Python - Data Munging in Pandas Part-4 | Python Courses in Tamil | Skillfloor - video Dailymotion

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Transcript

00:00Hello everyone. In this video, we will talk about data monging in pandas part 2.

00:07So, if we have specific titanic dataset, we will select titanic of name.

00:20In order to take multiple columns, we will extract multiple columns.

00:25Titanic of, we will extract multiple columns. The table is two-dimensional.

00:31Then, we will provide multiple columns in this form of 2D.

00:35Then, we will provide a table. We will choose multiple columns.

00:39Now, if we apply a specific column, we will choose columns.

00:45Now, we will extract female passengers details.

00:50First, we will check how female column is gender column.

00:54So, I am going to apply condition.

00:56Titanic of

01:06gender equal to female.

01:09So,

01:11Then, what result is false, true, false, true.

01:15So, if you have condition satisfied, you have true.

01:18If you have true, you have female.

01:20If you have true, you have female.

01:21If you have false, you have male.

01:23If you have female, you have female.

01:25Then, we will select the entire data.

01:27We will select the data frame.

01:29Then, we will select Titanic of.

01:31In this Titanic data frame, Titanic of gender equal to female.

01:34Then, we will select the condition satisfied columns.

01:37So, here, the number of rows is also known for the row.

01:38So, we will select the row.

01:42So, we will select the row.

01:43So, we will select the row.

01:47Then we will select the row.

01:49The row.

01:50From the row.

01:52These rows are a layer of female passengers.

01:54So, let us select the row row.

01:57specific name and age we will add in the form of 2d name,h

02:05extract

02:10this is our specific name and age

02:13this is female passengers from passenger class 1

02:18we will extract two conditions

02:21female venue is in the first class

02:24this is our specific name and use

02:27and use and python

02:29and use and python

02:32and just a and d use and operator

02:36so and

02:39next titanic of passenger class

02:43column name is same

02:46retrieve

02:49so if we look at gender

02:53female and passenger class

02:55we will extract the first class of people

02:58total

02:5994 people

03:02in the category

03:06this is the and condition

03:07two conditions

03:08satisfy

03:09and

03:10now

03:11passenger class 1

03:12and passenger class 2

03:14two

03:15we will extract the details

03:17in this class

03:18we will extract the details

03:19we will extract the

03:20passenger class 3

03:21also

03:22here

03:23one condition

03:24satisfy

03:25then

03:26or condition

03:27use

03:28so titanic of

03:30passenger class

03:32equal to equal to 2

03:33or

03:34passenger class

03:35equal to equal to 1

03:36or

03:37we will provide

03:38shift

03:39plus

03:40backward

03:41slash

03:42this is or

03:43operator

03:44so

03:46first and second

03:47passenger class

03:48details

03:49we will extract

03:50this is or condition

03:51this is or condition

03:52this is or condition

03:53or

03:54randomly satisfy

03:55and

03:56next is

03:57not condition

03:58if you want to

04:00not

04:02not

04:03to

04:04passenger class 2

04:05is

04:06passenger class 1

04:07passenger class 3

04:08is

04:09passenger class 3

04:10is

04:11then

04:12not

04:13titanic

04:14of

04:15passenger class

04:16equal to equal to 2

04:17to

04:18passenger class 2

04:19is

04:20retrieved

04:22so

04:23first class

04:24and third class

04:25passenger details

04:26extract

04:27not

04:28use

04:29not

04:30use

04:31equal to equal to

04:32not

04:33equal to

04:34provide

04:352

04:36same

04:37result

04:38provide

04:39multiple

04:40conditions

04:41multiple

04:42conditions

04:43now

04:443

04:45conditions

04:46in

04:47brackets

04:48separate

04:49by

04:50and

04:51provide

04:52result

04:53extract

04:54two

04:55dimension

04:56LOC

04:57integrate

04:58LOC

04:59integrate

05:00LOC

05:01integrate

05:02LOC

05:03now

05:04we

05:05show

05:06gender

05:07equal to

05:08female

05:10extract

05:11name

05:12name

05:13and

05:14age

05:15we

05:16provide

05:17LOC

05:18also

05:19use

05:20LOC

05:21all

05:22draw

05:23colon

05:24then

05:25name

05:26age

05:27we

05:28provide

05:29column

05:30labels

05:31and

05:33we

05:34extract

05:35direct

05:36we

05:37extract

05:38next

05:39we

05:40normal

05:41head

05:42we

05:43provide

05:44what

05:45show

05:46row

05:47first

05:48row

05:49next

05:50colon

05:51like

05:52last

05:53row

05:54we

05:55show

05:56we

05:57have

05:58method

05:59set

06:00option

06:01display

06:02maximum

06:03rows

06:04maximum

06:05rows

06:06fix

06:07we

06:08show

06:09now

06:10show

06:11titanic

06:12df

06:13we

06:14read

06:15titanic

06:16file

06:17set

06:18every

06:19rows

06:20show

06:21every

06:22rows

06:23like

06:24till

06:258

06:2691

06:27rows

06:28show

06:29maximum

06:30rows

06:31activate

06:33maximum

06:34rows

06:35activate

06:36now

06:37display

06:38maximum

06:39columns

06:40for example

06:4164

06:42columns

06:43we

06:44show

06:45normal

06:46data

06:47read

06:48display

06:49dot

06:50maximum

06:51underscore

06:52columns

06:53show

06:54maximum

06:55underscore

06:56columns

06:57change

06:58set

06:59actually

07:0012

07:01columns

07:02show

07:03next

07:12what

07:13is

07:14sorting

07:15sorting

07:16different

07:17approach

07:18how to do

07:19how to do

07:20first

07:23titanic

07:24dot

07:25sort

07:26values

07:27in the

07:28method

07:29first

07:30is

07:31by

07:32y

07:34equal to

07:35h

07:36then

07:37ascending

07:38order

07:39ascending

07:40order

07:41ascending

07:42equal to

07:43true

07:44descending

07:45order

07:46ascending

07:47equal to

07:48false

07:49so

07:50so

07:51what

07:52result

07:53is

07:540

07:55sorry

07:560

07:5742

07:59so

08:014

08:03month

08:04baby

08:05final

08:06maximum

08:07value

08:08h

08:09column

08:10at

08:11So, in 80, we will show the 4 values in the 80.

08:18This is descending order.

08:23Now, we will change the ascending order and descending order.

08:29Along with a filtering part.

08:32We will apply conditions and select the sort value.

08:39So, first, titanic of male passenger from passenger class 1.

08:46So, titanic of gender equal to equal to male and passenger class equal to 1.

08:52So, here we will run.

08:57Male from passenger class 1 extract.

09:00So, once after that, here we will change the sort value.

09:04We will change the sort value of 2 columns.

09:09So, first is age.

09:11Second is survive.

09:13If we change the sort value of 1 column, we will change the sort value of 2 columns.

09:19But, that is the list of the list we provide.

09:22Next, now, age is ascending order.

09:26That is the survive and descending order.

09:28Then, we will change the second ascending keyword.

09:32We will change the activation of the second ascending keyword.

09:34Now, what do we provide?

09:35The age is ascending order.

09:36The survive ascending order.

09:38So, the trend is ascending order.

09:39So, the name is true, true, true.

09:40Provide.

09:41So, the result is used to analyze the result.

09:44So, the result is used to analyze the search.

09:45so age

09:53now ascending order

09:54provide

09:54so 0.75

09:560.75

09:58here we have survived

10:00we can activate

10:02false

10:03and then descending order

10:04we will come

10:05not

10:06dot head

10:07provide

10:0820 columns

10:09show

10:10ok

10:18so here

10:22age column

10:24age column

10:259 months baby

10:26survived

10:27and then

10:28age 4

10:29survived

10:29and then

10:30difference

10:31we can analyze

10:31same age

10:34both survived

10:35and non-survived

10:36for example

10:4227 age

10:43so 27

10:44so 26

10:45this is the ascending order

10:47we have to sort it

10:48in the survey column

10:49we have not survived

10:52survived

10:53so

10:54we have to sort it

10:570

10:571

10:58activate

10:59because

10:592 columns

11:00we have to do

11:02true

11:02so age

11:04ascending order

11:05we have to sort it

11:08so we have to consider

11:0927 age

11:10because

11:11both survived

11:12and non-survived

11:13people

11:13so we have to put

11:1427

11:15first

11:160

11:16we have to

11:17surveyed

11:18column

11:18ascending order

11:19we have to print it

11:20so 0

11:201

11:21we have to follow

11:22so we have to

11:25activate all rows

11:26and then

11:27display.maximum

11:28rows

11:28activate

11:29then

11:29we have to

11:30easily

11:30visualize

11:30for all the

11:32steps

11:32here

11:33we have to

11:35provide

11:36more than

11:39two columns

11:39sorting

11:40apply

11:40visualize

11:41and

11:42we have to

11:43see

11:43the

11:44data

11:44munging

11:47in pandas

11:47part 4

11:48we have to

11:49see

11:49next video

11:49we have to

11:49see

11:50thank you

Python - Data Munging in Pandas Part-4 | Python Courses in Tamil | Skillfloor

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