Course Content
Pandas First Steps
Pandas First Steps
Filling 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'
.
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'
.
Everything was clear?
Filling 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'
.
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'
.
Everything was clear?
Filling 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'
.
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'
.
Everything was clear?
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'
.