Pandas Class 07: More on Data Selection in Pandas

Nafees AI Lab

Take your data selection skills further! Dive deeper into conditional filtering, Boolean indexing, and selecting based on logic. #AdvancedSelection #PandasFiltering #PythonData

Transcript

00:00and data selection

00:02to our discussion

00:04and there are two other things

00:06that we have learned

00:08which is how we compare them

00:10and how we compare them

00:12and how we compare them

00:14and how we compare them

00:16and how we compare them

00:18two columns

00:20two columns

00:22we have a simple

00:24command

00:26cross tab

00:28we have a PD

00:30which we have two columns

00:32which we compare

00:34we have two columns

00:36which we have to compare

00:38so we have to do

00:40what we have to do

00:42how do we compare them

00:44as a PD

00:46now you have to cross tab

00:48cross tab

00:50this cross tab

00:52which we have two columns

00:54to give them

00:56which we have

00:58to make a name

01:00and in a square bracket

01:02column

01:04and the other column

01:05and the other column

01:07and the other column

01:08and the other column

01:10and the other column

01:12which we have to do

01:14columns ko compare

01:15کر رہے ہیں

01:15अगर

01:17आप

01:17थोड़ा सा

01:18गोर करें

01:19तो ये

01:19जो आपका

01:20memory वाला

01:21column है

01:22वो ये है

01:22और आपका

01:23sim card वाला

01:24वो ये है

01:25तो आप

01:25इन दो columns

01:26को compare

01:27कर रहे है

01:27ठीक है

01:27और ये columns

01:28का जो structure है

01:29layout structure

01:30horizontal layout structure है

01:32ठीक है

01:32horizontally वो laid out है

01:34तो basically

01:34comparison ऐसे हो रहा

01:36ऐसे

01:36and let's say

01:38ऐसे

01:38आप compare कर रहे है

01:40ठीक है

01:40with sim card

01:42and a specific

01:43let's say memory

01:44और उसकी जो

01:45relative value है

01:47क्या वो exist करती है

01:48क्या नहीं करती

01:49जो भी उसकी value है

01:50ये जो 0 और 1 है

01:51ये बता रहा है

01:52आपको कि 0 का मतलब है

01:53इस sim card के अंदर

01:56ये value exist

01:57नहीं करती

01:57one का मतलब है

01:58exist करती है

01:59अब मुझे पता है

02:01naturally

02:01आपके जहन में

02:02एक question आ रहा है

02:03और वो ये कि

02:04यार बात ये है

02:05कि दोना सीख लिए

02:06what if

02:06अगर मुझे

02:07दो से ज्यादा column

02:08को compare करना पड़े

02:09more than two

02:11तब क्या करूँ

02:12उसके लिए

02:12command यूज होती है

02:14आपने वो यूज करनी है

02:15उसका नाम है

02:15group by

02:16group by

02:17group by

02:18by वाला by

02:19ये वाला by

02:20ठीक है

02:20group by

02:20चो चलिए इसको देखते है

02:22हमारे पास data किस format में है

02:24किस variable में

02:25phone underscore csv में

02:27phone underscore csv के उपर

02:29आपने group by का column

02:30group by की command यूज करनी

02:32अब

02:33इस group by के अंदर

02:36आपने principal component

02:38principal column

02:44करते हैं make को

02:45मैं make उठा लेता हूँ यहां से

02:47और आप कहते हैं कि यार make को compare करो

02:51किस के साथ

02:53अब बाइद वे इसको

02:54हमने make को एक

02:55round bracket में

02:57square bracket में देना है

02:58ठीक है

02:58इसको compare करना है

03:01बाकी सब के साथ

03:02जिनकी data type int है

03:04मैं इसको means का

03:05mean का function कॉल करता हूँ

03:08execute करते है

03:09अब जर आपने गवर करना है

03:11कि यह हुआ क्या है

03:12हमने जितने भी

03:14data types integer थी

03:17उसका comparison

03:18आपको make के साथ मिला हूँ है

03:20मिला

03:22इस axis के साथ

03:23किसका

03:23बाकी सब का

03:25बाकी सब में उनका

03:26जिनकी data type क्या थी

03:27integer थी

03:29अगर आपको याद नहीं है ना

03:31तो मैं इसी को पकड़ता हूँ

03:32control C करता हूँ

03:34control V और D types

03:35run करता हूँ

03:36run करते हैं

03:37अब जर आप गोर करें

03:38कि

03:39इस comparison के अंदर

03:41आपको सिर्फ वो वाले columns

03:45show हो रहे हैं

03:46जिनकी data type int है

03:49ये दो column show हो रहे हैं

03:50इदर आईए

03:51इनको track करें

03:52एक ये है

03:53और दूसरा जो है

03:55आपको ये नदर आ रहा है

03:56int 64, int 64

03:59ठीक है

04:00और दोनों memory और sim card

04:01दोनों int है

04:02और जो object थे

04:04वो यहाँ पर show नहीं हुए

04:06उनका comparison या उनके उपर

04:07वो operation परफॉर्म नहीं हुआ

04:08अब

04:10हम आगे चलते हैं

04:13और

04:14मैं आपको अब data plot करना

04:18सिखाने लगाँ

04:18ठीक है

04:19how to plot data

04:22सबसे पहले चले हम इस तरह करते हैं

04:25कि

04:25हम कोई एक column ले ले ले ले ले ठीक है

04:29और उसको plot करते है

04:30imagine करें कि

04:31let's say आप plot करना चाहते है

04:32सबसे पहले तो आपने note करना है

04:35कि int plot होगा सिरफ int

04:36int 64

04:37तो memory या sim card में से एक को plot कर सकते है

04:40तो चले पहले हम memory को plot करते है

04:42अचा plot करने के लिए सबसे पहले तो आपको column चाहिए

04:45column कैसे उठाते हैं

04:47column आप data जिसके अंदर पड़ा हूँ है

04:50वो लेते है

04:50square bracket लगाते है

04:52और column का नाम देते है

04:54अब column का नाम है memory के भी

04:55ठीक है यहां से उठा लिया

04:58और इसको उपर dot plot का method काल करते है

05:00ठीक है

05:02यह देखिए

05:03इसको execute किया

05:04और आपके सामने plot show हो गया

05:07अचा इसी तरह आपने

05:09अगर let's say

05:10दूसरा जो हमारे पास int type है

05:12वो sim cards है

05:13इसी तरह आप sim cards को भी plot कर सकते है

05:15ठीक है

05:16यह देखिए

05:16यहां पर मैं इसको पकड़ता हूँ

05:18और यहां पर sim card डाल देते है

05:19और इसको plot करते है

05:20और as you see

05:22sim card आपके पास plot हो गया

05:23अब हो सकता आपको यहां पर को problem है

05:26क्योंकि हमने matplotlib library कहीं भी import नहीं की

05:29हमने सिर्फ pandas को import किया

05:31और पांधा background के अंदर हमारे लिए matplotlib को import करके यह plot करता है

05:35तो यह जो plotting हो रही है matplotlib के अंदर हो रही है

05:37ठीक है

05:37और इनकेस अगर आपको कोई error आता है तो आपने काम करना है

05:40यहां पे यह library import करा लेना है

05:42import matplotlib matplotlib.pyplot asplt

05:51यह आपने import करा लेना है

05:53और by the way आपने एक चीज और भी यहां पे लिगनी है

05:56जो कि basically है matplotlib inline

06:02यानि कि इसी जगह पे इसको import करा के आगे execute करो

06:07और यह इस case में करना है जब आपके पास काम ना कर रही हो चीज

06:09ठीक है तो मैं इसको comment out कर देता हूँ

06:11क्योंकिए for us यह work कर रही है

06:13अच्छे चलें इस तरह करते हैं

06:16कि कुछ और plot की types देखते हैं

06:19मिसाल के तोर पर यह plot मैं पकड़ लेता हूँ

06:21और इसको change कर देते हैं

06:23मैं कहता हूँ यह हिस्ट एक plot type है

06:25histogram जिसको कहते है histogram

06:27अब histogram की खासियत यह है

06:31कि यह आपको data का spread बताता है

06:33कि भाई जो values का spread है

06:36वो कैसे है

06:37मैं इसको plot करता हूँ

06:39as you see

06:40हमारी जो data की values है

06:41sim card की

06:42वो four sim card जो है

06:43वो सबसे ज्यादा है

06:44three वाले कुछ हमारे पास phones है

06:47और जो कुछ phones हमारे पास five वाले है

06:50लेकिन सबसे ज्यादा जो phones है

06:52हमारे पास

06:53जो के eight phones है

06:54वो जो sim card लेते है

06:56वो four लेते है

06:57अच्छा अब

06:59मिसाल के तौर पे

07:00मैं इस तरह करता हूँ

07:01इसको chain करता हूँ

07:01और मैं sim card की बजाए

07:03जो four लेते है

07:04अच्छा अब

07:05मिसाल के तौर पे

07:06मैं इस तरह करता हूँ

07:07इसको chain करता हूँ

07:08और मैं sim card की बजाए

07:09Let's say, we plot

07:11I'll do this memory

07:12Memory KB

07:13So we'll go back to memory KB

07:16I'll do this

07:17Now I'll do this

07:17A very interesting example

07:19Because today

07:21Corona's talk about

07:22So this is basically what is

07:26This histogram

07:26And this is what is plot

07:29Now I'll do this

07:30Now I'll do this

07:31General pattern

07:31I'll do this

07:32Now I'll do this

07:34General pattern

07:34If I draw it

07:35Then this curve

07:36I'll do this

07:36And this

07:38Now I'll do this

07:40However

07:40Now I'll do this

07:41This is a jump

07:43And then

07:44This is a jump

07:45This is a jump

07:45Now I'll do this

07:46Now I'll do this

07:47If I draw it

07:48This plot

07:49If I draw it

07:50Simple

07:50X axis

07:51Y axis

07:52So this is normal

07:53This is draw it

07:55So this is normal

07:56By the way

07:56This two

07:56You'll look at it

07:57So this is normal distribution

08:00You'll look at it

08:01You'll look at the same data

08:03Okay

08:04Mathematics

08:06If I have to

08:07You'll look at it

08:09By the way

08:09You'll look at it

08:09But I'll do this

08:09Mathematics

08:10Jhaڑ گئے

08:10But I'll do this

08:11But I'll do this

08:12You'll look at it

08:12This will look at it

08:14Gaussian distribution

08:15Thick

08:16Distribution

08:17And this

08:18Is a better example

08:20You'll look at it

08:21You'll look at it

08:21Corona

08:21And Corona

08:22The model

08:23You'll look at it

08:24Number of patients

08:25This is one of the very important

08:55Function in mathematics

08:56And which we'll look at it

08:57Data science

08:58And machine learning

08:59In the way

09:00Use

09:01Now you'll look at it

09:02This is

09:03Distribution

09:04Now this distribution

09:05Is

09:06Distribution

09:07Is

09:08Data

09:09Spread

09:10Basically

09:11Is

09:12Limited

09:13Is

09:15Outliers

09:16You'll look at it

09:17You'll look at it

09:18You'll look at it

09:19You'll look at it

09:20You'll look at it

09:21Basically

09:22Major

09:23Data

09:24Features

09:25Co-represent

09:26You'll look at it

09:27You'll look at it

09:28You'll look at it

09:29One to ten thousand

09:30Good

09:31So you'll look at it

09:32Histogram

09:33Data spread

09:34Okay

09:35Now this

09:36We'll look at it

09:37Interesting

09:38We'll look at it

09:40Histogram

09:41Let's say

09:43Normal plot

09:44A good

09:45If I can never

09:46Plot

09:47I can never

09:48Plot

09:49A good

09:50No

09:51Plot

09:52Is

09:54Normal in

09:55Plot

09:56I can never

09:57Plot

09:58Plot

09:59I can't

09:59A good

10:01Plot

10:03And

10:04here

10:05My

10:06Plot

10:07As

10:07Plot

10:08Plot

10:09Plot

10:11Plot

10:12Plot

10:13but if someone has a price with a dollar sign,

10:17if someone has a dollar sign,

10:19it's a very important feature,

10:21we have a price plot.

10:23How can we plot this price?

10:25There are many different methods that you can use,

10:27and I strongly recommend that you can google this.

10:29However, I will give you a method here.

10:31It will be a little bit

10:33over-dose.

10:37Over-dose.

10:39But, you have a little bit of a limit,

10:41because you have to keep it from the limit and jump.

10:43So, how do we do this?

10:45Basically, you have to do this

10:47that we have to reformatting.

10:49Okay?

10:51I will just say that this dot will not be here.

10:53I will just say that this dollar sign will not be here.

10:55But you can do this.

10:57If you do this,

10:59you will have clean data and then

11:01we will convert this column.

11:03So basically,

11:05we will give this column to an integer convert.

11:07So I will go back to this column.

11:09we can select this column. How do we select this column? This is our line, which will represent

11:17this column. Now we can clean this column. How do we clean this column? How do we clean

11:23this column? We can clean this column. We can see that this is out of the scope. This

11:30is the scope of the lecture. We have a frame. We have a mental frame set. We have

11:36a state of the data engineering or the type of a direction. So we can decide

11:41when this column is not correct. So now, we will let our table

11:45look at the column. First of all, we have a function called here. We

11:51say that we have a replace. We have a dollar sign and a dot here.

12:04So now I will replace this.

12:06I will replace this.

12:08I will replace this.

12:10I will replace this.

12:12I will replace this.

12:14Back slash.

12:16Dollar sign.

12:18Dollar sign.

12:20I will replace this.

12:22Comma.

12:24Yeah.

12:26Comma.

12:28Comma.

12:30These two things.

12:32We will replace this.

12:34We will replace this.

12:36We will replace this.

12:38One empty string.

12:40Okay.

12:42If I will execute this.

12:44We will replace this.

12:46However.

12:48If you call it D type.

12:50This object.

12:52Data type.

12:54This object data type.

12:56We will convert this.

12:58Here.

13:00You can use type.

13:02And you can say.

13:04You can do int.

13:06Now.

13:08And you can execute.

13:10Int 32.

13:12Now.

13:14Now.

13:16Now.

13:18Now.

13:20assign a value. Let's say, you can say that this value is equal to this variable.

13:31Now, I will execute this. This is the value of this variable. Now, I will execute this variable.

13:41Actually, value is our object type.

13:47It is the capital value, which in this flow, means oh no.

13:56It is so, to see.

14:07price value plot.

14:09Let's close this lecture.

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