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This tutorial aims to explore the concepts in Pandas GroupBy Apply. Pandas is used as an advanced data analysis tool or package extension in Python.

When we have data in SQL tables, spreadsheets, or heterogeneous columns, Pandas is highly recommended. The data can be ordered or unordered, and time series data is also supported.

Pandas GroupBy-Apply Behavior

Let us try to understand how we can group by data and then apply a specific function to aggregate or calculate the values ​​of the data. GroupByThis helps us to combine or bring together certain data entries.

GroupByHelps us keep track of the different data entry points in our data. Let's see this in action.

We will create a dummy DataFrame to work with. Here, we create a DataFrame dframeand a few rows.

from pandas import *

our_data = {"mylabel": Series(["P", "R", "E", "E", "T", "S", "A", "P", "R", "E", "T"])}
dframe = DataFrame(our_data)

print(dframe)  # print output

Output:

   mylabel
0         P
1         R
2         E
3         E
4         T
5         S
6         A
7         P
8         R
9         E
10        T

Our DataFrame is labeled mylabeland has different data points and indices set up. Each letter is assigned a specific index.

These tags are what we will learn how to group and apply certain aggregation functions to.

groupby()Using Functions in Pandas

We can understand how to group the data with the help of the following code. As we can see, we are trying to group each letter and count their occurrences.

from pandas import *

our_data = {"mylabel": Series(["P", "R", "E", "E", "T", "S", "A", "P", "R", "E", "T"])}
dframe = DataFrame(our_data)


def perc(value, total):
    return value / float(total)


def gcou(values):
    return len(values)


grpd_count = dframe.groupby("mylabel").mylabel.agg(gcou)

print(grpd_count)  # prints output

Output:

mylabel
A    1
E    3
P    2
R    2
S    1
T    2
Name: mylabel, dtype: int64

We need to use the new DataFrame we created named grpd_countto apply any mathematical formulas. Here, we counted the number of each alphabet available.

groupby()Adding and apply()functions to Pandas

Let's manipulate the DataFrame grpd_countto divide the total counts for each letter by the sum of all counts. This idea is often used to measure 0 到 1the weight of an entity in the context of .

Values ​​closer to 1 have higher weights, while values ​​closer to 0 have lower weights, meaning that particular letter occurs less often than other letters.

Code example:

from pandas import *

our_data = {"mylabel": Series(["P", "R", "E", "E", "T", "S", "A", "P", "R", "E", "T"])}
dframe = DataFrame(our_data)


def perc(value, total):
    return value / float(total)


def gcou(values):
    return len(values)


grpd_count = dframe.groupby("mylabel").mylabel.agg(gcou)
mydata = grpd_count.apply(perc, total=dframe.mylabel.count())

print(mydata)  # prints output

Output:

mylabel
A    0.090909
E    0.272727
P    0.181818
R    0.181818
S    0.090909
T    0.181818
Name: mylabel, dtype: float64

After grouping the data in Pandas, we have successfully performed an operation.

So, with the help of techniques in Pandas Grouping By, we can effectively filter the data according to our needs and when required and based on one or more conditions and then apply certain functions or aggregations to the results.