1 00:00:00,030 --> 00:00:04,110 this is Torah I'm going to show you how 2 00:00:01,770 --> 00:00:06,869 to import data and analyze it with a 3 00:00:04,110 --> 00:00:09,420 Python and using the pandas package so 4 00:00:06,870 --> 00:00:13,049 let's say we have some data like you 5 00:00:09,420 --> 00:00:15,330 know something like time here in column 6 00:00:13,049 --> 00:00:19,500 and then we might have some sensor data 7 00:00:15,330 --> 00:00:21,930 that we've read in and in this case I'm 8 00:00:19,500 --> 00:00:25,230 going to have eight sensors okay so it's 9 00:00:21,930 --> 00:00:27,869 kind of like a table of sensors I want 10 00:00:25,230 --> 00:00:32,640 to import these and then maybe take the 11 00:00:27,869 --> 00:00:34,649 average of the sensors at least any good 12 00:00:32,640 --> 00:00:38,399 one so I might have some bad data in 13 00:00:34,649 --> 00:00:41,850 here as well and take the average and 14 00:00:38,399 --> 00:00:44,520 then export this okay so after it's 15 00:00:41,850 --> 00:00:48,989 imported you know just this raw data 16 00:00:44,520 --> 00:00:53,100 take the average and then export the 17 00:00:48,989 --> 00:00:56,280 combined table into another data file or 18 00:00:53,100 --> 00:00:58,170 back into something like excel ok so 19 00:00:56,280 --> 00:01:01,949 this is going to be the exercise let me 20 00:00:58,170 --> 00:01:04,619 just show you a couple things from the 21 00:01:01,949 --> 00:01:09,810 course website so if you go to AP 22 00:01:04,619 --> 00:01:14,250 monitor comm and then go to CH e 263 and 23 00:01:09,810 --> 00:01:17,159 then on the on the right hand side we 24 00:01:14,250 --> 00:01:20,100 have the Excel and VBA so we already did 25 00:01:17,159 --> 00:01:23,509 this example in Excel so we imported the 26 00:01:20,100 --> 00:01:26,580 data and then and then took this average 27 00:01:23,509 --> 00:01:28,619 and you can do that quickly in Excel 28 00:01:26,580 --> 00:01:32,009 sometimes when you're dealing with very 29 00:01:28,619 --> 00:01:34,950 large datasets though or you want to do 30 00:01:32,009 --> 00:01:38,040 this programmatically versus in an Excel 31 00:01:34,950 --> 00:01:40,500 we use something like Python ok and so 32 00:01:38,040 --> 00:01:42,869 I'm going to come here to the Python 33 00:01:40,500 --> 00:01:47,280 side and you can select data analysis 34 00:01:42,869 --> 00:01:49,560 here I did a former tutorial on using 35 00:01:47,280 --> 00:01:52,049 doing the same example but with just 36 00:01:49,560 --> 00:01:54,960 numpy ok without the extra pandas 37 00:01:52,049 --> 00:01:58,979 package and so for this one we're going 38 00:01:54,960 --> 00:02:01,250 to do it with pandas so to get the data 39 00:01:58,979 --> 00:02:04,740 file for this go ahead and just select 40 00:02:01,250 --> 00:02:08,639 this source code it'll download a zip 41 00:02:04,740 --> 00:02:09,369 file ok and then go grab that from your 42 00:02:08,639 --> 00:02:12,640 download 43 00:02:09,369 --> 00:02:14,980 the river is saved - I'm just going to 44 00:02:12,640 --> 00:02:17,798 copy that onto my desktop and then 45 00:02:14,980 --> 00:02:21,030 right-click it and extract all 46 00:02:17,799 --> 00:02:25,390 okay so I'll extract this folder and 47 00:02:21,030 --> 00:02:26,860 here I have an example script here's the 48 00:02:25,390 --> 00:02:28,750 solution right here that we're going to 49 00:02:26,860 --> 00:02:31,720 go through but more importantly we just 50 00:02:28,750 --> 00:02:35,530 have these data files okay so here it is 51 00:02:31,720 --> 00:02:38,709 in Excel and if we just open it up click 52 00:02:35,530 --> 00:02:41,230 enable editing and then I see that I 53 00:02:38,709 --> 00:02:43,209 have you know time and then my eight 54 00:02:41,230 --> 00:02:47,349 sensors one of the things that you'll 55 00:02:43,209 --> 00:02:50,340 see is that sensors five six and five 56 00:02:47,349 --> 00:02:53,470 through eight those are not a number 57 00:02:50,340 --> 00:02:55,870 okay as you can see those were bad 58 00:02:53,470 --> 00:02:59,590 sensors and so I want to just take an 59 00:02:55,870 --> 00:03:03,069 average of these four okay you also see 60 00:02:59,590 --> 00:03:04,599 that time does not start at zero okay so 61 00:03:03,069 --> 00:03:07,569 we're going to need to time shift that 62 00:03:04,599 --> 00:03:10,179 as well I'm going to go ahead and close 63 00:03:07,569 --> 00:03:12,458 this I also got the data in just a data 64 00:03:10,180 --> 00:03:14,049 file and that's just a text file without 65 00:03:12,459 --> 00:03:18,250 the headers 66 00:03:14,049 --> 00:03:22,180 I find that okay so no headers there but 67 00:03:18,250 --> 00:03:23,709 you can see the data in each row okay 68 00:03:22,180 --> 00:03:27,010 I'm going to go ahead and close that or 69 00:03:23,709 --> 00:03:29,650 you have a CSV file and if I right click 70 00:03:27,010 --> 00:03:31,599 this one and unless they just edit with 71 00:03:29,650 --> 00:03:35,260 a notepad you can see this is just a 72 00:03:31,599 --> 00:03:38,709 text file as well okay but it has the 73 00:03:35,260 --> 00:03:42,519 time sensor 1 through sensor 8 and then 74 00:03:38,709 --> 00:03:45,010 each row is a separate data point for 75 00:03:42,519 --> 00:03:46,959 each of those sensors okay so I'm going 76 00:03:45,010 --> 00:03:49,328 to actually work with this data with 77 00:03:46,959 --> 00:03:53,500 headers because pandas does a really 78 00:03:49,329 --> 00:03:56,730 good job with headers okay so let's just 79 00:03:53,500 --> 00:04:02,139 go ahead and open up a new ipython 80 00:03:56,730 --> 00:04:05,888 notebook ok when that opens and it'll 81 00:04:02,139 --> 00:04:07,720 come up with okay here and I'm going to 82 00:04:05,889 --> 00:04:10,660 go to my desktop I'm just going to go to 83 00:04:07,720 --> 00:04:13,599 that same folder that I just extracted 84 00:04:10,660 --> 00:04:16,449 because then I can access the the data 85 00:04:13,599 --> 00:04:18,250 file okay and so I'm going to select 86 00:04:16,449 --> 00:04:21,450 over on the right-hand side I'll then 87 00:04:18,250 --> 00:04:25,710 select new and then a new 88 00:04:21,450 --> 00:04:28,020 book here and okay so this is just a 89 00:04:25,710 --> 00:04:30,599 blank notebook I can rename it by 90 00:04:28,020 --> 00:04:38,250 clicking the untitled okay so I'll just 91 00:04:30,600 --> 00:04:39,630 call this import data and analyze okay 92 00:04:38,250 --> 00:04:40,230 so I'm going to import the data and 93 00:04:39,630 --> 00:04:42,960 analyze it 94 00:04:40,230 --> 00:04:44,490 let's go just import some of the 95 00:04:42,960 --> 00:04:47,210 packages that we're going to use for 96 00:04:44,490 --> 00:04:52,110 this so first of all I'll just import 97 00:04:47,210 --> 00:04:56,280 numpy as NP and I'll also do the same 98 00:04:52,110 --> 00:05:00,290 with pandas a lot of people do PD for 99 00:04:56,280 --> 00:05:04,799 pandas and then later I'm going to also 100 00:05:00,290 --> 00:05:06,980 import matplotlib and pipe lot but let's 101 00:05:04,800 --> 00:05:10,770 just go ahead and start with these two 102 00:05:06,980 --> 00:05:12,480 and so it's going to import those two 103 00:05:10,770 --> 00:05:16,979 and then I'm going to go ahead and 104 00:05:12,480 --> 00:05:19,800 insert a cell below okay and then what I 105 00:05:16,980 --> 00:05:24,890 want to do is now load I'll load the 106 00:05:19,800 --> 00:05:27,960 data file okay and so I'll just say data 107 00:05:24,890 --> 00:05:32,190 file these are going to be my results 108 00:05:27,960 --> 00:05:37,620 that's going to be equal to PD and then 109 00:05:32,190 --> 00:05:42,330 I'll do dot read CSV and I'll do my data 110 00:05:37,620 --> 00:05:47,670 with headers dot CSV okay I could also 111 00:05:42,330 --> 00:05:51,719 do read excel here and then do data with 112 00:05:47,670 --> 00:05:54,870 headers Excel SX okay so that is the 113 00:05:51,720 --> 00:05:59,550 excel file but let me go ahead and just 114 00:05:54,870 --> 00:06:03,600 do it with the CSV for now okay so I'm 115 00:05:59,550 --> 00:06:05,880 going to go ahead and import that okay 116 00:06:03,600 --> 00:06:10,560 and then I'll go ahead and insert a cell 117 00:06:05,880 --> 00:06:14,300 below okay so now I've got my data file 118 00:06:10,560 --> 00:06:16,920 and let me just go ahead and you know 119 00:06:14,300 --> 00:06:19,860 we're going to print a little bit of 120 00:06:16,920 --> 00:06:22,500 this data file just so we can see what's 121 00:06:19,860 --> 00:06:25,860 going on with this okay so I have print 122 00:06:22,500 --> 00:06:31,980 data file and let's say I just want to 123 00:06:25,860 --> 00:06:33,310 print the first just the first few rows 124 00:06:31,980 --> 00:06:37,000 of that 125 00:06:33,310 --> 00:06:39,220 okay so I'm going to go from zero to 126 00:06:37,000 --> 00:06:41,860 three that's going to be zero one and 127 00:06:39,220 --> 00:06:44,650 two and then let's just go and take a 128 00:06:41,860 --> 00:06:49,000 look at what it imported okay so I have 129 00:06:44,650 --> 00:06:51,609 my time column my s1 s2 s3 all the way 130 00:06:49,000 --> 00:06:54,580 to s8 you can see the not on numbers 131 00:06:51,610 --> 00:06:56,889 there for some of those okay now I want 132 00:06:54,580 --> 00:06:58,930 to parse this out maybe make it a little 133 00:06:56,889 --> 00:07:01,330 bit easier for me to work with it 134 00:06:58,930 --> 00:07:05,530 I want to exclude the sensors that were 135 00:07:01,330 --> 00:07:09,280 bad for example okay so let me go ahead 136 00:07:05,530 --> 00:07:11,919 and just grab time first of all I'll say 137 00:07:09,280 --> 00:07:13,989 that is a data file you know from my 138 00:07:11,919 --> 00:07:17,620 data file and then I can use the square 139 00:07:13,990 --> 00:07:20,140 brackets and just reference it by the 140 00:07:17,620 --> 00:07:26,530 column name from the header okay and so 141 00:07:20,140 --> 00:07:30,880 that will give me time and let's do 142 00:07:26,530 --> 00:07:34,450 another one now which is good parse out 143 00:07:30,880 --> 00:07:41,350 the sensors that are good okay so do 144 00:07:34,450 --> 00:07:45,310 sensors equals and then data file okay 145 00:07:41,350 --> 00:07:48,490 and I'm going to use the i-x and that is 146 00:07:45,310 --> 00:07:50,410 going to do go from all say all the rows 147 00:07:48,490 --> 00:07:56,919 and then I'll say what columns I want 148 00:07:50,410 --> 00:07:58,300 and I'll go from s1 to s4 okay and then 149 00:07:56,919 --> 00:07:59,919 let me go ahead and just print it just 150 00:07:58,300 --> 00:08:04,479 so I can see what this looks like 151 00:07:59,919 --> 00:08:10,299 I'll go ahead and print sensors and I'll 152 00:08:04,479 --> 00:08:13,590 go just the first six rows of that okay 153 00:08:10,300 --> 00:08:18,000 so it's going to be sensor zero to six 154 00:08:13,590 --> 00:08:20,590 okay so I can see some of my sensors 155 00:08:18,000 --> 00:08:22,690 that I just pronounced I I have new 156 00:08:20,590 --> 00:08:25,060 variable called sensors 157 00:08:22,690 --> 00:08:28,690 that's this table of just the sensor 158 00:08:25,060 --> 00:08:31,270 values and now what I want to do is I 159 00:08:28,690 --> 00:08:32,890 notice time does not start at zero maybe 160 00:08:31,270 --> 00:08:35,890 for the purposes of plotting let me go 161 00:08:32,890 --> 00:08:40,569 ahead and just subtract off this very 162 00:08:35,890 --> 00:08:43,169 first time value right here from all of 163 00:08:40,570 --> 00:08:46,800 the time 164 00:08:43,169 --> 00:08:49,589 a vector from all the time vectors okay 165 00:08:46,800 --> 00:08:56,550 so actually let me come down here and 166 00:08:49,589 --> 00:09:04,070 insert okay so I'll go ahead and delete 167 00:08:56,550 --> 00:09:08,040 that one and oh there it is the delete 168 00:09:04,070 --> 00:09:10,110 okay so now what I'm going to do is go 169 00:09:08,040 --> 00:09:14,339 ahead and just subtract off just that 170 00:09:10,110 --> 00:09:19,350 very first time okay so it's going to be 171 00:09:14,339 --> 00:09:21,779 equal time equals time - and then we're 172 00:09:19,350 --> 00:09:26,100 going to do time and just the very first 173 00:09:21,779 --> 00:09:31,910 value in that okay and then let's go to 174 00:09:26,100 --> 00:09:35,670 print time and we'll go zero to three 175 00:09:31,910 --> 00:09:37,920 okay and okay so there are my times 176 00:09:35,670 --> 00:09:40,740 right there's start zero and then goes 177 00:09:37,920 --> 00:09:42,329 to about one and then three so I don't 178 00:09:40,740 --> 00:09:45,360 start with some large value of 179 00:09:42,329 --> 00:09:49,739 subtracted off the initial starting 180 00:09:45,360 --> 00:09:52,380 value on that okay okay so let's go to 181 00:09:49,740 --> 00:09:56,040 the next line here and let's go and take 182 00:09:52,380 --> 00:09:59,790 our average of these so I want maybe I 183 00:09:56,040 --> 00:10:01,230 have three temperature sensors at 184 00:09:59,790 --> 00:10:03,209 different points in a reactor for 185 00:10:01,230 --> 00:10:06,959 example I just want to take an average 186 00:10:03,209 --> 00:10:11,160 of those at each time okay so I could do 187 00:10:06,959 --> 00:10:15,000 that if I do average and this will be 188 00:10:11,160 --> 00:10:18,300 the average of the row okay 189 00:10:15,000 --> 00:10:20,820 and that's going to be numpy and I'm 190 00:10:18,300 --> 00:10:23,099 going to use the mean and then I'm going 191 00:10:20,820 --> 00:10:26,819 to take the average of my sensors and 192 00:10:23,100 --> 00:10:30,660 then the next the next argument for this 193 00:10:26,819 --> 00:10:33,149 is over what index do I want to do this 194 00:10:30,660 --> 00:10:35,699 so if I do one that's going to be over 195 00:10:33,149 --> 00:10:41,310 the column so it'll take the average of 196 00:10:35,699 --> 00:10:44,790 the rows if I do average with the zero 197 00:10:41,310 --> 00:10:48,959 there instead of the one then it'll take 198 00:10:44,790 --> 00:10:50,670 the average of all of each column along 199 00:10:48,959 --> 00:10:55,300 the way okay so let me go ahead and just 200 00:10:50,670 --> 00:10:57,010 print and I'll do average 201 00:10:55,300 --> 00:11:01,270 I'll just print the first couple rows of 202 00:10:57,010 --> 00:11:05,910 that average row and I'll go one two 203 00:11:01,270 --> 00:11:09,430 three okay and then I'll also print 204 00:11:05,910 --> 00:11:12,160 average on my column and I don't think I 205 00:11:09,430 --> 00:11:17,709 need to connect that I'll just be for 206 00:11:12,160 --> 00:11:23,319 different values okay so there is I had 207 00:11:17,710 --> 00:11:25,840 for my average for s1 is 380 to s2 s3 s4 208 00:11:23,320 --> 00:11:29,170 so that's the column average and this is 209 00:11:25,840 --> 00:11:30,520 the row average that I just printed out 210 00:11:29,170 --> 00:11:33,219 okay so I'm more interested in the 211 00:11:30,520 --> 00:11:38,199 average row instead of the average 212 00:11:33,220 --> 00:11:41,470 column just for this example okay let me 213 00:11:38,200 --> 00:11:43,090 go ahead and insert another one okay so 214 00:11:41,470 --> 00:11:45,250 now what I want to do is now I want to 215 00:11:43,090 --> 00:11:48,850 put all of this information back 216 00:11:45,250 --> 00:11:52,210 together again like I showed here where 217 00:11:48,850 --> 00:11:54,250 you have you know this this average is 218 00:11:52,210 --> 00:11:56,320 on the right now I've cleaned up I've 219 00:11:54,250 --> 00:11:58,330 removed some of the not a number values 220 00:11:56,320 --> 00:12:02,170 I just want to put this back and then 221 00:11:58,330 --> 00:12:05,740 export it again okay so the way to do 222 00:12:02,170 --> 00:12:09,219 that is just go and create something 223 00:12:05,740 --> 00:12:11,470 like my data and then I'm going to 224 00:12:09,220 --> 00:12:14,230 create a new array just with the 225 00:12:11,470 --> 00:12:17,230 different values that I want to put into 226 00:12:14,230 --> 00:12:23,740 my final data file okay so I've got 227 00:12:17,230 --> 00:12:25,240 average row sensors and time okay and 228 00:12:23,740 --> 00:12:27,670 then what I'm going to do is I'm going 229 00:12:25,240 --> 00:12:31,510 to concatenate these I'm going to put 230 00:12:27,670 --> 00:12:35,469 them together so result equals and then 231 00:12:31,510 --> 00:12:37,900 I'll do PD dot kind of cat and that will 232 00:12:35,470 --> 00:12:41,830 put them together and I'll do my data 233 00:12:37,900 --> 00:12:45,340 and I want to do it along the second 234 00:12:41,830 --> 00:12:48,280 axis or axis equals 1 which will be take 235 00:12:45,340 --> 00:12:52,960 these columns column data and just put 236 00:12:48,280 --> 00:12:56,800 them left to right okay if I did if I 237 00:12:52,960 --> 00:12:58,240 did 0 then be like stacking rows on top 238 00:12:56,800 --> 00:13:01,620 of each other but these are stacking 239 00:12:58,240 --> 00:13:03,970 columns next to each other okay and then 240 00:13:01,620 --> 00:13:05,499 when I do that let me go ahead and just 241 00:13:03,970 --> 00:13:08,599 print 242 00:13:05,499 --> 00:13:13,819 result and I'll just print the first 243 00:13:08,599 --> 00:13:17,479 couple of rows on that okay and then you 244 00:13:13,819 --> 00:13:19,218 can see I have my time s1 through s4 and 245 00:13:17,479 --> 00:13:22,129 then my average which I haven't given it 246 00:13:19,219 --> 00:13:26,779 a name for that column it was just a 247 00:13:22,129 --> 00:13:29,779 numpy array okay now the next thing that 248 00:13:26,779 --> 00:13:31,219 I'll do is go ahead and just save it as 249 00:13:29,779 --> 00:13:34,729 something so now that I have my data 250 00:13:31,219 --> 00:13:39,949 file that I want to export I can do 251 00:13:34,729 --> 00:13:42,019 result and then dot two for example CSV 252 00:13:39,949 --> 00:13:46,699 file okay so like where it came from 253 00:13:42,019 --> 00:13:51,889 I'll just do result dot CSV I can also 254 00:13:46,699 --> 00:13:56,389 do something like instead of to CSV I 255 00:13:51,889 --> 00:14:00,679 can export it to an excel file okay so 256 00:13:56,389 --> 00:14:05,269 that's x LS X okay so I can export it to 257 00:14:00,679 --> 00:14:10,069 excel I can also do it to an HTML file 258 00:14:05,269 --> 00:14:13,809 as I just wanted a table that's exported 259 00:14:10,069 --> 00:14:18,139 to a web page okay so there's HTML 260 00:14:13,809 --> 00:14:22,129 actually I need to do HTM L even though 261 00:14:18,139 --> 00:14:24,769 my extension can be HTM or HTML and then 262 00:14:22,129 --> 00:14:27,619 I'm also going to export to the 263 00:14:24,769 --> 00:14:30,559 clipboard as well so when I run this 264 00:14:27,619 --> 00:14:32,779 it's going to I don't have to give this 265 00:14:30,559 --> 00:14:35,179 a file name what I'll do is we'll just 266 00:14:32,779 --> 00:14:40,189 put it out to the clipboard and then I 267 00:14:35,179 --> 00:14:42,019 can do the ctrl V to paste it somewhere 268 00:14:40,189 --> 00:14:44,059 I can paste it in like - like a word 269 00:14:42,019 --> 00:14:45,859 document or something like that you'll 270 00:14:44,059 --> 00:14:48,488 just put this data table out to a 271 00:14:45,859 --> 00:14:54,379 clipboard okay so let me go ahead and 272 00:14:48,489 --> 00:14:59,779 save go and save this and then let's go 273 00:14:54,379 --> 00:15:01,939 and run this last line right here okay 274 00:14:59,779 --> 00:15:04,489 and then it's going to export it let me 275 00:15:01,939 --> 00:15:06,919 go back into that folder so I can see a 276 00:15:04,489 --> 00:15:09,139 couple of these results right here if I 277 00:15:06,919 --> 00:15:12,589 open up the excel file that I just 278 00:15:09,139 --> 00:15:14,569 created I can see all of my data with 279 00:15:12,589 --> 00:15:16,819 the times the sensors and then the 280 00:15:14,569 --> 00:15:23,269 average value I can oh 281 00:15:16,819 --> 00:15:26,868 up a you know this is in HTML okay so 282 00:15:23,269 --> 00:15:29,239 there it is a web page and then here is 283 00:15:26,869 --> 00:15:35,149 my CSV file this is just a text file 284 00:15:29,239 --> 00:15:38,720 that's comma delimited and okay so I 285 00:15:35,149 --> 00:15:43,699 have all of my data here in this CSV 286 00:15:38,720 --> 00:15:45,559 file okay and then alright and then I'm 287 00:15:43,699 --> 00:15:48,019 going to show you clipboard as well so 288 00:15:45,559 --> 00:15:52,368 let's say I wanted to open up word for 289 00:15:48,019 --> 00:15:54,079 example and then I can open up a blank 290 00:15:52,369 --> 00:15:56,419 document it's on the clipboard right now 291 00:15:54,079 --> 00:16:00,559 so if I hit control V then it's just 292 00:15:56,419 --> 00:16:03,829 going to paste all the values in okay so 293 00:16:00,559 --> 00:16:09,219 there it was in clipboard okay so just a 294 00:16:03,829 --> 00:16:16,549 variety of different ways to export this 295 00:16:09,220 --> 00:16:20,629 now let's say we also want to maybe plot 296 00:16:16,549 --> 00:16:24,409 some of these results as well so just in 297 00:16:20,629 --> 00:16:28,959 order to use matplotlib and be able to 298 00:16:24,409 --> 00:16:32,359 show the values here I'm going to do the 299 00:16:28,959 --> 00:16:35,599 percent net plot live in line first of 300 00:16:32,359 --> 00:16:43,549 all before I import okay and then I'll 301 00:16:35,600 --> 00:16:48,519 do import mat plot lib pie plot as PLT 302 00:16:43,549 --> 00:16:53,720 and then let's go ahead and go and 303 00:16:48,519 --> 00:16:55,759 evaluate that and then I insert one more 304 00:16:53,720 --> 00:17:00,199 cell below so now I've got 305 00:16:55,759 --> 00:17:04,339 PLT and I'll just go ahead and create a 306 00:17:00,199 --> 00:17:07,459 new figure and then let me go ahead and 307 00:17:04,339 --> 00:17:12,490 just plot some of these data points I 308 00:17:07,459 --> 00:17:17,899 have time and then I'll plot sensors 309 00:17:12,490 --> 00:17:24,230 let's just plot s1 and then plot that as 310 00:17:17,898 --> 00:17:27,559 a red line okay and then let me add 311 00:17:24,230 --> 00:17:28,770 another one on here as well I'll plot 312 00:17:27,559 --> 00:17:33,149 time 313 00:17:28,770 --> 00:17:38,129 and the average rose let's see what I 314 00:17:33,150 --> 00:17:40,320 knew the average row okay so for every 315 00:17:38,130 --> 00:17:43,170 point on that plot just one sensor - 316 00:17:40,320 --> 00:17:46,850 then the average of all four sensors as 317 00:17:43,170 --> 00:17:50,750 well and I'll do that as a blue dotted 318 00:17:46,850 --> 00:17:57,840 trend and then let me go and add a 319 00:17:50,750 --> 00:18:02,340 legend to that and that will be to say 320 00:17:57,840 --> 00:18:08,730 that is going to be sensor one and then 321 00:18:02,340 --> 00:18:11,010 this one will be the average okay so 322 00:18:08,730 --> 00:18:14,310 there I've got a legend for my plot and 323 00:18:11,010 --> 00:18:17,250 we'll do an X label and a y label as 324 00:18:14,310 --> 00:18:23,370 well so this will be time maybe in 325 00:18:17,250 --> 00:18:27,620 seconds and okay 1y label and then we're 326 00:18:23,370 --> 00:18:32,760 almost done here so that'll be sensor 327 00:18:27,620 --> 00:18:36,959 values okay and then let's also just 328 00:18:32,760 --> 00:18:39,060 save this so I can view it here but I 329 00:18:36,960 --> 00:18:43,920 also maybe just want to save it as a PNG 330 00:18:39,060 --> 00:18:46,050 file let's all good export my like this 331 00:18:43,920 --> 00:18:50,220 plot that I'm creating and I'll do that 332 00:18:46,050 --> 00:18:51,600 with the save fig and I'll just say my 333 00:18:50,220 --> 00:18:56,820 plot dot PNG 334 00:18:51,600 --> 00:18:59,310 okay and I I think I am done now okay so 335 00:18:56,820 --> 00:19:02,149 let me go ahead and just execute this I 336 00:18:59,310 --> 00:19:05,340 can see the sensor one the average 337 00:19:02,150 --> 00:19:08,510 values for that and then let me go back 338 00:19:05,340 --> 00:19:14,699 into my directory where I'm working and 339 00:19:08,510 --> 00:19:16,620 it has my plot dot PNG so here it is you 340 00:19:14,700 --> 00:19:20,730 know in PNG format you can import that 341 00:19:16,620 --> 00:19:24,719 for example into a PowerPoint or other 342 00:19:20,730 --> 00:19:28,080 presentation format okay so I think 343 00:19:24,720 --> 00:19:32,100 we're done with this script now let me 344 00:19:28,080 --> 00:19:34,879 just go back here to the okay so just to 345 00:19:32,100 --> 00:19:39,990 review what we did is we just imported 346 00:19:34,880 --> 00:19:42,230 numpy and pandas we on input two we just 347 00:19:39,990 --> 00:19:44,750 read the CSV file 348 00:19:42,230 --> 00:19:47,840 just displayed some of the data the raw 349 00:19:44,750 --> 00:19:51,440 data just the first three rows assign 350 00:19:47,840 --> 00:19:53,659 this as time then just parse this we 351 00:19:51,440 --> 00:19:56,660 sliced some of the data just took 352 00:19:53,660 --> 00:20:00,350 sensors one through sensors four and 353 00:19:56,660 --> 00:20:01,670 then you reinitialize our time to start 354 00:20:00,350 --> 00:20:05,060 at zero 355 00:20:01,670 --> 00:20:08,300 we took some averages here on the row or 356 00:20:05,060 --> 00:20:10,040 column based averages and then put the 357 00:20:08,300 --> 00:20:13,730 data back together again 358 00:20:10,040 --> 00:20:17,000 with the row averages and then exported 359 00:20:13,730 --> 00:20:19,610 it to a CSV file to excel HTML and 360 00:20:17,000 --> 00:20:23,090 clipboard and then we as a final result 361 00:20:19,610 --> 00:20:25,879 we just plotted some of our data ok so 362 00:20:23,090 --> 00:20:28,610 that concludes this tutorial video if 363 00:20:25,880 --> 00:20:30,410 you're having a hard time with you know 364 00:20:28,610 --> 00:20:34,100 some of these with pandas you know don't 365 00:20:30,410 --> 00:20:36,620 or num num pie you know it just takes a 366 00:20:34,100 --> 00:20:38,870 little bit of practice and then it will 367 00:20:36,620 --> 00:20:41,689 be intuitive it'll actually probably be 368 00:20:38,870 --> 00:20:44,120 faster than something like Excel if you 369 00:20:41,690 --> 00:20:46,280 learn some of these commands there's a 370 00:20:44,120 --> 00:20:48,439 lot of other commands in pandas I've 371 00:20:46,280 --> 00:20:51,620 just kind of given you a very very basic 372 00:20:48,440 --> 00:20:54,470 introduction it's used extensively for 373 00:20:51,620 --> 00:20:57,830 example in the financial analysis sector 374 00:20:54,470 --> 00:21:01,750 or others but engineers and scientists 375 00:20:57,830 --> 00:21:01,750 also use it as well