{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "799724ca",
   "metadata": {},
   "source": [
    "# Exercise 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5784c7b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "from seaborn import load_dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56a0fc42",
   "metadata": {},
   "source": [
    "## Task 1: List and dict comprehensions\n",
    "\n",
    "Assume that we have a dictionary that stores some quality measures for models of different sizes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "568d7727",
   "metadata": {},
   "outputs": [],
   "source": [
    "results = {\n",
    "    \"large\": {\"acc\": 0.9, \"f1\": 0.85},\n",
    "    \"medium\": {\"acc\": 0.83, \"f1\": 0.87},\n",
    "    \"small\": {\"acc\": 0.7, \"f1\": 0.5},\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c793df5e",
   "metadata": {},
   "source": [
    "Create a variable called `acc` that maps the models to accurracy. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "280b285e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "3b722008",
   "metadata": {},
   "source": [
    "Filter the results dictionary such that only the information for models with an accurracy over 0.8 is kept."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "84c9f6a1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "b4ad4d8e",
   "metadata": {},
   "source": [
    "## Task 2: Create numpy arrays\n",
    "\n",
    "Create the following arrays:\n",
    "\n",
    "1. A three-dimensional array of shape `(2, 3, 4)` containing zeros\n",
    "2. A two-dimensional array with 4 rows and 5 columns that contain that is equivalent to the list `[[0.1, 0.2, 0.3, 0.4, 0.5], [0.6, 0.7, 0.8, 0.9, 1. ]]`. Do not just type in the numbers.\n",
    "3. Create a 3 x 3 identity matrix\n",
    "4. Create a 3 x 4 empty array (using `np.empty`) and compare it's entries with the ones your neighbor gets. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "41f09872",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e52e90e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bd5425ee",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a36a37b2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "8ea4e457",
   "metadata": {},
   "source": [
    "## Task 3: Numpy indexing\n",
    "\n",
    "Through the entire task, work with the arrays a and b from the lecture slides"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd3b3045",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.arange(5)\n",
    "b = np.arange(12).reshape(4, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da372b36",
   "metadata": {},
   "source": [
    "Select the middle element of a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a722b9e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "a0061ff4",
   "metadata": {},
   "source": [
    "Select all of a (but you need to put something into the square brackets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42bf1b56",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "b4b6d349",
   "metadata": {},
   "source": [
    "Select the last two rows of b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee0bc6e8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "4d1d8086",
   "metadata": {},
   "source": [
    "Select the last two columns of b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "748945bf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "418f1e37",
   "metadata": {},
   "source": [
    "Select the last two columns of the last two rows of b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4da771e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "62682bd1",
   "metadata": {},
   "source": [
    "## Task 4: Numpy calculations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "72f58069",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array([[0.5, 1.5], [2.5, 3.5]])\n",
    "y = np.diag([2, 3])\n",
    "z = np.array([2, 3])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8d7fc19",
   "metadata": {},
   "source": [
    "Do the following calculations with the arrays x, y, z\n",
    "\n",
    "1. Do a matrix multiplication of the two arrays x and y\n",
    "2. Do an elementwise multiplication of the matrices x and y\n",
    "3. Do an elementwise addition x and z\n",
    "4. Do an elementwise addition of x and `z.reshape(-1, 1)`\n",
    "5. Describe the difference between the last two tasks.\n",
    "6. Take the exponent of the array z\n",
    "7. Sum the two rows in x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "11eeca30",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ff1d5110",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7385d4cd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b82328e7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "9542ba4d",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f8a00b69",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b34514fc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d3ca5c28",
   "metadata": {},
   "source": [
    "## Task 5: File paths"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efecb1a6",
   "metadata": {},
   "source": [
    "Define a path called `ROOT` that leads to the directory in which you store all materials for this course. Define the path relative to this notebook and then convert it to an absolute path. Note: The solution is different for everyone and depends on the directory structure you chose. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "215b394b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "57f1c16b",
   "metadata": {},
   "source": [
    "Define a path to this notebook and use it to proof that this notebook exists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1432e22c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "69f8b814",
   "metadata": {},
   "source": [
    "## Task 6: Read and save DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "469ed6e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = load_dataset(\"iris\")\n",
    "iris.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ce13c47",
   "metadata": {},
   "source": [
    "Save this dataset in each of the file formats presented in the slides. Then re-load them into a DataFrame using the corresponding `read` function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e757c46d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "56f19854",
   "metadata": {},
   "source": [
    "Look at the dataset you reloaded from the csv file. What do you see? "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17f7aade",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "bbceda7a",
   "metadata": {},
   "source": [
    "## Task 7: Create Variables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44d44a9c",
   "metadata": {},
   "source": [
    "Add the square and the log of each numerical variable (i.e. all but \"species\") in the dataset. Use \"NAME_squared\" and \"log_NAME\" as naming conventions. Do not type in variable lists."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "01dbd37a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "0999da32",
   "metadata": {},
   "source": [
    "## Task 8: Select data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "643c711f",
   "metadata": {},
   "source": [
    "Select all rows where the species is setosa and the sepal_length is greater or equal to 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "380489cc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "511844a9",
   "metadata": {},
   "source": [
    "## Task 9: Errors and Tracebacks"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2475316",
   "metadata": {},
   "source": [
    "Write me a message in zulip where you describe this error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "06adc834",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data=np.ones(2, 2), columns=[\"a\", \"b\"])"
   ]
  }
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