1 00:00:00,350 --> 00:00:07,020 No that's how you have a basic understanding of pandas and you know how to use Jupiter notebooks. 2 00:00:07,020 --> 00:00:15,850 We can go ahead and learn how to load various kinds of files in python using pandas and Jupiter so I've 3 00:00:15,850 --> 00:00:18,730 got five files here. 4 00:00:18,740 --> 00:00:22,550 They contain exactly the same datasets. 5 00:00:22,610 --> 00:00:30,190 This is a text version or let me open the Excel version that will show a pretty overview of the data 6 00:00:30,490 --> 00:00:31,250 as you can see. 7 00:00:31,300 --> 00:00:38,980 So we've got seven lines of code including the header and we have also seven columns. 8 00:00:39,610 --> 00:00:44,010 So it's just some basic data of supermarkets. 9 00:00:44,440 --> 00:00:52,400 So the address, city, state, country, and the name of the supermarket and number of employees. 10 00:00:53,500 --> 00:00:58,180 And similarly we have exactly some of the same data but in different formats. 11 00:00:58,180 --> 00:01:02,940 So we have csv-s, and csv-s means comma separated values. 12 00:01:03,070 --> 00:01:11,140 So it's basically a text file where the values the columns are separated by commas as you can see here. 13 00:01:11,140 --> 00:01:18,740 So every column is separated by a coma, but it has a csv extension and it can be open with Excel. 14 00:01:18,760 --> 00:01:24,340 So if I open this now you'll see the same data set that you saw in the Excel file, we also have the 15 00:01:24,340 --> 00:01:32,140 same data separated by same line columns as you can see in here. 16 00:01:32,360 --> 00:01:32,670 And yeah. 17 00:01:33,640 --> 00:01:38,500 If you're working with data you're probably familiar with these kinds of files. 18 00:01:38,500 --> 00:01:45,820 So this is how to store data you need to have some conventions and using such convention then you 19 00:01:45,820 --> 00:01:49,760 use other programs such as Python to load these data. 20 00:01:50,050 --> 00:01:58,660 So when you load a csv file Python knows that the values will be separated by commas and it knows 21 00:01:58,930 --> 00:02:00,400 how to separate them. 22 00:02:00,400 --> 00:02:03,730 It knows how to extract values. 23 00:02:03,880 --> 00:02:06,290 So we'll open all these one by one. 24 00:02:06,340 --> 00:02:07,810 We also have a Json File 25 00:02:11,710 --> 00:02:18,340 which is yet another format to store data and looks like a Python dictionary actually. 26 00:02:18,370 --> 00:02:22,670 So we'll learn how to convert them to a pandas data frame as well. 27 00:02:22,720 --> 00:02:26,320 So all of these will be converted to pandas data frames. 28 00:02:26,400 --> 00:02:32,380 Yeah I'll go ahead and start Jupiter. Jupiter notebook. 29 00:02:32,920 --> 00:02:34,350 Here are my files. 30 00:02:34,390 --> 00:02:38,690 I'll go ahead and create a new Jupiter notebook for Python 3. 31 00:02:38,980 --> 00:02:45,910 Before I go ahead and load those files in Python there's a trick I do usually. I import os and then 32 00:02:45,970 --> 00:02:53,260 os.listdir and Alt Enter, execute that, you go to the next line and what you get is 33 00:02:53,500 --> 00:02:59,100 you get a list of files and folders as well, or file names that you have in the current directory. 34 00:02:59,650 --> 00:03:03,260 So no I don't have to switch to my folder to look at the names. 35 00:03:03,280 --> 00:03:04,780 I have everything in here. 36 00:03:04,810 --> 00:03:06,610 Now I can go ahead and import Pandas 37 00:03:09,280 --> 00:03:11,900 and let's start loading these files one by one. 38 00:03:12,340 --> 00:03:13,630 Let's say df1. 39 00:03:13,660 --> 00:03:20,450 So data frame one and that will be equal to pandas.read_csv. 40 00:03:23,280 --> 00:03:28,650 And then you have to pass the name of the file that you want to open supermarkets.csv. 41 00:03:28,690 --> 00:03:34,990 And just enter and maybe you want to print that out so df 42 00:03:35,020 --> 00:03:36,420 there. 43 00:03:36,750 --> 00:03:36,910 Yeah. 44 00:03:36,910 --> 00:03:43,270 What we got here is so we loaded the data frame first and then we printed that out so that we got this 45 00:03:43,570 --> 00:03:45,520 table nice table in there. 46 00:03:45,640 --> 00:03:48,930 That's how easy it is to load data from a csv file. 47 00:03:49,040 --> 00:03:55,120 Now the benefit of having the data in Python is that once you load them in Python you can do many many 48 00:03:55,120 --> 00:03:57,170 operations with your data. 49 00:03:57,220 --> 00:04:01,540 You can do statistics and add new columns you can. 50 00:04:01,540 --> 00:04:07,630 You can merge columns you can add the numbers of one column to the numbers of the other columns and 51 00:04:07,620 --> 00:04:13,530 at the ends you can export those data back to formats like csv, Excel, etc. 52 00:04:13,600 --> 00:04:19,630 So we're going to do that later but first let's see how we load data from several file formats in the 53 00:04:19,630 --> 00:04:20,130 next videos.