Pandas Class 05: Describing & Summarizing Data in Pandas

Nafees AI Lab

Explore quick summary statistics and how to describe datasets in Pandas. #DataSummary #DescribeData #PandasStats

Transcript

00:00So in this lecture, we will describe data.

00:02We will describe data for a important parameter.

00:06We will use attributes to describe data.

00:11Now, it is important that the attributes are found in the function.

00:17And the function and attributes are found in the way.

00:20First of all, we have noted that we have read CSV or to CSV followed with round brackets.

00:29So, if you have a function, you can call it with round brackets.

00:34When you call it, you want to use the function.

00:38However, when you call it, you can call it with round brackets.

00:43It is not followed.

00:45It is used for a simple value.

00:47We have data in the phone.csv.

00:50So, if you call it, you can call it with the function.csv.

00:55Now, remember, the types are basically.

01:00It is attributes.

01:02It is not followed by round brackets.

01:05Okay?

01:06So, the types are data.csv.

01:08We will explain how to execute.

01:12As you see, we have columns.

01:13Now, we have two columns, memory and sim card.

01:15They are in type.

01:16And the other columns are object type.

01:19So, if you want to see columns exist in my data.

01:25Again, this is my data.

01:27Data frame is in my phone.

01:29So, if you want to select columns

01:37We can use columns.

01:38We will set columns to this column.

01:39So, the forms are in type.

01:41We will select columns.

01:44We will select columns.

01:46So, if I zoom in here.

01:48Then, columns are in type.

01:50So, we need columns in type.

01:51We can mix columns.

01:52So, columns do we need columns.

01:53I will select columns.

01:54If I spend columns in times.

01:56Now, at the moment, if you think this is a very useful feature, this is a very useful feature.

02:06When we go to the next step, you can see how important it is.

02:10Now, let's go to the next step.

02:12If you look at the next step, you can show the next step.

02:16Now, let's go to the next step.

02:36If you look at the next step, you can see the next step.

02:42Now, let's go to the next step.

02:52Now, let's go to the next step.

02:54Now, let's go to the next step.

02:59Now, let's go to the next step.

03:11Now, let's go to the next step.

03:21Now, let's go to the next step.

03:23However, the price is followed by a dollar sign.

03:26So, if you look at the price, if you look at the next step,

03:31you can see the next step.

03:33Now, let's go to the next step.

03:35Now, let's go to the next step.

03:37Now, let's go, let's go.

03:38and so on.

03:49Now we have two things to use.

03:52We have a column attribute,

03:56D-type attribute,

03:57index.

03:59We have a simple method

04:01to combine.

04:03And that is info.

04:05We have a simple method.

04:12As you guessed,

04:13I have a simple method.

04:15Execute.

04:16Now we have data type,

04:19we have indexing and much more information.

04:25This is very cool.

04:27Mean values can be calculated.

04:30Again,

04:31we have data frame.

04:33I have a function called here.

04:36Means,

04:37this is mean.

04:38This is only int columns

04:41mean value display.

04:43Int columns mean value display.

04:46Okay?

04:48Now,

04:50if you have a hypothetical time series data,

04:53let's say,

04:54test is equal to

04:55pd.series

04:57with capital S

04:58R I E S

04:59I E S

05:00And in this series,

05:01I will generate a series.

05:02I will generate one,

05:03two, three.

05:04Execute.

05:05Test.

05:06Now,

05:07test.mean value call.

05:09Test.mean

05:11And this is mean method.

05:13I will execute.

05:14Two.

05:15This means,

05:16this data series,

05:18mean,

05:19two.

05:20How do we count?

05:22One plus two?

05:23Three.

05:24Three plus three?

05:25Six.

05:26Six.

05:27Total element is how many?

05:28Three.

05:29Six divided by three is equal to two.

05:31So,

05:32our answer is correct.

05:33So,

05:34mean we have a correct term.

05:35This is so cool.

05:36So,

05:37what current kām ہم کر سکتے ہیں?

05:38وہ یہ

05:39کہ ہم individual

05:41columns

05:42کو select کر سکتے ہیں.

05:43اب تک جو جتنا بھی کام ہم نے کیا

05:44وہ تو ہم پورے کے پورے data frame

05:46کے اوپر کرتے تھے نا.

05:47اس پورے data frame کے اوپر.

05:48However, imagine کریں

05:49میں

05:50mean لینا چاہتا ہوں

05:51لیکن میں mean

05:52صرف

05:53let's say

05:54same card کا لینا چاہتا ہوں.

05:55وہ کیسے لوں گا.

05:56حضر آپ نے غور کرنا ہے.

05:57یہ میرا data frame ہے.

05:59میں اس کو execute کرتا ہوں.

06:00یہ data frame آگیا نا.

06:01اب اس data frame کو اگر میں پکڑ لوں

06:04اور کہوں کہ

06:06square brackets ڈالو

06:08اور اس square brackets کے اندر string میں

06:10let's say میں sim card ڈال دیتا ہوں.

06:12اس column کا نام.

06:14ٹھیک ہے.

06:15اب میں اس کو execute کرتا ہوں.

06:16تو آپ ذرا غور کریں

06:17یہ صرف وہی column show ہو رہا ہے

06:19جس کا میں یہاں پر نام ڈال رہا ہوں.

06:21مثال کے طور پر

06:22میں اس میں memory ڈال رہا ہوں.

06:23memory

06:24یا memory

06:25execute کرتے ہیں

06:28اور

06:29as you see ہمارے پاس memory آ رہا ہے.

06:31نہ صرف یہ

06:32بلکہ اب آپ اس کے اوپر

06:33function call کر سکتے ہیں.

06:34مثال کے طور پر

06:35آپ یہی پہ کہیں sum

06:36execute کیا

06:37تو یہ آپ کو sum دے رہا ہے.

06:38پورے column کا.

06:39اگر اس کا dot sum

06:40call کرتے ہیں

06:41تو کبوم شاکالہ کا.

06:42یہ آپ کو

06:43سارے column کا

06:44sum return کر رہا ہے.

06:46Is this cool?

06:47Is this very cool.

06:48Okay.

06:51تو give it a go.

06:53اس کو try کریں ایک دفعہ.

06:54دیکھیں

06:55کہ آپ کے پاس کیا result آ رہا ہے

06:56and then we will start it.

06:58تو

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