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