This thread will cover methods of how you can calculate the mean of one series grouped by another series, this technique is useful as there are times when you group data by some categories and find it useful to calculate the mean with respect to other groups. We will discuss different techniques that can be used to calculate the mean, including the use of Pandas and NumPy libraries, and if you want to learn different techniques related to series, go through the following threads listed below:
- Dividing a numeric series into equal sized bins.
- Filtering valid emails from a series.
- Calculating series statistics.
- Computing autocorrelation of a numeric series.
- Filtering words from a series.
1. Using "groupby()" method:
pd.groupby()method in Pandas is a powerful function for grouping data based on one or more columns of a DataFrame and allowing to aggregate of the results.
- In the example code below, we group a series
valuesby another series
fruits, and then we find the mean of this grouped data using the
2. Using NumPy library:
- In this example code, we have used a dictionary comprehension along with NumPy’s
- For every unique value in the series
fruits, we have created a boolean mask using
fruits == keyand fetched values for that unique value. The mean is calculated on the fetched values (
values[fruits == key]) using the
3. Using "pd.pivot_table()" method:
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
fruitsseries by specifying the
indexargument, and found the mean of the
valuesseries which is specified in the
4. Using "pd.crosstab()" method:
pd.crosstab()method 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.
indexargument is the column to be used as the row index, the
columnsargument is the column to be used as the column index,
valuesis the column to be aggregated (optional), and
aggfuncis the aggregation function to be applied.
- Since we only have two series in our dataframe, we have used
5. Using list comprehension method:
- In this method, we iterate through all the unique values of series
unique()function to get unique values.
- Then, for each unique value, we get all values from series
valuesusing a boolean condition
df['fruits'] == groupand find their mean using the