WEBVTT 00:00.080 --> 00:01.160 Welcome back. 00:01.200 --> 00:07.280 We visualized our data frame and we succeeded in creating this table. 00:07.320 --> 00:12.760 Now let's explore our data use build a methods to quickly understand your data. 00:12.800 --> 00:17.760 Again guys, trust me, when working with simple tables it's very clear. 00:17.760 --> 00:22.280 It's obvious that that Diana has the highest grade. 00:22.280 --> 00:30.440 But when working with thousands and hundreds of rows and columns, it would be a very complex and complicated 00:30.440 --> 00:31.080 step. 00:31.200 --> 00:35.760 So what things we can do with our data? 00:35.760 --> 00:38.560 Let me display the first two rows. 00:38.560 --> 00:40.520 So I use the f. 00:40.760 --> 00:45.360 This is the data frame that we created before dot head. 00:45.400 --> 00:46.600 And we pass to. 00:46.800 --> 00:49.360 This includes first two rows. 00:49.400 --> 00:55.680 Let me run and see that the first two rows Alice and Bob. 00:55.960 --> 01:00.000 Those are the first two rows zero and one. 01:00.000 --> 01:09.780 If I need the last two rows so I can use df dot tail to and this is the last two rows. 01:09.940 --> 01:14.500 Charlie and Diana, if I need to get data set info. 01:14.820 --> 01:25.500 What the what this means df.info data set info provides a concise summary of the data frame, including 01:25.660 --> 01:34.620 data types of each column, number of non-null values, memory usage, and number of rows and columns. 01:34.780 --> 01:39.700 Okay, so let me run and see the output. 01:39.740 --> 01:46.740 Here we have the data set info class pandas data frame range index. 01:46.740 --> 01:51.300 For entries 0 to 3 we have four rows. 01:51.500 --> 01:54.260 The data columns total four columns. 01:54.260 --> 01:56.820 We have name, age, city, and grade. 01:56.820 --> 02:05.700 And you can notice that zero, one, two and three name, age, city and grade null null null null and 02:05.700 --> 02:06.340 others. 02:06.540 --> 02:14.450 And here we have the data type for the name is object, which is the string integer 64 for age. 02:14.610 --> 02:20.570 Object for the city, which is string and integer 64 for decades. 02:20.770 --> 02:21.690 Data types. 02:21.690 --> 02:26.810 Integers two and objects two for the columns and memory usage. 02:27.090 --> 02:29.370 260 bytes. 02:29.650 --> 02:35.530 Okay, this is how we get the information about the data set. 02:35.530 --> 02:40.810 So the range index five entries or four entries 0 to 3. 02:41.210 --> 02:47.890 We have four rows with index 0 to 3 data columns four columns in total. 02:48.050 --> 02:52.010 And others we don't have the null null. 02:52.050 --> 02:55.450 They are not null null null values. 02:55.450 --> 02:58.370 Nothing with null null values. 02:58.410 --> 03:08.890 Now let me use another like ready made and included function in that data frame which is describe function. 03:08.890 --> 03:12.770 We get basic statistics df dot describe. 03:13.090 --> 03:18.090 This will generate descriptive statistics for numerical columns. 03:18.290 --> 03:27.030 Count mean standard deviation, minimum maximum values, percentiles 25%, 50, 75 and others. 03:27.030 --> 03:30.390 Scroll down to get those basic statistics. 03:30.430 --> 03:36.230 We have two columns that works with integers, so age and grade. 03:36.270 --> 03:42.350 This will generate statistical data for those numerical columns. 03:42.590 --> 03:47.870 Age and grades count for mean 20.5. 03:47.950 --> 03:50.590 Standard deviation 1.29. 03:50.630 --> 03:59.270 Minimum 1,925% 19.7 50%, 75%, and maximum 22. 03:59.470 --> 04:02.390 Also, this is for the grade. 04:02.430 --> 04:07.350 Okay, so you can see how powerful is this library. 04:07.470 --> 04:14.990 It calculates all those statistics with one click and one command called df dot describe. 04:15.030 --> 04:15.510 Okay. 04:15.670 --> 04:25.710 So this is how we quickly understand and use the methods to understand our data and get statistics about.