Pandas GroupBy

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Groupby — pretty simple concept. We can create a group of categories and apply the function to the categories. It is a simple concept, but it is an extremely valuable technique that is widely used in data science. In real-world data science projects, you will be dealing with large amounts of data and trying to do things repeatedly, so for efficiency we use the Groupby concept. The Groupby concept is really important because it is able to combine data efficiently, both in terms of performance and the amount of code. Grouping mainly refers to a process involving one or more of the following steps:

  • Separation: is a process in which we divide data into groups by applying some conditions to datasets .
  • Application: is a process in which we apply a function to each group independently
  • Consolidation: is a process in which we combine different datasets after applying grouping and results into a data structure

The following image will help you understand the process involved in the Groupby concept.
1. Group the unique values ‚Äã‚Äãfrom the "Team" column.

2. There is now a bucket for each group

3. Throw other data into buckets

4. Apply the function to the weight column for each bucket.

Dividing data into groups

Splitting — it is a process in which we divide the data into groups by applying some conditions to the datasets. To separate data, we apply certain conditions to datasets. To separate data, we use groupby () this function is used to divide data into groups according to some criteria. Pandas objects can be subdivided into any of their axes. An abstract definition of grouping is to provide a mapping of labels to group names. Pandas datasets can be split into any objects. There are several ways to split data, for example:

Note: here we refer to grouping objects as keys.
Grouping data with one key:
To group data with one key, we only pass one key as an argument to the groupby function.


Now we group the data Name using the groupby () function.

# pandas module import

import pandas as pd


# Define a dictionary containing employee data

data1 = { ’ Name’ : [ ’Jai’ , ’Anuj’ , ’ Jai’ , ’ Princi’ ,

’ Gaurav’ , ’Anuj’ , ’Princi’ , ’Abhi’ ],

’ Age’ : [ 27 , 24 , 22 , 32 ,

33 , 36 , 27 , 32 ],

’Address’ : [ ’ Nagpur’ , ’Kanpur’ , ’Allahabad’ , ’Kannuaj’ ‚Äã‚Äã ,

’ Jaunpur’ , ’Kanpur’ , ’Allahabad’ , ’ Aligarh’ ],

’Qualification’ : [ ’Msc’ , ’ MA’ , ’MCA’ , ’ Phd’ ,

’B.Tech’ , ’ B.com’ , ’Msc’ , ’ MA’ ]}


# Convert dictionary to DataFrame

df = pd.DataFrame (data1)

print (df)

# using the groupby function
# with one key

df.groupby ( ’Name’ )

print (df.groupby ( ’Name’ ). groups)

Output:

Now we print the first records in all formed groups .

# apply the groupby () function to
# group data by name value

gk = df.groupby ( ’Name’ )


# Print first entries
# in all generated groups.
gk.first ()

Output:

Grouping data with multiple keys:
To group data with multiple keys, we transmit multiple keys in groupby function.

# pandas module import

import pandas as pd


# Define a dictionary containing employee data

data1 = { ’Name’ : [ ’Jai’ , ’ Anuj’ , ’Jai’ , ’ Princi’ ,

’Gaurav’ , ’ Anuj’ , ’Princi’ , ’ Abhi’ ],

’Age’ : [ 27 , 24 , 22 , 32 ,

33 , 36 , 27 , 32 ],

’Address’ : [ ’ Nagpur’ , ’Kanpur’ , ’ Allahabad’ , ’Kannuaj’ ‚Äã‚Äã ,

’Jaunpur’ , ’Kanpur’ , ’ Allahabad’ , ’Aligarh’ ],

’Qualification’ : [ ’Msc’ , ’ MA’ , ’MCA’ , ’ Phd’ ,

’B.Tech’ , ’B.com’ , ’ Msc’ , ’MA’ ]}


# Convert dictionary in DataFrame

df = pd. DataFrame (data1)

print (df)


Now we will group the Name and Qualification data together using multiple keys in groupby function work.

# Using multiple keys in
# groupby () function

df.groupby ([ ’ Name’ , ’Qualification’ ])

print (df.groupby ([ ’Name’ , ’ Qualification’ ]). groups)

Output:

Grouping data by key sorting:
Group keys are sorted by default during bulk operation. User can pass sort = False for possible speedups.

# pandas module import

import pandas as pd


# Define a dictionary containing employee data

data1 = { ’Name’ : [ ’Jai’ , ’ Anuj’ , ’Jai’ , ’ Princi’ ,

’Gaurav’ , ’Anuj’ , ’ Princi’ , ’Abhi’ ],

’Age’ : [ 27 , 24 , 22 , 32 ,

33 , 36 , 27 , 32 ],}


# Convert dictionary to DataFrame

df = pd.DataFrame (data1)

print (df)


Now we are applying groupby () no sorting

# using the groupby function
# without using sorting

df.groupby ([ ’Name’ ]). sum ()

Output:

Now we use groupby () using sort to achieve potential speedup.

# using the groupby function
# sorted

df .groupby ([ ’Name’ ], sort = False ). sum ( )

Output:

Grouping data with object attributes:
The groups attribute is similar to a dictionary, the keys of which are calculated unique groups, and the corresponding values ‚Äã‚Äãare the axis labels, belong to each group.

# pandas module import

import pandas as pd


# Define a dictionary containing employee data

data1 = { ’Name’ : [ ’ Jai’ , ’Anuj’ , ’ Jai ’ , ’ Princi’ ,

’Gaurav’ , ’ Anuj’ , ’Princi’ , ’ Abhi’ ],

’Age’ : [ 27 , 24 , 22 , 32 ,

33 , 36 , 27 , 32 ],

’Address’ : [ ’ Nagpur’ , ’ Kanpur’ , ’Allahabad’ , ’ Kannuaj’ ‚Äã‚Äã ,

’Jaunpur’ , ’Kanpur’ , ’ Allahabad’ , ’Aligarh’ ],

’ Qualification’ : [ ’Msc’ , ’MA’ , ’MCA’ , ’ Phd’ ,

’B.Tech’ , ’B.com’ , ’ Msc’ , ’MA’ ]}


# Convert dictionary to DataFrame

df = pd.DataFrame (data1)

print (df)


Now we group the data like we do in a dictionary using keys.

# using grouping keys
# data

df.groupby ( ’ Name’ ). groups

Output:

Iterating over the groups

To iterate over the groups item, we can iterate over an object similar to iterto.ols .

# pandas module import

import pandas as pd


# Define the dictionary containing the data about employees

data1 = { ’Name’ : [ ’ Jai’ , ’ Anuj’ , ’Jai’ , ’Princi’ ,

’Gaurav’ , ’ Anuj ’ , ’ Princi’ , ’Abhi’ ],

’ Age’ : [ 27 , 24 , 22 , 32 ,

33 , 36 , 27 , 32 ],

’ Address’ : [ ’ Nagpur’ , ’Kanpur’ , ’Allahabad’ , ’Kannuaj’ ‚Äã‚Äã ,

’ Jaunpur ’ , ’ Kanpur’ , ’Allahabad’ , ’ Aligarh’ ],

’Qualification’ : [ ’Msc’ , ’MA’ , ’ MCA’ , ’Phd’ ,

’B.Tech’ , ’ B.com’ , ’Msc’ , ’ MA ’ ]}


# Convert dictionary to DataFrame

df = pd.DataFrame (data1)

print (df)


We now repeat the group element just like we do in itertools.obj.

# element repetition
Group #

grp = df.groupby ( ’Name’ )

for name, group in grp:

print (name)

print (group)

print ()

Output:

Now we iterate over a group element containing multiple keys

# element repeat
# of the group containing
# multiple keys

grp = df.groupby ([ ’Name’ , ’ Qualification’ ])

for name, group in grp:

pr int (name)

print (group)

print ()

Output:
As shown in the output, the group name will be a tuple

Group selection

To select a group, we can select a group using GroupBy.get_group () . We can select a group by applying the function GroupBy.get_group this function will select one group.

# pandas module import

import pandas as pd


# Define a dictionary containing employee data

data1 = { ’Name’ : [ ’Jai’ , ’ Anuj’ , ’Jai’ , ’ Princi’ ,

’Gaurav’ , ’Anuj’ , ’ Princi’ , ’Abhi’ ],

print (name)

print (group)

print ()

Output:
As shown in the output, the group name will be a tuple

Group selection

To select a group, we can select a group using GroupBy.get_group () . We can select a group by applying the GroupBy.get_group function this function will select one group.

# pandas module import

import pandas as pd

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Pandas GroupBy __del__: Questions

How can I make a time delay in Python?

5 answers

I would like to know how to put a time delay in a Python script.

2973

Answer #1

import time
time.sleep(5)   # Delays for 5 seconds. You can also use a float value.

Here is another example where something is run approximately once a minute:

import time
while True:
    print("This prints once a minute.")
    time.sleep(60) # Delay for 1 minute (60 seconds).

2973

Answer #2

You can use the sleep() function in the time module. It can take a float argument for sub-second resolution.

from time import sleep
sleep(0.1) # Time in seconds

Pandas GroupBy __del__: Questions

How to delete a file or folder in Python?

5 answers

How do I delete a file or folder in Python?

2639

Answer #1


Path objects from the Python 3.4+ pathlib module also expose these instance methods:

We hope this article has helped you to resolve the problem. Apart from Pandas GroupBy, check other __del__-related topics.

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By the way, this material is also available in other languages:



Olivia Schteiner

Paris | 2023-03-22

select Python module is always a bit confusing 😭 Pandas GroupBy is not the only problem I encountered. Will get back tomorrow with feedback

Ken Richtgofen

Tallinn | 2023-03-22

I was preparing for my coding interview, thanks for clarifying this - Pandas GroupBy in Python is not the simplest one. Will get back tomorrow with feedback

Cornwall Schteiner

Abu Dhabi | 2023-03-22

select Python module is always a bit confusing 😭 Pandas GroupBy is not the only problem I encountered. Checked yesterday, it works!

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