e07-numpy-import-export-rozwiazania.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NumPy - Import i eksport - zadania"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Numpy Loadtext\n",
    "\n",
    "1. Użyj danych z sekcji \"Input\" (patrz poniżej)\n",
    "1. Wczytaj tekst z ``URL``\n",
    "1. Z pierwszej linii wybierz nazwy gatunków Irysów i zapisz rezultat jako str do ``species: ndarray``\n",
    "1. W pozostałych linii:\n",
    "\n",
    "    * Wczytaj kolumny z danymi i zapisz jako float do ``features: ndarray``\n",
    "    * Wczytaj ostatnią kolumnę z numerami gatunków i zapisz jako int do ``labels: ndarray``\n",
    "\n",
    "1. Wyświetl ``species``, ``labels`` i ``features``\n",
    "1. Porównaj wyniki z sekcją \"Output\" (patrz poniżej)\n",
    "\n",
    "Output:\n",
    "```python\n",
    "species: ndarray\n",
    "# array(['setosa', 'versicolor', 'virginica'], dtype='<U10')\n",
    "\n",
    "features: ndarray\n",
    "# array([[5.4, 3.9, 1.3, 0.4],\n",
    "#        [5.9, 3. , 5.1, 1.8],\n",
    "#        [6. , 3.4, 4.5, 1.6],\n",
    "#        [7.3, 2.9, 6.3, 1.8],\n",
    "#        ...\n",
    "#        [6.8, 3.2, 5.9, 2.3]])\n",
    "\n",
    "labels: ndarray\n",
    "# array([0, 2, 1, 2, ..., 0, 2, 2, 2])\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "URL = 'https://python.astrotech.io/_static/iris-dirty.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype='<U10')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "species = np.loadtxt(URL, delimiter=',', max_rows=1, usecols=(2,3,4), dtype=np.str)\n",
    "species"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.4, 3.9, 1.3, 0.4],\n",
       "       [5.9, 3. , 5.1, 1.8],\n",
       "       [6. , 3.4, 4.5, 1.6],\n",
       "       [7.3, 2.9, 6.3, 1.8],\n",
       "       [5.6, 2.5, 3.9, 1.1],\n",
       "       [5.4, 3.9, 1.3, 0.4],\n",
       "       [5.5, 2.6, 4.4, 1.2],\n",
       "       [5.7, 2.9, 4.2, 1.3],\n",
       "       [4.9, 3.1, 1.5, 0.1],\n",
       "       [6.7, 2.5, 5.8, 1.8],\n",
       "       [6.5, 3. , 5.2, 2. ],\n",
       "       [5.1, 3.3, 1.7, 0.5],\n",
       "       [4.6, 3.4, 1.4, 0.3],\n",
       "       [7.4, 2.8, 6.1, 1.9],\n",
       "       [7.7, 3.8, 6.7, 2.2],\n",
       "       [6.6, 3. , 4.4, 1.4],\n",
       "       [7.2, 3.2, 6. , 1.8],\n",
       "       [5.8, 2.7, 5.1, 1.9],\n",
       "       [6.5, 3. , 5.5, 1.8],\n",
       "       [6.1, 2.9, 4.7, 1.4],\n",
       "       [5.2, 3.5, 1.5, 0.2],\n",
       "       [5.9, 3.2, 4.8, 1.8],\n",
       "       [6.1, 3. , 4.6, 1.4],\n",
       "       [5.2, 4.1, 1.5, 0.1],\n",
       "       [5.1, 3.8, 1.9, 0.4],\n",
       "       [4.8, 3.4, 1.6, 0.2],\n",
       "       [6. , 2.2, 5. , 1.5],\n",
       "       [6.4, 3.2, 5.3, 2.3],\n",
       "       [4.9, 3. , 1.4, 0.2],\n",
       "       [6.3, 2.9, 5.6, 1.8],\n",
       "       [6. , 3. , 4.8, 1.8],\n",
       "       [4.3, 3. , 1.1, 0.1],\n",
       "       [6.9, 3.1, 4.9, 1.5],\n",
       "       [6.3, 2.3, 4.4, 1.3],\n",
       "       [6.4, 3.1, 5.5, 1.8],\n",
       "       [6.5, 3.2, 5.1, 2. ],\n",
       "       [5. , 3.3, 1.4, 0.2],\n",
       "       [5.5, 2.4, 3.7, 1. ],\n",
       "       [6.1, 3. , 4.9, 1.8],\n",
       "       [6.4, 3.2, 4.5, 1.5],\n",
       "       [5.5, 2.4, 3.8, 1.1],\n",
       "       [6.2, 2.9, 4.3, 1.3],\n",
       "       [6.2, 2.8, 4.8, 1.8],\n",
       "       [5.7, 2.5, 5. , 2. ],\n",
       "       [5.4, 3.4, 1.5, 0.4],\n",
       "       [5.6, 3. , 4.1, 1.3],\n",
       "       [6. , 2.7, 5.1, 1.6],\n",
       "       [5.2, 2.7, 3.9, 1.4],\n",
       "       [6.7, 3.1, 4.7, 1.5],\n",
       "       [5.5, 2.5, 4. , 1.3],\n",
       "       [6.7, 3.3, 5.7, 2.5],\n",
       "       [5.1, 3.7, 1.5, 0.4],\n",
       "       [7.7, 3. , 6.1, 2.3],\n",
       "       [6.6, 2.9, 4.6, 1.3],\n",
       "       [4.8, 3.4, 1.9, 0.2],\n",
       "       [4.8, 3. , 1.4, 0.1],\n",
       "       [4.4, 3.2, 1.3, 0.2],\n",
       "       [5.1, 3.8, 1.5, 0.3],\n",
       "       [5.4, 3. , 4.5, 1.5],\n",
       "       [4.6, 3.2, 1.4, 0.2],\n",
       "       [5.6, 3. , 4.5, 1.5],\n",
       "       [5.5, 3.5, 1.3, 0.2],\n",
       "       [6.5, 2.8, 4.6, 1.5],\n",
       "       [5.6, 2.8, 4.9, 2. ],\n",
       "       [5. , 3.5, 1.6, 0.6],\n",
       "       [5.7, 4.4, 1.5, 0.4],\n",
       "       [5.4, 3.4, 1.7, 0.2],\n",
       "       [4.8, 3.1, 1.6, 0.2],\n",
       "       [5. , 3.6, 1.4, 0.2],\n",
       "       [5.1, 3.5, 1.4, 0.3],\n",
       "       [5.7, 2.8, 4.1, 1.3],\n",
       "       [4.9, 3.1, 1.5, 0.2],\n",
       "       [6.4, 2.7, 5.3, 1.9],\n",
       "       [5. , 3.5, 1.3, 0.3],\n",
       "       [4.6, 3.6, 1. , 0.2],\n",
       "       [5.7, 2.8, 4.5, 1.3],\n",
       "       [6.3, 2.5, 4.9, 1.5],\n",
       "       [7.7, 2.8, 6.7, 2. ],\n",
       "       [7.2, 3.6, 6.1, 2.5],\n",
       "       [6.4, 2.9, 4.3, 1.3],\n",
       "       [4.9, 3.6, 1.4, 0.1],\n",
       "       [7.2, 3. , 5.8, 1.6],\n",
       "       [7. , 3.2, 4.7, 1.4],\n",
       "       [5.1, 3.5, 1.4, 0.2],\n",
       "       [5.8, 2.7, 3.9, 1.2],\n",
       "       [4.7, 3.2, 1.6, 0.2],\n",
       "       [6.9, 3.1, 5.4, 2.1],\n",
       "       [5.7, 2.6, 3.5, 1. ],\n",
       "       [5.7, 3.8, 1.7, 0.3],\n",
       "       [5.8, 2.7, 5.1, 1.9],\n",
       "       [4.4, 2.9, 1.4, 0.2],\n",
       "       [6.4, 2.8, 5.6, 2.2],\n",
       "       [6.1, 2.8, 4.7, 1.2],\n",
       "       [5.4, 3.7, 1.5, 0.2],\n",
       "       [6.4, 2.8, 5.6, 2.1],\n",
       "       [6.7, 3. , 5. , 1.7],\n",
       "       [4.9, 2.4, 3.3, 1. ],\n",
       "       [4.5, 2.3, 1.3, 0.3],\n",
       "       [5. , 3. , 1.6, 0.2],\n",
       "       [5.1, 2.5, 3. , 1.1],\n",
       "       [6.3, 3.4, 5.6, 2.4],\n",
       "       [6.5, 3. , 5.8, 2.2],\n",
       "       [6.7, 3. , 5.2, 2.3],\n",
       "       [6.2, 2.2, 4.5, 1.5],\n",
       "       [5.3, 3.7, 1.5, 0.2],\n",
       "       [6.1, 2.8, 4. , 1.3],\n",
       "       [5. , 2.3, 3.3, 1. ],\n",
       "       [6.8, 2.8, 4.8, 1.4],\n",
       "       [7.1, 3. , 5.9, 2.1],\n",
       "       [6.3, 2.7, 4.9, 1.8],\n",
       "       [5.1, 3.8, 1.6, 0.2],\n",
       "       [7.6, 3. , 6.6, 2.1],\n",
       "       [6.9, 3.1, 5.1, 2.3],\n",
       "       [5. , 3.4, 1.6, 0.4],\n",
       "       [6.7, 3.3, 5.7, 2.1],\n",
       "       [5.8, 2.6, 4. , 1.2],\n",
       "       [7.7, 2.6, 6.9, 2.3],\n",
       "       [5.8, 4. , 1.2, 0.2],\n",
       "       [5.2, 3.4, 1.4, 0.2],\n",
       "       [5.7, 3. , 4.2, 1.2],\n",
       "       [4.6, 3.1, 1.5, 0.2],\n",
       "       [7.9, 3.8, 6.4, 2. ],\n",
       "       [4.8, 3. , 1.4, 0.3],\n",
       "       [6.7, 3.1, 5.6, 2.4],\n",
       "       [5.5, 2.3, 4. , 1.3],\n",
       "       [6.2, 3.4, 5.4, 2.3],\n",
       "       [6.3, 2.5, 5. , 1.9],\n",
       "       [6.3, 3.3, 6. , 2.5],\n",
       "       [6.7, 3.1, 4.4, 1.4],\n",
       "       [4.4, 3. , 1.3, 0.2],\n",
       "       [6.1, 2.6, 5.6, 1.4],\n",
       "       [5.8, 2.7, 4.1, 1. ],\n",
       "       [5.4, 3.9, 1.7, 0.4],\n",
       "       [5. , 3.2, 1.2, 0.2],\n",
       "       [5.8, 2.8, 5.1, 2.4],\n",
       "       [4.7, 3.2, 1.3, 0.2],\n",
       "       [6.9, 3.2, 5.7, 2.3],\n",
       "       [5. , 2. , 3.5, 1. ],\n",
       "       [5.6, 2.7, 4.2, 1.3],\n",
       "       [6. , 2.2, 4. , 1. ],\n",
       "       [5.1, 3.4, 1.5, 0.2],\n",
       "       [6. , 2.9, 4.5, 1.5],\n",
       "       [5.6, 2.9, 3.6, 1.3],\n",
       "       [6.8, 3. , 5.5, 2.1],\n",
       "       [5.5, 4.2, 1.4, 0.2],\n",
       "       [6.3, 3.3, 4.7, 1.6],\n",
       "       [5.9, 3. , 4.2, 1.5],\n",
       "       [5. , 3.4, 1.5, 0.2],\n",
       "       [4.9, 2.5, 4.5, 1.7],\n",
       "       [6.3, 2.8, 5.1, 1.5],\n",
       "       [6.8, 3.2, 5.9, 2.3]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = np.loadtxt(URL, delimiter=',', skiprows=1, usecols=(0,1,2,3))\n",
    "features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 1, 2, 1, 0, 1, 1, 0, 2, 2, 0, 0, 2, 2, 1, 2, 2, 2, 1, 0, 1,\n",
       "       1, 0, 0, 0, 2, 2, 0, 2, 2, 0, 1, 1, 2, 2, 0, 1, 2, 1, 1, 1, 2, 2,\n",
       "       0, 1, 1, 1, 1, 1, 2, 0, 2, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 2, 0, 0,\n",
       "       0, 0, 0, 0, 1, 0, 2, 0, 0, 1, 1, 2, 2, 1, 0, 2, 1, 0, 1, 0, 2, 1,\n",
       "       0, 2, 0, 2, 1, 0, 2, 1, 1, 0, 0, 1, 2, 2, 2, 1, 0, 1, 1, 1, 2, 2,\n",
       "       0, 2, 2, 0, 2, 1, 2, 0, 0, 1, 0, 2, 0, 2, 1, 2, 2, 2, 1, 0, 2, 1,\n",
       "       0, 0, 2, 0, 2, 1, 1, 1, 0, 1, 1, 2, 0, 1, 1, 0, 2, 2, 2])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = np.loadtxt(URL, delimiter=',', skiprows=1, usecols=4, dtype=np.int)\n",
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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