Challenge: Imputing Missing Values
The SimpleImputer class is designed to handle missing data by automatically replacing missing values.
from sklearn.impute import SimpleImputer
imputer = SimpleImputer()
When initialized, it can also be customized by setting its parameters:
missing_value: specifies the placeholder for the missing values. By default, this isnp.nan;strategy: the strategy used to impute missing values.'mean'is the default value;fill_value: Specifies the value to use for filling missing values when thestrategyis'constant'. By default, this isNone.
Being a transformer, it has the following methods:
It is also necessary to decide which values to use for imputation.
A common approach is to replace missing numerical values with the mean and missing categorical values with the mode (most frequent value), as this minimally distorts the data distribution.
The choice is controlled by the strategy parameter:
strategy='mean': impute with the mean of each column;strategy='median': impute with the median of each column;strategy='most_frequent': impute with the mode of each column;strategy='constant': impute with a constant value specified in thefill_valueparameter.
The missing_values parameter defines which values are treated as missing. By default, this is NaN, but in some datasets it can be an empty string '' or another placeholder.
The SimpleImputer and many other transformers only work with DataFrames, not with pandas Series. Selecting a single column from a DataFrame using df['column'] returns a Series. To avoid this, you can use double brackets df[['column']] to ensure it returns a DataFrame instead:
imputer.fit_transform(df[['column']])
When the .fit_transform() method of SimpleImputer is applied, it returns a 2D array. Assigning values to a single column in a pandas DataFrame requires a 1D array (or Series).
df['column'] = ... # Requires 1D array or Series
imputer.fit_transform(df[['column']]) # Produces 2D array
The .ravel() method can be used to flatten the array into 1D before assignment:
df['column'] = imputer.fit_transform(df[['column']]).ravel()
This ensures that the imputed values are properly formatted and stored in the DataFrame column.
Swipe to start coding
You are given a DataFrame named df that contains information about penguins. The 'sex' column includes some missing (NaN) values. Your task is to fill these gaps using the most common category in this column.
- Import the
SimpleImputerclass fromsklearn.impute. - Create a
SimpleImputerobject with thestrategyparameter set to'most_frequent'. - Apply the imputer to the
'sex'column to replace all missing values. - Update the
'sex'column in thedfDataFrame with the imputed data.
Solution
Great! We dealt with the missing values problem in our dataset. We removed the rows with more than one null and imputed the 'sex' column with the most frequent valueΒ β MALE.
Thanks for your feedback!
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Completion rate improved to 3.13Challenge: Imputing Missing Values
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The SimpleImputer class is designed to handle missing data by automatically replacing missing values.
from sklearn.impute import SimpleImputer
imputer = SimpleImputer()
When initialized, it can also be customized by setting its parameters:
missing_value: specifies the placeholder for the missing values. By default, this isnp.nan;strategy: the strategy used to impute missing values.'mean'is the default value;fill_value: Specifies the value to use for filling missing values when thestrategyis'constant'. By default, this isNone.
Being a transformer, it has the following methods:
It is also necessary to decide which values to use for imputation.
A common approach is to replace missing numerical values with the mean and missing categorical values with the mode (most frequent value), as this minimally distorts the data distribution.
The choice is controlled by the strategy parameter:
strategy='mean': impute with the mean of each column;strategy='median': impute with the median of each column;strategy='most_frequent': impute with the mode of each column;strategy='constant': impute with a constant value specified in thefill_valueparameter.
The missing_values parameter defines which values are treated as missing. By default, this is NaN, but in some datasets it can be an empty string '' or another placeholder.
The SimpleImputer and many other transformers only work with DataFrames, not with pandas Series. Selecting a single column from a DataFrame using df['column'] returns a Series. To avoid this, you can use double brackets df[['column']] to ensure it returns a DataFrame instead:
imputer.fit_transform(df[['column']])
When the .fit_transform() method of SimpleImputer is applied, it returns a 2D array. Assigning values to a single column in a pandas DataFrame requires a 1D array (or Series).
df['column'] = ... # Requires 1D array or Series
imputer.fit_transform(df[['column']]) # Produces 2D array
The .ravel() method can be used to flatten the array into 1D before assignment:
df['column'] = imputer.fit_transform(df[['column']]).ravel()
This ensures that the imputed values are properly formatted and stored in the DataFrame column.
Swipe to start coding
You are given a DataFrame named df that contains information about penguins. The 'sex' column includes some missing (NaN) values. Your task is to fill these gaps using the most common category in this column.
- Import the
SimpleImputerclass fromsklearn.impute. - Create a
SimpleImputerobject with thestrategyparameter set to'most_frequent'. - Apply the imputer to the
'sex'column to replace all missing values. - Update the
'sex'column in thedfDataFrame with the imputed data.
Solution
Great! We dealt with the missing values problem in our dataset. We removed the rows with more than one null and imputed the 'sex' column with the most frequent valueΒ β MALE.
Switch to desktop for real-world practiceContinue from where you are using one of the options belowThanks for your feedback!
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