Hello everyone, I have a problem that I want to solve using the libraries available in Python, I did find a solution for it using list comprehension but I believe there are more efficient methods. The problem involves grouping data of one series and then calculating the mean of some other series based on this grouped data. The code I used is attached below, I have used two series and after creating a dataframe of those, I grouped the data based on the fruits series and calculated the mean of the values series based on these groups.
If there are efficient methods of doing this, please provide them below using the same or some other example.
Hi @mubashir_rizvi You can calculate the mean efficiently by using the pd.crosstab()` method which is used for creating a cross-tabulation (or contingency table) based on two or more columns of a dataFrame. It allows you to count the number of occurrences of each combination of values in the columns, and then display the results in a tabular format.
The index argument is the column to be used as the row index, the columns argument is the column to be used as the column index, values is the column to be aggregated (optional), and aggfunc is the aggregation function to be applied.
Since we only have two series in our dataframe, we have used fruits in both index and columns arguments.
Hello @mubashir_rizvi , the groupby() method in Pandas is a powerful function for grouping the data based on one or more columns of a data frame and allowing to aggregate of the results. Let’s understand it better by below example:
In the example code, we group a series values by another series fruits, and then we find the mean of this grouped data using the mean() function.
Hi @mubashir_rizvi , the pd.pivot_table() method is used for creating a spreadsheet-style pivot table based on a Pandas dataFrame. It allows you to summarize and aggregate data based on one or more columns, and then display the results in a tabular format. In this method, we have grouped the table by fruits series by specifying the index argument, and found the mean of the values series which is specified in the values argument.