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Title: Exploring Pandas DataFrame Value Counts, Including NaN
Introduction:
Pandas is a powerful data manipulation library in Python, and one of its most widely used features is the DataFrame. The value_counts function in Pandas is particularly handy for obtaining a frequency distribution of unique values in a DataFrame column. In this tutorial, we will explore how to use value_counts to include NaN values in the analysis.
Requirements:
Make sure you have Pandas installed in your Python environment. You can install it using:
Tutorial:
This will output something like:
Conclusion:
In this tutorial, we explored how to use Pandas' value_counts function to obtain a frequency distribution of unique values in a DataFrame column, including NaN values. This can be particularly useful for understanding the distribution of data and identifying missing values in your dataset. Experiment with different datasets and columns to gain a deeper understanding of your data using Pandas.
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