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From: =?UTF-8?q?Elias=20Ervel=C3=A4?= <elias.m.ervela@utu.fi>
Date: Tue, 11 Jan 2022 21:46:24 +0000
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+{"nbformat":4,"nbformat_minor":0,"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.6.10"},"colab":{"name":"DAKD2020Exercise1.ipynb","provenance":[]}},"cells":[{"cell_type":"markdown","metadata":{"id":"AjEZRpKRRPIv"},"source":["# Python tutorial\n","Python is a very common programming language in machine learning applications and is used in the exercises of this course. This tutorial is targeted for students that have at least elementary knowledge of programming in any language. In particular, you should know basic concepts such as variables, loops and functions. In this document we see how to use those building blocks in Python programs. We also cover some important packages such as NumPy, Pandas and Matplotlib. If you installed the [Anaconda Distribution](https://www.anaconda.com/distribution/) (Python version 3.x recommended) you should already have those packages, in addition to the Python language itself.\n","\n","**Notes:** 1) There are exercises in the end of this document. 2) A part of the tutorial uses the well-known Iris data set. You can download it [here](https://archive.ics.uci.edu/ml/machine-learning-databases/iris/).\n","\n","## Jupyter Notebook\n","Jupyter Notebook is an easy way to make documents containing both runnable program code and text (markdown). [Tutorials](https://www.dataquest.io/blog/jupyter-notebook-tutorial/) about Jupyter Notebook are easily found on the Internet but we also cover some of the most important concepts here. \n","\n","Notebook documents consist of two kinds of *cells*: *code* and *Markdown*. Code cells contain executable program code and Markdown cells contain text, possibly with images. You can execute the currently selected cell by pressing either **Ctrl+Enter** or **Shift+Enter**. The latter option also selects the next cell, making it easy to execute multiple consecutive cells after each other. These actions can also be found in the menu: Cell -> Run Cells. The entire Notebook can be executed from the menu: Kernel -> Restart & Run All.\n","\n","To add a new cell, press either a (insert *above* the current cell) or b (*below* the current cell) when a cell is selected but not editable. When editing a cell, pressing a or b will of course just add a letter to the document. To stop editing a cell, you may click the border area. To delete a cell, press d (*delete*) *twice*. To turn a cell into a Markdown cell, press m. By pressing y the current cell becomes a code cell.\n","\n","It is possible to execute a cell multiple times or to execute cells in an order that is different from their order in the document. This can be handy when you need to make small changes - there is no need to re-execute everything from the beginning. However, you need to remember that the state of the running program is determined by the order in which you run the cells, not by their order in the document.\n","\n","By pressing h (*help*) you can see what keyboard shortcuts are available. You can also access everything from the menus but it is faster to memorize at least the most commonly used keyboard shortcuts such as adding and executing cells.\n","\n","## Python programming basics\n","As our simplest use case, we can easily compute the values of certain expressions:"]},{"cell_type":"code","metadata":{"id":"2HrSzqvkRPIy","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1604417980382,"user_tz":-120,"elapsed":1723,"user":{"displayName":"Elias Ervelä","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhcVQbqAobpSX3NE6w5d6aZPU_VzlnsvBC9GkyMtw=s64","userId":"11858975235946053692"}},"outputId":"9dbfa16c-e7f1-4970-dd17-c3f809cfb7ea"},"source":["5 + 8*6 # Execute the cell by pressing Shift+Enter or Ctrl+Enter"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["53"]},"metadata":{"tags":[]},"execution_count":1}]},{"cell_type":"markdown","metadata":{"id":"f6t7fMT1RPI4"},"source":["Using variables is similar to other languages, although in Python there is no separate variable declaration. The data type of a variable depends on what is assigned to it."]},{"cell_type":"code","metadata":{"scrolled":true,"id":"YP603Z95RPI5","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1604418010789,"user_tz":-120,"elapsed":1393,"user":{"displayName":"Elias Ervelä","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhcVQbqAobpSX3NE6w5d6aZPU_VzlnsvBC9GkyMtw=s64","userId":"11858975235946053692"}},"outputId":"71241075-6387-49f8-cded-e6d5bff257aa"},"source":["a = 5\n","b, c = -1, 1 # We can assign values to more than one variable at the same time\n","s, x = 'a string', 2.1\n","print(a*b + x*c)\n","print(a**b) # Exponentiation\n","print(s + ' ' + str(a)) # An explicit conversion of a number to a string is necessary here\n","b, c = c, b # Swap the values of two variables\n","print(b, c)\n","s = a # Now s is no longer a string\n","print(s, type(s))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["-2.9\n","0.2\n","a string 5\n","1 -1\n","5 <class 'int'>\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"UN2mWdU9RPI-"},"source":["### Conditional statements and loops\n","Conditional statements and loops are similar to most programming languages. The 'for' loop of Python iterates over a collection. To iterate over a range of numeric values, we can use the *range()* function. Range can take a lower limit, an upper limit and a step size as the parameters. If only one parameter is specified, it is the upper limit (which is itself excluded), lower limit is 0 and step size 1."]},{"cell_type":"code","metadata":{"id":"-sGo8UjPRPI_","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1604418139966,"user_tz":-120,"elapsed":1293,"user":{"displayName":"Elias Ervelä","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhcVQbqAobpSX3NE6w5d6aZPU_VzlnsvBC9GkyMtw=s64","userId":"11858975235946053692"}},"outputId":"52f53ddc-a586-4213-ef3e-037f93b4117c"},"source":["if b > 0: # A conditional statement\n"," print('Positive')\n","elif b < 0: # There can be any number of \"else if\" -branches, including 0\n"," print('Negative')\n","else: # At most one \"else\"-branch\n"," print('Zero')\n"," \n","print()\n"," \n","for i in [-1, 1, -2, 6]: # Iterate over a list\n"," print(i, i**2)\n"," \n","print()\n"," \n","for i in range(10): # Repeat for i = 0, 1, ..., 9 (the upper limit, 10, is excluded)\n"," print(i)\n"," \n","print()\n"," \n","for i in range(-3, 8, 2): # Repeat for i = -3, -1, ..., 7\n"," print(i)\n","\n","print()\n","\n","i = -3\n","while i < 8: # The same as above but with a while loop\n"," print(i)\n"," i += 2 # Here we must remember to increase the value of i"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Positive\n","\n","-1 1\n","1 1\n","-2 4\n","6 36\n","\n","0\n","1\n","2\n","3\n","4\n","5\n","6\n","7\n","8\n","9\n","\n","-3\n","-1\n","1\n","3\n","5\n","7\n","\n","-3\n","-1\n","1\n","3\n","5\n","7\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"LuoQKJC1RPJD"},"source":["### Indentation\n","As seen above, in Python code blocks are defined by *indentation*, not by any particular block start and end characters (like '{' and '}' in Java). Some lines need to be ended in a colon (':'), e.g. conditional statements, loops and function definitions.\n","\n","### Functions\n","Functions are defined using the 'def' keyword:"]},{"cell_type":"code","metadata":{"id":"U2S2NIsMRPJE","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1604418194041,"user_tz":-120,"elapsed":1246,"user":{"displayName":"Elias Ervelä","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhcVQbqAobpSX3NE6w5d6aZPU_VzlnsvBC9GkyMtw=s64","userId":"11858975235946053692"}},"outputId":"186db615-5090-48fb-cb2c-13fb6a0a8da5"},"source":["def f(x): # A function definition\n"," return x**2\n","\n","f(5)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["25"]},"metadata":{"tags":[]},"execution_count":4}]},{"cell_type":"markdown","metadata":{"id":"PuSdjy7JRPJI"},"source":["### Data structures\n","The core language provides several data structures, such as 'list', 'tuple' and 'dictionary'. Lists and tuples are simple collections of values. Dictionaries consist of key-value pairs."]},{"cell_type":"code","metadata":{"id":"cqycm0ZSRPJI","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1604418229817,"user_tz":-120,"elapsed":1126,"user":{"displayName":"Elias Ervelä","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhcVQbqAobpSX3NE6w5d6aZPU_VzlnsvBC9GkyMtw=s64","userId":"11858975235946053692"}},"outputId":"cb5f11e4-73c7-4675-95e3-d99ea3f9781c"},"source":["l = [1, 2, 3] # A list\n","t = (1, 2, 3) # A tuple - similar to a list but cannot be modified after creation\n","l[0] = 0 # t[0] would not work because tuples are immutable\n","d = {'zero': 0, 'one': 1} # A dictionary\n","print(l, t, d)\n","print(l[1], d['one']) # l[1] is the second element of l - first index is 0\n","print(t[0:2]) # Elements at index 0 and 1 (end index is excluded)\n","print(t[0:3:2]) # Start index 0, end index 3, step size 2\n","print(t[::-1]) # Step size -1 (reverse), include all elements (start and end index empty)\n","l.append(4) # Append an element to the end of a list\n","d['many'] = 3 # Add a new key-value pair to a dictionary\n","print(l, d)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["[0, 2, 3] (1, 2, 3) {'zero': 0, 'one': 1}\n","2 1\n","(1, 2)\n","(1, 3)\n","(3, 2, 1)\n","[0, 2, 3, 4] {'zero': 0, 'one': 1, 'many': 3}\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"yHovlbTMRPJN"},"source":["#### Nice to know, not essential for this course:\n","There are many handy features in Python that you can use to save some work: list comprehensions, filter and map functions, lambda functions (a lambda function is an unnamed function that is defined where it is used). Here are some examples but we won't cover them any further because our needs are quite modest in this course."]},{"cell_type":"code","metadata":{"id":"7XM48sVJRPJO","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1604418447632,"user_tz":-120,"elapsed":507,"user":{"displayName":"Elias Ervelä","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhcVQbqAobpSX3NE6w5d6aZPU_VzlnsvBC9GkyMtw=s64","userId":"11858975235946053692"}},"outputId":"2ce7bf3b-8657-4aa6-e6f2-2d2ff1dc0f88"},"source":["print(l)\n","l1 = [x**2 for x in l] # List comprehension - can replace a loop in many cases\n","l2 = [x**2 for x in l if x > 0] # List comprehension with some elements omitted\n","print(l1, l2)\n","print(list(filter(lambda x: x > 0, l1))) # Filter: include only positive elements\n","print(list(map(lambda x: x**2, l1))) # Map: apply a function to all elements of a collection"],"execution_count":null,"outputs":[{"output_type":"stream","text":["[0, 2, 3, 4]\n","[0, 4, 9, 16] [4, 9, 16]\n","[4, 9, 16]\n","[0, 16, 81, 256]\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"-c9IKVzERPJV"},"source":["## NumPy\n","NumPy provides us with multi-dimensional arrays and methods for working with them. Among the methods are dot products of vectors, matrix products, calculating means and standard deviations, generating random numbers and many, many more. Let's start with the basics:"]},{"cell_type":"code","metadata":{"id":"UedZ8_VIRPJW","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1604419487990,"user_tz":-120,"elapsed":1305,"user":{"displayName":"Elias Ervelä","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhcVQbqAobpSX3NE6w5d6aZPU_VzlnsvBC9GkyMtw=s64","userId":"11858975235946053692"}},"outputId":"59e10d92-d64b-4159-9ccb-4ccf813fc616"},"source":["import numpy as np\n","vec1 = np.array([1, 2, 3]) # A one-dimensional array (vector)\n","vec2 = np.array([1, 1, -1])\n","print(np.dot(vec1, vec2)) # Dot product of vectors\n","print()\n","mat = np.array([[1, 1, 1], [1, -1, 1], [1, 1, -1]]) # A two-dimensional array (matrix)\n","print(mat)\n","print()\n","print(mat@vec1) # Matrix product (vec1 is interpreted as a column vector)\n","print()\n","print(mat*vec1) # Element-wise product, with broadcasting\n","print()\n","print(mat.T) # Transpose of a matrix\n","print()\n","print(np.linalg.inv(mat)) # Inverse of a matrix"],"execution_count":null,"outputs":[{"output_type":"stream","text":["0\n","\n","[[ 1 1 1]\n"," [ 1 -1 1]\n"," [ 1 1 -1]]\n","\n","[6 2 0]\n","\n","[[ 1 2 3]\n"," [ 1 -2 3]\n"," [ 1 2 -3]]\n","\n","[[ 1 1 1]\n"," [ 1 -1 1]\n"," [ 1 1 -1]]\n","\n","[[ 0. 0.5 0.5]\n"," [ 0.5 -0.5 -0. ]\n"," [ 0.5 -0. -0.5]]\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"_WPMKBtSSQ93"},"source":["Note that matrix product is denoted by the symbol @, not by * which is used for scalar multiplication or for element-wise product of the elements of two matrices. For example, if A and B are 3x3-matrices, (A\\*B)[0, 0] = A[0]\\*B[0] whereas (A@B)[0, 0] = A[0, 0]\\*B[0, 0] + A[0, 1]\\*B[1, 0] + A[0, 2]\\*B[2, 0].\n","\n","Above, we used the operator \\* where the left-side object was a matrix, but on the right there was a vector. This works because of an automatically applied mechanism known as *broadcasting*. Conceptually, the vector [1, 2, 3] (here interpreted as a row vector) was repeated three times to get a 3x3-matrix with all rows equal to [1, 2, 3]. The matrix mat was then multiplied element-wise with this new matrix."]},{"cell_type":"markdown","metadata":{"id":"4RGdcNjWRPJa"},"source":["In most applications we don't want to enter the arrays manually as that would be tedious and error-prone. Instead, we may read data (here, the [well-known Iris data set](https://archive.ics.uci.edu/ml/machine-learning-databases/iris/); you need to download iris.data from the page) from a file. If you are using this notebook via Google Colab, you will first need to upload the files you want to use, like this:"]},{"cell_type":"code","metadata":{"id":"L7WLfGAeSv1n","colab":{"resources":{"http://localhost:8080/nbextensions/google.colab/files.js":{"data":"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","ok":true,"headers":[["content-type","application/javascript"]],"status":200,"status_text":""}},"base_uri":"https://localhost:8080/","height":39},"executionInfo":{"status":"ok","timestamp":1604497037722,"user_tz":-120,"elapsed":3547,"user":{"displayName":"Elias Ervelä","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhcVQbqAobpSX3NE6w5d6aZPU_VzlnsvBC9GkyMtw=s64","userId":"11858975235946053692"}},"outputId":"e9a18eff-4b73-4ddf-dba3-dfc196376972"},"source":["from google.colab import files\n","uploaded = files.upload()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/html":["\n"," <input type=\"file\" id=\"files-a44c926d-7f22-4f97-a109-99da2161d00c\" name=\"files[]\" multiple disabled\n"," style=\"border:none\" />\n"," <output id=\"result-a44c926d-7f22-4f97-a109-99da2161d00c\">\n"," Upload widget is only available when the cell has been executed in the\n"," current browser session. Please rerun this cell to enable.\n"," </output>\n"," <script src=\"/nbextensions/google.colab/files.js\"></script> "],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"NntCqw2WSZEO"},"source":["If you are not using Google Colab, you may proceed directly here:"]},{"cell_type":"code","metadata":{"id":"iX0TLrg8RPJb","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1604422688893,"user_tz":-120,"elapsed":2412,"user":{"displayName":"Elias Ervelä","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhcVQbqAobpSX3NE6w5d6aZPU_VzlnsvBC9GkyMtw=s64","userId":"11858975235946053692"}},"outputId":"a9647f7a-f3c6-4bcf-8570-91c2e0c5f460"},"source":["orig_data = np.loadtxt('iris.data', dtype='object', delimiter=',')\n","print(orig_data)\n","# We see that only the four first columns are numeric, the fifth is the species. Let's get the\n","# numeric attributes into one array (contains numbers) and the species into a vector\n","X, y = orig_data[:, 0:4].astype(float), orig_data[:, 4]\n","print(X, y)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["[['5.1' '3.5' '1.4' '0.2' 'Iris-setosa']\n"," ['4.9' '3.0' '1.4' '0.2' 'Iris-setosa']\n"," ['4.7' '3.2' '1.3' '0.2' 'Iris-setosa']\n"," ['4.6' '3.1' '1.5' '0.2' 'Iris-setosa']\n"," ['5.0' '3.6' '1.4' '0.2' 'Iris-setosa']\n"," ['5.4' '3.9' '1.7' '0.4' 'Iris-setosa']\n"," ['4.6' '3.4' '1.4' '0.3' 'Iris-setosa']\n"," ['5.0' '3.4' '1.5' '0.2' 'Iris-setosa']\n"," ['4.4' '2.9' '1.4' '0.2' 'Iris-setosa']\n"," ['4.9' '3.1' '1.5' '0.1' 'Iris-setosa']\n"," ['5.4' '3.7' '1.5' '0.2' 'Iris-setosa']\n"," ['4.8' '3.4' '1.6' '0.2' 'Iris-setosa']\n"," ['4.8' '3.0' '1.4' '0.1' 'Iris-setosa']\n"," ['4.3' '3.0' '1.1' '0.1' 'Iris-setosa']\n"," ['5.8' '4.0' '1.2' '0.2' 'Iris-setosa']\n"," ['5.7' '4.4' '1.5' '0.4' 'Iris-setosa']\n"," ['5.4' '3.9' '1.3' '0.4' 'Iris-setosa']\n"," ['5.1' '3.5' '1.4' '0.3' 'Iris-setosa']\n"," ['5.7' '3.8' '1.7' '0.3' 'Iris-setosa']\n"," ['5.1' '3.8' '1.5' '0.3' 'Iris-setosa']\n"," ['5.4' '3.4' '1.7' '0.2' 'Iris-setosa']\n"," ['5.1' '3.7' '1.5' '0.4' 'Iris-setosa']\n"," ['4.6' '3.6' '1.0' '0.2' 'Iris-setosa']\n"," ['5.1' '3.3' '1.7' '0.5' 'Iris-setosa']\n"," ['4.8' '3.4' '1.9' '0.2' 'Iris-setosa']\n"," ['5.0' '3.0' '1.6' '0.2' 'Iris-setosa']\n"," ['5.0' '3.4' '1.6' '0.4' 'Iris-setosa']\n"," ['5.2' '3.5' '1.5' '0.2' 'Iris-setosa']\n"," ['5.2' '3.4' '1.4' '0.2' 'Iris-setosa']\n"," ['4.7' '3.2' '1.6' '0.2' 'Iris-setosa']\n"," ['4.8' '3.1' '1.6' '0.2' 'Iris-setosa']\n"," ['5.4' '3.4' '1.5' '0.4' 'Iris-setosa']\n"," ['5.2' '4.1' '1.5' '0.1' 'Iris-setosa']\n"," ['5.5' '4.2' '1.4' '0.2' 'Iris-setosa']\n"," ['4.9' '3.1' '1.5' '0.1' 'Iris-setosa']\n"," ['5.0' '3.2' '1.2' '0.2' 'Iris-setosa']\n"," ['5.5' '3.5' '1.3' '0.2' 'Iris-setosa']\n"," ['4.9' '3.1' '1.5' '0.1' 'Iris-setosa']\n"," ['4.4' '3.0' '1.3' '0.2' 'Iris-setosa']\n"," ['5.1' '3.4' '1.5' '0.2' 'Iris-setosa']\n"," ['5.0' '3.5' '1.3' '0.3' 'Iris-setosa']\n"," ['4.5' '2.3' '1.3' '0.3' 'Iris-setosa']\n"," ['4.4' '3.2' '1.3' '0.2' 'Iris-setosa']\n"," ['5.0' '3.5' '1.6' '0.6' 'Iris-setosa']\n"," ['5.1' '3.8' '1.9' '0.4' 'Iris-setosa']\n"," ['4.8' '3.0' '1.4' '0.3' 'Iris-setosa']\n"," ['5.1' '3.8' '1.6' '0.2' 'Iris-setosa']\n"," ['4.6' '3.2' '1.4' '0.2' 'Iris-setosa']\n"," ['5.3' '3.7' '1.5' '0.2' 'Iris-setosa']\n"," ['5.0' '3.3' '1.4' '0.2' 'Iris-setosa']\n"," ['7.0' '3.2' '4.7' '1.4' 'Iris-versicolor']\n"," ['6.4' '3.2' '4.5' '1.5' 'Iris-versicolor']\n"," ['6.9' '3.1' '4.9' '1.5' 'Iris-versicolor']\n"," ['5.5' '2.3' '4.0' '1.3' 'Iris-versicolor']\n"," ['6.5' '2.8' '4.6' '1.5' 'Iris-versicolor']\n"," ['5.7' '2.8' '4.5' '1.3' 'Iris-versicolor']\n"," ['6.3' '3.3' '4.7' '1.6' 'Iris-versicolor']\n"," ['4.9' '2.4' '3.3' '1.0' 'Iris-versicolor']\n"," ['6.6' '2.9' '4.6' '1.3' 'Iris-versicolor']\n"," ['5.2' '2.7' '3.9' '1.4' 'Iris-versicolor']\n"," ['5.0' '2.0' '3.5' '1.0' 'Iris-versicolor']\n"," ['5.9' '3.0' '4.2' '1.5' 'Iris-versicolor']\n"," ['6.0' '2.2' '4.0' '1.0' 'Iris-versicolor']\n"," ['6.1' '2.9' '4.7' '1.4' 'Iris-versicolor']\n"," ['5.6' '2.9' '3.6' '1.3' 'Iris-versicolor']\n"," ['6.7' '3.1' '4.4' '1.4' 'Iris-versicolor']\n"," ['5.6' '3.0' '4.5' '1.5' 'Iris-versicolor']\n"," ['5.8' '2.7' '4.1' '1.0' 'Iris-versicolor']\n"," ['6.2' '2.2' '4.5' '1.5' 'Iris-versicolor']\n"," ['5.6' '2.5' '3.9' '1.1' 'Iris-versicolor']\n"," ['5.9' '3.2' '4.8' '1.8' 'Iris-versicolor']\n"," ['6.1' '2.8' '4.0' '1.3' 'Iris-versicolor']\n"," ['6.3' '2.5' '4.9' '1.5' 'Iris-versicolor']\n"," ['6.1' '2.8' '4.7' '1.2' 'Iris-versicolor']\n"," ['6.4' '2.9' '4.3' '1.3' 'Iris-versicolor']\n"," ['6.6' '3.0' '4.4' '1.4' 'Iris-versicolor']\n"," ['6.8' '2.8' '4.8' '1.4' 'Iris-versicolor']\n"," ['6.7' '3.0' '5.0' '1.7' 'Iris-versicolor']\n"," ['6.0' '2.9' '4.5' '1.5' 'Iris-versicolor']\n"," ['5.7' '2.6' '3.5' '1.0' 'Iris-versicolor']\n"," ['5.5' '2.4' '3.8' '1.1' 'Iris-versicolor']\n"," ['5.5' '2.4' '3.7' '1.0' 'Iris-versicolor']\n"," ['5.8' '2.7' '3.9' '1.2' 'Iris-versicolor']\n"," ['6.0' '2.7' '5.1' '1.6' 'Iris-versicolor']\n"," ['5.4' '3.0' '4.5' '1.5' 'Iris-versicolor']\n"," ['6.0' '3.4' '4.5' '1.6' 'Iris-versicolor']\n"," ['6.7' '3.1' '4.7' '1.5' 'Iris-versicolor']\n"," ['6.3' '2.3' '4.4' '1.3' 'Iris-versicolor']\n"," ['5.6' '3.0' '4.1' '1.3' 'Iris-versicolor']\n"," ['5.5' '2.5' '4.0' '1.3' 'Iris-versicolor']\n"," ['5.5' '2.6' '4.4' '1.2' 'Iris-versicolor']\n"," ['6.1' '3.0' '4.6' '1.4' 'Iris-versicolor']\n"," ['5.8' '2.6' '4.0' '1.2' 'Iris-versicolor']\n"," ['5.0' '2.3' '3.3' '1.0' 'Iris-versicolor']\n"," ['5.6' '2.7' '4.2' '1.3' 'Iris-versicolor']\n"," ['5.7' '3.0' '4.2' '1.2' 'Iris-versicolor']\n"," ['5.7' '2.9' '4.2' '1.3' 'Iris-versicolor']\n"," ['6.2' '2.9' '4.3' '1.3' 'Iris-versicolor']\n"," ['5.1' '2.5' '3.0' '1.1' 'Iris-versicolor']\n"," ['5.7' '2.8' '4.1' '1.3' 'Iris-versicolor']\n"," ['6.3' '3.3' '6.0' '2.5' 'Iris-virginica']\n"," ['5.8' '2.7' '5.1' '1.9' 'Iris-virginica']\n"," ['7.1' '3.0' '5.9' '2.1' 'Iris-virginica']\n"," ['6.3' '2.9' '5.6' '1.8' 'Iris-virginica']\n"," ['6.5' '3.0' '5.8' '2.2' 'Iris-virginica']\n"," ['7.6' '3.0' '6.6' '2.1' 'Iris-virginica']\n"," ['4.9' '2.5' '4.5' '1.7' 'Iris-virginica']\n"," ['7.3' '2.9' '6.3' '1.8' 'Iris-virginica']\n"," ['6.7' '2.5' '5.8' '1.8' 'Iris-virginica']\n"," ['7.2' '3.6' '6.1' '2.5' 'Iris-virginica']\n"," ['6.5' '3.2' '5.1' '2.0' 'Iris-virginica']\n"," ['6.4' '2.7' '5.3' '1.9' 'Iris-virginica']\n"," ['6.8' '3.0' '5.5' '2.1' 'Iris-virginica']\n"," ['5.7' '2.5' '5.0' '2.0' 'Iris-virginica']\n"," ['5.8' '2.8' '5.1' '2.4' 'Iris-virginica']\n"," ['6.4' '3.2' '5.3' '2.3' 'Iris-virginica']\n"," ['6.5' '3.0' '5.5' '1.8' 'Iris-virginica']\n"," ['7.7' '3.8' '6.7' '2.2' 'Iris-virginica']\n"," ['7.7' '2.6' '6.9' '2.3' 'Iris-virginica']\n"," ['6.0' '2.2' '5.0' '1.5' 'Iris-virginica']\n"," ['6.9' '3.2' '5.7' '2.3' 'Iris-virginica']\n"," ['5.6' '2.8' '4.9' '2.0' 'Iris-virginica']\n"," ['7.7' '2.8' '6.7' '2.0' 'Iris-virginica']\n"," ['6.3' '2.7' '4.9' '1.8' 'Iris-virginica']\n"," ['6.7' '3.3' '5.7' '2.1' 'Iris-virginica']\n"," ['7.2' '3.2' '6.0' '1.8' 'Iris-virginica']\n"," ['6.2' '2.8' '4.8' '1.8' 'Iris-virginica']\n"," ['6.1' '3.0' '4.9' '1.8' 'Iris-virginica']\n"," ['6.4' '2.8' '5.6' '2.1' 'Iris-virginica']\n"," ['7.2' '3.0' '5.8' '1.6' 'Iris-virginica']\n"," ['7.4' '2.8' '6.1' '1.9' 'Iris-virginica']\n"," ['7.9' '3.8' '6.4' '2.0' 'Iris-virginica']\n"," ['6.4' '2.8' '5.6' '2.2' 'Iris-virginica']\n"," ['6.3' '2.8' '5.1' '1.5' 'Iris-virginica']\n"," ['6.1' '2.6' '5.6' '1.4' 'Iris-virginica']\n"," ['7.7' '3.0' '6.1' '2.3' 'Iris-virginica']\n"," ['6.3' '3.4' '5.6' '2.4' 'Iris-virginica']\n"," ['6.4' '3.1' '5.5' '1.8' 'Iris-virginica']\n"," ['6.0' '3.0' '4.8' '1.8' 'Iris-virginica']\n"," ['6.9' '3.1' '5.4' '2.1' 'Iris-virginica']\n"," ['6.7' '3.1' '5.6' '2.4' 'Iris-virginica']\n"," ['6.9' '3.1' '5.1' '2.3' 'Iris-virginica']\n"," ['5.8' '2.7' '5.1' '1.9' 'Iris-virginica']\n"," ['6.8' '3.2' '5.9' '2.3' 'Iris-virginica']\n"," ['6.7' '3.3' '5.7' '2.5' 'Iris-virginica']\n"," ['6.7' '3.0' '5.2' '2.3' 'Iris-virginica']\n"," ['6.3' '2.5' '5.0' '1.9' 'Iris-virginica']\n"," ['6.5' '3.0' '5.2' '2.0' 'Iris-virginica']\n"," ['6.2' '3.4' '5.4' '2.3' 'Iris-virginica']\n"," ['5.9' '3.0' '5.1' '1.8' 'Iris-virginica']]\n","[[5.1 3.5 1.4 0.2]\n"," [4.9 3. 1.4 0.2]\n"," [4.7 3.2 1.3 0.2]\n"," [4.6 3.1 1.5 0.2]\n"," [5. 3.6 1.4 0.2]\n"," [5.4 3.9 1.7 0.4]\n"," [4.6 3.4 1.4 0.3]\n"," [5. 3.4 1.5 0.2]\n"," [4.4 2.9 1.4 0.2]\n"," [4.9 3.1 1.5 0.1]\n"," [5.4 3.7 1.5 0.2]\n"," [4.8 3.4 1.6 0.2]\n"," [4.8 3. 1.4 0.1]\n"," [4.3 3. 1.1 0.1]\n"," [5.8 4. 1.2 0.2]\n"," [5.7 4.4 1.5 0.4]\n"," [5.4 3.9 1.3 0.4]\n"," [5.1 3.5 1.4 0.3]\n"," [5.7 3.8 1.7 0.3]\n"," [5.1 3.8 1.5 0.3]\n"," [5.4 3.4 1.7 0.2]\n"," [5.1 3.7 1.5 0.4]\n"," [4.6 3.6 1. 0.2]\n"," [5.1 3.3 1.7 0.5]\n"," [4.8 3.4 1.9 0.2]\n"," [5. 3. 1.6 0.2]\n"," [5. 3.4 1.6 0.4]\n"," [5.2 3.5 1.5 0.2]\n"," [5.2 3.4 1.4 0.2]\n"," [4.7 3.2 1.6 0.2]\n"," [4.8 3.1 1.6 0.2]\n"," [5.4 3.4 1.5 0.4]\n"," [5.2 4.1 1.5 0.1]\n"," [5.5 4.2 1.4 0.2]\n"," [4.9 3.1 1.5 0.1]\n"," [5. 3.2 1.2 0.2]\n"," [5.5 3.5 1.3 0.2]\n"," [4.9 3.1 1.5 0.1]\n"," [4.4 3. 1.3 0.2]\n"," [5.1 3.4 1.5 0.2]\n"," [5. 3.5 1.3 0.3]\n"," [4.5 2.3 1.3 0.3]\n"," [4.4 3.2 1.3 0.2]\n"," [5. 3.5 1.6 0.6]\n"," [5.1 3.8 1.9 0.4]\n"," [4.8 3. 1.4 0.3]\n"," [5.1 3.8 1.6 0.2]\n"," [4.6 3.2 1.4 0.2]\n"," [5.3 3.7 1.5 0.2]\n"," [5. 3.3 1.4 0.2]\n"," [7. 3.2 4.7 1.4]\n"," [6.4 3.2 4.5 1.5]\n"," [6.9 3.1 4.9 1.5]\n"," [5.5 2.3 4. 1.3]\n"," [6.5 2.8 4.6 1.5]\n"," [5.7 2.8 4.5 1.3]\n"," [6.3 3.3 4.7 1.6]\n"," [4.9 2.4 3.3 1. ]\n"," [6.6 2.9 4.6 1.3]\n"," [5.2 2.7 3.9 1.4]\n"," [5. 2. 3.5 1. ]\n"," [5.9 3. 4.2 1.5]\n"," [6. 2.2 4. 1. ]\n"," [6.1 2.9 4.7 1.4]\n"," [5.6 2.9 3.6 1.3]\n"," [6.7 3.1 4.4 1.4]\n"," [5.6 3. 4.5 1.5]\n"," [5.8 2.7 4.1 1. ]\n"," [6.2 2.2 4.5 1.5]\n"," [5.6 2.5 3.9 1.1]\n"," [5.9 3.2 4.8 1.8]\n"," [6.1 2.8 4. 1.3]\n"," [6.3 2.5 4.9 1.5]\n"," [6.1 2.8 4.7 1.2]\n"," [6.4 2.9 4.3 1.3]\n"," [6.6 3. 4.4 1.4]\n"," [6.8 2.8 4.8 1.4]\n"," [6.7 3. 5. 1.7]\n"," [6. 2.9 4.5 1.5]\n"," [5.7 2.6 3.5 1. ]\n"," [5.5 2.4 3.8 1.1]\n"," [5.5 2.4 3.7 1. ]\n"," [5.8 2.7 3.9 1.2]\n"," [6. 2.7 5.1 1.6]\n"," [5.4 3. 4.5 1.5]\n"," [6. 3.4 4.5 1.6]\n"," [6.7 3.1 4.7 1.5]\n"," [6.3 2.3 4.4 1.3]\n"," [5.6 3. 4.1 1.3]\n"," [5.5 2.5 4. 1.3]\n"," [5.5 2.6 4.4 1.2]\n"," [6.1 3. 4.6 1.4]\n"," [5.8 2.6 4. 1.2]\n"," [5. 2.3 3.3 1. ]\n"," [5.6 2.7 4.2 1.3]\n"," [5.7 3. 4.2 1.2]\n"," [5.7 2.9 4.2 1.3]\n"," [6.2 2.9 4.3 1.3]\n"," [5.1 2.5 3. 1.1]\n"," [5.7 2.8 4.1 1.3]\n"," [6.3 3.3 6. 2.5]\n"," [5.8 2.7 5.1 1.9]\n"," [7.1 3. 5.9 2.1]\n"," [6.3 2.9 5.6 1.8]\n"," [6.5 3. 5.8 2.2]\n"," [7.6 3. 6.6 2.1]\n"," [4.9 2.5 4.5 1.7]\n"," [7.3 2.9 6.3 1.8]\n"," [6.7 2.5 5.8 1.8]\n"," [7.2 3.6 6.1 2.5]\n"," [6.5 3.2 5.1 2. ]\n"," [6.4 2.7 5.3 1.9]\n"," [6.8 3. 5.5 2.1]\n"," [5.7 2.5 5. 2. ]\n"," [5.8 2.8 5.1 2.4]\n"," [6.4 3.2 5.3 2.3]\n"," [6.5 3. 5.5 1.8]\n"," [7.7 3.8 6.7 2.2]\n"," [7.7 2.6 6.9 2.3]\n"," [6. 2.2 5. 1.5]\n"," [6.9 3.2 5.7 2.3]\n"," [5.6 2.8 4.9 2. ]\n"," [7.7 2.8 6.7 2. ]\n"," [6.3 2.7 4.9 1.8]\n"," [6.7 3.3 5.7 2.1]\n"," [7.2 3.2 6. 1.8]\n"," [6.2 2.8 4.8 1.8]\n"," [6.1 3. 4.9 1.8]\n"," [6.4 2.8 5.6 2.1]\n"," [7.2 3. 5.8 1.6]\n"," [7.4 2.8 6.1 1.9]\n"," [7.9 3.8 6.4 2. ]\n"," [6.4 2.8 5.6 2.2]\n"," [6.3 2.8 5.1 1.5]\n"," [6.1 2.6 5.6 1.4]\n"," [7.7 3. 6.1 2.3]\n"," [6.3 3.4 5.6 2.4]\n"," [6.4 3.1 5.5 1.8]\n"," [6. 3. 4.8 1.8]\n"," [6.9 3.1 5.4 2.1]\n"," [6.7 3.1 5.6 2.4]\n"," [6.9 3.1 5.1 2.3]\n"," [5.8 2.7 5.1 1.9]\n"," [6.8 3.2 5.9 2.3]\n"," [6.7 3.3 5.7 2.5]\n"," [6.7 3. 5.2 2.3]\n"," [6.3 2.5 5. 1.9]\n"," [6.5 3. 5.2 2. ]\n"," [6.2 3.4 5.4 2.3]\n"," [5.9 3. 5.1 1.8]] ['Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n"," 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n"," 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n"," 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n"," 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n"," 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n"," 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n"," 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n"," 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n"," 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n"," 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n"," 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n"," 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n"," 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n"," 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n"," 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n"," 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n"," 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n"," 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n"," 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n"," 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n"," 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n"," 'Iris-versicolor' 'Iris-versicolor' 'Iris-virginica' 'Iris-virginica'\n"," 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n"," 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n"," 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n"," 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n"," 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n"," 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n"," 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n"," 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n"," 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n"," 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n"," 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n"," 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica']\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"8c-WckBURPJe"},"source":["For a description of what the columns represent, see [the Iris data set page](https://archive.ics.uci.edu/ml/datasets/iris) (Attributes)."]},{"cell_type":"code","metadata":{"id":"N47HKNEtRPJf"},"source":["# We will use the column names later\n","col_names = ['sepal length', 'sepal width', 'petal length', 'petal width', 'class']"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"fI4GZsgIRPJj"},"source":["Let's compute the means of all four attributes. First, for all four classes of the flowers. Then, for Iris setosa only."]},{"cell_type":"code","metadata":{"id":"0_5Yz8OARPJj","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1604422803536,"user_tz":-120,"elapsed":2539,"user":{"displayName":"Elias Ervelä","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhcVQbqAobpSX3NE6w5d6aZPU_VzlnsvBC9GkyMtw=s64","userId":"11858975235946053692"}},"outputId":"70c88ace-47f6-4ca9-8bc1-4e354eadfb69"},"source":["print(np.mean(X, axis=0))\n","print(np.mean(X[y == 'Iris-setosa'], axis=0))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["[5.84333333 3.054 3.75866667 1.19866667]\n","[5.006 3.418 1.464 0.244]\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"0FbX070lGDnT","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1604423694691,"user_tz":-120,"elapsed":1216,"user":{"displayName":"Elias Ervelä","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhcVQbqAobpSX3NE6w5d6aZPU_VzlnsvBC9GkyMtw=s64","userId":"11858975235946053692"}},"outputId":"4acabc9d-8c31-42b1-df0f-71118454e0a1"},"source":["print(np.mean([[1,2,3],[4,5,6],[7,8,9]],axis=0))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["[4. 5. 6.]\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"WSndnIxGRPJo"},"source":["Here, the parameter 'axis' specifies that we are interested in column-wise means (0 actually means *rows*; one needs to *iterate over all rows* to get the mean value of a column). If we don't specify any axis, we will get the mean of all values of the array.\n","\n","In the second case we created a *Boolean vector* by the condition 'y == 'Iris-setosa''. The condition is *True* for all rows where the flower is of class 'Iris-setosa' and *False* for all other rows. Such a vector can be used to index an array. The result is that a row of the array is included if and only if the Boolean vector contains the value *True* in the corresponding position. The size of the Boolean vector should be equal to the number of rows in the array. We could also use a vector containing the indices of the wanted rows, and such a vector can be obtained using the 'where' function."]},{"cell_type":"code","metadata":{"id":"7ArJ4aTbRPJo","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1604423803274,"user_tz":-120,"elapsed":2271,"user":{"displayName":"Elias Ervelä","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhcVQbqAobpSX3NE6w5d6aZPU_VzlnsvBC9GkyMtw=s64","userId":"11858975235946053692"}},"outputId":"8120868a-dac3-436e-a54e-bfc913d11a82"},"source":["np.mean(X[np.where(y=='Iris-setosa')], axis=0)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([5.006, 3.418, 1.464, 0.244])"]},"metadata":{"tags":[]},"execution_count":23}]},{"cell_type":"markdown","metadata":{"id":"_n1mq9q-RPJs"},"source":["We can generate many kinds of random sequences with NumPy. This includes samples from various kinds of statistical distributions or randomly selected samples from a given collection, with or without replacement. The latter case, samples from a collection without replacement, are also *permutations*."]},{"cell_type":"code","metadata":{"scrolled":false,"id":"ka_OtZc3RPJt","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1604423859559,"user_tz":-120,"elapsed":1131,"user":{"displayName":"Elias Ervelä","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhcVQbqAobpSX3NE6w5d6aZPU_VzlnsvBC9GkyMtw=s64","userId":"11858975235946053692"}},"outputId":"84442449-9cbb-4587-f41a-fad3055c0107"},"source":["print(np.random.uniform(low=1, high=3, size=20)) # Samples from a uniform distributions\n","shuffled_classes = np.random.choice(y, size=len(y), replace=False) # Random permutation of y\n","print(shuffled_classes)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["[1.00946479 1.74323771 1.93787052 1.71097019 2.21355561 2.51288984\n"," 2.60060803 2.82108909 1.21750855 2.17268318 1.66045995 1.29568077\n"," 1.1848672 1.66955724 2.14624474 1.75735649 1.24518886 1.84378699\n"," 2.29753815 2.06729687]\n","['Iris-versicolor' 'Iris-virginica' 'Iris-setosa' 'Iris-versicolor'\n"," 'Iris-virginica' 'Iris-versicolor' 'Iris-virginica' 'Iris-setosa'\n"," 'Iris-setosa' 'Iris-setosa' 'Iris-virginica' 'Iris-virginica'\n"," 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n"," 'Iris-virginica' 'Iris-virginica' 'Iris-setosa' 'Iris-versicolor'\n"," 'Iris-virginica' 'Iris-setosa' 'Iris-virginica' 'Iris-versicolor'\n"," 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-virginica'\n"," 'Iris-versicolor' 'Iris-virginica' 'Iris-virginica' 'Iris-setosa'\n"," 'Iris-setosa' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n"," 'Iris-virginica' 'Iris-setosa' 'Iris-versicolor' 'Iris-setosa'\n"," 'Iris-versicolor' 'Iris-versicolor' 'Iris-virginica' 'Iris-setosa'\n"," 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-setosa'\n"," 'Iris-virginica' 'Iris-virginica' 'Iris-versicolor' 'Iris-versicolor'\n"," 'Iris-versicolor' 'Iris-virginica' 'Iris-versicolor' 'Iris-setosa'\n"," 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-versicolor'\n"," 'Iris-setosa' 'Iris-versicolor' 'Iris-virginica' 'Iris-virginica'\n"," 'Iris-versicolor' 'Iris-setosa' 'Iris-setosa' 'Iris-virginica'\n"," 'Iris-setosa' 'Iris-virginica' 'Iris-setosa' 'Iris-virginica'\n"," 'Iris-virginica' 'Iris-versicolor' 'Iris-virginica' 'Iris-versicolor'\n"," 'Iris-virginica' 'Iris-versicolor' 'Iris-setosa' 'Iris-setosa'\n"," 'Iris-versicolor' 'Iris-setosa' 'Iris-versicolor' 'Iris-setosa'\n"," 'Iris-setosa' 'Iris-virginica' 'Iris-versicolor' 'Iris-virginica'\n"," 'Iris-virginica' 'Iris-setosa' 'Iris-virginica' 'Iris-setosa'\n"," 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-versicolor'\n"," 'Iris-virginica' 'Iris-virginica' 'Iris-setosa' 'Iris-versicolor'\n"," 'Iris-setosa' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n"," 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-virginica' 'Iris-setosa'\n"," 'Iris-setosa' 'Iris-versicolor' 'Iris-versicolor' 'Iris-setosa'\n"," 'Iris-virginica' 'Iris-setosa' 'Iris-versicolor' 'Iris-setosa'\n"," 'Iris-virginica' 'Iris-setosa' 'Iris-virginica' 'Iris-virginica'\n"," 'Iris-virginica' 'Iris-versicolor' 'Iris-setosa' 'Iris-versicolor'\n"," 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-setosa'\n"," 'Iris-setosa' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n"," 'Iris-virginica' 'Iris-setosa' 'Iris-virginica' 'Iris-virginica'\n"," 'Iris-virginica' 'Iris-virginica' 'Iris-versicolor' 'Iris-setosa'\n"," 'Iris-setosa' 'Iris-virginica' 'Iris-versicolor' 'Iris-virginica'\n"," 'Iris-versicolor' 'Iris-versicolor' 'Iris-setosa' 'Iris-versicolor']\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"4QWO7suURPJx"},"source":["## Pandas\n","NumPy is a powerful library but for interactively working with tabular data it is not always ideal. For instance, we would quite likely want to *label* our columns in some way so that we don't need to look at a separate vector containing the labels whenever we have forgotten what the columns stand for. We might also want to use those labels as column indices instead of always using numbers from zero to the number of columns minus one. Perhaps we would like to do the same with rows, too.\n","\n","Pandas is a package providing just that and more. The basic data structures of Pandas are Series (1D array, similar to a NumPy vector but item indices (labels) need not be integers) and DataFrame (similar to 2D NumPy array, with freely selectable row and column labels).\n","\n","Let's load the Iris data into a Pandas DataFrame."]},{"cell_type":"code","metadata":{"id":"R9WYFrIURPJ2","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1604424105448,"user_tz":-120,"elapsed":894,"user":{"displayName":"Elias Ervelä","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhcVQbqAobpSX3NE6w5d6aZPU_VzlnsvBC9GkyMtw=s64","userId":"11858975235946053692"}},"outputId":"161e16fb-824b-478c-e8cd-b997e2e911e1"},"source":["import pandas as pd\n","# Way 1: from a file\n","df1 = pd.read_csv('iris.data', names=col_names)\n","# Way 2: from a NumPy array\n","df2 = pd.DataFrame(orig_data, columns=col_names)\n","print(df1.dtypes, df2.dtypes)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["sepal length float64\n","sepal width float64\n","petal length float64\n","petal width float64\n","class object\n","dtype: object sepal length object\n","sepal width object\n","petal length object\n","petal width object\n","class object\n","dtype: object\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"p5XcvqivRPJ5"},"source":["We note that the two ways of getting the data led to different data types of the columns. This is because in the NumPy array all values were of type object. In a Pandas DataFrame different columns can have different data types but in NumPy arrays that is not supported.\n","\n","If we want to work with Pandas instead of NumPy, we would usually prefer the first way. The read_csv funtion of Pandas can also get the names of the columns from the file. Had we omitted 'names=col_names', the first row of the file would have been used as column names. In the present case that does not make sense because the file does not contain column labels. If numbers 0, 1, ... are acceptable as column names, we could omit the names parameter and add a new parameter 'header=None'. If some column contains values that we want to use as row names, we can add the parameter 'index_col= <column number or name>'.\n","\n","Now that we have loaded the data, let's do something with it. Like with NumPy, we can compute row and column means and select rows or columns from the table."]},{"cell_type":"code","metadata":{"id":"FHqdLVWkRPJ6","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1604424531372,"user_tz":-120,"elapsed":2586,"user":{"displayName":"Elias Ervelä","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhcVQbqAobpSX3NE6w5d6aZPU_VzlnsvBC9GkyMtw=s64","userId":"11858975235946053692"}},"outputId":"90ca9c5d-fac5-4f9e-cd42-f7a5685e1439"},"source":["print(df1.mean()) \n","print(df1.mean(axis=1)) # Row means\n","setosa = df1[df1['class'] == 'Iris-setosa']\n","print(setosa.mean()) # Attribute (column) means for Iris setosa"],"execution_count":null,"outputs":[{"output_type":"stream","text":["sepal length 5.843333\n","sepal width 3.054000\n","petal length 3.758667\n","petal width 1.198667\n","dtype: float64\n","0 2.550\n","1 2.375\n","2 2.350\n","3 2.350\n","4 2.550\n"," ... \n","145 4.300\n","146 3.925\n","147 4.175\n","148 4.325\n","149 3.950\n","Length: 150, dtype: float64\n","sepal length 5.006\n","sepal width 3.418\n","petal length 1.464\n","petal width 0.244\n","dtype: float64\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"V6I64ALoRPJ-"},"source":["As we saw above, the syntax is slightly different from NumPy when we want to compute, e.g., the means of some columns. Here, mean is a function (method) of the DataFrame object and it is therefore called like 'df1.mean()', whereas in NumPy we used 'np.mean(X, axis=0)'. Also note that with Pandas we did not get the mean of the entire array when we did not specify an axis.\n","\n","We can convert a Pandas DataFrame to a NumPy array. The recommended way to do that depends on Pandas version. In older versions of Pandas one can use df.values, and the newer versions also support a method df.to_numpy()"]},{"cell_type":"code","metadata":{"id":"Kmdo8CtIRPJ-","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1604424795246,"user_tz":-120,"elapsed":1246,"user":{"displayName":"Elias Ervelä","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhcVQbqAobpSX3NE6w5d6aZPU_VzlnsvBC9GkyMtw=s64","userId":"11858975235946053692"}},"outputId":"cb53b9c0-c75a-4885-a3ca-4dbc345836eb"},"source":["np.mean(df1.iloc[:, 0:4].values)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["3.4636666666666662"]},"metadata":{"tags":[]},"execution_count":29}]},{"cell_type":"markdown","metadata":{"id":"KRDpLtNlRPKC"},"source":["Here we converted a part of the DataFrame into a NumPy array and then computed the mean of all values.\n","\n","There are many ways to index a DataFrame. One can use df[<column name>], df[Boolean_vector], df.loc[...] and df.iloc[...]. The first two can be a bit confusing at first. This is because the first one selects columns and the second one selects rows, despite the calls looking very similar to each other. Also, in NumPy we can select several rows of an array by a vector of row indices but in Pandas df[vector_of_labels] selects columns (unlike df[Boolean_vector] which selects rows).\n","\n","To select rows or colums by numeric indices instead of row/column labels, one can use df.iloc[rows, columns]. Above, we selected all rows (indices ':' on the first axis) and four first columns (indices '0:4' on the second axis). When using iloc, the labels of the rows and columns are ignored, even if they happen to be numbers.\n","\n","The final way, df.loc[rows, columns] works similarly to iloc but selection is done by row and column labels. One difference is that, for example, df.loc[:, start:end] contains also the column df[end] whereas it is omitted when using iloc. Here are some examples:"]},{"cell_type":"code","metadata":{"scrolled":true,"id":"ficfnRWURPKC","colab":{"base_uri":"https://localhost:8080/","height":229},"executionInfo":{"status":"error","timestamp":1605029754858,"user_tz":-120,"elapsed":982,"user":{"displayName":"Elias Ervelä","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhcVQbqAobpSX3NE6w5d6aZPU_VzlnsvBC9GkyMtw=s64","userId":"11858975235946053692"}},"outputId":"1fd11a7e-2c12-42f1-c5f2-688326d682ab"},"source":["#print(df1['class']) # Select a column by name\n","#print(df1.iloc[:5, 4]) # Select 5 rows and one column by index\n","print(df1.iloc[:5, 0:4]) # Select 5 rows and 4 columns by index\n","print(df1.loc[:5, 'sepal length':'class']) # Select 5 rows and columns between 'sepal length' and 'class' -\n"," # including the column 'class'. Here that selects all columns."],"execution_count":null,"outputs":[{"output_type":"error","ename":"NameError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)","\u001b[0;32m<ipython-input-1-bedb714ad762>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m#print(df1['class']) # Select a column by name\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;31m#print(df1.iloc[:5, 4]) # Select 5 rows and one column by index\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# Select 5 rows and 4 columns by index\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'sepal length'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m'class'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# Select 5 rows and columns between 'sepal length' and 'class' -\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;31m# including the column 'class'. Here that selects all columns.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mNameError\u001b[0m: name 'df1' is not defined"]}]},{"cell_type":"markdown","metadata":{"id":"k8th7YKeRPKE"},"source":["Pandas can also compute correlations and draw scatter plots and histograms for us."]},{"cell_type":"code","metadata":{"id":"m3-lA5QlRPKF","colab":{"base_uri":"https://localhost:8080/","height":166},"executionInfo":{"status":"ok","timestamp":1604425399546,"user_tz":-120,"elapsed":827,"user":{"displayName":"Elias Ervelä","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhcVQbqAobpSX3NE6w5d6aZPU_VzlnsvBC9GkyMtw=s64","userId":"11858975235946053692"}},"outputId":"0b0e6ade-87c5-4ad9-d2a4-e66787a425eb"},"source":["df1.corr() # Correlations between columns"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n"," .dataframe tbody tr th:only-of-type {\n"," vertical-align: middle;\n"," }\n","\n"," .dataframe tbody tr th {\n"," vertical-align: top;\n"," }\n","\n"," .dataframe thead th {\n"," text-align: right;\n"," }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n"," <thead>\n"," <tr style=\"text-align: right;\">\n"," <th></th>\n"," <th>sepal length</th>\n"," <th>sepal width</th>\n"," <th>petal length</th>\n"," <th>petal width</th>\n"," </tr>\n"," </thead>\n"," <tbody>\n"," <tr>\n"," <th>sepal length</th>\n"," <td>1.000000</td>\n"," <td>-0.109369</td>\n"," <td>0.871754</td>\n"," <td>0.817954</td>\n"," </tr>\n"," <tr>\n"," <th>sepal width</th>\n"," <td>-0.109369</td>\n"," <td>1.000000</td>\n"," <td>-0.420516</td>\n"," <td>-0.356544</td>\n"," </tr>\n"," <tr>\n"," <th>petal length</th>\n"," <td>0.871754</td>\n"," <td>-0.420516</td>\n"," <td>1.000000</td>\n"," <td>0.962757</td>\n"," </tr>\n"," <tr>\n"," <th>petal width</th>\n"," <td>0.817954</td>\n"," <td>-0.356544</td>\n"," <td>0.962757</td>\n"," <td>1.000000</td>\n"," </tr>\n"," </tbody>\n","</table>\n","</div>"],"text/plain":[" sepal length sepal width petal length petal width\n","sepal length 1.000000 -0.109369 0.871754 0.817954\n","sepal width -0.109369 1.000000 -0.420516 -0.356544\n","petal length 0.871754 -0.420516 1.000000 0.962757\n","petal width 0.817954 -0.356544 0.962757 1.000000"]},"metadata":{"tags":[]},"execution_count":35}]},{"cell_type":"code","metadata":{"id":"626JaV73RPKJ","colab":{"base_uri":"https://localhost:8080/","height":572},"executionInfo":{"status":"ok","timestamp":1604425419575,"user_tz":-120,"elapsed":2586,"user":{"displayName":"Elias Ervelä","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhcVQbqAobpSX3NE6w5d6aZPU_VzlnsvBC9GkyMtw=s64","userId":"11858975235946053692"}},"outputId":"fe950548-805c-49a1-fac9-0e90ea4cf32e"},"source":["%matplotlib inline\n","# Without the above line the figure may not be shown. This 'magic incantation' is only needed once in a Jupyter\n","# Notebook document, before the first time we draw a figure.\n","\n","pd.plotting.scatter_matrix(df1)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fac4a6e43c8>,\n"," <matplotlib.axes._subplots.AxesSubplot object at 0x7fac4a6be6a0>,\n"," <matplotlib.axes._subplots.AxesSubplot object at 0x7fac4a66f908>,\n"," <matplotlib.axes._subplots.AxesSubplot object at 0x7fac4a61cb70>],\n"," [<matplotlib.axes._subplots.AxesSubplot object at 0x7fac4a5cedd8>,\n"," <matplotlib.axes._subplots.AxesSubplot object at 0x7fac4a58c080>,\n"," <matplotlib.axes._subplots.AxesSubplot object at 0x7fac4a5ba2e8>,\n"," <matplotlib.axes._subplots.AxesSubplot object at 0x7fac4a56a518>],\n"," [<matplotlib.axes._subplots.AxesSubplot object at 0x7fac4a56a588>,\n"," <matplotlib.axes._subplots.AxesSubplot object at 0x7fac4a4cea20>,\n"," <matplotlib.axes._subplots.AxesSubplot object at 0x7fac4a501c88>,\n"," <matplotlib.axes._subplots.AxesSubplot object at 0x7fac4a4b5ef0>],\n"," [<matplotlib.axes._subplots.AxesSubplot object at 0x7fac4a476198>,\n"," <matplotlib.axes._subplots.AxesSubplot object at 0x7fac4a427400>,\n"," <matplotlib.axes._subplots.AxesSubplot object at 0x7fac4a3dc668>,\n"," <matplotlib.axes._subplots.AxesSubplot object at 0x7fac4a38e8d0>]],\n"," dtype=object)"]},"metadata":{"tags":[]},"execution_count":36},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 16 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"hv5AdpOsRPKL"},"source":["Pandas offers many functionalities not covered in this tutorial. For instance, one can make queries involving multiple tables, join tables etc. For more details, see [comparison with SQL](https://pandas.pydata.org/pandas-docs/stable/getting_started/comparison/comparison_with_sql.html). However, we won't need advanced functionality during this course. So, let's proceed to the next topic."]},{"cell_type":"markdown","metadata":{"id":"pZZGwSzuRPKM"},"source":["## Matplotlib\n","When we get a new data set, we usually want to make different kinds of plots to see what is in the data. There are other use cases for plots, too, such as reporting our results in a visual way. Matplotlib is one of many possible tools for visualization. Basic usage of Matplotlib is quite easy but more advanced plotting can be a bit cumbersome. In this course our needs are relatively simple.\n","\n","Let's start by plotting a function of one variable $y=f(x)$."]},{"cell_type":"code","metadata":{"id":"O_52yLjLRPKN","colab":{"base_uri":"https://localhost:8080/","height":282},"executionInfo":{"status":"ok","timestamp":1604425633938,"user_tz":-120,"elapsed":1000,"user":{"displayName":"Elias Ervelä","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhcVQbqAobpSX3NE6w5d6aZPU_VzlnsvBC9GkyMtw=s64","userId":"11858975235946053692"}},"outputId":"32270817-c9df-4a74-cc74-48ea24f816ef"},"source":["import matplotlib.pyplot as plt\n","\n","x = np.linspace(0, 2*np.pi, 50)\n","y = np.sin(x)\n","\n","plt.plot(x, y, 'o', color='blue') # Draw points without connecting them by line segments\n","plt.plot(x, y, color='black') # Connect consecutive points with line segments"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[<matplotlib.lines.Line2D at 0x7fac4a25dc18>]"]},"metadata":{"tags":[]},"execution_count":37},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"quHImpYuRPKQ"},"source":["It is possible to make a plot consisting of several subplots."]},{"cell_type":"code","metadata":{"id":"8-_fyY24RPKR","colab":{"base_uri":"https://localhost:8080/","height":282},"executionInfo":{"status":"ok","timestamp":1604425862014,"user_tz":-120,"elapsed":1309,"user":{"displayName":"Elias Ervelä","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhcVQbqAobpSX3NE6w5d6aZPU_VzlnsvBC9GkyMtw=s64","userId":"11858975235946053692"}},"outputId":"cb48ce66-61ec-475a-cb8c-7ee4d67a1d77"},"source":["plt.subplot(2, 2, 1)\n","plt.hist(df1['sepal length']) # Histogram\n","\n","plt.subplot(2, 2, 2)\n","plt.scatter(df1['sepal length'], df1['petal length']) # Scatter plot\n","\n","# Next, we will try plotting a Pandas DataFrame. First we put some data to the DataFrame\n","plt.subplot(2, 2, 3)\n","x2 = np.linspace(10, 20, 500)\n","y2 = np.sin(x2)\n","y3 = np.cos(x2)\n","y4 = y2/x2\n","df = pd.DataFrame(columns=['sin(x)', 'cos(x)', 'sin(x)/x'])\n","df['sin(x)'] = y2\n","df['cos(x)'] = y3\n","df['sin(x)/x'] = y4\n","df = df.set_index(x2)\n","plt.plot(df) # Plots all columns of the data frame\n","plt.legend(df.columns) # Let's add a legend - although it won't look nice here\n","\n","# If we plot a sequence of y values, the x values will be 0, 1, ... in the plot. Here,\n","# that gives a rather misleading impression of the function we are plotting.\n","plt.subplot(2, 2, 4)\n","plt.plot(df['sin(x)'].values)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[<matplotlib.lines.Line2D at 0x7fac48b2b518>]"]},"metadata":{"tags":[]},"execution_count":39},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 4 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"J9OLNnjKRPKU"},"source":["We can add a title and axis labels to a plot:"]},{"cell_type":"code","metadata":{"id":"Xztd6yiQRPKV","colab":{"base_uri":"https://localhost:8080/","height":312},"executionInfo":{"status":"ok","timestamp":1604425949475,"user_tz":-120,"elapsed":1451,"user":{"displayName":"Elias Ervelä","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhcVQbqAobpSX3NE6w5d6aZPU_VzlnsvBC9GkyMtw=s64","userId":"11858975235946053692"}},"outputId":"0daa90f6-c309-4f3e-df71-112894fb9359"},"source":["plt.scatter(df1['sepal length'], df1['petal length'])\n","plt.title('Sepal and petal length')\n","plt.xlabel('Sepal length (cm)')\n","plt.ylabel('Petal length (cm)')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0, 0.5, 'Petal length (cm)')"]},"metadata":{"tags":[]},"execution_count":40},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"7lCMdVu6RPKZ"},"source":["If you need to know Matplotlib in greater detail, you might check, for example, [Pyplot tutorial](https://matplotlib.org/3.1.1/tutorials/introductory/pyplot.html#sphx-glr-tutorials-introductory-pyplot-py).\n","\n","But now, it is time for some exercises!\n","\n","(Optional) If you are using Google Colab, start again with uploading the required files 'exercise_data.csv'."]},{"cell_type":"markdown","metadata":{"id":"rDSIdFtWYpFc"},"source":["## Exercises\n","\n","(Optional) If you are using Google Colab, start again with uploading the required files 'exercise_data.csv'."]},{"cell_type":"code","metadata":{"id":"uNTBQ4NHYYh_","colab":{"resources":{"http://localhost:8080/nbextensions/google.colab/files.js":{"data":"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","ok":true,"headers":[["content-type","application/javascript"]],"status":200,"status_text":""}},"base_uri":"https://localhost:8080/","height":73},"executionInfo":{"status":"ok","timestamp":1604426069139,"user_tz":-120,"elapsed":46732,"user":{"displayName":"Elias Ervelä","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhcVQbqAobpSX3NE6w5d6aZPU_VzlnsvBC9GkyMtw=s64","userId":"11858975235946053692"}},"outputId":"0cbc2659-f5e3-49f8-9229-34b530788a8e"},"source":["from google.colab import files\n","uploaded = files.upload()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/html":["\n"," <input type=\"file\" id=\"files-367b7cb4-c679-4b8e-af44-2f1db3264a26\" name=\"files[]\" multiple disabled\n"," style=\"border:none\" />\n"," <output id=\"result-367b7cb4-c679-4b8e-af44-2f1db3264a26\">\n"," Upload widget is only available when the cell has been executed in the\n"," current browser session. Please rerun this cell to enable.\n"," </output>\n"," <script src=\"/nbextensions/google.colab/files.js\"></script> "],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["Saving exercise_data.csv to exercise_data.csv\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"vNrsmeEZRPKa"},"source":["Use the included data matrix 'exercise_data.csv'. Solve the following exercises.\n","\n","1. Load the data into a Pandas data frame.\n","1. Find all objects of class 1 whose height is over 25 units. Print all of their attributes. *Hint*: you can combine the results of two comparisons using the '&' operator (class is 1 *and* height is over 25). Parentheses may be needed.\n","1. Compute the mean of each attribute for the whole data.\n","1. Make a plot of weights (y axis) vs. heights (x axis).\n","1. Make a plot of weights (y axis) vs. \"size\" (=$height \\cdot width^2$, x axis).\n","1. Like 4-5 but limited to objects of class 1.\n","1. Do you see anything interesting in the plots?"]},{"cell_type":"code","metadata":{"id":"AHWRFqW9RPKb","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1604426284979,"user_tz":-120,"elapsed":1235,"user":{"displayName":"Elias Ervelä","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhcVQbqAobpSX3NE6w5d6aZPU_VzlnsvBC9GkyMtw=s64","userId":"11858975235946053692"}},"outputId":"092a4455-32c4-427d-9270-030216ce9bb5"},"source":["# 1\n","df = pd.read_csv('exercise_data.csv')\n","print(df)"],"execution_count":null,"outputs":[{"output_type":"stream","text":[" Unnamed: 0 height width weight class\n","0 0 18.022890 5.182596 50.527110 1\n","1 1 16.194651 4.383156 28.829918 1\n","2 2 18.150373 4.556691 38.565482 1\n","3 3 13.846924 3.798662 16.413877 1\n","4 4 27.364656 7.777790 166.997659 1\n",".. ... ... ... ... ...\n","124 124 14.394126 1.623162 20.133995 3\n","125 125 14.715369 1.571027 18.098519 3\n","126 126 14.995934 1.613972 20.634388 3\n","127 127 14.345058 1.604480 18.507403 3\n","128 128 14.879172 1.675843 20.413188 3\n","\n","[129 rows x 5 columns]\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"t3BzyT_0RPKh","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1604426544852,"user_tz":-120,"elapsed":1304,"user":{"displayName":"Elias Ervelä","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhcVQbqAobpSX3NE6w5d6aZPU_VzlnsvBC9GkyMtw=s64","userId":"11858975235946053692"}},"outputId":"c1c67970-c7c0-4c51-fc7d-6f9ca4ffa5ff"},"source":["# 2\n","print(df[(df['class'] == 1) & (df['height'] > 25)])"],"execution_count":null,"outputs":[{"output_type":"stream","text":[" Unnamed: 0 height width weight class\n","4 4 27.364656 7.777790 166.997659 1\n","16 16 25.640493 6.852879 122.388594 1\n","21 21 25.343498 7.226682 135.671038 1\n","33 33 26.893780 7.624959 164.284870 1\n","44 44 32.085549 9.016029 263.542786 1\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"ZfD-wN4IRPKn","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1604426615351,"user_tz":-120,"elapsed":1229,"user":{"displayName":"Elias Ervelä","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhcVQbqAobpSX3NE6w5d6aZPU_VzlnsvBC9GkyMtw=s64","userId":"11858975235946053692"}},"outputId":"e863fbf7-a95b-4079-e9d2-a1838a43505a"},"source":["# 3\n","print(df.mean())"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Unnamed: 0 64.000000\n","height 31.938985\n","width 15.431009\n","weight 735.292601\n","class 1.860465\n","dtype: float64\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"XT1_G5O1RPKq","colab":{"base_uri":"https://localhost:8080/","height":282},"executionInfo":{"status":"ok","timestamp":1604427079116,"user_tz":-120,"elapsed":1561,"user":{"displayName":"Elias Ervelä","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhcVQbqAobpSX3NE6w5d6aZPU_VzlnsvBC9GkyMtw=s64","userId":"11858975235946053692"}},"outputId":"58dd0976-d6a3-4558-8fdb-aa44f66c5ebe"},"source":["# 4\n","plt.plot(df['height'],df['weight'],'o')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[<matplotlib.lines.Line2D at 0x7fac47006080>]"]},"metadata":{"tags":[]},"execution_count":63},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"e4AuqUARRPKt","colab":{"base_uri":"https://localhost:8080/","height":282},"executionInfo":{"status":"ok","timestamp":1604427082895,"user_tz":-120,"elapsed":1007,"user":{"displayName":"Elias Ervelä","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhcVQbqAobpSX3NE6w5d6aZPU_VzlnsvBC9GkyMtw=s64","userId":"11858975235946053692"}},"outputId":"2311a613-6d8d-48f3-b0ef-8c247cbcbdeb"},"source":["# 5\n","plt.plot(df['height']*df['width']**2,df['weight'],'o')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[<matplotlib.lines.Line2D at 0x7fac46fdaf60>]"]},"metadata":{"tags":[]},"execution_count":64},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"JBroLJuuRPKw","colab":{"base_uri":"https://localhost:8080/","height":513},"executionInfo":{"status":"ok","timestamp":1604427992903,"user_tz":-120,"elapsed":4156,"user":{"displayName":"Elias Ervelä","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhcVQbqAobpSX3NE6w5d6aZPU_VzlnsvBC9GkyMtw=s64","userId":"11858975235946053692"}},"outputId":"914e6886-d714-4b3c-9deb-3c62e633ba11"},"source":["# 6\n","df_class1 = df[df['class'] == 1]\n","plt.plot(df_class1['height'],df_class1['weight'],'o')\n","plt.show()\n","\n","df_class1 = df[df['class'] == 1]\n","plt.plot(df_class1['height']*df_class1['width']**2,df_class1['weight'],'o')\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"XoDpWJ_gRPK1","colab":{"base_uri":"https://localhost:8080/","height":52},"executionInfo":{"status":"ok","timestamp":1604428174907,"user_tz":-120,"elapsed":2085,"user":{"displayName":"Elias Ervelä","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhcVQbqAobpSX3NE6w5d6aZPU_VzlnsvBC9GkyMtw=s64","userId":"11858975235946053692"}},"outputId":"55ebb101-df86-4746-8435-3d83a065ecaa"},"source":["# 7\n","\"\"\"\n","Weight and height definetly have positive correlation.\n","Weight and \"size\" have definetly linear correlation.\n","The class 1 is definetly smaller in height and weight and size than other classes.\n","\"\"\""],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"},"text/plain":["'\\nWeight and height definetly have positive correlation.\\nWeight and \"size\" have definetly linear correlation.\\nThe class 1 is definetly smaller in height and weight and size than other classes.\\n'"]},"metadata":{"tags":[]},"execution_count":81}]},{"cell_type":"markdown","metadata":{"id":"rbgBuoIrkbBM"},"source":["Finally, if you want to export your Notebook as PDF you can use the following script. On the first run it asks for authentication for using Google Drive. After that you can run it like any other cell, and it will (eventually) produce a PDF and prompt you to download it."]},{"cell_type":"code","metadata":{"id":"T55jRTBRje6N","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1604428297266,"user_tz":-120,"elapsed":102182,"user":{"displayName":"Elias Ervelä","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhcVQbqAobpSX3NE6w5d6aZPU_VzlnsvBC9GkyMtw=s64","userId":"11858975235946053692"}},"outputId":"b9514aa8-de76-4553-be82-f2ae92b24416"},"source":["!wget -nc https://raw.githubusercontent.com/brpy/colab-pdf/master/colab_pdf.py\n","from colab_pdf import colab_pdf\n","colab_pdf('DAKD2020Exercise1.ipynb')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2020-11-03 18:29:57-- https://raw.githubusercontent.com/brpy/colab-pdf/master/colab_pdf.py\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1301 (1.3K) [text/plain]\n","Saving to: ‘colab_pdf.py’\n","\n","colab_pdf.py 100%[===================>] 1.27K --.-KB/s in 0s \n","\n","2020-11-03 18:29:57 (63.8 MB/s) - ‘colab_pdf.py’ saved [1301/1301]\n","\n","Mounted at /content/drive/\n","Ign:1 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64 InRelease\n","Get:2 https://cloud.r-project.org/bin/linux/ubuntu bionic-cran40/ InRelease [3,626 B]\n","Ign:3 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64 InRelease\n","Get:4 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64 Release [697 B]\n","Hit:5 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64 Release\n","Get:6 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64 Release.gpg [836 B]\n","Get:7 http://security.ubuntu.com/ubuntu bionic-security InRelease [88.7 kB]\n","Get:8 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic InRelease [15.9 kB]\n","Hit:9 http://archive.ubuntu.com/ubuntu bionic InRelease\n","Get:10 https://cloud.r-project.org/bin/linux/ubuntu bionic-cran40/ Packages [40.1 kB]\n","Get:11 http://archive.ubuntu.com/ubuntu bionic-updates InRelease [88.7 kB]\n","Get:13 http://ppa.launchpad.net/graphics-drivers/ppa/ubuntu bionic InRelease [21.3 kB]\n","Ign:14 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64 Packages\n","Get:14 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64 Packages [405 kB]\n","Get:15 http://archive.ubuntu.com/ubuntu bionic-backports InRelease [74.6 kB]\n","Get:16 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic/main Sources [1,688 kB]\n","Get:17 http://security.ubuntu.com/ubuntu bionic-security/universe amd64 Packages [1,353 kB]\n","Get:18 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 Packages [2,118 kB]\n","Get:19 http://security.ubuntu.com/ubuntu bionic-security/main amd64 Packages [1,749 kB]\n","Get:20 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 Packages [2,167 kB]\n","Get:21 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic/main amd64 Packages [864 kB]\n","Get:22 http://ppa.launchpad.net/graphics-drivers/ppa/ubuntu bionic/main amd64 Packages [48.9 kB]\n","Fetched 10.7 MB in 4s (2,520 kB/s)\n","Reading package lists... Done\n","Building dependency tree \n","Reading state information... Done\n","33 packages can be upgraded. Run 'apt list --upgradable' to see them.\n","Reading package lists... Done\n","Building dependency tree \n","Reading state information... Done\n","The following additional packages will be installed:\n"," fonts-droid-fallback fonts-lato fonts-lmodern fonts-noto-mono fonts-texgyre\n"," javascript-common libcupsfilters1 libcupsimage2 libgs9 libgs9-common\n"," libijs-0.35 libjbig2dec0 libjs-jquery libkpathsea6 libpotrace0 libptexenc1\n"," libruby2.5 libsynctex1 libtexlua52 libtexluajit2 libzzip-0-13 lmodern\n"," poppler-data preview-latex-style rake ruby ruby-did-you-mean ruby-minitest\n"," ruby-net-telnet ruby-power-assert ruby-test-unit ruby2.5\n"," rubygems-integration t1utils tex-common tex-gyre texlive-base\n"," texlive-binaries texlive-latex-base texlive-latex-extra\n"," texlive-latex-recommended texlive-pictures texlive-plain-generic tipa\n","Suggested packages:\n"," fonts-noto apache2 | lighttpd | httpd poppler-utils ghostscript\n"," fonts-japanese-mincho | fonts-ipafont-mincho fonts-japanese-gothic\n"," | fonts-ipafont-gothic fonts-arphic-ukai fonts-arphic-uming fonts-nanum ri\n"," ruby-dev bundler debhelper gv | postscript-viewer perl-tk xpdf-reader\n"," | pdf-viewer texlive-fonts-recommended-doc texlive-latex-base-doc\n"," python-pygments icc-profiles libfile-which-perl\n"," libspreadsheet-parseexcel-perl texlive-latex-extra-doc\n"," texlive-latex-recommended-doc texlive-pstricks dot2tex prerex ruby-tcltk\n"," | libtcltk-ruby texlive-pictures-doc vprerex\n","The following NEW packages will be installed:\n"," fonts-droid-fallback fonts-lato fonts-lmodern fonts-noto-mono fonts-texgyre\n"," javascript-common libcupsfilters1 libcupsimage2 libgs9 libgs9-common\n"," libijs-0.35 libjbig2dec0 libjs-jquery libkpathsea6 libpotrace0 libptexenc1\n"," libruby2.5 libsynctex1 libtexlua52 libtexluajit2 libzzip-0-13 lmodern\n"," poppler-data preview-latex-style rake ruby ruby-did-you-mean ruby-minitest\n"," ruby-net-telnet ruby-power-assert ruby-test-unit ruby2.5\n"," rubygems-integration t1utils tex-common tex-gyre texlive-base\n"," texlive-binaries texlive-fonts-recommended texlive-generic-recommended\n"," texlive-latex-base texlive-latex-extra texlive-latex-recommended\n"," texlive-pictures texlive-plain-generic texlive-xetex tipa\n","0 upgraded, 47 newly installed, 0 to remove and 33 not upgraded.\n","Need to get 146 MB of archives.\n","After this operation, 460 MB of additional disk space will be used.\n","Get:1 http://archive.ubuntu.com/ubuntu bionic/main amd64 fonts-droid-fallback all 1:6.0.1r16-1.1 [1,805 kB]\n","Get:2 http://archive.ubuntu.com/ubuntu bionic/main amd64 fonts-lato all 2.0-2 [2,698 kB]\n","Get:3 http://archive.ubuntu.com/ubuntu bionic/main amd64 poppler-data all 0.4.8-2 [1,479 kB]\n","Get:4 http://archive.ubuntu.com/ubuntu bionic/main amd64 tex-common all 6.09 [33.0 kB]\n","Get:5 http://archive.ubuntu.com/ubuntu bionic/main amd64 fonts-lmodern all 2.004.5-3 [4,551 kB]\n","Get:6 http://archive.ubuntu.com/ubuntu bionic/main amd64 fonts-noto-mono all 20171026-2 [75.5 kB]\n","Get:7 http://archive.ubuntu.com/ubuntu bionic/universe amd64 fonts-texgyre all 20160520-1 [8,761 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