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Advanced Techniques in pandas
course content

Course Content

Advanced Techniques in pandas

Advanced Techniques in pandas

1. Getting Familiar With Indexing and Selecting Data
Outputting Columns by TitleOutputting Rows by IndexSelecting Specific Rows and ColumnsLearning More About IndexationGetting Familiar With lambda FunctionsExpanding Functionality of the .iloc[] Attribute
2. Dealing With Conditions
Setting ConditionSelecting Data Based on ConditionDealing With Several ConditionsMaking Your Code BeautifulConditions Quiz
3. Extracting Data
Is Data in ...?Combining Your KnowledgeBetween MethodExtracting Specific DataFinding the Smallest Values of a ColumnFinding the Largest Values of a ColumnFinding the Correlation
4. Aggregating Data
Getting Familiar With the .groupby() MethodGrouping by Several ColumnsComplicated GroupingAdvanced GroupingDealing With Pivot TablesCreating a Pivot Table
5. Preprocessing Data
Checking for Missing ValuesCalculating the Number of Missing ValuesWhat Will We Do With the NaN Values?How to Delete Only NaN Values?Filling In the Missing ValuesManaging Categorical VariablesChecking the Column TypeManaging an Incorrect ColumnRenaming the Column

bookHow to Delete Only NaN Values?

You've managed one way how of dealing with NaN values, but let's think about what we can do with the others. Look at the number of outliers in each column one more time:

In the previous chapter, you received the result:

PassengerId0
Survived0
Pclass0
Name0
Sex0
Age86
SibSp0
Parch0
Ticket0
Fare1
Embarked0

The simplest way is to delete all rows that contain missing values. For instance, 86 rows with ages are missing, as well as 1 row in the column 'Fare'. Let's figure out how we can delete them. In pandas, you can do so using one simple method, similar to the one in the previous chapter, called .dropna():

Here, you can use inplace=True to save all changes. Let's talk about the function. If a row in the column 'Age' has a NaN value, this method will delete all values in the same row. The whole row will be deleted if at least one value in the row is missing.

Task

Your task here is to delete all missing values and check if you did everything correctly.

  1. Drop the NaN values using the inplace=True argument.
  2. Calculate the number of NaN values.

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Section 5. Chapter 4
toggle bottom row

bookHow to Delete Only NaN Values?

You've managed one way how of dealing with NaN values, but let's think about what we can do with the others. Look at the number of outliers in each column one more time:

In the previous chapter, you received the result:

PassengerId0
Survived0
Pclass0
Name0
Sex0
Age86
SibSp0
Parch0
Ticket0
Fare1
Embarked0

The simplest way is to delete all rows that contain missing values. For instance, 86 rows with ages are missing, as well as 1 row in the column 'Fare'. Let's figure out how we can delete them. In pandas, you can do so using one simple method, similar to the one in the previous chapter, called .dropna():

Here, you can use inplace=True to save all changes. Let's talk about the function. If a row in the column 'Age' has a NaN value, this method will delete all values in the same row. The whole row will be deleted if at least one value in the row is missing.

Task

Your task here is to delete all missing values and check if you did everything correctly.

  1. Drop the NaN values using the inplace=True argument.
  2. Calculate the number of NaN values.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 5. Chapter 4
toggle bottom row

bookHow to Delete Only NaN Values?

You've managed one way how of dealing with NaN values, but let's think about what we can do with the others. Look at the number of outliers in each column one more time:

In the previous chapter, you received the result:

PassengerId0
Survived0
Pclass0
Name0
Sex0
Age86
SibSp0
Parch0
Ticket0
Fare1
Embarked0

The simplest way is to delete all rows that contain missing values. For instance, 86 rows with ages are missing, as well as 1 row in the column 'Fare'. Let's figure out how we can delete them. In pandas, you can do so using one simple method, similar to the one in the previous chapter, called .dropna():

Here, you can use inplace=True to save all changes. Let's talk about the function. If a row in the column 'Age' has a NaN value, this method will delete all values in the same row. The whole row will be deleted if at least one value in the row is missing.

Task

Your task here is to delete all missing values and check if you did everything correctly.

  1. Drop the NaN values using the inplace=True argument.
  2. Calculate the number of NaN values.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

You've managed one way how of dealing with NaN values, but let's think about what we can do with the others. Look at the number of outliers in each column one more time:

In the previous chapter, you received the result:

PassengerId0
Survived0
Pclass0
Name0
Sex0
Age86
SibSp0
Parch0
Ticket0
Fare1
Embarked0

The simplest way is to delete all rows that contain missing values. For instance, 86 rows with ages are missing, as well as 1 row in the column 'Fare'. Let's figure out how we can delete them. In pandas, you can do so using one simple method, similar to the one in the previous chapter, called .dropna():

Here, you can use inplace=True to save all changes. Let's talk about the function. If a row in the column 'Age' has a NaN value, this method will delete all values in the same row. The whole row will be deleted if at least one value in the row is missing.

Task

Your task here is to delete all missing values and check if you did everything correctly.

  1. Drop the NaN values using the inplace=True argument.
  2. Calculate the number of NaN values.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 5. Chapter 4
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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