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Sure, I'd be happy to help you with that! Pandas is a powerful Python library for data manipulation and analysis, and the value_counts() function is particularly useful for obtaining the count of unique values in a DataFrame column. Let's create a tutorial with a code example to demonstrate how to use value_counts() per column in Pandas.
Pandas is a popular data manipulation library in Python, and it provides a convenient method called value_counts() to get the count of unique values in a DataFrame column. In this tutorial, we will explore how to use value_counts() to obtain the count of unique values for each column in a Pandas DataFrame.
Make sure you have Pandas installed. If not, you can install it using:
Now, let's import Pandas and create a sample DataFrame for demonstration:
Now, let's use value_counts() to get the count of unique values for each column in the DataFrame:
This code iterates through each column in the DataFrame and prints the count of unique values for each column.
When you run the above code, you will get an output showing the value counts for each column in the DataFrame:
Using value_counts() in Pandas is a simple yet powerful way to analyze the distribution of unique values in each column of a DataFrame. This can be particularly useful in understanding the data and identifying patterns or anomalies.
Feel free to adapt this tutorial to your specific use case, and explore further options and parameters provided by the value_counts() function in the Pandas documentation.
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