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ML Introduction with scikit-learn
ML Introduction with scikit-learn
Challenge: Imputing Missing Values
The SimpleImputer class is designed to handle missing data by automatically replacing missing values.
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:
However, we also need to choose the value to impute.
The popular approach is to impute missing numerical values with the mean and missing categorical values with the mode (the most frequent value), as such imputation minimally impacts the distribution of the values.
The approach can be controlled using the strategy parameter:
strategy='mean': impute with mean along each column;strategy='median': impute with median along each column;strategy='most_frequent': impute with mode along each column;strategy='constant': impute with constant number specified infill_valueparameter.
The missing_values parameter controls what values are considered missing. By default, it is NaN, but in different datasets, it can be an empty string '' or anything else.
When you use the .fit_transform() method of the SimpleImputer, it produces a 2D array as output. However, when updating a single column in a pandas DataFrame, you need a 1D array (or a Series).
To convert the 2D array into a 1D array suitable for assignment to a DataFrame column, you can apply the .ravel() method. This method flattens the array. Here's how you can update a column after imputation:
This approach ensures that the imputed values are correctly formatted and assigned back to the DataFrame.
Swipe to show code editor
Your task is to impute the NaN values of the 'sex' column using SimpleImputer. Since you are dealing with a categorical column, you will replace null values with the most frequent value (the most common approach).
- Import the
SimpleImputer. - Create a
SimpleImputerobject with the desiredstrategy. - Impute the missing of the
'sex'column using theimputerobject.
Once you've completed this task, click the button below the code to check your 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 value – MALE.
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантівДякуємо за ваш відгук!
Challenge: Imputing Missing Values
The SimpleImputer class is designed to handle missing data by automatically replacing missing values.
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:
However, we also need to choose the value to impute.
The popular approach is to impute missing numerical values with the mean and missing categorical values with the mode (the most frequent value), as such imputation minimally impacts the distribution of the values.
The approach can be controlled using the strategy parameter:
strategy='mean': impute with mean along each column;strategy='median': impute with median along each column;strategy='most_frequent': impute with mode along each column;strategy='constant': impute with constant number specified infill_valueparameter.
The missing_values parameter controls what values are considered missing. By default, it is NaN, but in different datasets, it can be an empty string '' or anything else.
When you use the .fit_transform() method of the SimpleImputer, it produces a 2D array as output. However, when updating a single column in a pandas DataFrame, you need a 1D array (or a Series).
To convert the 2D array into a 1D array suitable for assignment to a DataFrame column, you can apply the .ravel() method. This method flattens the array. Here's how you can update a column after imputation:
This approach ensures that the imputed values are correctly formatted and assigned back to the DataFrame.
Swipe to show code editor
Your task is to impute the NaN values of the 'sex' column using SimpleImputer. Since you are dealing with a categorical column, you will replace null values with the most frequent value (the most common approach).
- Import the
SimpleImputer. - Create a
SimpleImputerobject with the desiredstrategy. - Impute the missing of the
'sex'column using theimputerobject.
Once you've completed this task, click the button below the code to check your 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 value – MALE.
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантівДякуємо за ваш відгук!
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів