Künstliche Intelligenz und Maschinelles Lernen in der Praxis

1. 00:00In this unity we now want to see how programming libraries work,
2. 00:04having just looked at each other, how the foundations of Python are constructed.
3. 00:09programming libraries are Now something very, very exciting.
4. 00:13And that's what you can imagine. because we are now Python code for example
5. 00:18, we can write this It's easy to spread.
6. 00:21Not only we can, but we can also completely different developers.
7. 00:25And the great thing is that, through this Dissemination finally we code also from
8. 00:30other programmers.
9. 00:32For example, if the The challenges and the solution
10. 00:38and then developed it publicly we can make this code available to us
11. 00:42look and research and then use it yourself if necessary.
12. 00:46Now, there are programming libraries that are in the end, a hodgepodge of code
13. 00:52, not just maybe from one person, but perhaps from several developers
14. 00:56are well known and are well used, we would like to use our own
15. 01:02to solve challenges.
16. 01:03And finally, you can imagine that programming libraries
17. 01:08are, after all, something like tools.
18. 01:10That is, this function, which we can just reuse.
19. 01:13And now there are tools. For example, the
20. 01:17to run data science.
21. 01:19Let us look at an example see what a library like that is.
22. 01:24how it is built, how to use it and then towards the end of the
23. 01:30Let's take a very quick look at what unity is. Librarize it like this in the Data section
24. 01:34Science. For this example, we will We're going to look at the Pandas Library.
25. 01:41suitable for this purpose, for example: and then process data.
26. 01:48And what we take for that is for example, the following CSV file.
27. 01:52So CSV is just a data format. that often looks like you can
28. 01:58first row comma-separates the column , then simply the values,
29. 02:03, then again comma-separated.
30. 02:04So a file that you can easily into an Excel workbook.
31. 02:09And now we don't want an Excel folder , but write a Python program,
32. 02:13what this file reads and then maybe processed.
33. 02:17And exactly for this purpose there are There are, for example, pandas.
34. 02:22So this library of pandas can that is simply the name of this
35. 02:26Library, so like you would call a hammer,
36. 02:30can be used to Library called pandas.
37. 02:32This is what the developers have We decided that it should be.
38. 02:36And after now, pandas, this Python library installed on our system,
39. 02:42we can then Import Python code, that is, just like this
40. 02:48tool, what we want to use, or, if you like, tool box,
41. 02:51to place on our tool table and then be able to work with it.
42. 02:56It's very easy in Python via "import pandas as PD".
43. 03:01So PD is just, after all, that we say we don't always want to
44. 03:04Write out pandas. This is too long, we prefer to write PD.
45. 03:08And when we do that, we kind of go along with it. Execute this command the tool with us
46. 03:14placed on the table and can now use it.
47. 03:16And now, for example, we have just under this file path, i.e. /data/housing.csv
48. 03:23this CSV file we want to import.
49. 03:26And Pandas offers a functionality, namely with read CSV.
50. 03:31It is as in the previous Video simply shows a functionality that
51. 03:37has been developed. Not by but from the Pandas developers.
52. 03:41What we can do is, run this function now.
53. 03:45The result in the variable DF, which is short for data frame, and then
54. 03:51of the ECB. As we can see now, this CSV file has been successful
55. 03:55and we can now work with it.
56. 04:01Now I just said that We didn't write this code.
57. 04:05that the Pandas developers and developers.
58. 04:09We can now just jump in the Source code behind it because pandas
59. 04:13is completely open.
60. 04:15So it's an open-source project. We are asking quickly determine how much work is behind it.
61. 04:19And that's what we're looking at. single file and pandas consists of many
62. 04:25Files. And we already see here alone that it's 1497 lines of code, that's really quite
63. 04:33a lot. That really means every single line of code has some instruction, be it now
64. 04:38an import statement from another library, or hold function descriptions.
65. 04:44And now we can just with ctrl-F after the read CSV file,
66. 04:48Find function description and must skip a few values.
67. 04:56And then come to the function signature and see how the
68. 05:00Developer of pandas this feature is written with a whole lot of arguments.
69. 05:05That is, things that we pass to the function such as the file path.
70. 05:10And if we keep scrolling down. then we finally see the source code.
71. 05:15Yes, that means we can now look behind it how this function is actually implemented
72. 05:21has been reported. Now it can be quite elaborate. such a library by the source code alone
73. 05:26to understand and apply.
74. 05:28And because Pandas is established and by many, is used by many developers, there is
75. 05:35a very good documentation. So a Documentation made for humans.
76. 05:40We can also have a look and see for example, the documentation page for
77. 05:47pandas.read_csv, see Here again, all the arguments.
78. 05:50but can now be found here Read documentation page very well
79. 05:55and also simple and understandable how to use these arguments.
80. 05:58for example, filepath_or_buffer, This is a string and we can now
81. 06:05here in the description in the free text, as
82. 06:08We will use this argument best.
83. 06:14Now, of course, you have to know. Pandas offers even more functionality.
84. 06:19So there is not only read.CSV, but also for example, to filter options.
85. 06:24Something I can say in my data Frame, just show me those values.
86. 06:29which are used for the total rooms attribute have a value greater than 1000.
87. 06:34When we do this, we see the values, of course not all
88. 06:40but the values that just meet this condition, for example.
89. 06:45Now you have to know that Pandas provides such functionality
90. 06:52that there is a read.csv, or that there is data filtering.
91. 06:57And it can't be that easy because that's just so many functionalities
92. 07:03where the question then often arises, how do we know I think I'm going to do that with pandas.
93. 07:08can? And the simplest answer is actually, that if you don't know
94. 07:15, mostly building up the knowledge, Just by getting started.
95. 07:18That is, you have an idea of something that you want to program, for example
96. 07:22a calculator or a Machine learning model or whatever,
97. 07:26and you get going until you realize, that you encounter a problem,
98. 07:30what you don't It's as simple as that.
99. 07:33Yes, we could just do that be reading the file, so that we do not
100. 07:38independently, the function , which reads this CSV file for us.
101. 07:43That can be, for example.
102. 07:45So what we could do is we could do it with a microscope. simple Google search or any other
103. 07:50Search engine Python read csv file and search for that term; and
104. 07:58would then be highly likely to Libraries or simple
105. 08:03How to kick That's the problem.
106. 08:06And if we then, for example, , you can search for this solution
107. 08:10and read the documentation and then apply stop.
108. 08:14And typically, when you do this then you can remember that for
109. 08:20next time. Or you hit it over and over and must search for it every time.
110. 08:24But you know at least that it somehow this library seemed to give.
111. 08:28And so it just happens that you have to time builds knowledge about such tools and
112. 08:33for example, if I know CSV files I want to read, oh, there were pandas.
113. 08:38that was this library, the I can do another research now
114. 08:41and then maybe hit the solution I need.
115. 08:44Exactly, and with that, you have Libraries finally just a
116. 08:49powerful tool to use.
117. 08:51And that's not just for Pandas, so not only pandas is the only
118. 08:57Programming library, but it also exists, for example, in the data
119. 09:01Science area libraries for visualization techniques.
120. 09:04For example, there is matplotlib for or to perform more complex mathematics,
121. 09:10e.g. more complex Formulas or numerical principles,
122. 09:14There is a numpy for that. What we will also be watching in particular,
123. 09:18I look forward to it personally a very special one,
124. 09:21is to view scikit-learn, i.e. standard machine learning library for
125. 09:28Python. And, really, we're going to be doing this kind of programming. Libraries of current course in many places
126. 09:34reconnect with the and to many places.

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