I think you need add reset_index, then parameter ascending=False to sort_values because sort return:

Here, by setting numeric_only = True, the count() technique is computing the number of non-missing values for the numeric columns only. Now let’s see how to sort rows from the result of pandas groupby and drop duplicate rows from pandas DataFrame. Analogous to len(df.index), len(df.columns) is the faster of the two methods (but takes more characters to type). Row Count of a Series: len(s), s.size, len(s.index) len(s) Alternatively, you can also get the group count by using agg() or aggregate() function and passing the aggregate count function as a param. reset_index() function is used to set the index on DataFrame. By using this approach you can compute multiple aggregations.Let’s take a look at the how the method looks and the default arguments that exist: # The value_counts() Method Explained The Pandas value_counts method can also be used to bin data into different equal sized groups. This method is a convenience function for the Pandas .cut() method, and provides the number of values in each group.

normalize (bool, default False) - If True then the object returned will contain the relative frequencies of the unique values. I’ll explain exactly what the technique does, how the syntax works, and I’ll show you step-by-step examples so you can see Pandas count in action. shape is more versatile and more convenient than len(), especially for interactive work (just needs to be added at the end), but len is a bit faster (see also this answer). Let’s split the data into four numeric groups using the bin= parameter: # Binning data with Pandas value_countsWhen it comes to pulling basic counts within Pandas, it’s easy to find a function that will work for your use case, and the three above should be your go-to functions.

We will get counts for the column course_difficulty from our dataframe. # count of all unique values for the column course_difficulty To perform column-wise COUNTIF/SUMIF, you can use axis=0 argument (which it is by default). The range here (the first 3 rows) is selected using iloc. df.loc['COUNTIF'] = (df.iloc[:3] > 1).sum()Note that size and count are not identical, the former counts all rows per group, the latter counts non-null rows only. See this other answer of mine for more. Now that we have missing values in our DataFrame, let’s apply the method with its default parameters and see how the results look: # Seeing value counts The method has only optional parameters, meaning if you simply want to calculate value counts you can apply the method directly without needing to worry about any arguments being passed in. Loading a Sample Pandas DataFrame To follow along with this tutorial, load the dataset below by copying and pasting the provided code. If you have your own data, feel free to use that dataset but your results will, of course, vary. # Loading a Sample Pandas DataFrame

I honestly think this is a misunderstanding of how people think about axes, and using terminology in a counter-intuitive way. The value_counts function works only on Pandas series objects, and can be quite useful. Unlike the other functions in this list, value_counts will provide the count of each unique record within the series.

Frequently asked questions about KEYWORD

In this section, you’ll learn how to apply the Pandas .value_counts() method to a Pandas column. For example, if you wanted to count the number of times each value appears in the Students column, you can simply apply the function onto that column. Students': [10.0, 20.0, 10.0, 40.0, 20.0, 10.0, None, 20.0, 20.0, 40.0, 10.0, 30.0, 30.0, 10.0, 10.0, 10.0, 40.0, 20.0]