Python - Data Munging in Pandas Part - 2 | Python Courses in Tamil | Skillfloor - video Dailymotion

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Transcript

00:00Hello everyone! In this video, we are going to talk about data monkey in pandas part.

00:13So first, we are going to talk about titanic.describe.

00:18So, titanic.describe, we can access the numeric columns to the data set.

00:24So, we can provide that information.

00:27Let's say, if a passenger id is survived, all these are numeric columns.

00:30So, what information we can provide is count.

00:34We can provide non-null values.

00:37We can provide non-null values.

00:41So, we can provide that specific value.

00:43In age, we have 714.

00:46So, we can provide 150 missing values.

00:52Next, mean.

00:54What is average value?

00:56Then, standard deviation, spreadness level.

00:59Average spreadness level.

01:01Then, qualify that.

01:03Minimum value.

01:05Maximum value.

01:06And then,

01:07In this center, we can provide 25, 50, 75.

01:10This is percentage value.

01:12Percentile value.

01:14What we can go to detail.

01:18So, describe just like that.

01:20We can provide a lot of inference.

01:22We can provide a lot of inference.

01:24So, first.

01:25If we don't consider an age column,

01:26If we don't consider an age column,

01:27In the 25, 50, 75 percentile.

01:29We can calculate the age data set.

01:31We can sort the ascending to descending order.

01:35Like,

01:36Now,

01:37Now,

01:38Now,

01:39Now,

01:40Then,

01:41Now,

01:42Now,

01:43Now,

01:44Now,

01:45Now,

01:46Now,

01:47Now,

01:48Now,

01:49Now,

01:50Now,

01:51Now,

01:52In it,

01:53How

01:56Now,

01:57Now,

01:58and

02:03Now,

02:05Now,

02:07Next.

02:08Now,

02:13Now,

02:15Now,

02:19Now,

02:20The next 50% value is 28, we show that.

02:24So that 28 is considered, I consider almost 50% data.

02:30First 50% data is considered, I consider almost 28 years of people.

02:37That's how we analyze it. Compare it.

02:40In the whole dataset, 50% data comes to 28 years of people.

02:45That's how we analyze it.

02:46The maximum value is 80.

02:48So what we say is 100 percentile value.

02:51If you consider people in the data set,

02:55all of them are 80.

02:58That's 100 percentile.

03:00So in comparison analysis, we do each and every column.

03:05Then, followed by that,

03:07this is an important thing,

03:10by making yourself describe.

03:12Over a Numeric column,

03:14we analyze the distribution.

03:17Without making use of this plot.

03:19If we look at this plot,

03:21this is a normal distribution.

03:23Skewed or left skewed or right skewed,

03:25we say specific.

03:27That's how we look at it.

03:29In case of normal distribution,

03:32the mean is equal to mean.

03:35Okay.

03:36This is one side left.

03:38Now, skewed data,

03:40this is right skewed,

03:41this is left skewed.

03:42So here,

03:43mode,

03:44median,

03:45mean.

03:46Now, mean,

03:47greater than median,

03:48if we say,

03:49right skewed data.

03:51then,

03:52here is actually mean.

03:53Then,

03:54median,

03:55mode.

03:56Then, left skewed data,

03:58this is left skewed data.

04:00Then,

04:01left skewed data,

04:02this is left skewed data.

04:05Then,

04:06right skewed data,

04:07we consider,

04:08this is mode,

04:09this is median,

04:10this is mean.

04:12Then,

04:13this is median,

04:14this is mean.

04:16Then,

04:17mean value,

04:18median is equal to right skewed data.

04:20This is right skewed data.

04:21Now,

04:22mean is less than median,

04:25this is left skewed data.

04:26This is left skewed data.

04:29First,

04:31we consider passenger ID.

04:33So,

04:34passenger ID is equal to mean value.

04:36median value is shown.

04:37median value is shown.

04:38median value is shown.

04:39median value is shown.

04:40So,

04:41median value is shown.

04:42So,

04:43median value is shown.

04:44So,

04:45median value is shown.

04:46So,

04:47mean and 50% data,

04:49we compare.

04:50mean data is 446.

04:51Then,

04:52median data is 446.

04:53So,

04:54the two of us,

04:55same value is shown.

04:56mean equal to median,

04:57then,

04:58passenger ID is shown.

04:59normal distribution is shown.

05:00Then,

05:01survey column is shown.

05:03So,

05:04survey column is shown.

05:05mean value is 0.38.

05:07Then,

05:08median value is 0.00.

05:10This is skewed data.

05:11So,

05:12left skewed or right skewed

05:13and how to check.

05:14mean value,

05:16median value is 0.00.

05:18So,

05:19when mean is greater than median,

05:21it is right skewed data.

05:23So,

05:24over column,

05:26we analyze the specific column.

05:28Now,

05:29passenger class mean value is 2.3.

05:32Then,

05:33median value is 3.

05:34Now,

05:35mean value is less than median.

05:37So,

05:38this is left skewed.

05:39So,

05:40distribution analysis,

05:42we can easily do specific

05:44without making use of this plot.

05:47Using describe.

05:48right.

05:54So,

05:55next number,

05:56unique,

05:57nunique,

05:58and value counts.

05:59So,

06:00basic number,

06:01unique,

06:02and value counts.

06:03so,

06:04value counts.

06:05So,

06:06value counts is

06:07each and every unique category

06:08and value counts.

06:09each and every unique categories

06:10are.

06:11So,

06:12that we can specify.

06:13Then,

06:14unique values

06:15count

06:16specify.

06:17nunique,

06:18nunique,

06:19unique classes

06:20count

06:21specify.

06:22Now,

06:23male and female

06:24two values are.

06:25that we can provide.

06:26Then,

06:27titanic of gender.value counts.

06:30So,

06:31value counts

06:32nunique,

06:33each and every unique categories

06:34provide.

06:35male and female.

06:36So,

06:37male and female

06:38count

06:39that we can specify

06:40value counts.

06:42that we can specify

06:43value counts.

06:44Now,

06:45next,

06:46same process

06:47we can check the

06:48Embarked column.

06:55Embarked column

06:56s, c and q

06:58three values

06:59are.

07:00So,

07:01next

07:02is

07:03total number

07:04of unique values

07:05check.

07:07Three.

07:08Then,

07:09s, c, q

07:10will be

07:11one of the values

07:12that we can see.

07:13So,

07:14every category

07:15has the same data.

07:16This is value counts

07:17from the

07:18value counts

07:19that we can provide.

07:20For numeric

07:21and categorical

07:22data

07:23we can support.

07:24Now,

07:25age

07:26people

07:27have

07:28people

07:29and

07:30people

07:31have

07:3288

07:33unique

07:34categories

07:35and

07:36there are null values.

07:37there are null values

07:38and we can

07:39preprocess

07:40that we can

07:41pre-process

07:42do

07:43this.

07:44So,

07:45we can check

07:46this.

07:47So,

07:48in one category,

07:49there are

07:50number of people.

07:51value counts

07:52in 24

07:53age

07:54category

07:5530

07:56people

07:57and

07:58you

07:59have

08:0027

08:01people

08:02so,

08:03we can check

08:04easily

08:05by making use of

08:06unique

08:07and unique

08:08and value counts.

08:09This is the most important method.

08:11data

08:12mongling

08:13in pandas

08:14part 2

08:15mongling

08:16in pandas

08:17part 2

08:18mongling

08:19in pandas

08:20part 2

08:21next video

08:22we will see

08:23thank you

08:24for

08:26our

08:27hands

08:28to

08:29the

08:31body

08:32p

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Python - Data Munging in Pandas Part - 2 | Python Courses in Tamil | Skillfloor

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