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 ​​from 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.
# 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 Name
using the groupby ()
function.
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 ​​are 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 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).
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:
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