Applied Data Science Coding: How to get class distribution in Data

Class distribution refers to the number of instances or samples that belong to each class in a dataset. In machine learning, class distribution is an important aspect to consider, as it can affect the performance of a model. For example, if a dataset is highly imbalanced, with a large number of instances belonging to one class and a small number of instances belonging to another class, this can lead to a bias in the model, where it will perform better on the majority class and worse on the minority class.

To get the class distribution in data in Python, you can use various libraries and functions, depending on the format of the data and the specific task.

One way to get the class distribution in data is to use the pandas.value_counts() function from the Pandas library. This function can be applied to a specific column or series in a DataFrame, which contains the class labels. It returns the number of occurrences of each unique value in the column, as a Series object.

Another way to get the class distribution in data is to use the numpy.unique() function from the Numpy library, in combination with the numpy.bincount() function. The numpy.unique() function can be applied to an array, which contains the class labels. It returns the unique values in the array, along with the number of occurrences of each unique value, using the numpy.bincount() function.

In summary, class distribution refers to the number of instances or samples that belong to each class in a dataset. In machine learning, class distribution is an important aspect to consider. To get the class distribution in data in Python, you can use the pandas.value_counts() function from the Pandas library, which can be applied to a specific column or series in a DataFrame, or the numpy.unique() function from the Numpy library in combination with the numpy.bincount() function which can be applied to an array. These functions return the number of occurrences of each unique value in the data.

In this Applied Machine Learning & Data Science Recipe, the reader will learn: How to get class distribution in Data.