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It's also faster if you don't sort the result: %timeit df.groupby(['num_legs', 'num_wings'])['num_legs'].count() s a reason why the Pandas developers named it this way, but it only makes sense if you really understand what axes are. Again, I strongly suggest you avoid this alternate notation, and simply use axis = 1. I explain this more in the FAQ section. Using groupby and value_counts we can count the number of certificate types for each type of course difficulty. Group by course difficulty and value counts for course certificate type

Everything that I’m about to explain assumes that you’ve imported Pandas and that you already have a dataframe that you’re working with. sort_values(['count'], ascending=False) df = df[['STNAME','CTYNAME']].groupby(['STNAME'])['CTYNAME'] \ Value_counts() with sort_index(ascending=True) sorts by index (column that you are running value_counts() on: Value_counts() sorted alphabetically I think you need add reset_index, then parameter ascending=False to sort_values because sort return:Again though, there are some optional parameters that control how the technique works. Let’s look at those parameters. The parameters of Pandas count

I don't know exactly how your df looks like. But if you have to sort the frequency of several categories by its count, it is easier to slice a Series from the df and sort the series: series = df.count().sort_values(ascending=False) To call the count method with a dataframe, you simply type the name of the dataframe, and then .count(). 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.level (nt or str, optional): If the axis is a MultiIndex, count along a particular level, collapsing into a DataFrame. A str specifies the level name.

In case you want to get the row count in the middle of a chained operation, you can use: df.pipe(len) To follow along with the tutorial below, feel free to copy and paste the code below into your favourite text editor to load a sample Pandas Dataframe that we’ll use to count rows! import pandas as pd In this tutorial, you learned how to use the .value_counts() method to calculate a frequency table counting the values in a Series or DataFrame. The section below provides a recap of what you learned: Binning makes it easy to understand the idea being conveyed. We can easily see that most of the people out of the total population rated courses above 4.5. With just a few outliers where the rating is below 4.15 (only 7 rated courses lower than 4.15). 7.) value_counts() displaying the NaN values But if you set numeric_only = True, the count method will return the counts for the numeric variables only (integers, floats, etc).

df[df.columns[0]].count() was omitted in the above discussion because no commenter has identified a case where it is useful. It is exponentially slow, and long to type. It provides the number of non-NaN values in the first column. The values None, NaN, NaT, pandas.NA are considered NA. Parameters : axis {0 or ‘index’, 1 or ‘columns’}, default 0 Hence, we can see that value counts is a handy tool, and we can do some interesting analysis with this single line of code.

Syntax - df['your_column'].value_counts(ascending=True) # count of all unique values for the column course_difficultyBut this method is not so efficient when the Dataframe grows in size and contains thousands of rows and columns. To give an efficient there are three methods available which are listed below: The Pandas value_counts() method can be applied to both a DataFrame column or to an entire DataFrame. The behavior varies slightly between the two methods. However, for the most part, the methods will work exactly the same. There’ll be a note indicating if there are differences between the two methods. based to the answer that was given and some improvements this is my approach def PercentageMissin(Dataset): This could be useful information during data cleaning. It could also be useful if you’re building a machine learning model, since some model types will not tolerate missing values.