e07-numpy-import-export-lekcja.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NumPy - Import i eksport - lekcja"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "URL = 'https://python.astrotech.io/_static/iris.csv'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Wczytywanie danych\n",
    "- z pliku\n",
    "- z sieci\n",
    "\n",
    "- `np.loadtxt()`: `.csv`, `.txt`, `.dat`\n",
    "- `np.load()`: `.npy`\n",
    "- `np.loads()`: `.pkl`\n",
    "- `np.fromstring()`: ...\n",
    "- `np.fromregex()`: ...\n",
    "- `np.genfromtxt()`: ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.loadtxt(URL, skiprows=1, delimiter=',', usecols=(0,1,2,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = np.loadtxt(URL, skiprows=1, delimiter=',', usecols=4, dtype=np.str)\n",
    "species = np.unique(y)\n",
    "\n",
    "y[ y == 'setosa' ] = 0\n",
    "y[ y == 'virginica' ] = 1\n",
    "y[ y == 'versicolor' ] = 2\n",
    "y = y.astype(np.int8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "header = np.loadtxt(URL, max_rows=1, delimiter=',', dtype=np.str, usecols=(0,1,2,3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Eksport\n",
    "\n",
    "- plik tekstowy `np.savetxt()`: `.csv`, `.txt`, `.dat`\n",
    "- plik binarny: `np.save()`: `.npy`\n",
    "- plik binarny: `np.savez()`: `.npz`\n",
    "- plik binarny: `np.savez_compressed()`: `.npz`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.40,3.90,1.30,0.40\r\n",
      "5.90,3.00,5.10,1.80\r\n",
      "6.00,3.40,4.50,1.60\r\n",
      "7.30,2.90,6.30,1.80\r\n",
      "5.60,2.50,3.90,1.10\r\n",
      "5.40,3.90,1.30,0.40\r\n",
      "5.50,2.60,4.40,1.20\r\n",
      "5.70,2.90,4.20,1.30\r\n",
      "4.90,3.10,1.50,0.10\r\n",
      "6.70,2.50,5.80,1.80\r\n",
      "6.50,3.00,5.20,2.00\r\n",
      "5.10,3.30,1.70,0.50\r\n",
      "4.60,3.40,1.40,0.30\r\n",
      "7.40,2.80,6.10,1.90\r\n",
      "7.70,3.80,6.70,2.20\r\n",
      "6.60,3.00,4.40,1.40\r\n",
      "7.20,3.20,6.00,1.80\r\n",
      "5.80,2.70,5.10,1.90\r\n",
      "6.50,3.00,5.50,1.80\r\n",
      "6.10,2.90,4.70,1.40\r\n",
      "5.20,3.50,1.50,0.20\r\n",
      "5.90,3.20,4.80,1.80\r\n",
      "6.10,3.00,4.60,1.40\r\n",
      "5.20,4.10,1.50,0.10\r\n",
      "5.10,3.80,1.90,0.40\r\n",
      "4.80,3.40,1.60,0.20\r\n",
      "6.00,2.20,5.00,1.50\r\n",
      "6.40,3.20,5.30,2.30\r\n",
      "4.90,3.00,1.40,0.20\r\n",
      "6.30,2.90,5.60,1.80\r\n",
      "6.00,3.00,4.80,1.80\r\n",
      "4.30,3.00,1.10,0.10\r\n",
      "6.90,3.10,4.90,1.50\r\n",
      "6.30,2.30,4.40,1.30\r\n",
      "6.40,3.10,5.50,1.80\r\n",
      "6.50,3.20,5.10,2.00\r\n",
      "5.00,3.30,1.40,0.20\r\n",
      "5.50,2.40,3.70,1.00\r\n",
      "6.10,3.00,4.90,1.80\r\n",
      "6.40,3.20,4.50,1.50\r\n",
      "5.50,2.40,3.80,1.10\r\n",
      "6.20,2.90,4.30,1.30\r\n",
      "6.20,2.80,4.80,1.80\r\n",
      "5.70,2.50,5.00,2.00\r\n",
      "5.40,3.40,1.50,0.40\r\n",
      "5.60,3.00,4.10,1.30\r\n",
      "6.00,2.70,5.10,1.60\r\n",
      "5.20,2.70,3.90,1.40\r\n",
      "6.70,3.10,4.70,1.50\r\n",
      "5.50,2.50,4.00,1.30\r\n",
      "6.70,3.30,5.70,2.50\r\n",
      "5.10,3.70,1.50,0.40\r\n",
      "7.70,3.00,6.10,2.30\r\n",
      "6.60,2.90,4.60,1.30\r\n",
      "4.80,3.40,1.90,0.20\r\n",
      "4.80,3.00,1.40,0.10\r\n",
      "4.40,3.20,1.30,0.20\r\n",
      "5.10,3.80,1.50,0.30\r\n",
      "5.40,3.00,4.50,1.50\r\n",
      "4.60,3.20,1.40,0.20\r\n",
      "5.60,3.00,4.50,1.50\r\n",
      "5.50,3.50,1.30,0.20\r\n",
      "6.50,2.80,4.60,1.50\r\n",
      "5.60,2.80,4.90,2.00\r\n",
      "5.00,3.50,1.60,0.60\r\n",
      "5.70,4.40,1.50,0.40\r\n",
      "5.40,3.40,1.70,0.20\r\n",
      "4.80,3.10,1.60,0.20\r\n",
      "5.00,3.60,1.40,0.20\r\n",
      "5.10,3.50,1.40,0.30\r\n",
      "5.70,2.80,4.10,1.30\r\n",
      "4.90,3.10,1.50,0.20\r\n",
      "6.40,2.70,5.30,1.90\r\n",
      "5.00,3.50,1.30,0.30\r\n",
      "4.60,3.60,1.00,0.20\r\n",
      "5.70,2.80,4.50,1.30\r\n",
      "6.30,2.50,4.90,1.50\r\n",
      "7.70,2.80,6.70,2.00\r\n",
      "7.20,3.60,6.10,2.50\r\n",
      "6.40,2.90,4.30,1.30\r\n",
      "4.90,3.60,1.40,0.10\r\n",
      "7.20,3.00,5.80,1.60\r\n",
      "7.00,3.20,4.70,1.40\r\n",
      "5.10,3.50,1.40,0.20\r\n",
      "5.80,2.70,3.90,1.20\r\n",
      "4.70,3.20,1.60,0.20\r\n",
      "6.90,3.10,5.40,2.10\r\n",
      "5.70,2.60,3.50,1.00\r\n",
      "5.70,3.80,1.70,0.30\r\n",
      "5.80,2.70,5.10,1.90\r\n",
      "4.40,2.90,1.40,0.20\r\n",
      "6.40,2.80,5.60,2.20\r\n",
      "6.10,2.80,4.70,1.20\r\n",
      "5.40,3.70,1.50,0.20\r\n",
      "6.40,2.80,5.60,2.10\r\n",
      "6.70,3.00,5.00,1.70\r\n",
      "4.90,2.40,3.30,1.00\r\n",
      "4.50,2.30,1.30,0.30\r\n",
      "5.00,3.00,1.60,0.20\r\n",
      "5.10,2.50,3.00,1.10\r\n",
      "6.30,3.40,5.60,2.40\r\n",
      "6.50,3.00,5.80,2.20\r\n",
      "6.70,3.00,5.20,2.30\r\n",
      "6.20,2.20,4.50,1.50\r\n",
      "5.30,3.70,1.50,0.20\r\n",
      "6.10,2.80,4.00,1.30\r\n",
      "5.00,2.30,3.30,1.00\r\n",
      "6.80,2.80,4.80,1.40\r\n",
      "7.10,3.00,5.90,2.10\r\n",
      "6.30,2.70,4.90,1.80\r\n",
      "5.10,3.80,1.60,0.20\r\n",
      "7.60,3.00,6.60,2.10\r\n",
      "6.90,3.10,5.10,2.30\r\n",
      "5.00,3.40,1.60,0.40\r\n",
      "6.70,3.30,5.70,2.10\r\n",
      "5.80,2.60,4.00,1.20\r\n",
      "7.70,2.60,6.90,2.30\r\n",
      "5.80,4.00,1.20,0.20\r\n",
      "5.20,3.40,1.40,0.20\r\n",
      "5.70,3.00,4.20,1.20\r\n",
      "4.60,3.10,1.50,0.20\r\n",
      "7.90,3.80,6.40,2.00\r\n",
      "4.80,3.00,1.40,0.30\r\n",
      "6.70,3.10,5.60,2.40\r\n",
      "5.50,2.30,4.00,1.30\r\n",
      "6.20,3.40,5.40,2.30\r\n",
      "6.30,2.50,5.00,1.90\r\n",
      "6.30,3.30,6.00,2.50\r\n",
      "6.70,3.10,4.40,1.40\r\n",
      "4.40,3.00,1.30,0.20\r\n",
      "6.10,2.60,5.60,1.40\r\n",
      "5.80,2.70,4.10,1.00\r\n",
      "5.40,3.90,1.70,0.40\r\n",
      "5.00,3.20,1.20,0.20\r\n",
      "5.80,2.80,5.10,2.40\r\n",
      "4.70,3.20,1.30,0.20\r\n",
      "6.90,3.20,5.70,2.30\r\n",
      "5.00,2.00,3.50,1.00\r\n",
      "5.60,2.70,4.20,1.30\r\n",
      "6.00,2.20,4.00,1.00\r\n",
      "5.10,3.40,1.50,0.20\r\n",
      "6.00,2.90,4.50,1.50\r\n",
      "5.60,2.90,3.60,1.30\r\n",
      "6.80,3.00,5.50,2.10\r\n",
      "5.50,4.20,1.40,0.20\r\n",
      "6.30,3.30,4.70,1.60\r\n",
      "5.90,3.00,4.20,1.50\r\n",
      "5.00,3.40,1.50,0.20\r\n",
      "4.90,2.50,4.50,1.70\r\n",
      "6.30,2.80,5.10,1.50\r\n",
      "6.80,3.20,5.90,2.30\r\n"
     ]
    }
   ],
   "source": [
    "FILE = r'iris.csv'\n",
    "np.savetxt(FILE, X, fmt='%.2f', delimiter=',')\n",
    "\n",
    "%cat $FILE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Łączenie Tablic\n",
    "\n",
    "- `np.concatenate()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "setosa = (y == 0)\n",
    "sepal_length = (header == 'sepal_length')\n",
    "sepal_width = (header == 'sepal_width')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "sl = X[setosa, sepal_length]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "sw = X[setosa, sepal_width]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5.4, 5.4, 4.9, 5.1, 4.6, 5.2, 5.2, 5.1, 4.8, 4.9, 4.3, 5. , 5.4,\n",
       "       5.1, 4.8, 4.8, 4.4, 5.1, 4.6, 5.5, 5. , 5.7, 5.4, 4.8, 5. , 5.1,\n",
       "       4.9, 5. , 4.6, 4.9, 5.1, 4.7, 5.7, 4.4, 5.4, 4.5, 5. , 5.3, 5.1,\n",
       "       5. , 5.8, 5.2, 4.6, 4.8, 4.4, 5.4, 5. , 4.7, 5.1, 5.5, 5. ])"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.9, 3.9, 3.1, 3.3, 3.4, 3.5, 4.1, 3.8, 3.4, 3. , 3. , 3.3, 3.4,\n",
       "       3.7, 3.4, 3. , 3.2, 3.8, 3.2, 3.5, 3.5, 4.4, 3.4, 3.1, 3.6, 3.5,\n",
       "       3.1, 3.5, 3.6, 3.6, 3.5, 3.2, 3.8, 2.9, 3.7, 2.3, 3. , 3.7, 3.8,\n",
       "       3.4, 4. , 3.4, 3.1, 3. , 3. , 3.9, 3.2, 3.2, 3.4, 4.2, 3.4])"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5.4, 5.4, 4.9, 5.1, 4.6, 5.2, 5.2, 5.1, 4.8, 4.9, 4.3, 5. , 5.4,\n",
       "       5.1, 4.8, 4.8, 4.4, 5.1, 4.6, 5.5, 5. , 5.7, 5.4, 4.8, 5. , 5.1,\n",
       "       4.9, 5. , 4.6, 4.9, 5.1, 4.7, 5.7, 4.4, 5.4, 4.5, 5. , 5.3, 5.1,\n",
       "       5. , 5.8, 5.2, 4.6, 4.8, 4.4, 5.4, 5. , 4.7, 5.1, 5.5, 5. , 3.9,\n",
       "       3.9, 3.1, 3.3, 3.4, 3.5, 4.1, 3.8, 3.4, 3. , 3. , 3.3, 3.4, 3.7,\n",
       "       3.4, 3. , 3.2, 3.8, 3.2, 3.5, 3.5, 4.4, 3.4, 3.1, 3.6, 3.5, 3.1,\n",
       "       3.5, 3.6, 3.6, 3.5, 3.2, 3.8, 2.9, 3.7, 2.3, 3. , 3.7, 3.8, 3.4,\n",
       "       4. , 3.4, 3.1, 3. , 3. , 3.9, 3.2, 3.2, 3.4, 4.2, 3.4])"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.concatenate((sl, sw))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = np.empty((51, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "result[:,0] = sl\n",
    "result[:,1] = sw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.4, 3.9],\n",
       "       [5.4, 3.9],\n",
       "       [4.9, 3.1],\n",
       "       [5.1, 3.3],\n",
       "       [4.6, 3.4],\n",
       "       [5.2, 3.5],\n",
       "       [5.2, 4.1],\n",
       "       [5.1, 3.8],\n",
       "       [4.8, 3.4],\n",
       "       [4.9, 3. ],\n",
       "       [4.3, 3. ],\n",
       "       [5. , 3.3],\n",
       "       [5.4, 3.4],\n",
       "       [5.1, 3.7],\n",
       "       [4.8, 3.4],\n",
       "       [4.8, 3. ],\n",
       "       [4.4, 3.2],\n",
       "       [5.1, 3.8],\n",
       "       [4.6, 3.2],\n",
       "       [5.5, 3.5],\n",
       "       [5. , 3.5],\n",
       "       [5.7, 4.4],\n",
       "       [5.4, 3.4],\n",
       "       [4.8, 3.1],\n",
       "       [5. , 3.6],\n",
       "       [5.1, 3.5],\n",
       "       [4.9, 3.1],\n",
       "       [5. , 3.5],\n",
       "       [4.6, 3.6],\n",
       "       [4.9, 3.6],\n",
       "       [5.1, 3.5],\n",
       "       [4.7, 3.2],\n",
       "       [5.7, 3.8],\n",
       "       [4.4, 2.9],\n",
       "       [5.4, 3.7],\n",
       "       [4.5, 2.3],\n",
       "       [5. , 3. ],\n",
       "       [5.3, 3.7],\n",
       "       [5.1, 3.8],\n",
       "       [5. , 3.4],\n",
       "       [5.8, 4. ],\n",
       "       [5.2, 3.4],\n",
       "       [4.6, 3.1],\n",
       "       [4.8, 3. ],\n",
       "       [4.4, 3. ],\n",
       "       [5.4, 3.9],\n",
       "       [5. , 3.2],\n",
       "       [4.7, 3.2],\n",
       "       [5.1, 3.4],\n",
       "       [5.5, 4.2],\n",
       "       [5. , 3.4]])"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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