Introduction to the usage of R language and Python data aggregation function

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R language and Pandas of Python have very rich data aggregation functions, so let's take an inventory of the usage of these functions today.

R language:

Transform

Mutate

Aggregate

Grouy_by+summarize

Ddply

Python:

Groupby

Pivot.table

In R, the quickest way to create new variables is through transform (of course you can choose to use a custom function), which supports the creation of multiple variables based on the same data box.

The classic Yingwei dataset is still used here to demonstrate:

Iris1%group_by (Species)% >% summarize (sums=sum (Sepal.Length))

Grouping aggregation in the R language will greatly improve its execution efficiency if it uses vector functions to operate:

Tapply (iris$Sepal.Length,iris$Species,mean)

Tapply (iris$Sepal.Length,iris$Species,sum)

Tapply (X, INDEX, FUN = NULL, … , simplify = TRUE)

Tapply is a fast grouping aggregation function, its parameters are easy to understand, by providing a metric, a sub-category field, an aggregation function can complete the simple answer data aggregation function.

Library (plyr)

Ddply (iris,. (Species), summarize,means=mean (Sepal.Length))

Ddply (iris,. (Species), summarize,means=sum (Sepal.Length))

Ddply (.data, .aggregate, .fun =) # generally only needs to provide data boxes, aggregate classification fields, and the final aggregate function and aggregate variable formula. It is used in the same way as the built-in tpply.

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Python:

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Import pandas as pd

Import numpy as np

The data aggregation tools used in Python mainly include groupby function, agg function, povit_table and so on.

Groupby

Agg

Povit_table

Iris=pd.read_csv ("C:/Users/RAINDU/Desktop/iris.csv", sep= ",")

Iris.head ()

Iris.describe ()

The grouping data can be aggregated quickly by using the groupby method in pandas.

Iris.groupby ('Species') [' Sepal.Length'] .mean ()

Iris.groupby ('Species') [' Sepal.Length'] .sum ()

Iris.groupby ('Species') [' Sepal.Length'] .agg ([len,np.sum,np.mean])

Iris.groupby ('Species') [' Sepal.Length'] .agg ({'count':len,'sum':np.sum,'mean':np.mean})

# Custom name the output:

You can use the corresponding aggregate function directly to aggregate only one variable, and you can use the agg function to aggregate multiple variables.

Pd.pivot_table (iris,index= ["Species"], values= ["Sepal.Length"], aggfunc= [len,np.sum,np.mean], margins=False)

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