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This style of Pandas coding is atypical, but it can be very useful when you’re doing data cleaning, data exploration, or data analysis. Note that you can also do the same thing if you set axis = 'columns'. axis = 'columns' is the same as axis = 1. Having said that, I strongly discourage this notation, because it’s extremely confusing. Setting axis = 'columns' actually gives you the number of non-missing values for the rows. There

In both cases, a Series is returned. This makes sense for DataFrames as well since all groups share the same row-count. Group-wise Non-Null Row Count: GroupBy.count I won’t explain the reasons here about why the axes are numbered like this. It’s just something you need to memorize: axis-0 points downward, and axis-1 points horizontally. Let’s see the basic usage of this method using a dataset. I’ll be using the Coursera Course Dataset from Kaggle for the live demo. I have also published an accompanying notebook on git, in case you want to get my code. We will get counts for the column course_difficulty from our dataframe. # count of all unique values for the column course_difficulty But we can see that several of the rows displayed have 13 or 14 non-missing values. In fact, the first row has only 14 values. That means that some of these rows have missing values. That might be okay, but maybe not, depending on what you’re doing.Remember that a dataframe column is actually a Pandas series. Additionally, we can retrieve a column from a dataframe using so-called “dot syntax.” Zero Values Missing Values % of Total Values Total Zero Missing Values % Total Zero Missing Values Data Type Hence, we can see that value counts is a handy tool, and we can do some interesting analysis with this single line of code.

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 bins (int, optional) - Rather than count values, group them into half-open bins, a convenience for pd.cut, only works with numeric data.

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print ("Your selected dataframe has " + str(df.shape[1]) + " columns and " + str(df.shape[0]) + " Rows.\n" This dataframe has 15 columns. If you look carefully at the output above, you’ll actually see some NaN values. We’ll be able to count the non- NaN values with count(). You can also use an alternative notation, with axis = "columns" or axis = "rows". I strongly discourage you from using this notation, because it’s highly confusing. I explain why in the FAQ section.

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. The dataset only has three columns, two of which can be considered categorical. The data covers different grades and class types, as well as indicating how many students are in that grade and level. Creating a Pandas Frequency Table with value_countsNow we are ready to use value_counts function. Let begin with the basic application of the function.

At normal data sizes each option will finish in under a second. So the "fastest" option is actually whichever one lets you work the fastest, which can be len(df) or df.shape[0] if you already have a subsetted df and want to just add .shape[0] briefly in an interactive session. normalize (bool, default False) - If True then the object returned will contain the relative frequencies of the unique values. Now, let’s create a DataFrame with a few rows and columns, execute these examples, and validate the results. Our DataFrame contains column names Courses, Fee, Duration, and Discount. If you need to name index column and rename a column, with counts in the dataframe you can convert to dataframe in a slightly different way. value_counts = df['course_difficulty'].value_counts() I think you need add reset_index, then parameter ascending=False to sort_values because sort return:

A quick introduction to Pandas Count

This is one of my favourite uses of the value_counts() function and an underutilized one too. Groupby is a very powerful pandas method. You can group by one column and count the values of another column per this column value using value_counts. I honestly think this is a misunderstanding of how people think about axes, and using terminology in a counter-intuitive way. Also, notice that all of the functions are on separate lines. To make this work, we need to enclose the whole expression inside of parenthesis. df['course_difficulty'].value_counts(normalize=True) value_counts as percentages 6.) value_counts() to bin continuous data into discrete intervals