WEBVTT

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Hello and welcome to Deterred by tinted ordeal on data frames he had with Villone multi index and index

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hierarchy.

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Let us begin first import deliberated.

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Great after that, define a least outside.

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InterCloud read two more times.

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Then enter color green three times.

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They find one more liste inside.

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One, two, three, again, one, two, three.

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Execute.

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Great.

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Now define a variable.

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Higher index in this variable.

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We're going to store a list in that list, zip function.

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And deep barometers outside and inside.

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Execute.

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Now, take this variable here.

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Index.

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Great.

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So this is a list of people's.

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Using that stuffed up, well, today will define a multi index, define a variable multi index is equal

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to BD Dot Multi Index.

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Dort from double's.

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Inter barometer as higher index.

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Then one more parameter names.

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Gullahs.

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And number.

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Now, take this multi index.

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Great.

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So this is the multi index that we have defined here.

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Now using that multi index, define a data frame.

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B, F, bd dot data frame.

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First, we have to specify the data.

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Data is equal to that end and.

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And in parentheses, six comma, two six rows and two columns after that specify.

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Index.

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Index is equal to multi index.

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And at the unspecified columns.

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A and B.

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Now, take this data out from the F.

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Great.

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So this is a multi index data frame.

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This is the index level one.

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This one, the red and green, this column.

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And this is the index level.

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Second one, two, three, one, two, three.

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Let us understand how we how defined a multi-level data frame.

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Scroll up.

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First view, how important the libraries.

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After that, we help, we find to list outside and inside.

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Then using this list to be how do we find a variable?

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Higher index.

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This one, a list of doubles.

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And using this list of doubles, we have defined multi index.

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And Vedette Multi Index, we have defined a data frame.

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Be index data, frame this one, index level one and index level two.

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And this is data data.

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In column A and B, let us understand how to call the data from multi index data frame.

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By the name of data frame B, F dot a low, C specify index.

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Read.

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So this is the output here.

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We have specified level one index that is read.

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And this is the output in level one index grade.

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There is level two index.

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And this data, let us understand how to select level two index type dot Alosi.

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Specify indexed level to execute.

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Great.

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So this is the data from index level to to get that data.

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We have to specify.

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Index level one.

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And then we have to specify index level to like these.

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And after that, we can grab a column.

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Also column A.

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Great.

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So this is how we can call the data from multi index data frame.

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Let us see one more example.

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The F Dot LLC.

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Index level one is green.

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Great Dane Dart, LLC.

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We will grab these two.

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So this is the output from index level one and index level two.

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Now grab the data from Colombe.

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Zero point seven for this one.

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So this is all about grabbing data.

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No check.

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The index stays in this data frame.

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The F dot index dot names.

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Colors and numbers.

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Colors and numbers.

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These two are the indexes.

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We can change these indexes.

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Let us see how Kopit is based.

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Is equal to specify a list.

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C o.

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L.

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That is four color and.

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And you.

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And that is four numbers.

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Take the data from D.F..

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Great.

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So here we have successfully changed the index's call and name.

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Previously it was colors and numbers.

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So this way we can change the indexes.

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Let us understand a function for might be index cross section.

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Take the data out for him.

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B f.

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So this is our data frame to grab the level one index trade.

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We use a Locy method like this.

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B.

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F dot.

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A, Locy.

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And then index level one read.

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And this is the output.

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We can do the same with cross-section function type D.F. dot X s and in parentheses index name read.

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Same output.

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So this is the cross-section function in cross-section function.

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We can't specify other parameters.

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Also.

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Let us see how.

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So these are the parameters that we can specify key axes, level and drop level, specify key is equal

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to one.

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And level is equal to number.

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Execute.

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Great.

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So this is the output.

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Let us understand this output.

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We have specified key is equal to one.

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This one Vun.

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In the data frame we have given is two times in index grade and in index green.

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And after that we have specified the level of this key, which is now.

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Key and level, and this is the output.

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So here we have selected all the data under key one and level number so we can say that the cross-section

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function has the ability to go inside the multi level index.

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As you can see here at a time, we have selected all the data from Key VUN.

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This is the ability to go inside the multi level index.

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In this tutorial, we have learned a lot of things.

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Let us vides.

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Data frames part three.

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First, we have defined a multi level index.

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This one.

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And we have defined this multi level index from a list of double's.

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After that, we have defined a data frame, multi-level data frame this one.

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There are two levels in this data frame, level one and level two.

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And after that, we help understood how to grab the data from multi level data frame.

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To do that, we use this method.

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Doc Alosi method, then we help understood how to change the indexes.

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And at the end, V.L. understood cross-section function.

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This one dot, it's as so despite Antigo deal on data frames, part three NCEA.

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I will see you in the next one.

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buildOn Happy learning.