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Would you like to inspect the original subtitles? These are the user uploaded subtitles that are being translated: 1 00:00:11,650 --> 00:00:13,780 Hi, everyone, and welcome in this new video. 2 00:00:14,380 --> 00:00:21,700 In this video, I will show you quickly how to import the data, so will not take many time to do it 3 00:00:21,700 --> 00:00:27,070 because we have already done this many times in the previous chapter. 4 00:00:27,400 --> 00:00:33,160 But I just want to highlight a very important points in this chapter. 5 00:00:33,700 --> 00:00:44,200 So first, we need to install white finance, marketing finance to plot the candlestick and the tea 6 00:00:44,200 --> 00:00:45,340 a library. 7 00:00:45,640 --> 00:00:50,500 The TR library, which will allow us to create technical indicators and. 8 00:00:51,900 --> 00:01:00,960 It will be very useful to read, for example, the RSA easily to we in stealth, then then we import 9 00:01:01,110 --> 00:01:02,160 some libraries. 10 00:01:02,250 --> 00:01:07,890 So the installed libraries and then pipe and in secure some. 11 00:01:09,090 --> 00:01:10,860 Very known Labor is. 12 00:01:13,070 --> 00:01:21,470 Then we are playing disco to have the growth in the document, and then we just need to import some 13 00:01:21,470 --> 00:01:23,960 data using Yahoo Finance. 14 00:01:24,470 --> 00:01:27,650 Then we can rename the column. 15 00:01:29,340 --> 00:01:36,540 And the only new thing here is the creation of a new column name dates. 16 00:01:38,020 --> 00:01:45,310 This column will be very important to pluck the candlestick and the only. 17 00:01:46,960 --> 00:01:51,850 Utility of this column is to plot the candlestick. 18 00:01:51,880 --> 00:01:59,920 So if you don't want to split them, you don't need to have to create this column, so let me show you. 19 00:02:01,160 --> 00:02:04,130 What we will have with this column, so. 20 00:02:06,000 --> 00:02:11,250 Using this tube line of good, we have. 21 00:02:12,840 --> 00:02:22,350 Treat the former for the dates, we're create a new format because initially we have the former year, 22 00:02:23,070 --> 00:02:34,770 then month, then Dean, OK, and here we just want the dates as a number without any date formal. 23 00:02:35,430 --> 00:02:41,280 It's because my sleep demand this type of dates. 24 00:02:41,700 --> 00:02:42,090 So. 25 00:02:44,020 --> 00:02:45,730 Here we just create. 26 00:02:47,550 --> 00:02:49,320 A column with. 27 00:02:50,550 --> 00:02:51,330 I will show you. 28 00:02:54,780 --> 00:02:58,740 A column which is equal to the index. 29 00:02:59,400 --> 00:03:07,290 So at this step, we don't have trade anything and here we are playing the. 30 00:03:09,670 --> 00:03:13,070 Matt liquidates point, they too. 31 00:03:13,700 --> 00:03:21,160 No function to attribute data to have to date in a number. 32 00:03:21,610 --> 00:03:25,150 So it's a little bit technique, but. 33 00:03:26,190 --> 00:03:27,330 In reality. 34 00:03:29,520 --> 00:03:30,600 It's very simple. 35 00:03:30,710 --> 00:03:31,080 OK. 36 00:03:31,650 --> 00:03:40,770 It's exactly the same dates, one as a date former and another as a no and multiple athlete demand the 37 00:03:40,770 --> 00:03:42,120 dates as a number. 38 00:03:42,300 --> 00:03:44,810 So we need to create dates. 39 00:03:45,300 --> 00:03:47,310 No, that's it. 40 00:03:47,640 --> 00:03:51,300 And then we just rename the column, etc.. 41 00:03:51,390 --> 00:03:54,660 Exactly as in the previous chapter. 3563

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