Menu iconMenu iconData Analysis Foundations with Python
Data Analysis Foundations with Python

Chapter 6: Data Manipulation with Pandas

6.1 DataFrames and Series

Welcome to Chapter 6, where we'll explore the amazing world of Pandas, an essential library for data analysis in Python. Pandas is a powerful tool that provides a wide range of data manipulation capabilities, catering to a variety of data formats and types. Not only can it help you with cleaning and transforming data, but it can also assist in creating stunning visualizations that can significantly enhance your data analysis.  

As we dive deeper into this chapter, we'll start by introducing you to the basics of Pandas, specifically the DataFrame and Series data structures. These structures will be your closest allies when it comes to handling complex data analysis tasks. By understanding how to use these structures, you'll be able to perform a wide range of data manipulations, including combining, filtering, and transforming data. Additionally, we'll also cover some of the more advanced features of Pandas, including grouping, pivoting, and reshaping data.

Whether you're a beginner or an experienced data analyst, this chapter will help you unlock the full potential of Pandas. So, get ready to embark on an exciting journey that will equip you with the skills and knowledge you need to become a master of data analysis!

Pandas is a powerful library for data manipulation in Python. It provides two primary data structures— DataFrame and Series—that are designed to help you manage and manipulate data effectively.

In the world of data science, it's essential to have a good understanding of these two data structures. A DataFrame is a two-dimensional table, where each column can have a different data type, and each row represents a single record. It's similar to a spreadsheet in Excel, but with more advanced functionality. On the other hand, a Series is a one-dimensional array-like object that can hold any data type, including integers, floats, and strings.

Both DataFrame and Series offer a wide range of built-in functions and methods that simplify data manipulation tasks. For instance, you can use them to filter, group, sort, join, and merge data, among other things. They also provide an intuitive and straightforward syntax that makes it easy to perform complex operations with minimal code.

In conclusion, if you want to become proficient in data manipulation with Python, you need to master DataFrame and Series. By understanding their differences and capabilities, you'll be able to leverage their full potential and take your data analysis skills to the next level.

6.1.1 DataFrame

A DataFrame is a highly versatile data structure that is essentially a two-dimensional, size-mutable, and potentially heterogeneous tabular data structure with labeled axes (rows and columns).

Similar to a spreadsheet or a SQL table, a DataFrame provides a convenient way to store and manipulate data. However, unlike a spreadsheet, a DataFrame can handle a much larger dataset and is highly optimized for data analysis tasks. Additionally, DataFrames are widely used in the field of data science and are considered to be an essential tool for conducting various data analysis tasks, including data wrangling, data cleaning, data transformation, and data visualization.

Overall, a DataFrame is a powerful and flexible data structure that is an indispensable tool for any data analyst or data scientist.

Here's how to create a basic DataFrame:

import pandas as pd

# Create a DataFrame from a dictionary
data = {'Name': ['Alice', 'Bob', 'Charlie'],
        'Age': [25, 30, 35],
        'Occupation': ['Engineer', 'Doctor', 'Artist']}
df = pd.DataFrame(data)

# Display the DataFrame
print(df)

Output:

      Name  Age Occupation
0    Alice   25   Engineer
1      Bob   30     Doctor
2  Charlie   35     Artist

6.1.2 Series

A Series is a type of data structure in pandas library. It is a one-dimensional labeled array that contains data of any type. It can be thought of as a single column in a DataFrame. This means that the Series can be used to store a single column of data, such as a list of numbers, names, or any other data type. In pandas, you can create a Series from a list, array, or dictionary.

The Series can be very useful in data analysis and manipulation, as it allows you to perform various operations on the data stored in it. For instance, you can sort the data, filter it, or group it based on certain conditions.

Furthermore, you can also perform mathematical operations on the data, such as addition, subtraction, multiplication, and division. Overall, the Series is an essential data structure in pandas that can help you to work with data in a more efficient and effective way.

Example:

# Create a Series from a list
ages = [25, 30, 35]
age_series = pd.Series(ages, name='Age')

# Display the Series
print(age_series)

Output:

0    25
1    30
2    35
Name: Age, dtype: int64

6.1.3 DataFrame vs Series

While both DataFrames and Series are highly flexible and versatile, there are several important differences between the two that may impact which one you choose to use depending on your specific needs.

For example, while Series are more memory-efficient for single-column data and are often returned when you query a single column from a DataFrame, DataFrames offer more functionalities that may be useful in certain scenarios. For instance, DataFrames allow for multiple columns with different data types, which can be very helpful when working with complex datasets.

Additionally, DataFrames have built-in methods for merging and joining data from different sources, which can save time and effort when dealing with large datasets. Finally, another advantage of DataFrames over Series is that they can be easily exported to a variety of file formats, including CSV and Excel, which can be very useful when sharing data with others or integrating it into other applications.

Here's how you can select a column as a Series from a DataFrame:

# Select the 'Age' column from the DataFrame
age_from_df = df['Age']

# Display the Series
print(age_from_df)

Output:

0    25
1    30
2    35
Name: Age, dtype: int64

6.1.4 DataFrame Methods and Attributes

When working with DataFrames, you don't always need to dive into the data to gain insights. There are various ways to learn about a DataFrame without even looking at its contents. For instance, you can check the shape of the DataFrame to see how many rows and columns it contains.

You can also check the data types of each column, which can provide clues about the nature of the data. Additionally, you can use the info() method to get a summary of the DataFrame's columns, including their data types and the number of non-null values. By exploring these characteristics of a DataFrame, you can gain a better understanding of its structure and make more informed decisions about how to manipulate or analyze the data it contains.

Here are some methods to explore the basic information:

  • df.head(): Returns the first 5 rows of the DataFrame.
  • df.tail(): Returns the last 5 rows of the DataFrame.
  • df.info(): Provides a concise summary of the DataFrame including data types and non-null values.
  • df.describe(): Gives statistical insights into numerical columns.

Example:

# Get the first 5 rows
print(df.head())

# Get summary information
print(df.info())

# Get statistical information
print(df.describe())

6.1.5 Series Methods and Attributes

When working with series, you will find that not only do they provide a range of different data types to work with, but they also come with a wide variety of methods and attributes that can make data manipulation and analysis more streamlined and efficient.

By leveraging these built-in tools and functions, you can save yourself time and effort while still ensuring that your data is accurate and easy to work with. Whether you are a seasoned data analyst or a beginner just starting out, mastering the use of series is an essential step towards becoming a more effective and productive data professional.

Some important ones are:

  • s.size: Returns the number of elements in the Series.
  • s.mean(): Returns the mean value.
  • s.std(): Returns the standard deviation.
  • s.unique(): Returns unique values.

Example:

# Get the size of the Series
print(age_series.size)

# Get the mean age
print(age_series.mean())

# Get unique ages
print(age_series.unique())

6.1.6 Changing Data Types

In some cases, it may become necessary to alter the data types of columns or Series for a variety of reasons. For instance, doing so may be essential for more efficient processing or to facilitate the execution of specific operations.

Additionally, such changes may be required to address the constraints of a particular environment, such as when working with limited memory or processing power. In such cases, it is important to carefully consider the implications of the changes being made and to test the revised data types thoroughly to ensure that they continue to support the desired outcomes.

In DataFrame

# Change the data type of a single column
df['column_name'] = df['column_name'].astype('new_data_type')

# Change data types of multiple columns
df = df.astype({'column1': 'new_data_type1', 'column2': 'new_data_type2'})

In Series

# Changing the Series data type
s = s.astype('new_data_type')

For example, if you have a DataFrame df and you want to change the data type of the age column to float:

df['age'] = df['age'].astype('float')

6.1 DataFrames and Series

Welcome to Chapter 6, where we'll explore the amazing world of Pandas, an essential library for data analysis in Python. Pandas is a powerful tool that provides a wide range of data manipulation capabilities, catering to a variety of data formats and types. Not only can it help you with cleaning and transforming data, but it can also assist in creating stunning visualizations that can significantly enhance your data analysis.  

As we dive deeper into this chapter, we'll start by introducing you to the basics of Pandas, specifically the DataFrame and Series data structures. These structures will be your closest allies when it comes to handling complex data analysis tasks. By understanding how to use these structures, you'll be able to perform a wide range of data manipulations, including combining, filtering, and transforming data. Additionally, we'll also cover some of the more advanced features of Pandas, including grouping, pivoting, and reshaping data.

Whether you're a beginner or an experienced data analyst, this chapter will help you unlock the full potential of Pandas. So, get ready to embark on an exciting journey that will equip you with the skills and knowledge you need to become a master of data analysis!

Pandas is a powerful library for data manipulation in Python. It provides two primary data structures— DataFrame and Series—that are designed to help you manage and manipulate data effectively.

In the world of data science, it's essential to have a good understanding of these two data structures. A DataFrame is a two-dimensional table, where each column can have a different data type, and each row represents a single record. It's similar to a spreadsheet in Excel, but with more advanced functionality. On the other hand, a Series is a one-dimensional array-like object that can hold any data type, including integers, floats, and strings.

Both DataFrame and Series offer a wide range of built-in functions and methods that simplify data manipulation tasks. For instance, you can use them to filter, group, sort, join, and merge data, among other things. They also provide an intuitive and straightforward syntax that makes it easy to perform complex operations with minimal code.

In conclusion, if you want to become proficient in data manipulation with Python, you need to master DataFrame and Series. By understanding their differences and capabilities, you'll be able to leverage their full potential and take your data analysis skills to the next level.

6.1.1 DataFrame

A DataFrame is a highly versatile data structure that is essentially a two-dimensional, size-mutable, and potentially heterogeneous tabular data structure with labeled axes (rows and columns).

Similar to a spreadsheet or a SQL table, a DataFrame provides a convenient way to store and manipulate data. However, unlike a spreadsheet, a DataFrame can handle a much larger dataset and is highly optimized for data analysis tasks. Additionally, DataFrames are widely used in the field of data science and are considered to be an essential tool for conducting various data analysis tasks, including data wrangling, data cleaning, data transformation, and data visualization.

Overall, a DataFrame is a powerful and flexible data structure that is an indispensable tool for any data analyst or data scientist.

Here's how to create a basic DataFrame:

import pandas as pd

# Create a DataFrame from a dictionary
data = {'Name': ['Alice', 'Bob', 'Charlie'],
        'Age': [25, 30, 35],
        'Occupation': ['Engineer', 'Doctor', 'Artist']}
df = pd.DataFrame(data)

# Display the DataFrame
print(df)

Output:

      Name  Age Occupation
0    Alice   25   Engineer
1      Bob   30     Doctor
2  Charlie   35     Artist

6.1.2 Series

A Series is a type of data structure in pandas library. It is a one-dimensional labeled array that contains data of any type. It can be thought of as a single column in a DataFrame. This means that the Series can be used to store a single column of data, such as a list of numbers, names, or any other data type. In pandas, you can create a Series from a list, array, or dictionary.

The Series can be very useful in data analysis and manipulation, as it allows you to perform various operations on the data stored in it. For instance, you can sort the data, filter it, or group it based on certain conditions.

Furthermore, you can also perform mathematical operations on the data, such as addition, subtraction, multiplication, and division. Overall, the Series is an essential data structure in pandas that can help you to work with data in a more efficient and effective way.

Example:

# Create a Series from a list
ages = [25, 30, 35]
age_series = pd.Series(ages, name='Age')

# Display the Series
print(age_series)

Output:

0    25
1    30
2    35
Name: Age, dtype: int64

6.1.3 DataFrame vs Series

While both DataFrames and Series are highly flexible and versatile, there are several important differences between the two that may impact which one you choose to use depending on your specific needs.

For example, while Series are more memory-efficient for single-column data and are often returned when you query a single column from a DataFrame, DataFrames offer more functionalities that may be useful in certain scenarios. For instance, DataFrames allow for multiple columns with different data types, which can be very helpful when working with complex datasets.

Additionally, DataFrames have built-in methods for merging and joining data from different sources, which can save time and effort when dealing with large datasets. Finally, another advantage of DataFrames over Series is that they can be easily exported to a variety of file formats, including CSV and Excel, which can be very useful when sharing data with others or integrating it into other applications.

Here's how you can select a column as a Series from a DataFrame:

# Select the 'Age' column from the DataFrame
age_from_df = df['Age']

# Display the Series
print(age_from_df)

Output:

0    25
1    30
2    35
Name: Age, dtype: int64

6.1.4 DataFrame Methods and Attributes

When working with DataFrames, you don't always need to dive into the data to gain insights. There are various ways to learn about a DataFrame without even looking at its contents. For instance, you can check the shape of the DataFrame to see how many rows and columns it contains.

You can also check the data types of each column, which can provide clues about the nature of the data. Additionally, you can use the info() method to get a summary of the DataFrame's columns, including their data types and the number of non-null values. By exploring these characteristics of a DataFrame, you can gain a better understanding of its structure and make more informed decisions about how to manipulate or analyze the data it contains.

Here are some methods to explore the basic information:

  • df.head(): Returns the first 5 rows of the DataFrame.
  • df.tail(): Returns the last 5 rows of the DataFrame.
  • df.info(): Provides a concise summary of the DataFrame including data types and non-null values.
  • df.describe(): Gives statistical insights into numerical columns.

Example:

# Get the first 5 rows
print(df.head())

# Get summary information
print(df.info())

# Get statistical information
print(df.describe())

6.1.5 Series Methods and Attributes

When working with series, you will find that not only do they provide a range of different data types to work with, but they also come with a wide variety of methods and attributes that can make data manipulation and analysis more streamlined and efficient.

By leveraging these built-in tools and functions, you can save yourself time and effort while still ensuring that your data is accurate and easy to work with. Whether you are a seasoned data analyst or a beginner just starting out, mastering the use of series is an essential step towards becoming a more effective and productive data professional.

Some important ones are:

  • s.size: Returns the number of elements in the Series.
  • s.mean(): Returns the mean value.
  • s.std(): Returns the standard deviation.
  • s.unique(): Returns unique values.

Example:

# Get the size of the Series
print(age_series.size)

# Get the mean age
print(age_series.mean())

# Get unique ages
print(age_series.unique())

6.1.6 Changing Data Types

In some cases, it may become necessary to alter the data types of columns or Series for a variety of reasons. For instance, doing so may be essential for more efficient processing or to facilitate the execution of specific operations.

Additionally, such changes may be required to address the constraints of a particular environment, such as when working with limited memory or processing power. In such cases, it is important to carefully consider the implications of the changes being made and to test the revised data types thoroughly to ensure that they continue to support the desired outcomes.

In DataFrame

# Change the data type of a single column
df['column_name'] = df['column_name'].astype('new_data_type')

# Change data types of multiple columns
df = df.astype({'column1': 'new_data_type1', 'column2': 'new_data_type2'})

In Series

# Changing the Series data type
s = s.astype('new_data_type')

For example, if you have a DataFrame df and you want to change the data type of the age column to float:

df['age'] = df['age'].astype('float')

6.1 DataFrames and Series

Welcome to Chapter 6, where we'll explore the amazing world of Pandas, an essential library for data analysis in Python. Pandas is a powerful tool that provides a wide range of data manipulation capabilities, catering to a variety of data formats and types. Not only can it help you with cleaning and transforming data, but it can also assist in creating stunning visualizations that can significantly enhance your data analysis.  

As we dive deeper into this chapter, we'll start by introducing you to the basics of Pandas, specifically the DataFrame and Series data structures. These structures will be your closest allies when it comes to handling complex data analysis tasks. By understanding how to use these structures, you'll be able to perform a wide range of data manipulations, including combining, filtering, and transforming data. Additionally, we'll also cover some of the more advanced features of Pandas, including grouping, pivoting, and reshaping data.

Whether you're a beginner or an experienced data analyst, this chapter will help you unlock the full potential of Pandas. So, get ready to embark on an exciting journey that will equip you with the skills and knowledge you need to become a master of data analysis!

Pandas is a powerful library for data manipulation in Python. It provides two primary data structures— DataFrame and Series—that are designed to help you manage and manipulate data effectively.

In the world of data science, it's essential to have a good understanding of these two data structures. A DataFrame is a two-dimensional table, where each column can have a different data type, and each row represents a single record. It's similar to a spreadsheet in Excel, but with more advanced functionality. On the other hand, a Series is a one-dimensional array-like object that can hold any data type, including integers, floats, and strings.

Both DataFrame and Series offer a wide range of built-in functions and methods that simplify data manipulation tasks. For instance, you can use them to filter, group, sort, join, and merge data, among other things. They also provide an intuitive and straightforward syntax that makes it easy to perform complex operations with minimal code.

In conclusion, if you want to become proficient in data manipulation with Python, you need to master DataFrame and Series. By understanding their differences and capabilities, you'll be able to leverage their full potential and take your data analysis skills to the next level.

6.1.1 DataFrame

A DataFrame is a highly versatile data structure that is essentially a two-dimensional, size-mutable, and potentially heterogeneous tabular data structure with labeled axes (rows and columns).

Similar to a spreadsheet or a SQL table, a DataFrame provides a convenient way to store and manipulate data. However, unlike a spreadsheet, a DataFrame can handle a much larger dataset and is highly optimized for data analysis tasks. Additionally, DataFrames are widely used in the field of data science and are considered to be an essential tool for conducting various data analysis tasks, including data wrangling, data cleaning, data transformation, and data visualization.

Overall, a DataFrame is a powerful and flexible data structure that is an indispensable tool for any data analyst or data scientist.

Here's how to create a basic DataFrame:

import pandas as pd

# Create a DataFrame from a dictionary
data = {'Name': ['Alice', 'Bob', 'Charlie'],
        'Age': [25, 30, 35],
        'Occupation': ['Engineer', 'Doctor', 'Artist']}
df = pd.DataFrame(data)

# Display the DataFrame
print(df)

Output:

      Name  Age Occupation
0    Alice   25   Engineer
1      Bob   30     Doctor
2  Charlie   35     Artist

6.1.2 Series

A Series is a type of data structure in pandas library. It is a one-dimensional labeled array that contains data of any type. It can be thought of as a single column in a DataFrame. This means that the Series can be used to store a single column of data, such as a list of numbers, names, or any other data type. In pandas, you can create a Series from a list, array, or dictionary.

The Series can be very useful in data analysis and manipulation, as it allows you to perform various operations on the data stored in it. For instance, you can sort the data, filter it, or group it based on certain conditions.

Furthermore, you can also perform mathematical operations on the data, such as addition, subtraction, multiplication, and division. Overall, the Series is an essential data structure in pandas that can help you to work with data in a more efficient and effective way.

Example:

# Create a Series from a list
ages = [25, 30, 35]
age_series = pd.Series(ages, name='Age')

# Display the Series
print(age_series)

Output:

0    25
1    30
2    35
Name: Age, dtype: int64

6.1.3 DataFrame vs Series

While both DataFrames and Series are highly flexible and versatile, there are several important differences between the two that may impact which one you choose to use depending on your specific needs.

For example, while Series are more memory-efficient for single-column data and are often returned when you query a single column from a DataFrame, DataFrames offer more functionalities that may be useful in certain scenarios. For instance, DataFrames allow for multiple columns with different data types, which can be very helpful when working with complex datasets.

Additionally, DataFrames have built-in methods for merging and joining data from different sources, which can save time and effort when dealing with large datasets. Finally, another advantage of DataFrames over Series is that they can be easily exported to a variety of file formats, including CSV and Excel, which can be very useful when sharing data with others or integrating it into other applications.

Here's how you can select a column as a Series from a DataFrame:

# Select the 'Age' column from the DataFrame
age_from_df = df['Age']

# Display the Series
print(age_from_df)

Output:

0    25
1    30
2    35
Name: Age, dtype: int64

6.1.4 DataFrame Methods and Attributes

When working with DataFrames, you don't always need to dive into the data to gain insights. There are various ways to learn about a DataFrame without even looking at its contents. For instance, you can check the shape of the DataFrame to see how many rows and columns it contains.

You can also check the data types of each column, which can provide clues about the nature of the data. Additionally, you can use the info() method to get a summary of the DataFrame's columns, including their data types and the number of non-null values. By exploring these characteristics of a DataFrame, you can gain a better understanding of its structure and make more informed decisions about how to manipulate or analyze the data it contains.

Here are some methods to explore the basic information:

  • df.head(): Returns the first 5 rows of the DataFrame.
  • df.tail(): Returns the last 5 rows of the DataFrame.
  • df.info(): Provides a concise summary of the DataFrame including data types and non-null values.
  • df.describe(): Gives statistical insights into numerical columns.

Example:

# Get the first 5 rows
print(df.head())

# Get summary information
print(df.info())

# Get statistical information
print(df.describe())

6.1.5 Series Methods and Attributes

When working with series, you will find that not only do they provide a range of different data types to work with, but they also come with a wide variety of methods and attributes that can make data manipulation and analysis more streamlined and efficient.

By leveraging these built-in tools and functions, you can save yourself time and effort while still ensuring that your data is accurate and easy to work with. Whether you are a seasoned data analyst or a beginner just starting out, mastering the use of series is an essential step towards becoming a more effective and productive data professional.

Some important ones are:

  • s.size: Returns the number of elements in the Series.
  • s.mean(): Returns the mean value.
  • s.std(): Returns the standard deviation.
  • s.unique(): Returns unique values.

Example:

# Get the size of the Series
print(age_series.size)

# Get the mean age
print(age_series.mean())

# Get unique ages
print(age_series.unique())

6.1.6 Changing Data Types

In some cases, it may become necessary to alter the data types of columns or Series for a variety of reasons. For instance, doing so may be essential for more efficient processing or to facilitate the execution of specific operations.

Additionally, such changes may be required to address the constraints of a particular environment, such as when working with limited memory or processing power. In such cases, it is important to carefully consider the implications of the changes being made and to test the revised data types thoroughly to ensure that they continue to support the desired outcomes.

In DataFrame

# Change the data type of a single column
df['column_name'] = df['column_name'].astype('new_data_type')

# Change data types of multiple columns
df = df.astype({'column1': 'new_data_type1', 'column2': 'new_data_type2'})

In Series

# Changing the Series data type
s = s.astype('new_data_type')

For example, if you have a DataFrame df and you want to change the data type of the age column to float:

df['age'] = df['age'].astype('float')

6.1 DataFrames and Series

Welcome to Chapter 6, where we'll explore the amazing world of Pandas, an essential library for data analysis in Python. Pandas is a powerful tool that provides a wide range of data manipulation capabilities, catering to a variety of data formats and types. Not only can it help you with cleaning and transforming data, but it can also assist in creating stunning visualizations that can significantly enhance your data analysis.  

As we dive deeper into this chapter, we'll start by introducing you to the basics of Pandas, specifically the DataFrame and Series data structures. These structures will be your closest allies when it comes to handling complex data analysis tasks. By understanding how to use these structures, you'll be able to perform a wide range of data manipulations, including combining, filtering, and transforming data. Additionally, we'll also cover some of the more advanced features of Pandas, including grouping, pivoting, and reshaping data.

Whether you're a beginner or an experienced data analyst, this chapter will help you unlock the full potential of Pandas. So, get ready to embark on an exciting journey that will equip you with the skills and knowledge you need to become a master of data analysis!

Pandas is a powerful library for data manipulation in Python. It provides two primary data structures— DataFrame and Series—that are designed to help you manage and manipulate data effectively.

In the world of data science, it's essential to have a good understanding of these two data structures. A DataFrame is a two-dimensional table, where each column can have a different data type, and each row represents a single record. It's similar to a spreadsheet in Excel, but with more advanced functionality. On the other hand, a Series is a one-dimensional array-like object that can hold any data type, including integers, floats, and strings.

Both DataFrame and Series offer a wide range of built-in functions and methods that simplify data manipulation tasks. For instance, you can use them to filter, group, sort, join, and merge data, among other things. They also provide an intuitive and straightforward syntax that makes it easy to perform complex operations with minimal code.

In conclusion, if you want to become proficient in data manipulation with Python, you need to master DataFrame and Series. By understanding their differences and capabilities, you'll be able to leverage their full potential and take your data analysis skills to the next level.

6.1.1 DataFrame

A DataFrame is a highly versatile data structure that is essentially a two-dimensional, size-mutable, and potentially heterogeneous tabular data structure with labeled axes (rows and columns).

Similar to a spreadsheet or a SQL table, a DataFrame provides a convenient way to store and manipulate data. However, unlike a spreadsheet, a DataFrame can handle a much larger dataset and is highly optimized for data analysis tasks. Additionally, DataFrames are widely used in the field of data science and are considered to be an essential tool for conducting various data analysis tasks, including data wrangling, data cleaning, data transformation, and data visualization.

Overall, a DataFrame is a powerful and flexible data structure that is an indispensable tool for any data analyst or data scientist.

Here's how to create a basic DataFrame:

import pandas as pd

# Create a DataFrame from a dictionary
data = {'Name': ['Alice', 'Bob', 'Charlie'],
        'Age': [25, 30, 35],
        'Occupation': ['Engineer', 'Doctor', 'Artist']}
df = pd.DataFrame(data)

# Display the DataFrame
print(df)

Output:

      Name  Age Occupation
0    Alice   25   Engineer
1      Bob   30     Doctor
2  Charlie   35     Artist

6.1.2 Series

A Series is a type of data structure in pandas library. It is a one-dimensional labeled array that contains data of any type. It can be thought of as a single column in a DataFrame. This means that the Series can be used to store a single column of data, such as a list of numbers, names, or any other data type. In pandas, you can create a Series from a list, array, or dictionary.

The Series can be very useful in data analysis and manipulation, as it allows you to perform various operations on the data stored in it. For instance, you can sort the data, filter it, or group it based on certain conditions.

Furthermore, you can also perform mathematical operations on the data, such as addition, subtraction, multiplication, and division. Overall, the Series is an essential data structure in pandas that can help you to work with data in a more efficient and effective way.

Example:

# Create a Series from a list
ages = [25, 30, 35]
age_series = pd.Series(ages, name='Age')

# Display the Series
print(age_series)

Output:

0    25
1    30
2    35
Name: Age, dtype: int64

6.1.3 DataFrame vs Series

While both DataFrames and Series are highly flexible and versatile, there are several important differences between the two that may impact which one you choose to use depending on your specific needs.

For example, while Series are more memory-efficient for single-column data and are often returned when you query a single column from a DataFrame, DataFrames offer more functionalities that may be useful in certain scenarios. For instance, DataFrames allow for multiple columns with different data types, which can be very helpful when working with complex datasets.

Additionally, DataFrames have built-in methods for merging and joining data from different sources, which can save time and effort when dealing with large datasets. Finally, another advantage of DataFrames over Series is that they can be easily exported to a variety of file formats, including CSV and Excel, which can be very useful when sharing data with others or integrating it into other applications.

Here's how you can select a column as a Series from a DataFrame:

# Select the 'Age' column from the DataFrame
age_from_df = df['Age']

# Display the Series
print(age_from_df)

Output:

0    25
1    30
2    35
Name: Age, dtype: int64

6.1.4 DataFrame Methods and Attributes

When working with DataFrames, you don't always need to dive into the data to gain insights. There are various ways to learn about a DataFrame without even looking at its contents. For instance, you can check the shape of the DataFrame to see how many rows and columns it contains.

You can also check the data types of each column, which can provide clues about the nature of the data. Additionally, you can use the info() method to get a summary of the DataFrame's columns, including their data types and the number of non-null values. By exploring these characteristics of a DataFrame, you can gain a better understanding of its structure and make more informed decisions about how to manipulate or analyze the data it contains.

Here are some methods to explore the basic information:

  • df.head(): Returns the first 5 rows of the DataFrame.
  • df.tail(): Returns the last 5 rows of the DataFrame.
  • df.info(): Provides a concise summary of the DataFrame including data types and non-null values.
  • df.describe(): Gives statistical insights into numerical columns.

Example:

# Get the first 5 rows
print(df.head())

# Get summary information
print(df.info())

# Get statistical information
print(df.describe())

6.1.5 Series Methods and Attributes

When working with series, you will find that not only do they provide a range of different data types to work with, but they also come with a wide variety of methods and attributes that can make data manipulation and analysis more streamlined and efficient.

By leveraging these built-in tools and functions, you can save yourself time and effort while still ensuring that your data is accurate and easy to work with. Whether you are a seasoned data analyst or a beginner just starting out, mastering the use of series is an essential step towards becoming a more effective and productive data professional.

Some important ones are:

  • s.size: Returns the number of elements in the Series.
  • s.mean(): Returns the mean value.
  • s.std(): Returns the standard deviation.
  • s.unique(): Returns unique values.

Example:

# Get the size of the Series
print(age_series.size)

# Get the mean age
print(age_series.mean())

# Get unique ages
print(age_series.unique())

6.1.6 Changing Data Types

In some cases, it may become necessary to alter the data types of columns or Series for a variety of reasons. For instance, doing so may be essential for more efficient processing or to facilitate the execution of specific operations.

Additionally, such changes may be required to address the constraints of a particular environment, such as when working with limited memory or processing power. In such cases, it is important to carefully consider the implications of the changes being made and to test the revised data types thoroughly to ensure that they continue to support the desired outcomes.

In DataFrame

# Change the data type of a single column
df['column_name'] = df['column_name'].astype('new_data_type')

# Change data types of multiple columns
df = df.astype({'column1': 'new_data_type1', 'column2': 'new_data_type2'})

In Series

# Changing the Series data type
s = s.astype('new_data_type')

For example, if you have a DataFrame df and you want to change the data type of the age column to float:

df['age'] = df['age'].astype('float')