Identifying and handling missing values is an important step in the data pre-processing phase of any machine learning project. Missing values can occur due to various reasons such as data collection errors, missing data due to privacy concerns, or simply because the value was not recorded. However, when building a model, missing values can cause problems such as reduced accuracy and bias in the results.
Identifying missing values:
1. Using the isnull() method:
This method returns a boolean mask of the same shape as the data passed, where True indicates a missing value.
2. Using the notna() method:
This method returns the opposite output as compared to the isnull() method, indicating which values are not missing i.e., returns False where there is a missing value.
3. Using the isna() method:
This method is equivalent to the isnull() method and can be used in the same way and also returns the same output.
4. Using the sum() method:
This method can be used to count the number of missing values in each column of a DataFrame by combining it with the isnull() or isna() method.
Handling missing values:
Handling missing values is important because they can have a significant impact on the analysis and results of a dataset. Missing values can lead to biased or incorrect conclusions, as well as decreased accuracy and reliability of the model.
1. Using the interpolate() method:
This method can be used to interpolate missing values based on the values of the other rows, such as linear interpolation. By default, the method uses linear interpolation, but it also allows other methods to be specified, such as polynomial interpolation, using the method parameter.
2. Using the dropna() method:
This method can be used to remove the rows that have more than a certain number of missing values. In this example, we are removing all the rows that have more than 1 missing value using the thread parameter.