WEBVTT 00:00.080 --> 00:00.840 Welcome back. 00:00.880 --> 00:07.560 As a quick recap of what we did before, we installed TensorFlow generated random numbers and set the 00:07.560 --> 00:08.160 seeds. 00:08.200 --> 00:14.480 Also, we created those variables size and price linked together by this equation. 00:14.680 --> 00:25.000 Now we're going to convert numpy array into pandas dataframe in order to be plotted and in a table format. 00:25.280 --> 00:35.680 Okay, so first of all let me introduce uh the and let's clarify some concepts we need to concatenate. 00:35.680 --> 00:38.920 And in the previous videos we learned about concatenation. 00:38.920 --> 00:48.560 And we did this step before okay I love writing uh the notes uh, like this because they are very useful 00:48.560 --> 00:50.320 when going back to this. 00:50.440 --> 00:51.680 Uh ipy. 00:51.880 --> 00:58.960 And B file using TensorFlow file and the notebook, everything will be clarified. 00:59.110 --> 01:08.590 So data equals to PD dot data frame and concatenate this size and price. 01:08.630 --> 01:17.350 Set the axis equals to y and the columns named as size and the price in thousand dollars. 01:17.470 --> 01:23.430 What we did here we are converting numpy array into pandas dataframe. 01:23.470 --> 01:29.270 As you learned before, we generated random arrays here. 01:29.350 --> 01:32.150 Size and price like this. 01:32.350 --> 01:37.510 So before concatenation we have a size array and a price array. 01:37.670 --> 01:42.910 The size array for example 1.372.771.. 01:42.950 --> 01:46.430 Did you remember the range from 1 to 3. 01:46.750 --> 01:49.070 Also the price array. 01:49.310 --> 01:51.110 Also we set the range. 01:51.350 --> 01:56.510 And here we have the size and arrays without concatenation. 01:56.510 --> 02:02.890 So think about them as two arrays with 100 rows and one column. 02:02.930 --> 02:07.130 After concatenation, we get a combined array. 02:07.170 --> 02:17.810 So the first element in the array size is combined with the first element of the price array. 02:17.970 --> 02:20.170 We have this element. 02:20.210 --> 02:25.210 The first one represents the size and the second one represents the price. 02:25.250 --> 02:25.770 Okay. 02:25.890 --> 02:32.530 This is a combined array 100 rows and two columns. 02:32.530 --> 02:35.930 So you, uh, see the difference guys. 02:36.050 --> 02:36.530 How? 02:36.770 --> 02:45.170 Before concatenation we get two arrays 100 rows and one column, but two arrays here after concatenation 02:45.170 --> 02:53.170 we have only one array containing two row two columns and 100 rows. 02:53.210 --> 02:53.730 Okay. 02:54.050 --> 02:57.560 So this is about the MP dot concatenate. 02:57.600 --> 03:05.840 The axis equals to one means join side by side horizontally instead of stacking vertically. 03:05.840 --> 03:13.080 So x is one means I need to set them as a horizontal manner side by side. 03:13.200 --> 03:16.080 Then we created the columns. 03:16.080 --> 03:21.960 So those columns will represent the size here and the price. 03:22.000 --> 03:27.720 Okay so the first one is the size, the second one is the price. 03:27.720 --> 03:30.720 And the title of the table would be. 03:30.760 --> 03:34.360 And the title of the columns would be size and the price. 03:34.360 --> 03:36.200 Exactly like in this format. 03:36.200 --> 03:36.720 Okay. 03:36.880 --> 03:37.360 Don't worry. 03:37.400 --> 03:42.800 We're going to run this, um, this cell and see the results. 03:42.800 --> 03:43.680 Let's run. 03:43.720 --> 03:47.880 I got this error because I restarted my computer. 03:48.080 --> 03:56.620 So let me start from the beginning, importing TensorFlow and generating the random data before running 03:56.620 --> 03:57.220 this cell. 03:57.260 --> 03:58.500 And here we go. 03:58.540 --> 04:02.460 We have this generated data size. 04:02.500 --> 04:08.860 They see the title of the first column and the price the title of the second column. 04:09.060 --> 04:14.740 And we get the first ten results using data dot head. 04:14.780 --> 04:19.900 If you want to get all the data, we use data directly. 04:19.900 --> 04:20.980 And here we go. 04:21.020 --> 04:24.540 We have 100 rows with two columns. 04:24.780 --> 04:28.300 Okay guys this is how we set. 04:28.340 --> 04:37.420 And uh, we, we control the generated data and how we display the them into a data frame. 04:37.740 --> 04:38.220 Okay. 04:38.260 --> 04:45.100 So here we converted numpy array into pandas data frame okay. 04:45.380 --> 04:47.340 So this is our data. 04:47.380 --> 04:49.700 Now we can work with it.