Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.Filling Null Values | Analyzing the Data
Pandas First Steps
course content

Course Content

Pandas First Steps

Pandas First Steps

1. The Very First Steps
What is pandas? SeriesCreating a SeriesDataFrameSeries QuizCreating a DataFrame QuizAdding a New ColumnInserting a New ColumnDeleting a Row/Column Matching the FunctionsWorking with ColumnsExtracting Columns Quiziloc BasicsUsing iloc
2. Reading Files in Pandas
CSV FilesTXT FilesWorking with Files QuizAccessing the Columns
3. Analyzing the Data
Viewing the DataHead QuizHead and Tail QuizInformation on the DataColumn Names and Data TypesFinding Null ValuesIdentifying Null Values QuizDropping Null ValuesFilling Null ValuesNull Values QuizDescribing the DataCalculating Descriptive Statisticsmax() and min()Statistical Operations Quizsum() and count()Unique Values

bookFilling Null Values

To handle NaN values while retaining each row of the dataframe, we can utilize the fillna() method. This allows us to populate each empty cell with a specific value (like a string or number) rather than eliminating it.

To replace NaN values with the number 0:

Task

You're working with a dataframe named data_frame. Your goal is to replace the NaN values in this dataframe with the string 'no'.

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 3. Chapter 9
toggle bottom row

bookFilling Null Values

To handle NaN values while retaining each row of the dataframe, we can utilize the fillna() method. This allows us to populate each empty cell with a specific value (like a string or number) rather than eliminating it.

To replace NaN values with the number 0:

Task

You're working with a dataframe named data_frame. Your goal is to replace the NaN values in this dataframe with the string 'no'.

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 3. Chapter 9
toggle bottom row

bookFilling Null Values

To handle NaN values while retaining each row of the dataframe, we can utilize the fillna() method. This allows us to populate each empty cell with a specific value (like a string or number) rather than eliminating it.

To replace NaN values with the number 0:

Task

You're working with a dataframe named data_frame. Your goal is to replace the NaN values in this dataframe with the string 'no'.

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!

To handle NaN values while retaining each row of the dataframe, we can utilize the fillna() method. This allows us to populate each empty cell with a specific value (like a string or number) rather than eliminating it.

To replace NaN values with the number 0:

Task

You're working with a dataframe named data_frame. Your goal is to replace the NaN values in this dataframe with the string 'no'.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 3. Chapter 9
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
some-alt