify emerging trends and topics.

Overall, the use of time-series data in a variety of fields has become increasingly important, as it provides a valuable tool for understanding and predicting patterns over time.

Example:

`# Simple time-series plot`

import pandas as pd

time_series_data = pd.DataFrame({

'Date': pd.date_range(start='1/1/2022', periods=10, freq='D'),

'Stock_Price': [1, 2, 3, 4, 3, 4, 5, 6, 7, 8]

})

time_series_data.plot(x='Date', y='Stock_Price', kind='line')

plt.title('Time-Series Data')

plt.show()

Understanding different types of data is an essential aspect of exploratory data analysis (EDA). It involves learning how to visualize and handle data effectively, which is crucial for your data journey. In the upcoming sections, we will provide you with detailed insights into how each type of data requires a unique approach for effective analysis.

By gaining proficiency in these techniques, you will be well-equipped to handle complex data sets and draw meaningful conclusions from them. This, in turn, will help you derive valuable insights and make informed decisions in various fields, including business, finance, healthcare, and more.

### 8.2.5 Multivariate Data

Multivariate data analysis is a technique that involves examining multiple variables simultaneously to uncover patterns, trends or correlations that may be missed by analyzing variables independently. For instance, when making a decision about purchasing a car, you may consider factors such as mileage, price, year of manufacture, and brand. By examining how these variables are related, you can make a more informed decision.

One popular way of visualizing multivariate data is using a `pairplot`

. A pairplot is a matrix of scatter plots for each pair of variables, which provides a bird's-eye view of the relationships between all the variables involved. Through the use of a pairplot, one can easily identify correlations and outliers within the data. Moreover, this plot can be used to determine which variables are most influential in a given outcome.

In addition to pair plots, multivariate data analysis techniques can be used to develop models that can predict outcomes based on the relationship between multiple variables. These models can be used to forecast trends, identify patterns, and make informed decisions. By using multivariate data analysis, one can gain a more comprehensive understanding of complex data sets and make informed decisions based on the relationships between multiple variables.

Here's a Python example that uses Seaborn to create a `pairplot`

:

`import seaborn as sns`

import matplotlib.pyplot as plt

import pandas as pd

# Create a DataFrame

df = pd.DataFrame({

'Height': [5.9, 5.8, 5.6, 6.1, 5.7],

'Weight': [75, 80, 77, 89, 94],

'Age': [21, 22, 20, 19, 18]

})

# Create a pairplot

sns.pairplot(df)

plt.suptitle('Multivariate Data Visualization', y=1.02)

plt.show()

In the pairplot above, you can visually examine how `Height`

, `Weight`

, and `Age`

interact with each other. This can be very useful for identifying patterns or anomalies in the data.

### 8.2.6 Geospatial Data

Geospatial data is a type of data that contains information about the geographical location of objects or events. This type of data is highly valuable since it provides a wide range of information that can be used in various fields.

For instance, it can provide detailed information about the weather patterns of a particular region, the location of natural resources, and the population density of an area. This data can also be used to study the impact of human activities on the environment, and to develop strategies to mitigate them.

The complexity of geospatial data can vary widely, ranging from simple latitude and longitude coordinates of a city to a multi-layer map containing a wide range of information. Overall, geospatial data is an essential tool in many industries and plays a crucial role in our understanding of the world around us.

Here's a simple example that plots the geographical coordinates (latitude and longitude) of three cities: New York, Los Angeles, and Chicago.

`import matplotlib.pyplot as plt`

# Sample coordinates: [latitude, longitude]

locations = [

[40.7128, -74.0060], # New York

[34.0522, -118.2437], # Los Angeles

[41.8781, -87.6298], # Chicago

]

# Unzip the coordinates

latitudes, longitudes = zip(*locations)

# Create a scatter plot

plt.scatter(longitudes, latitudes)

plt.xlabel('Longitude')

plt.ylabel('Latitude')

plt.title('Geospatial Data Visualization')

plt.show()

This is a basic example that can be expanded in several ways to enhance its functionality and usefulness. For instance, you can include additional layers such as roads, landmarks, or other relevant data that might be useful to your specific application.

By introducing these additional types of data, you can gain a more comprehensive understanding of the types of data that you might encounter in real-world data analysis scenarios. This can help you to better prepare for such scenarios and to develop more accurate and reliable data analysis models. Additionally, by incorporating more data layers into your analysis, you can also increase the depth and complexity of your analysis, allowing you to uncover more insights and trends that might not be apparent from a more basic analysis.

## 8.2 Types of Data

### 8.2.1 Numerical Data

**Discrete Data **

**Continuous Data**

Example:

`# Example code to plot discrete and continuous data`

import matplotlib.pyplot as plt

import numpy as np

# Discrete Data

discrete_data = np.random.choice([1, 2, 3, 4, 5], 50)

plt.subplot(1, 2, 1)

plt.hist(discrete_data, bins=5)

plt.title('Discrete Data')

# Continuous Data

continuous_data = np.random.normal(5, 2, 50)

plt.subplot(1, 2, 2)

plt.hist(continuous_data, bins=5)

plt.title('Continuous Data')

plt.tight_layout()

plt.show()

### 8.2.2 Categorical Data

**Nominal Data**

**Ordinal Data**:

Example:

`# Example code to plot nominal and ordinal data using bar plots`

import seaborn as sns

# Nominal Data

sns.countplot(x=["Apple", "Banana", "Apple", "Orange", "Banana", "Apple", "Orange"])

plt.title('Nominal Data')

plt.show()

# Ordinal Data

sns.countplot(x=["Poor", "Average", "Excellent", "Poor", "Average"])

plt.title('Ordinal Data')

plt.show()

### 8.2.3 Textual Data

Example:

`# Simple example using word frequency`

from collections import Counter

text_data = "Exploratory Data Analysis is important for data science."

word_count = Counter(text_data.split())

print("Word Frequency:", word_count)

### 8.2.4 Time-Series Data

Example:

`# Simple time-series plot`

import pandas as pd

time_series_data = pd.DataFrame({

'Date': pd.date_range(start='1/1/2022', periods=10, freq='D'),

'Stock_Price': [1, 2, 3, 4, 3, 4, 5, 6, 7, 8]

})

time_series_data.plot(x='Date', y='Stock_Price', kind='line')

plt.title('Time-Series Data')

plt.show()

### 8.2.5 Multivariate Data

`pairplot`

. A pairplot is a matrix of scatter plots for each pair of variables, which provides a bird's-eye view of the relationships between all the variables involved. Through the use of a pairplot, one can easily identify correlations and outliers within the data. Moreover, this plot can be used to determine which variables are most influential in a given outcome.

Here's a Python example that uses Seaborn to create a `pairplot`

:

`import seaborn as sns`

import matplotlib.pyplot as plt

import pandas as pd

# Create a DataFrame

df = pd.DataFrame({

'Height': [5.9, 5.8, 5.6, 6.1, 5.7],

'Weight': [75, 80, 77, 89, 94],

'Age': [21, 22, 20, 19, 18]

})

# Create a pairplot

sns.pairplot(df)

plt.suptitle('Multivariate Data Visualization', y=1.02)

plt.show()

`Height`

, `Weight`

, and `Age`

interact with each other. This can be very useful for identifying patterns or anomalies in the data.

### 8.2.6 Geospatial Data

`import matplotlib.pyplot as plt`

# Sample coordinates: [latitude, longitude]

locations = [

[40.7128, -74.0060], # New York

[34.0522, -118.2437], # Los Angeles

[41.8781, -87.6298], # Chicago

]

# Unzip the coordinates

latitudes, longitudes = zip(*locations)

# Create a scatter plot

plt.scatter(longitudes, latitudes)

plt.xlabel('Longitude')

plt.ylabel('Latitude')

plt.title('Geospatial Data Visualization')

plt.show()