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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. Below, I show examples of each of the methods described in the table above. First, the setup - df = pd.DataFrame({ The Pandas .count() method is, unfortunately, the slowest method of the three methods listed here. The .shape attribute and the len() function are vectorized and take the same length of time regardless of how large a dataframe is. The .count() method takes significantly longer with with larger dataframes. Also for COUNTIF (similar to the pandas equivalent of COUNTIFS), it suffices to sum over the condition while for SUMIF, we need to index the frame. df['COUNTIF'] = (df[['A', 'B']] > 1).sum(axis=1) To perform row-wise COUNTIF/SUMIF, you can use axis=1 argument. Again, the range is given as a list of columns ( ['A', 'B']) similar to how range is fed to COUNTIF.
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. For multiple conditions e.g. COUNTIFS/SUMIFS, a convenient method is query because it's very fast for large frames (where performance actually matters) and you don't need to worry about parentheses, bitwise-and etc. For example, to compute =SUMIFS(C2:C8, A2:A8,">1", B2:B8, "<3"), you can use df.query("A>1 and B<3")['C'].sum() The syntax of the count method is fairly simple, but there are a few ways to use it and a few parameters that can modify its functionality. Let’s see how this looks without passing in any arguments, which will be covered off later in the tutorial. # Calculating a Frequency Table of a DataFrame Column
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We’ll look at examples of how to count the records in a dataframe, how to count the records in a single column, and a few other uses.
One final comment on the axis parameter: to understand this parameter, you really need to understand axes. For an explanation of how axes work, you should read our tutorial on Numpy axes (Numpy axes are very similar to dataframe axes). numeric_only (optional) The numeric_only parameter enables you to force the count method to only return counts for numeric variables.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 Zero Values Missing Values % of Total Values Total Zero Missing Values % Total Zero Missing Values Data Type 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 Again, there are some additional parameters that you can call that will modify the technique. Dataframe Column Syntax
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 The .value_counts() can also be applied the multiple columns. The benefit of applying the method to the entire DataFrame is that you gain access to the subset= parameter. This allows you to pass in a list of columns, which will return the values in the cross-section of columns. Let’s see what the distribution of values is across the Grade and Class Type columns. # Applying .value_counts() to multiple columns
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Alternatively, you can also use size() to get the rows count for each group. You can use df.groupby(['Courses','Duration']).size() to get a total number of elements for each group Courses and Duration. columns We will get counts for the column course_difficulty from our dataframe. # count of all unique values for the column course_difficulty
Typically, I use the count() technique to count the non-missing values for the columns. But there might be times when you need to examine the rows instead. One of the benefits of the .count() method is that it can ignore missing values. >> print(df.count()) In the next section, you’ll learn how to calculate a Pandas value counts table that uses normalized percentages, rather than values. Calculating a Pandas Frequecy Table with Percentages This tells us that we have 891 records in our dataset and that we don't have any NA values. 1. ) value_counts() with default parameters So if your dataframe is named your_dataframe, you can use the code your_dataframe.count() to count the number of non-missing values in each of the columns.
And here, we can see that many of the variables – like survived, pclass, and class – have 891 values. These variables are fully populated. 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. You can use df.groupby(['Courses','Fee']).Courses.transform('count') to add a new column containing the groups counts into the DataFrame.
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