Скачать или смотреть pandas value counts include nan

Download this code from https://codegive.com
Title: A Comprehensive Guide to Pandas value_counts Including NaN Handling
Introduction:
Pandas is a powerful data manipulation library in Python, and the value_counts method is particularly useful for exploring the distribution of values in a Series. This tutorial will walk you through the usage of value_counts with a focus on including NaN (Not a Number) values.
If you haven't installed Pandas yet, you can do so using the following command:
Let's start by importing the Pandas library into your Python script or Jupyter notebook.
The value_counts method is typically applied to a Pandas Series to count unique values. Here's a basic example:
By default, value_counts excludes NaN values. To include them, set the dropna parameter to False.
You can also apply value_counts to a DataFrame column. Here's an example:
The value_counts method in Pandas is a handy tool for exploring the distribution of values in a Series or DataFrame column. By setting the dropna parameter to False, you can include NaN values in the count, providing a more comprehensive analysis of your data. Experiment with these examples to gain a better understanding of how to leverage value_counts in your data analysis workflows.
ChatGPT
Title: Comprehensive Guide to Pandas value_counts Including NaN Values
Introduction:
Pandas is a powerful data manipulation library in Python, and the value_counts function is particularly useful for counting unique values in a Series. In this tutorial, we'll explore how to use value_counts effectively, including handling NaN values.
This basic example demonstrates how to use value_counts to count the occurrences of unique values in the 'Category' column.
Now, let's introduce NaN values to the DataFrame and see how value_counts handles them.
By setting the dropna parameter to False, value_counts includes NaN values in the output. This is useful when you want to see the distribution of missing values along with the actual counts.
You can sort the result based on counts using the sort_values method.
This sorts the unique values based on their counts in descending order.
If you want to get the relative frequencies (percentages) instead of counts, you can use the normalize parameter.
Setting normalize to True returns the proportions of unique values in the Series.
Visualizing the value counts can provide a clearer understanding of the data distribution.
This code snippet uses Matplotlib to create a bar plot representing the counts of each unique value in the 'Category