Python - Data Munging in Pandas Part-3 | Python Courses in Tamil | Skillfloor - video Dailymotion

Skillfloor

Welcome to Skillfloor’s Python course in Tamil!   Join us and improve your Python programming and data science expertise!  Our Website: Visit 🔗 http://www.skillfloor.com  Our Blogs: Visit 🔗 https://skillfloor.com/blog/  DEVELOPMENT TRAINING IN CHENNAI https://skillfloor.com/development-training-in-chennai  DEVELOPMENT TRAINING IN COIMBATORE https://skillfloor.com/development-training-in-coimbatore  Our Development Courses: Certified Python Developer Visit 🔗https://skillfloor.com/certified-python-developer Certified Data BASE Developer Visit 🔗https://skillfloor.com/certified-data-base-developer Certified Android App Developer Visit 🔗https://skillfloor.com/certified-android-app-developer Certified IOS App Developer Visit 🔗https://skillfloor.com/certified-ios-app-developer Certified Flutter Developer Visit 🔗https://skillfloor.com/certified-flutter-developer Certified Full Stack Developer Visit 🔗https://skillfloor.com/certified-full-stack-developer Certified Front End Developer Visit 🔗https://skillfloor.com/certified-front-end-developer  Our Classroom Locations: Bangalore - https://maps.app.goo.gl/ZKTSJNCKTihQqfgx6 Chennai - https://maps.app.goo.gl/36gvPAnwqVWWoWD47 Coimbatore - https://maps.app.goo.gl/BvEpAWtdbDUuTf1G6 Hyderabad - https://maps.app.goo.gl/NyPwrN35b3EoUDHCA Ahmedabad - https://maps.app.goo.gl/uSizg8qngBMyLhC76 Pune - https://maps.app.goo.gl/JbGVtDgNQA7hpJYj9  Our Additional Course: Analytics Course https://skillfloor.com/analytics-courses https://skillfloor.com/analytics-training-in-bangalore Artificial Intelligence Course https://skillfloor.com/artificial-intelligence-courses https://skillfloor.com/artificial-intelligence-training-in-bangalore Data Science Course https://skillfloor.com/data-science-courses https://skillfloor.com/data-science-course-in-bangalore Digital Marketing https://skillfloor.com/digital-marketing-courses https://skillfloor.com/digital-marketing-courses-in-bangalore Ethical Hacking https://skillfloor.com/ethical-hacking-courses https://skillfloor.com/cyber-security-training-in-bangalore  #python #pandas #datamunging #datamerge #datajoin #datascience #pythontamil #dataanalysis #pythontutorial #learnpython #pythonprogramming #tamilcoding #pythonforbeginners #codingintamil #pythoncourse #datatransformation #datawrangling #machinelearning #programmingtamil #skillfloor

Transcript

00:00Hello everyone, in this video, we are going to talk about Data Munging in Pandas Part 3.

00:07So, in the last class, we will use describe and use numerical data analysis.

00:16So, when we include the object data type columns,

00:21Titanic.describe include equal to O,

00:23we will include the object data type columns.

00:25Here, for example, name, gender, ticket, cabin and name.

00:31These are categorical columns.

00:33So, for each and every categorical columns,

00:35we will analyze what data analysis is like count.

00:38Count is the normal values.

00:40How much number of normal values is each and every column.

00:43Next, we will show unique values.

00:46For example, gender is male and female.

00:50So, there are two classes.

00:52So, that counts are shown.

00:54Embark is S, C and Q.

00:56So, total is 3.

00:57So, this is unique category data.

00:59Count is shown.

01:00Next, now is frequency.

01:03Frequency is the value count.

01:05Any particular part is the value count.

01:08For example, Titanic of gender.

01:12This is the value count.

01:13If we look at the value count,

01:15we'll show it.

01:16This will show it.

01:17Male is the 577 people.

01:18Female is the 314 people.

01:19Female is the 314 people.

01:21This will show it.

01:23Male is the female and the female.

01:25Male is the same.

01:26So, frequency is the maximum occurrence.

01:30Mode value is the 577.

01:33Next, 577.

01:34It is the same.

01:36What we have to adjust is mill.

01:38So, top is mill.

01:39This is the over column.

01:42Now, we check the m-backed column.

01:47What is it?

01:49We have 644 pair of 644 pair.

01:53Ship is board.

01:55Then, we have 644 m-backed column.

01:59Then, this is the top value.

02:02This is the over column.

02:04Let's check the name.

02:05Let's check the name.

02:06Let's check the name.

02:08Because, the name is the first name.

02:11Now, there is a brand Mr. Owen Harris.

02:14So, we check the top value.

02:16So, here is the value count.

02:18So, we check the value count.

02:19In the over column, check the value count.

02:22It is the same as the frequency.

02:24We say the same as the value.

02:26So, the frequency is the same as the value.

02:28What value is the top value.

02:30This is basically describe include equal.

02:32Describe include equal to O.

02:35This is the object data type columns.

02:36This is the normal describe value.

02:39We check the name.

02:40We check the name.

02:41Like minimum value, maximum, percentile values.

02:44So, we do this analysis.

02:46Now, we check the name.

02:48We check the title and enter the value media head.

02:50We check the order node.

02:51Here, we check the clicator data unit.

02:54So, we check the identity set.

02:55Now, we check the other actions.

02:57Now, we tell you.

02:58So, these are the types of gaps.

02:59The values name represents due to the value one.

03:01To use 1.

03:01We show the食 box.

03:02This case here.

03:03There is one duplicate value.

03:05How do we remove it?

03:07titanic.drop__duplicates

03:09If we remove it, we will show an output

03:12That will affect the original titanic dataset.

03:15In the original dataset,

03:17Inplace is equal to true.

03:19Activate.

03:21We will affect the original dataset.

03:23In our titanic dataset, we will know

03:25there are changes in our titanic dataset.

03:27Next, we are going to select D type.

03:33Now, we will add specific numeric data type columns

03:35We will add titanic.select__dtypes

03:39We will provide two arguments.

03:41That is include and exclude.

03:43Now, we will add specific int and float columns

03:47We will add include equal to list

03:49Int and float

03:51Integer or float data types

03:53We will add include

03:55We will add include

03:57We will add exclude equal to object

03:59We will add exclude equal to object

04:01We will add the object

04:03We will provide the other task

04:05Next, we have exclude

04:07We will add same result

04:09We will add specific data types

04:11Now, we will add

04:15Categorical data

04:17We will add include

04:19we will add include

04:21Object columns

04:23Now, let us know what column specific terms is,

04:27titanic.select.select.com

04:29This is a data frame.

04:31This is column names.

04:34We will show the column names.

04:37This is select underscore detect.

04:40We drop it, then followed by select underscore detect.

04:46First, we can describe it,

04:48then duplicate data is removed,

04:50then specific data type

04:52We will extract a column in order to describe.

04:54So, let us draw a specific column

04:56What do we should do on titanic dot we should be draw?

05:00So, draw last column.

05:02Let's say, experiment.

05:04We Gmbugged column add,

05:05Embugged

05:06and we provide this access

05:08So the access is equal to 1

05:10If it happens to make changes,

05:12then we do All Simple

05:14Inplace equal to true

05:16So the changes regardless of me,

05:18we show all a child.

05:20We can Dog End.

05:21So, if we know the changes in place, we will give an entire column to access equal to 1.

05:31Now, let's check the changes.

05:33Let's run the changes.

05:35Next, we print the titanic.

05:37So, the titanic show is the last column.

05:43So, this is the final cabin.

05:47So, the last column is finally removed.

05:55Next, we will rename it.

05:59For example, we will rename the sex column.

06:03So, titanic.rename.

06:05This is the dictionary format.

06:07Which column is the same.

06:09So, the key will replace the same.

06:12We will be case sensitive.

06:14We will replace the gender.

06:16Then, if we change the axis, we can change the column.

06:19So, the axis is equal to 1.

06:21Then, in place equal to true.

06:24So, in place equal to true, we will check whether sex column is gender.

06:31So, once after that, I will print titanic.

06:34So, if you change the changes.

06:37You will analyze the changes.

06:38That is, sex has been changed to gender.

06:43So, this is how you drop, rename, duplicates value check.

06:47This was the type of data, that is the form of pandas part 3.

06:52See you at the next video.

06:54Part 3 is done in the next video. Thank you.

Python - Data Munging in Pandas Part-3 | Python Courses in Tamil | Skillfloor

Category

Transcript

Be the first to comment

Recommended