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diff --git a/notebooks/Block 0/Checking_Correct_Installation.ipynb b/notebooks/Block 0/Checking_Correct_Installation.ipynb
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@@ -0,0 +1,131 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Checking your installation\n",
+ "\n",
+ "Please check that the notebook below runs smoothly."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'3.7.7 (default, May 6 2020, 11:45:54) [MSC v.1916 64 bit (AMD64)]'"
+ ]
+ },
+ "execution_count": 1,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import sys\n",
+ "sys.version #Should work and give 3.7."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'2.1.0'"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import tensorflow as tf\n",
+ "tf.__version__ #Should work and give 2.1.0"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'1.19.1'"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import numpy as np \n",
+ "np.__version__ #Should work and give something > 1.19"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.collections.PathCollection at 0x17a7a929508>"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "plt.scatter(range(100), np.sin(0.1 * np.array(range(100))))"
+ ]
+ }
+ ],
+ "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": "3.7.7"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/Block 0/Exercise Sheet - Basics Numpy.ipynb b/notebooks/Block 0/Exercise Sheet - Basics Numpy.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..ce2bb78b87ba4652d57442c3bbf0448f18a0d259
--- /dev/null
+++ b/notebooks/Block 0/Exercise Sheet - Basics Numpy.ipynb
@@ -0,0 +1,473 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Problem 1 - Introduction Numpy"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This is just a short programming quiz that asks you use a few NumPy features. It is meant to give you a little practice if you don't have NumPy experience"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Input as Array: None\n",
+ "Input minus min: None\n",
+ "Input Array: None\n",
+ "Multiply 1:\n",
+ "None\n",
+ "Multiply 2:\n",
+ "None\n",
+ "Multiply 3:\n",
+ "None\n",
+ "Mean == None\n"
+ ]
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "\n",
+ "\n",
+ "def prepare_inputs(inputs):\n",
+ " # TODO: create a 2-dimensional ndarray from the given 1-dimensional list;\n",
+ " # assign it to input_array\n",
+ " input_array = None\n",
+ " \n",
+ " # TODO: find the minimum value in input_array and subtract that\n",
+ " # value from all the elements of input_array. Store the\n",
+ " # result in inputs_minus_min\n",
+ " inputs_minus_min = None\n",
+ "\n",
+ " # TODO: find the maximum value in inputs_minus_min and divide\n",
+ " # all of the values in inputs_minus_min by the maximum value.\n",
+ " # Store the results in inputs_div_max.\n",
+ " inputs_div_max = None\n",
+ "\n",
+ " # return the three arrays we've created\n",
+ " return input_array, inputs_minus_min, inputs_div_max\n",
+ " \n",
+ "\n",
+ "def multiply_inputs(m1, m2):\n",
+ " # TODO: Check the shapes of the matrices m1 and m2. \n",
+ " # m1 and m2 will be ndarray objects.\n",
+ " #\n",
+ " # Return False if the shapes cannot be used for matrix\n",
+ " # multiplication. You may not use a transpose\n",
+ " pass\n",
+ "\n",
+ "\n",
+ " # TODO: If you have not returned False, then calculate the matrix product\n",
+ " # of m1 and m2 and return it. Do not use a transpose,\n",
+ " # but you swap their order if necessary\n",
+ " pass\n",
+ " \n",
+ "\n",
+ "def find_mean(values):\n",
+ " # TODO: Return the average of the values in the given Python list\n",
+ " pass\n",
+ "\n",
+ "\n",
+ "input_array, inputs_minus_min, inputs_div_max = prepare_inputs([-1,2,7])\n",
+ "print(\"Input as Array: {}\".format(input_array))\n",
+ "print(\"Input minus min: {}\".format(inputs_minus_min))\n",
+ "print(\"Input Array: {}\".format(inputs_div_max))\n",
+ "\n",
+ "print(\"Multiply 1:\\n{}\".format(multiply_inputs(np.array([[1,2,3],[4,5,6]]), np.array([[1],[2],[3],[4]]))))\n",
+ "print(\"Multiply 2:\\n{}\".format(multiply_inputs(np.array([[1,2,3],[4,5,6]]), np.array([[1],[2],[3]]))))\n",
+ "print(\"Multiply 3:\\n{}\".format(multiply_inputs(np.array([[1,2,3],[4,5,6]]), np.array([[1,2]]))))\n",
+ "\n",
+ "print(\"Mean == {}\".format(find_mean([1,3,4])))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Problem 2 - Body Mass Index"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The goal is to write a function which can calculate the Body Mass Index (BMI) of a given dataset. BMI is given by:\n",
+ "\n",
+ "$$ BMI = \\frac{Weight}{Length^2} $$\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The given weights and lengths result in BMI's: \n",
+ "None\n"
+ ]
+ }
+ ],
+ "source": [
+ "# import numpy\n",
+ "import numpy as np\n",
+ "np.set_printoptions(precision=2)\n",
+ "\n",
+ "def BMI(weight, length):\n",
+ " # Function returning the BMI of input vectors m and l\n",
+ " return \n",
+ "\n",
+ "\n",
+ "m_example = [60, 72, 57, 90, 95, 72]\n",
+ "l_example = [1.75, 1.80, 1.65, 1.90, 1.74, 1.91]\n",
+ "print(\"The given weights and lengths result in BMI's: \\n{}\".format(BMI(m_example, l_example)))\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Problem 3 - Using Pandas: Weather"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In this problem you will have to make use of **Pandas** to load and analyse a small, fictional database weather.csv (the same as used in the Script).\n",
+ "The data containes the temperature in 4 cities over 6 months. Download the dataset and save it somewhere practically, possibly in the same folder as this notebook."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# First import Pandas\n",
+ "import pandas as pd\n",
+ "\n",
+ "# load the database using pandas.read_csv. The file location is different for everyone. If you made a new folder in this folder named \"data\", the path would be ./data/weather.csv\n",
+ "# Example: data = pd.read_csv(\"/data/weather.csv\")\n",
+ "data = None\n",
+ "\n",
+ "data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Viewing Data using Indices:\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Using the names of rows and columns (not integer indices), find:\n",
+ "- Temperature in Basel for all months\n",
+ "- Temperature in Chur in February\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Temperature in Basel is:\n",
+ " None \n",
+ "\n",
+ "Temperature in Chur in February is:\n",
+ " None\n"
+ ]
+ }
+ ],
+ "source": [
+ "t_basel = None\n",
+ "t_chur_feb = None\n",
+ "\n",
+ "print(\"Temperature in Basel is:\\n\", t_basel, \"\\n\")\n",
+ "print(\"Temperature in Chur in February is:\\n\", t_chur_feb)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Changing Indices"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we will look at the indices themselves. \n",
+ "- Find the name of column 3 using Python\n",
+ "- Change \"Basel\" to \"Bern\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "None\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Find the name of column 3:\n",
+ "name_c3 = None\n",
+ "print(name_c3)\n",
+ " \n",
+ "# Change \"Basel\" to \"Bern\"\n",
+ "\n",
+ "data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### .mean() Method:\n",
+ "Now find :\n",
+ "- the average temperature in Chur (for the given Period)\n",
+ "- the average temperature in Spring in Switzerland (spring is Mar, Apr, May, mean of all cities)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Average temperature in Chur was: None\n",
+ "Average temperature in Spring was: 0\n"
+ ]
+ }
+ ],
+ "source": [
+ "t_chur_mean = None\n",
+ "\n",
+ "\n",
+ "t_spring_mean = 0\n",
+ "# Hint: mean() on a DataFrame gives the result in a Serries. The axis of the function to be applied on can be set with axis={index (0), columns (1)}.\n",
+ "# To find the mean of the whole matrix, the mean method can be applied twice\n",
+ "\n",
+ "\n",
+ "print(\"Average temperature in Chur was: {}\".format(t_chur_mean))\n",
+ "print(\"Average temperature in Spring was: {}\".format(round(t_spring_mean, 1)))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### .sort_values() Method:\n",
+ "- Sort the data based on the temperature in Zürich\n",
+ "- Sort the data based on decreasing temperature in Basel"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Sort data based on Zurich"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Sort data based on Basel, descending with temperature"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Problem 4: Fuel Consumption\n",
+ "In this exercise, you will analyse a dataset containing information on Fuel consumption of cars in the eighties. The data contains 3 columns, the weight of the car in Pounds(1 Pound = 0.454 kg), the range in Miles per Gallon(1 mile=1.61 km; 1 gallon=3.79 l), and the type of car. Download the dataset and save it somewhere practically, possibly in the same folder as this notebook."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# First import Pandas\n",
+ "import pandas as pd\n",
+ "\n",
+ "# load the database using pandas.read_csv. The file location is different for everyone. If you made a new folder in this folder named \"data\", the path would be ./data/d.fuel.dat\n",
+ "# Example: data = pd.read_csv(\"/data/d.fuel.dat\")\n",
+ "data = None\n",
+ "\n",
+ "data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " To get a quick overview, we can view only the first 5 rows of the dataset. Print the first five rows using:\n",
+ " - **dataframe.loc**\n",
+ " - **DataFrame.head()** "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Print the first 5 rows using data.loc\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# print the first 5 rows using data.head()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### .mean() Method:\n",
+ "Now find :\n",
+ "- the average range of all cars\n",
+ "- the average range of all cars with type \"Medium\" (hint, select all rows with a certain constraint using **DataFrame[DataFrame[** *column* **].isin([** *values* **])]**\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Average miles per galon is: \n",
+ "0 \n",
+ "Average miles per galon for all Medium type cars is: \n",
+ "0\n"
+ ]
+ }
+ ],
+ "source": [
+ "avg_mpg = 0\n",
+ "avg_medium = 0\n",
+ "\n",
+ "print(\"Average miles per galon is: \\n{}\".format(round(avg_mpg, 2)), \"\\nAverage miles per galon for all Medium type cars is: \\n{}\".format(round(avg_medium,2)))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Conversion to SI units\n",
+ "- Create a Series containing the range in km/l and another Series containing the weight in kg.\n",
+ "- Find the average of these new Vectors\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Empty DataFrame\n",
+ "Columns: []\n",
+ "Index: []\n",
+ "Empty DataFrame\n",
+ "Columns: []\n",
+ "Index: []\n",
+ "\n",
+ "Average Kilometer per liter is: \n",
+ "Series([], dtype: float64) \n",
+ "Average weight in kilogram is: \n",
+ "Series([], dtype: float64)\n"
+ ]
+ }
+ ],
+ "source": [
+ "t_kml = pd.DataFrame()\n",
+ "t_kg = pd.DataFrame()\n",
+ "\n",
+ "print(t_kml.head())\n",
+ "print(t_kg.head())\n",
+ "\n",
+ "avg_kml = t_kml.mean()\n",
+ "avg_kg = t_kg.mean()\n",
+ "print(\"\\nAverage Kilometer per liter is: \\n{}\".format(round(avg_kml, 2)), \"\\nAverage weight in kilogram is: \\n{}\".format(round(avg_kg,2)))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python [conda env:root]",
+ "language": "python",
+ "name": "conda-root-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/Block 0/Solution - Basics Numpy.ipynb b/notebooks/Block 0/Solution - Basics Numpy.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..fd04a3a2c286252570ce427931eff90e4ede4242
--- /dev/null
+++ b/notebooks/Block 0/Solution - Basics Numpy.ipynb
@@ -0,0 +1,1507 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Solution to Problem 1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Use the numpy library\n",
+ "import numpy as np\n",
+ "np.set_printoptions(precision=2)\n",
+ "\n",
+ "\n",
+ "######################################################\n",
+ "#\n",
+ "# MESSAGE TO STUDENTS:\n",
+ "#\n",
+ "# This file contains a solution to the coding quiz. Feel free\n",
+ "# to look at it when you are stuck, but try to solve the\n",
+ "# problem on your own first.\n",
+ "#\n",
+ "######################################################\n",
+ "\n",
+ "\n",
+ "def prepare_inputs(inputs):\n",
+ " # TODO: create a 2-dimensional ndarray from the given 1-dimensional list;\n",
+ " # assign it to input_array\n",
+ " input_array = np.array([inputs])\n",
+ " \n",
+ " # TODO: find the minimum value in input_array and subtract that\n",
+ " # value from all the elements of input_array. Store the\n",
+ " # result in inputs_minus_min\n",
+ " # We can use NumPy's min function and element-wise division\n",
+ " inputs_minus_min = input_array - np.min(input_array)\n",
+ "\n",
+ " # TODO: find the maximum value in inputs_minus_min and divide\n",
+ " # all of the values in inputs_minus_min by the maximum value.\n",
+ " # Store the results in inputs_div_max.\n",
+ " # We can use NumPy's max function and element-wise division\n",
+ " inputs_div_max = inputs_minus_min / np.max(inputs_minus_min)\n",
+ "\n",
+ " return input_array, inputs_minus_min, inputs_div_max\n",
+ " \n",
+ "\n",
+ "def multiply_inputs(m1, m2):\n",
+ " # Check the shapes of the matrices m1 and m2. \n",
+ " # m1 and m2 will be ndarray objects.\n",
+ " #\n",
+ " # Return False if the shapes cannot be used for matrix\n",
+ " # multiplication. You may not use a transpose\n",
+ " if m1.shape[0] != m2.shape[1] and m1.shape[1] != m2.shape[0]: \n",
+ " return False\n",
+ "\n",
+ " # Have not returned False, so calculate the matrix product\n",
+ " # of m1 and m2 and return it. Do not use a transpose,\n",
+ " # but you swap their order if necessary\n",
+ " if m1.shape[1] == m2.shape[0]:\n",
+ " return np.matmul(m1, m2) \n",
+ " else:\n",
+ " return np.matmul(m2, m1) \n",
+ "\n",
+ "\n",
+ "def find_mean(values):\n",
+ " # Return the average of the values in the given Python list\n",
+ " # NumPy has a lot of helpful methods like this.\n",
+ " return np.mean(values)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Input as Array: [[-1 2 7]]\n",
+ "Input minus min: [[0 3 8]]\n",
+ "Input Array: [[0. 0.38 1. ]]\n",
+ "Multiply 1:\n",
+ "False\n",
+ "Multiply 2:\n",
+ "[[14]\n",
+ " [32]]\n",
+ "Multiply 3:\n",
+ "[[ 9 12 15]]\n",
+ "Mean == 2.6666666666666665\n"
+ ]
+ }
+ ],
+ "source": [
+ "\n",
+ "input_array, inputs_minus_min, inputs_div_max = prepare_inputs([-1,2,7])\n",
+ "print(\"Input as Array: {}\".format(input_array))\n",
+ "print(\"Input minus min: {}\".format(inputs_minus_min))\n",
+ "print(\"Input Array: {}\".format(inputs_div_max))\n",
+ "\n",
+ "print(\"Multiply 1:\\n{}\".format(multiply_inputs(np.array([[1,2,3],[4,5,6]]), np.array([[1],[2],[3],[4]]))))\n",
+ "print(\"Multiply 2:\\n{}\".format(multiply_inputs(np.array([[1,2,3],[4,5,6]]), np.array([[1],[2],[3]]))))\n",
+ "print(\"Multiply 3:\\n{}\".format(multiply_inputs(np.array([[1,2,3],[4,5,6]]), np.array([[1,2]]))))\n",
+ "\n",
+ "print(\"Mean == {}\".format(find_mean([1,3,4])))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Solution to Problem 2 - Body Mass Index"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The given weights and lengths result in BMI's: \n",
+ "[19.59 22.22 20.94 24.93 31.38 19.74]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# import numpy\n",
+ "import numpy as np\n",
+ "np.set_printoptions(precision=2)\n",
+ "\n",
+ "def BMI(weight, length):\n",
+ " # Function returning the BMI of input vectors m and l\n",
+ " m = np.array(weight) # Create arrays from inputs\n",
+ " l = np.array(length)\n",
+ " \n",
+ " # Calculate all BMI's simultaniously\n",
+ " BMI = np.divide(m, l**2) \n",
+ " # Or just\n",
+ " BMI = m/l**2 \n",
+ " \n",
+ " return BMI\n",
+ "\n",
+ "m_example = [60, 72, 57, 90, 95, 72]\n",
+ "l_example = [1.75, 1.80, 1.65, 1.90, 1.74, 1.91]\n",
+ "print(\"The given weights and lengths result in BMI's: \\n{}\".format(BMI(m_example, l_example)))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Solution to Problem 3 - Using Pandas: Weather"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "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>Luzern</th>\n",
+ " <th>Basel</th>\n",
+ " <th>Chur</th>\n",
+ " <th>Zurich</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>Jan</th>\n",
+ " <td>2</td>\n",
+ " <td>5</td>\n",
+ " <td>-3</td>\n",
+ " <td>4</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Feb</th>\n",
+ " <td>5</td>\n",
+ " <td>6</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Mar</th>\n",
+ " <td>10</td>\n",
+ " <td>11</td>\n",
+ " <td>13</td>\n",
+ " <td>8</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Apr</th>\n",
+ " <td>16</td>\n",
+ " <td>12</td>\n",
+ " <td>14</td>\n",
+ " <td>17</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>May</th>\n",
+ " <td>21</td>\n",
+ " <td>23</td>\n",
+ " <td>21</td>\n",
+ " <td>20</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Jun</th>\n",
+ " <td>25</td>\n",
+ " <td>21</td>\n",
+ " <td>23</td>\n",
+ " <td>27</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Luzern Basel Chur Zurich\n",
+ "Jan 2 5 -3 4\n",
+ "Feb 5 6 1 0\n",
+ "Mar 10 11 13 8\n",
+ "Apr 16 12 14 17\n",
+ "May 21 23 21 20\n",
+ "Jun 25 21 23 27"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# First import Pandas\n",
+ "import pandas as pd\n",
+ "\n",
+ "# load the database using pandas.read_csv \n",
+ "data = pd.read_csv(\"./Daten_Serie_1/weather.csv\")\n",
+ "data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Viewing Data using Indices:\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Temperature in Basel is:\n",
+ " Jan 5\n",
+ "Feb 6\n",
+ "Mar 11\n",
+ "Apr 12\n",
+ "May 23\n",
+ "Jun 21\n",
+ "Name: Basel, dtype: int64 \n",
+ "\n",
+ "Temperature in Chur in February is:\n",
+ " 1\n"
+ ]
+ }
+ ],
+ "source": [
+ "t_basel = data[\"Basel\"]\n",
+ "t_chur_feb = data.loc[\"Feb\", \"Chur\"]\n",
+ "\n",
+ "print(\"Temperature in Basel is:\\n\", t_basel, \"\\n\")\n",
+ "print(\"Temperature in Chur in February is:\\n\", t_chur_feb)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Changing Indices"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Zurich\n"
+ ]
+ },
+ {
+ "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>Luzern</th>\n",
+ " <th>Bern</th>\n",
+ " <th>Chur</th>\n",
+ " <th>Zurich</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>Jan</th>\n",
+ " <td>2</td>\n",
+ " <td>5</td>\n",
+ " <td>-3</td>\n",
+ " <td>4</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Feb</th>\n",
+ " <td>5</td>\n",
+ " <td>6</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Mar</th>\n",
+ " <td>10</td>\n",
+ " <td>11</td>\n",
+ " <td>13</td>\n",
+ " <td>8</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Apr</th>\n",
+ " <td>16</td>\n",
+ " <td>12</td>\n",
+ " <td>14</td>\n",
+ " <td>17</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>May</th>\n",
+ " <td>21</td>\n",
+ " <td>23</td>\n",
+ " <td>21</td>\n",
+ " <td>20</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Jun</th>\n",
+ " <td>25</td>\n",
+ " <td>21</td>\n",
+ " <td>23</td>\n",
+ " <td>27</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Luzern Bern Chur Zurich\n",
+ "Jan 2 5 -3 4\n",
+ "Feb 5 6 1 0\n",
+ "Mar 10 11 13 8\n",
+ "Apr 16 12 14 17\n",
+ "May 21 23 21 20\n",
+ "Jun 25 21 23 27"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Find the name of column 3:\n",
+ "all_columns = data.columns\n",
+ "name_c3 = all_columns[3]\n",
+ "# Oder \n",
+ "name_c3 = data.columns.values[3]\n",
+ "print(name_c3)\n",
+ " \n",
+ "# Change \"Basel\" to \"Bern\"\n",
+ "data.columns.values[1] = \"Bern\"\n",
+ "data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### .mean() Method:\n",
+ "Now find :\n",
+ "- the average temperature in Chur (for the given Period)\n",
+ "- the average temperature in Spring in Switzerland (spring is Mar, Apr, May, mean of all cities)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Average temperature in Chur was: 11.5\n",
+ "Average temperature in Spring was: 15.5\n"
+ ]
+ }
+ ],
+ "source": [
+ "t_chur_mean = data.loc[:, \"Chur\"].mean()\n",
+ "t_spring_mean = data.loc[\"Mar\":\"May\", :].mean().mean()\n",
+ "# Hint: mean() on a DataFrame gives the result in a Serries. The axis of the function to be applied on can be set with axis={index (0), columns (1)}.\n",
+ "# To find the mean of the whole matrix, the mean method can be applied twice\n",
+ "\n",
+ "\n",
+ "print(\"Average temperature in Chur was: {}\".format(t_chur_mean))\n",
+ "print(\"Average temperature in Spring was: {}\".format(round(t_spring_mean, 1)))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### .sort_values() Method:\n",
+ "- Sort the data based on the temperature in Zürich\n",
+ "- Sort the data based on decreasing temperature in Basel"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "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>Luzern</th>\n",
+ " <th>Bern</th>\n",
+ " <th>Chur</th>\n",
+ " <th>Zurich</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>Feb</th>\n",
+ " <td>5</td>\n",
+ " <td>6</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Jan</th>\n",
+ " <td>2</td>\n",
+ " <td>5</td>\n",
+ " <td>-3</td>\n",
+ " <td>4</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Mar</th>\n",
+ " <td>10</td>\n",
+ " <td>11</td>\n",
+ " <td>13</td>\n",
+ " <td>8</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Apr</th>\n",
+ " <td>16</td>\n",
+ " <td>12</td>\n",
+ " <td>14</td>\n",
+ " <td>17</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>May</th>\n",
+ " <td>21</td>\n",
+ " <td>23</td>\n",
+ " <td>21</td>\n",
+ " <td>20</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Jun</th>\n",
+ " <td>25</td>\n",
+ " <td>21</td>\n",
+ " <td>23</td>\n",
+ " <td>27</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Luzern Bern Chur Zurich\n",
+ "Feb 5 6 1 0\n",
+ "Jan 2 5 -3 4\n",
+ "Mar 10 11 13 8\n",
+ "Apr 16 12 14 17\n",
+ "May 21 23 21 20\n",
+ "Jun 25 21 23 27"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data.sort_values(\"Zurich\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "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>Luzern</th>\n",
+ " <th>Bern</th>\n",
+ " <th>Chur</th>\n",
+ " <th>Zurich</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>May</th>\n",
+ " <td>21</td>\n",
+ " <td>23</td>\n",
+ " <td>21</td>\n",
+ " <td>20</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Jun</th>\n",
+ " <td>25</td>\n",
+ " <td>21</td>\n",
+ " <td>23</td>\n",
+ " <td>27</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Apr</th>\n",
+ " <td>16</td>\n",
+ " <td>12</td>\n",
+ " <td>14</td>\n",
+ " <td>17</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Mar</th>\n",
+ " <td>10</td>\n",
+ " <td>11</td>\n",
+ " <td>13</td>\n",
+ " <td>8</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Feb</th>\n",
+ " <td>5</td>\n",
+ " <td>6</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Jan</th>\n",
+ " <td>2</td>\n",
+ " <td>5</td>\n",
+ " <td>-3</td>\n",
+ " <td>4</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Luzern Bern Chur Zurich\n",
+ "May 21 23 21 20\n",
+ "Jun 25 21 23 27\n",
+ "Apr 16 12 14 17\n",
+ "Mar 10 11 13 8\n",
+ "Feb 5 6 1 0\n",
+ "Jan 2 5 -3 4"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data.sort_values(\"Basel\", ascending=False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Solution to Problem 4: Fuel Consumption"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "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>X</th>\n",
+ " <th>weight</th>\n",
+ " <th>mpg</th>\n",
+ " <th>type</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>1</td>\n",
+ " <td>2560</td>\n",
+ " <td>33</td>\n",
+ " <td>Small</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>2</td>\n",
+ " <td>2345</td>\n",
+ " <td>33</td>\n",
+ " <td>Small</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>3</td>\n",
+ " <td>1845</td>\n",
+ " <td>37</td>\n",
+ " <td>Small</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>4</td>\n",
+ " <td>2260</td>\n",
+ " <td>32</td>\n",
+ " <td>Small</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>5</td>\n",
+ " <td>2440</td>\n",
+ " <td>32</td>\n",
+ " <td>Small</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5</th>\n",
+ " <td>6</td>\n",
+ " <td>2285</td>\n",
+ " <td>26</td>\n",
+ " <td>Small</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>6</th>\n",
+ " <td>7</td>\n",
+ " <td>2275</td>\n",
+ " <td>33</td>\n",
+ " <td>Small</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7</th>\n",
+ " <td>8</td>\n",
+ " <td>2350</td>\n",
+ " <td>28</td>\n",
+ " <td>Small</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>8</th>\n",
+ " <td>9</td>\n",
+ " <td>2295</td>\n",
+ " <td>25</td>\n",
+ " <td>Small</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>9</th>\n",
+ " <td>10</td>\n",
+ " <td>1900</td>\n",
+ " <td>34</td>\n",
+ " <td>Small</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>10</th>\n",
+ " <td>11</td>\n",
+ " <td>2390</td>\n",
+ " <td>29</td>\n",
+ " <td>Small</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>11</th>\n",
+ " <td>12</td>\n",
+ " <td>2075</td>\n",
+ " <td>35</td>\n",
+ " <td>Small</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>12</th>\n",
+ " <td>13</td>\n",
+ " <td>2330</td>\n",
+ " <td>26</td>\n",
+ " <td>Small</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>13</th>\n",
+ " <td>14</td>\n",
+ " <td>3320</td>\n",
+ " <td>20</td>\n",
+ " <td>Sporty</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>14</th>\n",
+ " <td>15</td>\n",
+ " <td>2885</td>\n",
+ " <td>27</td>\n",
+ " <td>Sporty</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>15</th>\n",
+ " <td>16</td>\n",
+ " <td>3310</td>\n",
+ " <td>19</td>\n",
+ " <td>Sporty</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>16</th>\n",
+ " <td>17</td>\n",
+ " <td>2695</td>\n",
+ " <td>30</td>\n",
+ " <td>Sporty</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17</th>\n",
+ " <td>18</td>\n",
+ " <td>2170</td>\n",
+ " <td>33</td>\n",
+ " <td>Sporty</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>18</th>\n",
+ " <td>19</td>\n",
+ " <td>2710</td>\n",
+ " <td>27</td>\n",
+ " <td>Sporty</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>19</th>\n",
+ " <td>20</td>\n",
+ " <td>2775</td>\n",
+ " <td>24</td>\n",
+ " <td>Sporty</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>20</th>\n",
+ " <td>21</td>\n",
+ " <td>2840</td>\n",
+ " <td>26</td>\n",
+ " <td>Sporty</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>21</th>\n",
+ " <td>22</td>\n",
+ " <td>2485</td>\n",
+ " <td>28</td>\n",
+ " <td>Sporty</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>22</th>\n",
+ " <td>23</td>\n",
+ " <td>2670</td>\n",
+ " <td>27</td>\n",
+ " <td>Compact</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>23</th>\n",
+ " <td>24</td>\n",
+ " <td>2640</td>\n",
+ " <td>23</td>\n",
+ " <td>Compact</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>24</th>\n",
+ " <td>25</td>\n",
+ " <td>2655</td>\n",
+ " <td>26</td>\n",
+ " <td>Compact</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25</th>\n",
+ " <td>26</td>\n",
+ " <td>3065</td>\n",
+ " <td>25</td>\n",
+ " <td>Compact</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>26</th>\n",
+ " <td>27</td>\n",
+ " <td>2750</td>\n",
+ " <td>24</td>\n",
+ " <td>Compact</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>27</th>\n",
+ " <td>28</td>\n",
+ " <td>2920</td>\n",
+ " <td>26</td>\n",
+ " <td>Compact</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>28</th>\n",
+ " <td>29</td>\n",
+ " <td>2780</td>\n",
+ " <td>24</td>\n",
+ " <td>Compact</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>29</th>\n",
+ " <td>30</td>\n",
+ " <td>2745</td>\n",
+ " <td>25</td>\n",
+ " <td>Compact</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>30</th>\n",
+ " <td>31</td>\n",
+ " <td>3110</td>\n",
+ " <td>21</td>\n",
+ " <td>Compact</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>31</th>\n",
+ " <td>32</td>\n",
+ " <td>2920</td>\n",
+ " <td>21</td>\n",
+ " <td>Compact</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>32</th>\n",
+ " <td>33</td>\n",
+ " <td>2645</td>\n",
+ " <td>23</td>\n",
+ " <td>Compact</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>33</th>\n",
+ " <td>34</td>\n",
+ " <td>2575</td>\n",
+ " <td>24</td>\n",
+ " <td>Compact</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>34</th>\n",
+ " <td>35</td>\n",
+ " <td>2935</td>\n",
+ " <td>23</td>\n",
+ " <td>Compact</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>35</th>\n",
+ " <td>36</td>\n",
+ " <td>2920</td>\n",
+ " <td>27</td>\n",
+ " <td>Compact</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>36</th>\n",
+ " <td>37</td>\n",
+ " <td>2985</td>\n",
+ " <td>23</td>\n",
+ " <td>Compact</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>37</th>\n",
+ " <td>38</td>\n",
+ " <td>3265</td>\n",
+ " <td>20</td>\n",
+ " <td>Medium</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>38</th>\n",
+ " <td>39</td>\n",
+ " <td>2880</td>\n",
+ " <td>21</td>\n",
+ " <td>Medium</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>39</th>\n",
+ " <td>40</td>\n",
+ " <td>2975</td>\n",
+ " <td>22</td>\n",
+ " <td>Medium</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>40</th>\n",
+ " <td>41</td>\n",
+ " <td>3450</td>\n",
+ " <td>22</td>\n",
+ " <td>Medium</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>41</th>\n",
+ " <td>42</td>\n",
+ " <td>3145</td>\n",
+ " <td>22</td>\n",
+ " <td>Medium</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>42</th>\n",
+ " <td>43</td>\n",
+ " <td>3190</td>\n",
+ " <td>22</td>\n",
+ " <td>Medium</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>43</th>\n",
+ " <td>44</td>\n",
+ " <td>3610</td>\n",
+ " <td>23</td>\n",
+ " <td>Medium</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>44</th>\n",
+ " <td>45</td>\n",
+ " <td>2885</td>\n",
+ " <td>23</td>\n",
+ " <td>Medium</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>45</th>\n",
+ " <td>46</td>\n",
+ " <td>3480</td>\n",
+ " <td>21</td>\n",
+ " <td>Medium</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>46</th>\n",
+ " <td>47</td>\n",
+ " <td>3200</td>\n",
+ " <td>22</td>\n",
+ " <td>Medium</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>47</th>\n",
+ " <td>48</td>\n",
+ " <td>2765</td>\n",
+ " <td>21</td>\n",
+ " <td>Medium</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>48</th>\n",
+ " <td>49</td>\n",
+ " <td>3220</td>\n",
+ " <td>21</td>\n",
+ " <td>Medium</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>49</th>\n",
+ " <td>50</td>\n",
+ " <td>3480</td>\n",
+ " <td>23</td>\n",
+ " <td>Medium</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50</th>\n",
+ " <td>51</td>\n",
+ " <td>3325</td>\n",
+ " <td>23</td>\n",
+ " <td>Large</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>51</th>\n",
+ " <td>52</td>\n",
+ " <td>3855</td>\n",
+ " <td>18</td>\n",
+ " <td>Large</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>52</th>\n",
+ " <td>53</td>\n",
+ " <td>3850</td>\n",
+ " <td>20</td>\n",
+ " <td>Large</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>53</th>\n",
+ " <td>54</td>\n",
+ " <td>3195</td>\n",
+ " <td>18</td>\n",
+ " <td>Van</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>54</th>\n",
+ " <td>55</td>\n",
+ " <td>3735</td>\n",
+ " <td>18</td>\n",
+ " <td>Van</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>55</th>\n",
+ " <td>56</td>\n",
+ " <td>3665</td>\n",
+ " <td>18</td>\n",
+ " <td>Van</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>56</th>\n",
+ " <td>57</td>\n",
+ " <td>3735</td>\n",
+ " <td>19</td>\n",
+ " <td>Van</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>57</th>\n",
+ " <td>58</td>\n",
+ " <td>3415</td>\n",
+ " <td>20</td>\n",
+ " <td>Van</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>58</th>\n",
+ " <td>59</td>\n",
+ " <td>3185</td>\n",
+ " <td>20</td>\n",
+ " <td>Van</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>59</th>\n",
+ " <td>60</td>\n",
+ " <td>3690</td>\n",
+ " <td>19</td>\n",
+ " <td>Van</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " X weight mpg type\n",
+ "0 1 2560 33 Small\n",
+ "1 2 2345 33 Small\n",
+ "2 3 1845 37 Small\n",
+ "3 4 2260 32 Small\n",
+ "4 5 2440 32 Small\n",
+ "5 6 2285 26 Small\n",
+ "6 7 2275 33 Small\n",
+ "7 8 2350 28 Small\n",
+ "8 9 2295 25 Small\n",
+ "9 10 1900 34 Small\n",
+ "10 11 2390 29 Small\n",
+ "11 12 2075 35 Small\n",
+ "12 13 2330 26 Small\n",
+ "13 14 3320 20 Sporty\n",
+ "14 15 2885 27 Sporty\n",
+ "15 16 3310 19 Sporty\n",
+ "16 17 2695 30 Sporty\n",
+ "17 18 2170 33 Sporty\n",
+ "18 19 2710 27 Sporty\n",
+ "19 20 2775 24 Sporty\n",
+ "20 21 2840 26 Sporty\n",
+ "21 22 2485 28 Sporty\n",
+ "22 23 2670 27 Compact\n",
+ "23 24 2640 23 Compact\n",
+ "24 25 2655 26 Compact\n",
+ "25 26 3065 25 Compact\n",
+ "26 27 2750 24 Compact\n",
+ "27 28 2920 26 Compact\n",
+ "28 29 2780 24 Compact\n",
+ "29 30 2745 25 Compact\n",
+ "30 31 3110 21 Compact\n",
+ "31 32 2920 21 Compact\n",
+ "32 33 2645 23 Compact\n",
+ "33 34 2575 24 Compact\n",
+ "34 35 2935 23 Compact\n",
+ "35 36 2920 27 Compact\n",
+ "36 37 2985 23 Compact\n",
+ "37 38 3265 20 Medium\n",
+ "38 39 2880 21 Medium\n",
+ "39 40 2975 22 Medium\n",
+ "40 41 3450 22 Medium\n",
+ "41 42 3145 22 Medium\n",
+ "42 43 3190 22 Medium\n",
+ "43 44 3610 23 Medium\n",
+ "44 45 2885 23 Medium\n",
+ "45 46 3480 21 Medium\n",
+ "46 47 3200 22 Medium\n",
+ "47 48 2765 21 Medium\n",
+ "48 49 3220 21 Medium\n",
+ "49 50 3480 23 Medium\n",
+ "50 51 3325 23 Large\n",
+ "51 52 3855 18 Large\n",
+ "52 53 3850 20 Large\n",
+ "53 54 3195 18 Van\n",
+ "54 55 3735 18 Van\n",
+ "55 56 3665 18 Van\n",
+ "56 57 3735 19 Van\n",
+ "57 58 3415 20 Van\n",
+ "58 59 3185 20 Van\n",
+ "59 60 3690 19 Van"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# First import Pandas\n",
+ "import pandas as pd\n",
+ "\n",
+ "# load the database using pandas.read_csv with options: sep=\",\" and index_col=0\n",
+ "# data = None\n",
+ "data = pd.read_csv(\"./Daten_Serie_1/d.fuel.dat\")\n",
+ "\n",
+ "data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " To get a quick overview, we can view only the first 5 rows of the dataset. Print the first five rows using:\n",
+ " - **dataframe.loc**\n",
+ " - **DataFrame.head()** "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "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>X</th>\n",
+ " <th>weight</th>\n",
+ " <th>mpg</th>\n",
+ " <th>type</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>2</td>\n",
+ " <td>2345</td>\n",
+ " <td>33</td>\n",
+ " <td>Small</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>3</td>\n",
+ " <td>1845</td>\n",
+ " <td>37</td>\n",
+ " <td>Small</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>4</td>\n",
+ " <td>2260</td>\n",
+ " <td>32</td>\n",
+ " <td>Small</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>5</td>\n",
+ " <td>2440</td>\n",
+ " <td>32</td>\n",
+ " <td>Small</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5</th>\n",
+ " <td>6</td>\n",
+ " <td>2285</td>\n",
+ " <td>26</td>\n",
+ " <td>Small</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " X weight mpg type\n",
+ "1 2 2345 33 Small\n",
+ "2 3 1845 37 Small\n",
+ "3 4 2260 32 Small\n",
+ "4 5 2440 32 Small\n",
+ "5 6 2285 26 Small"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Print the first 5 rows using data.loc\n",
+ "data.loc[1:5,:]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "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>X</th>\n",
+ " <th>weight</th>\n",
+ " <th>mpg</th>\n",
+ " <th>type</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>1</td>\n",
+ " <td>2560</td>\n",
+ " <td>33</td>\n",
+ " <td>Small</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>2</td>\n",
+ " <td>2345</td>\n",
+ " <td>33</td>\n",
+ " <td>Small</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>3</td>\n",
+ " <td>1845</td>\n",
+ " <td>37</td>\n",
+ " <td>Small</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>4</td>\n",
+ " <td>2260</td>\n",
+ " <td>32</td>\n",
+ " <td>Small</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>5</td>\n",
+ " <td>2440</td>\n",
+ " <td>32</td>\n",
+ " <td>Small</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " X weight mpg type\n",
+ "0 1 2560 33 Small\n",
+ "1 2 2345 33 Small\n",
+ "2 3 1845 37 Small\n",
+ "3 4 2260 32 Small\n",
+ "4 5 2440 32 Small"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# print the first 5 rows using data.head()\n",
+ "data.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### .mean() Method:\n",
+ "Now find :\n",
+ "- the average range of all cars\n",
+ "- the average range of all cars with type \"Medium\" (hint, select all rows with a certain constraint using **DataFrame[DataFrame[** *column* **].isin([** *values* **])]**\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Average miles per galon is: \n",
+ "24.58 \n",
+ "Average miles per galon for all Medium type cars is: \n",
+ "21.77\n"
+ ]
+ }
+ ],
+ "source": [
+ "data.mean() # Gives all averages\n",
+ "avg_mpg = data.mean()[\"mpg\"] # Gives the average mpg\n",
+ "\n",
+ "avg_medium = data[data[\"type\"].isin([\"Medium\"])].mean()[\"mpg\"]\n",
+ "\n",
+ "print(\"Average miles per galon is: \\n{}\".format(round(avg_mpg, 2)), \"\\nAverage miles per galon for all Medium type cars is: \\n{}\".format(round(avg_medium,2)))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Conversion to SI units\n",
+ "- Create a Series containing the range in km/l and another Series containing the weight in kg.\n",
+ "- Find the average of these new Vectors\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0 14.018470\n",
+ "1 14.018470\n",
+ "2 15.717678\n",
+ "3 13.593668\n",
+ "4 13.593668\n",
+ "Name: mpg, dtype: float64\n",
+ "0 1162.24\n",
+ "1 1064.63\n",
+ "2 837.63\n",
+ "3 1026.04\n",
+ "4 1107.76\n",
+ "Name: weight, dtype: float64\n",
+ "\n",
+ "Average Kilometer per liter is: \n",
+ "10.44 \n",
+ "Average weight in kilogram is: \n",
+ "1316.98\n"
+ ]
+ }
+ ],
+ "source": [
+ "t_kml = data[\"mpg\"]*1.61/3.79\n",
+ "t_kg = data[\"weight\"]*0.454\n",
+ "\n",
+ "print(t_kml.head())\n",
+ "print(t_kg.head())\n",
+ "\n",
+ "avg_kml = t_kml.mean()\n",
+ "avg_kg = t_kg.mean()\n",
+ "print(\"\\nAverage Kilometer per liter is: \\n{}\".format(round(avg_kml, 2)), \"\\nAverage weight in kilogram is: \\n{}\".format(round(avg_kg,2)))"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python [conda env:root]",
+ "language": "python",
+ "name": "conda-root-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/requirements.txt b/requirements.txt
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..30d634363c8c94c1fe5412d74cedcc061c4e5571 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -0,0 +1,5 @@
+numpy==1.19.1
+pandas==1.1.3
+matplotlib==3.3.1
+opencv-python==4.1.2.30
+tensorflow==2.1.0
\ No newline at end of file