Advanced Techniques in pandas

Advanced Techniques in pandas

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1. Getting Familiar With Indexing and Selecting Data

This section will teach you how to output specific columns by their titles or indices. Also, you will get acquainted with the ways you can select rows by indices.

Outputting Columns by Title

Outputting Rows by Index

Selecting Specific Rows and Columns

Learning More About Indexation

Getting Familiar With lambda Functions

Expanding Functionality of the .iloc[] Attribute

2. Dealing With Conditions

Here, you will learn how to extract data that has specific conditions. Also, you will learn how to combine them and even create your own.

Setting Condition

Selecting Data Based on Condition

Dealing With Several Conditions

Making Your Code Beautiful

Conditions Quiz

3. Extracting Data

In this section, you will expand your knowledge on setting different data conditions. You will learn to check if your data is in a defined list of values or between two values. You will also learn how to find the largest and smallest values.

Is Data in ...?

Combining Your Knowledge

Extracting Specific Data

Finding the Smallest Values of a Column

Finding the Largest Values of a Column

Finding the Correlation

4. Aggregating Data

This section is one of the most fascinating of the course. Here, you will learn how to group data in different ways. It will help you work as a data analyst to find out information on specific data groups.

Getting Familiar With the .groupby() Method

Grouping by Several Columns

Complicated Grouping

Advanced Grouping

Dealing With Pivot Tables

Creating a Pivot Table

5. Preprocessing Data

This section is one of the most significant for a data analyst because if the data contains missing data values in the incorrect format, it will be impossible to work with. Thus, you will learn how to deal with such inappropriate values here.

Checking for Missing Values

Calculating the Number of Missing Values

What Will We Do With the NaN Values?

How to Delete Only NaN Values?

Filling In the Missing Values

Managing Categorical Variables

Checking the Column Type

Managing an Incorrect Column

Renaming the Column

Зміст курсу

Advanced Techniques in pandas

Advanced Techniques in pandas

1. Getting Familiar With Indexing and Selecting Data

Outputting Columns by Title Outputting Rows by Index Selecting Specific Rows and Columns Learning More About Indexation Getting Familiar With lambda Functions Expanding Functionality of the .iloc[] Attribute

2. Dealing With Conditions

Setting Condition Selecting Data Based on Condition Dealing With Several Conditions Making Your Code Beautiful Conditions Quiz

3. Extracting Data

Is Data in ...?Combining Your Knowledge Between Method Extracting Specific Data Finding the Smallest Values of a Column Finding the Largest Values of a Column Finding the Correlation

4. Aggregating Data

Getting Familiar With the .groupby() Method Grouping by Several Columns Complicated Grouping Advanced Grouping Dealing With Pivot Tables Creating a Pivot Table

5. Preprocessing Data

Checking for Missing Values Calculating the Number of Missing Values What Will We Do With the NaN Values?How to Delete Only NaN Values?Filling In the Missing Values Managing Categorical Variables Checking the Column Type Managing an Incorrect Column Renaming the Column

Calculating the Number of Missing Values

It should be noted that it isn't convenient to check each value of the dataset for the NaN. It is more convenient to see the number of missing values to conclude columns where we have NaNs. As you remember, we have two functions to check for the missing values. To calculate the sum, just use the .sum() function. Thus, in general, we have 2 options for outputting the number of NaNs for each column:

Okay, nothing complicated. Let's move on the task.

Завдання

Swipe to show code editor

Calculate the number of missing values for the dataset using one of the mentioned functions.
Output the result.

Try to draw your own conclusions.

Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів

Все було зрозуміло?

Дякуємо за ваш відгук!

Секція 5. Розділ 2

toggle bottom row

Calculating the Number of Missing Values

It should be noted that it isn't convenient to check each value of the dataset for the NaN. It is more convenient to see the number of missing values to conclude columns where we have NaNs. As you remember, we have two functions to check for the missing values. To calculate the sum, just use the .sum() function. Thus, in general, we have 2 options for outputting the number of NaNs for each column:

Okay, nothing complicated. Let's move on the task.

Завдання

Swipe to show code editor

Calculate the number of missing values for the dataset using one of the mentioned functions.
Output the result.

Try to draw your own conclusions.

Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів

Все було зрозуміло?

Дякуємо за ваш відгук!

Секція 5. Розділ 2

toggle bottom row

Calculating the Number of Missing Values

It should be noted that it isn't convenient to check each value of the dataset for the NaN. It is more convenient to see the number of missing values to conclude columns where we have NaNs. As you remember, we have two functions to check for the missing values. To calculate the sum, just use the .sum() function. Thus, in general, we have 2 options for outputting the number of NaNs for each column:

Okay, nothing complicated. Let's move on the task.

Завдання

Swipe to show code editor

Calculate the number of missing values for the dataset using one of the mentioned functions.
Output the result.

Try to draw your own conclusions.

Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів

Все було зрозуміло?

Дякуємо за ваш відгук!

It should be noted that it isn't convenient to check each value of the dataset for the NaN. It is more convenient to see the number of missing values to conclude columns where we have NaNs. As you remember, we have two functions to check for the missing values. To calculate the sum, just use the .sum() function. Thus, in general, we have 2 options for outputting the number of NaNs for each column:

Okay, nothing complicated. Let's move on the task.

Завдання

Swipe to show code editor

Calculate the number of missing values for the dataset using one of the mentioned functions.
Output the result.

Try to draw your own conclusions.

Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів

Секція 5. Розділ 2

Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів

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