One-hot encoding is a technique used in machine learning and data preprocessing to represent categorical variables as binary vectors. In one-hot encoding, each category or label in a categorical variable is represented as a binary vector, where each element corresponds to a unique category. The process involves the following steps: For example, consider a dataset…
In pandas, you can give a specific sorting order to categorical values by creating a categorical variable with an ordered category. Here’s an example: In this example: This can be useful when you want to ensure that certain operations, such as sorting or plotting, take into account the natural order of the days of the…
To cap or clip outliers in a column, you can use the clip method in pandas. The clip method allows you to set a minimum and maximum threshold for the values in a DataFrame or a specific column. Here’s an example: Clipping is a simple method, and it’s important to consider the impact on your…
hen dealing with missing data of type categorical, several methods can be used to impute the missing values. Here are some common approaches: The choice of imputation method depends on the nature of the data, the underlying patterns, and the goals of the analysis. Always consider the context of the data and the potential impact…
leaning a dataset involves handling missing values, correcting errors, and preparing the data for analysis. Here are common steps to clean a dataset using Python and pandas: Always document the steps taken during the cleaning process for transparency and reproducibility. Additionally, it’s crucial to thoroughly understand the context of the data and the goals of…
