Aggregation
Aggregation is used to get the mean, average, variance and standard deviation of all column in a dataframe or particular column in a data frame.
- sum(): It returns the sum of the data frame
Syntax:
dataframe[‘column].sum()
- mean(): It returns the mean of the particular column in a data frame
Syntax:
dataframe[‘column].mean()
- std(): It returns the standard deviation of that column.
Syntax:
dataframe[‘column].std()
- var(): It returns the variance of that column
dataframe[‘column’].var()
- min(): It returns the minimum value in column
Syntax:
dataframe[‘column’].min()
- max(): It returns maximum value in column
Syntax:
dataframe[‘column’].max()
Example:
In the below program we will aggregate data.
Python3
# importing pandas as pd for using data frame import pandas as pd # creating dataframe with student details dataframe = pd.DataFrame({ 'id' : [ 7058 , 4511 , 7014 , 7033 ], 'name' : [ 'sravan' , 'manoj' , 'aditya' , 'bhanu' ], 'Maths_marks' : [ 99 , 97 , 88 , 90 ], 'Chemistry_marks' : [ 89 , 99 , 99 , 90 ], 'telugu_marks' : [ 99 , 97 , 88 , 80 ], 'hindi_marks' : [ 99 , 97 , 56 , 67 ], 'social_marks' : [ 79 , 97 , 78 , 90 ], }) # display dataframe dataframe |
Output:
Python3
# getting all minimum values from # all columns in a dataframe print (dataframe. min ()) print ( "-----------------------------------------" ) # minimum value from a particular # column in a data frame print (dataframe[ 'Maths_marks' ]. min ()) print ( "-----------------------------------------" ) # computing maximum values print (dataframe. max ()) print ( "-----------------------------------------" ) # computing sum print (dataframe. sum ()) print ( "-----------------------------------------" ) # finding count print (dataframe.count()) print ( "-----------------------------------------" ) # computing standard deviation print (dataframe.std()) print ( "-----------------------------------------" ) # computing variance print (dataframe.var()) |
Output:
Pandas Groupby: Summarising, Aggregating, and Grouping data in Python
GroupBy is a pretty simple concept. We can create a grouping of categories and apply a function to the categories. It’s a simple concept, but it’s an extremely valuable technique that’s widely used in data science. In real data science projects, you’ll be dealing with large amounts of data and trying things over and over, so for efficiency, we use Groupby concept. Groupby concept is really important because of its ability to summarize, aggregate, and group data efficiently.