WEBVTT 00:00.120 --> 00:01.040 Welcome back. 00:01.080 --> 00:04.520 We learned about Pandas and Matplot library. 00:04.560 --> 00:06.960 Now let's do a mini project. 00:06.960 --> 00:08.800 Analyze a real data set. 00:09.000 --> 00:14.840 Let's load and analyze a real CSV file from online resource. 00:14.840 --> 00:18.640 By the way, you can upload any Excel sheet, any spreadsheet. 00:18.800 --> 00:27.160 Here to the files and link it, but I prefer using online things online URLs. 00:27.200 --> 00:32.320 Here we're going to load data from URL, explore it and visualize the results. 00:32.360 --> 00:36.440 Go to Google type demo CSV file online. 00:36.600 --> 00:42.480 There are a lot of websites that gives you free CSV files like this website. 00:42.520 --> 00:45.440 Don't click on it because it will download it. 00:45.640 --> 00:50.520 We need to copy the link, so copy link from here. 00:50.720 --> 00:55.680 Go to this cell and paste the name. 00:55.720 --> 01:02.210 Also you can check other sample files, but I prefer this GitHub. 01:02.330 --> 01:05.930 In order to do that, we need an online data set. 01:05.930 --> 01:13.570 So data URL equals this string DF data frame equals to PD Panda. 01:13.770 --> 01:19.090 The pandas a powerful data analysis and manipulation library for Python. 01:19.290 --> 01:21.970 As we talked about it in the previous videos. 01:22.090 --> 01:23.330 Dot read key. 01:24.370 --> 01:29.050 So here we are reading a CSV from a data URL. 01:29.490 --> 01:32.890 Then we need to use this to display it. 01:33.090 --> 01:35.170 Run the cell and here we go. 01:35.410 --> 01:41.730 We have John Doe and other, but there are no column names. 01:41.730 --> 01:46.810 Because this file doesn't contain any column name. 01:46.850 --> 01:48.170 Go to GitHub. 01:48.170 --> 01:50.410 Search for sample CSV files. 01:50.410 --> 01:54.050 This is good repository github.com slash data. 01:55.210 --> 01:56.810 Sample CSV files. 01:56.810 --> 01:58.570 This is a good one. 01:58.730 --> 02:04.570 You can get the description for the files. 02:04.570 --> 02:12.890 For example index country email I need to get CSV files with numeric values in order to calculate the 02:12.890 --> 02:16.050 mean and do other jobs on it. 02:16.050 --> 02:18.610 So here we have people schema. 02:18.610 --> 02:23.130 We have organizations products and others okay. 02:23.410 --> 02:27.890 So you can select products copy link from here. 02:27.890 --> 02:30.730 Also you can download it by clicking on it. 02:30.770 --> 02:32.650 Open it with Excel. 02:32.650 --> 02:33.690 And here we go. 02:33.970 --> 02:42.850 We have index name description brand category, price currency stock and others okay we need to calculate 02:42.850 --> 02:44.450 the average price. 02:44.450 --> 02:50.970 So let me copy this link address and paste it here. 02:51.170 --> 02:56.130 Then run this cell and the data is being visualized okay. 02:56.290 --> 03:01.210 So this is how to load data from online CSV file. 03:01.250 --> 03:09.820 Again guys you can upload your CSV files here and copy the link to to to to directly visualize them. 03:09.860 --> 03:16.140 Now let's visualize the data with our Matplot library. 03:16.380 --> 03:21.700 Here we have many columns we need to get the numeric ones. 03:21.740 --> 03:28.660 I love using the price and also you can use the stack and get the index. 03:28.700 --> 03:31.860 Okay so you can use the stack. 03:32.020 --> 03:35.700 Let's start with print average stack. 03:35.900 --> 03:38.620 Print df dot mean. 03:38.780 --> 03:43.380 And we need to use the numeric values only. 03:43.420 --> 03:48.540 Actually not visualizing the data like statistics run. 03:48.540 --> 03:49.740 And here we go. 03:49.780 --> 03:56.860 We have the index the price the stock internal ID and data type. 03:56.860 --> 03:59.740 So those are the statistics of the data. 03:59.780 --> 04:08.030 You can get the average for the numeric values index because index is an integer, you don't need this. 04:08.070 --> 04:10.630 The stock, the price. 04:10.630 --> 04:12.430 Those are very useful. 04:12.510 --> 04:15.630 Now let's visualize the data. 04:15.670 --> 04:19.350 Let's let's plot the stocking price. 04:19.590 --> 04:24.870 So here we have plt dot figure figure size. 04:24.870 --> 04:27.950 We need to get the figure size ten by six. 04:27.990 --> 04:30.510 It's good plt dot plot. 04:30.550 --> 04:38.310 Here we need to define the values and the the numeric things that we need to display. 04:38.310 --> 04:42.950 So def and what things we need to display. 04:42.950 --> 04:48.350 We need to display the stock price okay. 04:48.470 --> 04:54.070 Marker equals to oh color blue line width equals to two. 04:54.230 --> 04:59.150 The title would be stock price Y label is price. 04:59.470 --> 05:04.630 The plot dot grid equals to true and plot dot show. 05:04.790 --> 05:07.110 We need to call this function. 05:07.110 --> 05:09.190 Let's run and here we go. 05:09.430 --> 05:11.350 This is the stock price. 05:11.350 --> 05:19.590 You can notice that the we have 100 company and the stock price is from 0 to 1000. 05:19.630 --> 05:27.630 You see that for example the company number 20 the stock price is below 200. 05:27.670 --> 05:33.990 If we go up to the to here we need to get all the tabular format. 05:33.990 --> 05:36.430 And the company number 20. 05:36.670 --> 05:42.990 The stock price is one six $3 $163 okay. 05:43.030 --> 05:45.590 So it's below 200. 05:45.750 --> 05:48.670 So this data visualization is real. 05:48.870 --> 05:58.270 And it's very very helpful for statistical things for data analysis, for machine learning and others. 05:58.270 --> 06:02.590 So we're going to use those libraries in the next videos.