WEBVTT

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Hello, everyone, and welcome to this bitin tutorial here we will understand group by method.

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Let us begin.

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We use group by method to line up the data together and call aggregate functions on that.

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In simple words, we can say that group by function allows us to group together rules based on columns.

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And after that, we can perform aggregate operations first import the library reads.

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Import numpty as ENPI.

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Then import Bendat.

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As beedi execute.

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Do understand a group by function B, how to define a data frame.

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And we will define this data frame on the basis of a dictionary.

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So define a dictionary.

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First name of the dictionary is deemed data.

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Putting the key company.

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Then valued as at least.

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Apple.

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Apple.

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Facebook.

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Facebook.

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Then Google.

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And one more time, Google.

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No passing one more key.

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Person, this is also a list.

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Mark.

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Dom, John.

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Sara, Mia and Emma.

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No.

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Add one more key value pair key ad sales, then add at least two hundred, 150, 350, 120 for you do

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60 180.

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That seat execute.

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Check this dictionary team data.

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Great, in this dictionary, there are three key value pairs.

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These are the keys.

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And these are the values.

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Now, define a data frame.

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B.

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F.

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BD dot data frame.

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Specified team data.

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Execute.

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No, Jake, this data frame the F.

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Great, in this data frame, there are three columns and six roads.

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No, we can understand the group by function.

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Have the name of data from the EFF F, then group by function, specify parameter by.

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Bike company.

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So this is the group by object.

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And it is pointing towards me, Muddy, to get the actual output store.

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This is a variable.

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Variable be.

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Now take BEE and apply aggregate function.

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I will apply mean.

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So this is the output data.

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It's aggregated here.

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Total Apple sales, total Facebook sales and total Google sales.

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We can apply other aggregate functions.

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Also.

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Type variable B, now use some.

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This is the output.

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Let's see one more aggregate function.

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SDD, this is for standard deviation.

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Great.

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We can apply this function for a specific company.

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Also, let us see how.

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A variable, B, not aggregate function some then dot a low, C, specify a company, Apple.

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So this is the output for this specific company.

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Apple sales 350.

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We can do all these operations in one lane.

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Also, let us see how the name of the data framed D.F. then grew by function grew by.

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On the basis of company.

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He added, We can use aggregate function some.

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Execute.

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So this is the result we can add.

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Company name also.

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F b..

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So this is a very adult.

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So this way we can perform all the operations in one lane.

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Also.

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Let us see other aggregate functions.

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B, dot count.

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This function returns the count.

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There are two person in Apple, two in FBI and two in Google.

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And there are total two to two values of sales.

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Baby Dot Max.

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So these are the maximum values of each company.

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And these are deep person names.

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In similar vein, we can apply in function, be dort mean.

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These are the minimum values of taels for each company.

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Let us understand one more function with group by method.

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Group by method describe.

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First to use.

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Group by method.

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B f dot.

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Group by

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barometer by is equal to company.

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Then God describe.

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So this is the output in this output v. count mean standard deviation, minimum value, first quartile,

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second quartile, third quartile and maximum values.

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We can get transfers of that.

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Let us see how.

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Based dot dance booths.

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So this is the transports.

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So these are the values cound mean standard deviation minimum.

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These are the three quartiles first, second and third, and then maximum values we can use to describe

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function on the basis of a single company.

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Also, let us see how Paiste then Dot Alosi enter name of the company.

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And this is our output.

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All the values on the basis of company F B, so despite and ditto the NCA.

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Let us revise what we have learned in this ordeal.

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First, we have defined A data frame B F and we have defined this data frame on the basis of a dictionary.

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This one.

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After that, we help understood aggregate functions with group by function.

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This is our first aggregate function mean.

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Then we help understood some standard deviation and other functions like Cound, Max and Min.

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And at the end, V.L. understood group by method with described function described function returns.

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All these values.

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So this patented Odille Ungrouped by Method NCEA.

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I will see you in the next one, will then.

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Happy learning.