robert-marik/00-uvod-python.ipynb

## 00-uvod-python.ipynb
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      "name": "python3",
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/gist/robert-marik/a3b7b095cbb625433e4f223c782ea6d4/00-uvod-python.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ipkYKsIQbJF5"
      },
      "source": [
        "Toto je zápisník ukazující začátečníkům možnosti jazyka Python. Můžete si ho číst jako statický dokument. Můžete si ho tlačítkem \"Open in Colab\" otevřit jako interaktivní dokument, kde budete moci spouštět výpočty a zadávat své vlastní úlohy.\n",
        "\n",
        "# Reprodukovatelná a škálkovatelná věda \n",
        "\n",
        "Výpočty do odborných prací je možno dělat na kalkulačce (pravěk) nebo v interkativních  nástrojích typu MS Excel (středověk). Nevýhoda kalkulaček ze zřejmá. Výhoda MS Excel je, že s tímto programem může člověk pracovat po kratičkém zaškolení a hodně věcí si naklikat přes menu. Jsou však ještě lepší možnosti. \n",
        "\n",
        "Kromě výše uvedeného existují ještě nástroje pro dávkové zpracování dat. V tomto případě se nepostupuje interaktivně, ale uživatel sestaví posloupnost příkazů a interpreter provede tyto příkazy podobně, jako spuštění programu. Výhody jsou následující.\n",
        "\n",
        "* Možnost pracovat s daty neomezené velikosti.\n",
        "* Snadná reprodukovatelnost.\n",
        "* Lepší přehlednost v rozsáhlejších projektech.\n",
        "* Možnost zařazení datové anlaýzy jako stavebního kamene delšího řetězce.\n",
        "* Možnost neomezeného opakování stejné analýzy v cyklu nad jinými daty.\n",
        "\n",
        "Nejznámnější nástroje pro dávkové zpracování dat v technické praxi jsou [Matlab](https://cs.wikipedia.org/wiki/MATLAB) a [Python](https://cs.wikipedia.org/wiki/Python). Nevýhoda neinteraktivních nástrojů je, že si nemůžeme požadované výstupy zajistit klikáním v menu. Jedná se však o nástroje s širokou uživatelskou základnou a v době kvalitního vyhledávání na Internetu není těžké \"vygooglit\" na webech s dotazy a odpověďmi příkazy nutné pro kreslení grafů různých typů a další dílčí úkoly spojené se zpracováním dat. Výhodou jsou mnohem větší možnosti, než má Excel. Ukázka grafů je například v článku [Tvorba grafů v Jupyter Notebooku s využitím knihovny Matplotlib ](https://www.root.cz/clanky/tvorba-grafu-v-jupyter-notebooku-s-vyuzitim-knihovny-matplotlib/). (Odkazovaný článek je vlastně věnovaný přístupu pomocí jazyka Python, protože Maplotlib je knihovna pro kreslení grafů v jazyce Python a Jupyter je prostředí, ve kterém příkazy spouštíme.)\n",
        "\n",
        "## Python v prostředí JupyterNotebook\n",
        "\n",
        "Jupyter Notebook je skvělá kalkulačka ve webovém prohlížeči. Šedá políčka značí spustitelný kód. Ten je možné spustit kliknutím na šipku nebo klávesovou zkratkou Shift+Enter. Políčka se spousští postupně a proměnné vytvořené v jednom políčku jsou k dispozici v celém sešitu. "
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Aritmetika s čísly"
      ],
      "metadata": {
        "id": "QmDaZU7iki_O"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ocyvF71N72Gx",
        "outputId": "95e8b371-7ddb-4984-db29-221dabc9770c"
      },
      "source": [
        "a = 4 # promenna a obsahuje nastavenou hodnotu\n",
        "b = a + 2  # promenna b bude o dve vetsi nez promenna a\n",
        "a = b**2   # promenna a se nahradi druhou mocninnou promenne b, promenna b zustava na sve hodnote\n",
        "a  # tisk hodnoty promenne a"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "36"
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Proměnné si udrží hodnoty i v dalších políčkách. V dalším políčku zmenšíme hodnotu $a$ o 30."
      ],
      "metadata": {
        "id": "k6u7R_P915wP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "a-30"
      ],
      "metadata": {
        "id": "Eh4O4KTT6WGY",
        "outputId": "21e151dd-9690-421c-f65e-eedca47e3625",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Operace se zapisují běžným způsobem jako například v Excelu, pouze umocňování se označuje dvojicí hvězdiček. Takto vypadá druhá mocnina dvanáctky."
      ],
      "metadata": {
        "id": "xlTJDwdgk2Il"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "3**2"
      ],
      "metadata": {
        "id": "i1_m650Akzve"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Následující políčko s kódem `1**2 + 2**2 + 3**2 + 4**2`\n",
        "sčítá druhé mocniny přirozených čísel až do čísla 4. Opravte políčko tak, aby sečetlo druhou mocninu přirozených čísel až do čísla 8."
      ],
      "metadata": {
        "id": "K0XOGDkBlHWg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "1**2 + 2**2 + 3**2 + 4**2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PAk5a9OMlG5T",
        "outputId": "00319a04-7ba5-494b-f00e-e7fc9cd83ba8"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "30"
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Hrátky s textem\n",
        "\n",
        "Textový řetezec se bere jako seznam znaků a je možné ho ukládat do proměnná, přistupovat k prvnímu nebo poslednímu znaku, k několika prvním nebo několika posledním znakům, ke skupině znaků uprostřed atd. Pozor na to, že Python čísluje od nuly a první znak má tedy index nulový. Pokud je index záporný, znamená to pořadí od konce."
      ],
      "metadata": {
        "id": "J3C7h3Rn6gxo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "retezec=\"MENDELU\"\n",
        "retezec"
      ],
      "metadata": {
        "id": "nF0Adnda6hOX",
        "outputId": "d40d9168-2024-4acf-aace-d4ebb5c96553",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'MENDELU'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "retezec[0]"
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        "id": "xMewcMvb6_zW",
        "outputId": "adef4288-bc49-4bc4-dc1b-757c2bdf87d1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'M'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "retezec[-2]"
      ],
      "metadata": {
        "id": "0MJoGW9i7Crv",
        "outputId": "422087f4-5153-4a64-af4e-bd42a903c74d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'L'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "retezec[:4]"
      ],
      "metadata": {
        "id": "Y37bYfFc6rP0",
        "outputId": "8186dcf0-1256-4dce-fe9c-d8b66580aeda",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'MEND'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "retezec[-3:]"
      ],
      "metadata": {
        "id": "hm5HFmI86wmG",
        "outputId": "a5d2247d-ea87-4f67-9d07-8aa111c51df8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'ELU'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "retezec[2:4]"
      ],
      "metadata": {
        "id": "BU58KUQP65RY",
        "outputId": "04735ffe-42b2-4763-e5ce-94dbfce8c5cb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'ND'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "retezec + \" je prostě \" + retezec + \".\""
      ],
      "metadata": {
        "id": "SDFfyMub7JB2",
        "outputId": "e105684a-86ba-4870-8a21-6ba4ea2d79ce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'MENDELU je prostě MENDELU.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "veta = \"\".join([retezec,\" je prostě \",retezec,\".\"])\n",
        "veta"
      ],
      "metadata": {
        "id": "JIJEMBxg7SgO",
        "outputId": "6c75eb7e-ed6f-4d58-bd8e-960d17fb3309",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'MENDELU je prostě MENDELU.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "veta[:-5]"
      ],
      "metadata": {
        "id": "v_4Z-aZ37yNe",
        "outputId": "4c4e1084-0caf-43f3-b5d5-cc6481fa94ec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'MENDELU je prostě MEN'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(10*(retezec+\"-\"))[:-1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "uv3XO0aoly4a",
        "outputId": "fe216543-640e-41ad-badf-549197c4c0d8"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'MENDELU-MENDELU-MENDELU-MENDELU-MENDELU-MENDELU-MENDELU-MENDELU-MENDELU-MENDELU'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"-\".join([retezec for i in range(10)])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "c5Yl5JJJl5VQ",
        "outputId": "22868cd4-6b38-4575-fcac-a53b7ad7f817"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'MENDELU-MENDELU-MENDELU-MENDELU-MENDELU-MENDELU-MENDELU-MENDELU-MENDELU-MENDELU'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Práce se seznamem hodnot"
      ],
      "metadata": {
        "id": "ccvyn4Fy6nFo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n"
      ],
      "metadata": {
        "id": "j1wTKsdkAtT9"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = [0, 1, 2, 3, 4, 5, 6]\n",
        "y = [0, 1, 4, 9, 16, 25, 36]\n",
        "plt.plot(x,y)\n"
      ],
      "metadata": {
        "id": "-nVRNkm88vm1",
        "outputId": "f42e29ce-6e77-4877-f4de-33f94b6f363d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f0b6b97b050>]"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = [i**2 for i in range(9)]\n",
        "y"
      ],
      "metadata": {
        "id": "0XZ1lCQtATEG",
        "outputId": "adc45985-a7fa-4a83-d8e6-19ae13eb29fb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0, 1, 4, 9, 16, 25, 36, 49, 64]"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sum(y)"
      ],
      "metadata": {
        "id": "Z5A7gUSY_f-8",
        "outputId": "564e9bfc-1e94-4763-9a3e-075f8d50068c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "204"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(y)"
      ],
      "metadata": {
        "id": "5_1l2QEmAa6X",
        "outputId": "b6505459-2be0-4832-edb3-3ce38975bba6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f0b6a98f290>]"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Práce s poli `np.array`\n",
        "\n",
        "S poli typu `np.array` se poracuje podobně jako se seznamy ale dokážeme s nimi dělat rovnou matematické operace a zápis je pohodlnější."
      ],
      "metadata": {
        "id": "yg6YWG4__3hS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "a = [1,2,3,4,5]\n",
        "A = np.array(a)\n",
        "\n",
        "print(A)\n",
        "\n",
        "for i in A[1:]:\n",
        "    print(\"Prvek je \"+str(i))\n",
        "\n",
        "b = [10,20,30,40,50]\n",
        "B = np.array(b)\n",
        "\n",
        "#print(a*b)  # toto by zpusobilo chybu\n",
        "print(A*B)\n",
        "print(100*A+3)\n",
        "print(A**2)\n",
        "print(B**A)\n",
        "print(10*a)"
      ],
      "metadata": {
        "id": "uJmzVA_jA9Fh",
        "outputId": "c12cc481-7f97-421e-a2d3-7aafcd642751",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1 2 3 4 5]\n",
            "Prvek je 2\n",
            "Prvek je 3\n",
            "Prvek je 4\n",
            "Prvek je 5\n",
            "[ 10  40  90 160 250]\n",
            "[103 203 303 403 503]\n",
            "[ 1  4  9 16 25]\n",
            "[       10       400     27000   2560000 312500000]\n",
            "[1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = [0.01*i for i in range(100)]\n",
        "y = [i**2 for i in x]\n",
        "plt.plot(x,y)\n",
        "plt.grid()\n",
        "len(x)"
      ],
      "metadata": {
        "id": "HvXRD8KoBNdv",
        "outputId": "7ee303e7-e568-4ec0-ee51-045b312cc38e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "100"
            ]
          },
          "metadata": {},
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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V3LJgDQdLT/P0rRlkDuzudEnSSgp0EeHzwye5deEaTlfX8eLMsaT37uJ0SdIGCnSREJez5xgzFuUQExXOK3dcxMAecU6XJG2kQBcJYe/nHeGHi9eRFB/DczPGkNy5vdMlyTlQoIuEqKw19SfZGpbUiWemjdaFKYKAAl0kxDQ+L8s3BnTjHzeNokO0oiAY6F0UCSG1dS7uW7qVxav3cc2oJP547QgidV6WoKFAFwkRFdW13PXSet7fVqRD+YOUAl0kBJSUVXHbs7lsLjzBA5OHcvOFaU6XJF6gQBcJcgXFZUxflMPh0kqemJrON4f2cLok8RIFukgQy91zjJnP5RJmDC/NGseo1M5OlyRepEAXCVJvbzrE3Us2kBQfw6Lpo+md0MHpksTLFOgiQcZay+Mf7uKhd7eT0bszT92SQecOUU6XJT6gQBcJIjV1Ln71zy28nLufK8/vxUPXjdCFnEOIAl0kSJRW1HDn4rV8kl/CXf/Tj7svG6DdEkOMAl0kCOwtKWf6ohz2H6vgz989n+vSk50uSRzg1iFixpiJxpjtxph8Y8zPzzLuWmOMNcZkeK5EETmb1QUlXDXvE46VV/PCjLEK8xDWYqAbY8KBecDlwBDgBmPMkGbGxQE/BlZ7ukgRad6SnP1MXbCazh2ieOPO8Yztm+B0SeIgd9bQxwD51toCa201kAVMbmbcA8AfgUoP1icizahzWR58K4+fvraJcX0T+Oed40nrqt0SQ52x1p59gDHXAROttTMblm8GxlprZzcaMwr4pbX2WmNMNnCvtTa3me81C5gFkJiYmJ6VldWmosvKyoiNjW3TcwNZKPYdij3D2fuuqLE8samKTcV1XJIawY2DoggPC/yNn3qv3TNhwoS11tpmp7XPeaOoMSYM+AswraWx1tr5wHyAjIwMm5mZ2abXzM7Opq3PDWSh2Hco9gxn7nv30XJmPpvD3hIXD1w1jJvH9fZ9cV6i9/rcuRPoB4CURsvJDfd9IQ4YBmQ37CLVA1hqjLmyubV0EWmblTuL+eGL6wgPMzw/YywXnqf5cvlv7gR6DtDfGNOH+iCfAtz4xYPW2lKg6xfLZ5tyEZHWs9by9Mrd/P6dbfTvHsfTt2aQ0kWXipOvajHQrbW1xpjZwHIgHFhord1qjJkL5Fprl3q7SJFQVVlTx5zXN/PP9QeYOLQHD3/vfF1dSM7IrU+GtXYZsKzJffedYWzmuZclIgdOnOaO59ey5WAp935zAD+c0E9HfspZ6Ve9iB/KK6njnr9/TE2ti6duzuDSIYlOlyQBQIEu4ke+mC//U04l/brH8uTN6fTtFnq78knbKNBF/ERZVS0/e3UTb28+REZiOIvuHE+s5sulFfRpEfED+UVl3PHCWgqKy5hz+SAGuPYpzKXV3Do5l4h4z1ubDjL5sY85Xl7NCzPH8v1vnKeNn9ImWgUQcUh1rYvfLdvGok/3MCo1nnk3jaJnpxiny5IApkAXccDBE6f54eJ1rN93gtvG92HOpEFEhusPZjk3CnQRH1vxeRF3L9lAbZ1l3o2j+PaInk6XJEFCgS7iI7V1Lv787x088eEuhvTsyLybRtFHp7wVD1Kgi/jAgROnueul9azde5wbx6Zy33eG6OLN4nEKdBEvey/vCPe+spHaOheP3jCSK8/v5XRJEqQU6CJeUlVbx++Xfc6iT/cwtFdHHrtRUyziXQp0ES/ILyrjrpfWk3foJNMuSmPOpEFER2iKRbxLgS7iQdZaXs7Zz2/ezKNdZBgLbs3gksE6sZb4hgJdxENOVFQz5/XNvLPlMBf2TeCR6y+gR6d2TpclIUSBLuIBn+46yj0vb6SkvIo5lw/i9q/3JSwILtwsgUWBLnIOqmrrePjfO3hqZQF9Ejrw1C3jGZ7cyemyJEQp0EXaaPvhU/w4az2fHz7F1HGp/GLSYNpH6UdKnKNPn0gr1bksCz4u4M/Ld9AxJoKF0zL4n0Ha8CnOU6CLtML+YxX8ZMlG1uw5xreGJvK7q4eTEBvtdFkigAJdxC3WWl5cvY/fLdtGuDE8/N3zuWZUks5bLn5FgS7SgoMnTvOz1zaxcudRvt6/K3+4dgRJ8TpvufgfBbrIGVhrycrZz2/f3obLWh68ahg3jU3VWrn4LQW6SDMKj1cw5/XNrNx5lAv7JvDHa0eQmtDe6bJEzkqBLtKIy2V5ftVe/vju5xjgwauGceOYVB0kJAFBgS7SIL+ojJ+/toncvce5eEA3fnf1MJI7a61cAocCXUJeda2LJz7cxWP/yScmKlx7sEjAUqBLSFu79zhzXt/EjiNlfGdET+6/Yijd4rRfuQQmBbqEpNLTNTz07ucsXrOPnh3b6WhPCQoKdAkp1lre3HSIuW/mcay8itvG9+HuywYQG60fBQl8+hRLyCgoLuO+f23l4/yjDE/qxKLpoxmWpDMjSvBwK9CNMROBvwHhwNPW2j80efweYCZQCxQDt1lr93q4VpE2OV1dxz+y83nywwKiI8OYO3koN43tTbh2RZQg02KgG2PCgXnAZUAhkGOMWWqtzWs0bD2QYa2tMMb8AHgIuN4bBYu4y1rL8q1HeOCtPA6cOM1VF/TiF98eTPc4XUVIgpM7a+hjgHxrbQGAMSYLmAx8GejW2hWNxq8CpnqySJHWyi8qY+5beXy0o5iBiXFkzRrHuL4JTpcl4lXuBHoSsL/RciEw9izjZwDvnEtRIm11srKGR9/fyaJP9xATGc7/fWcIt1zYm8jwMKdLE/E6Y609+wBjrgMmWmtnNizfDIy11s5uZuxUYDbwDWttVTOPzwJmASQmJqZnZWW1qeiysjJiY2Pb9NxAFop9u9uzy1pWFtby2s5qTlXD15MjuLZ/FJ2iA3OeXO916Ght3xMmTFhrrc1o7jF31tAPACmNlpMb7vsvxphLgV9yhjAHsNbOB+YDZGRk2MzMTDde/quys7Np63MDWSj27U7Pn+0q4YG38sg7VEF6787cf8UQRiTH+6ZAL9F7HTo82bc7gZ4D9DfG9KE+yKcANzYeYIwZCTxJ/Zp8kUcqE2lBQXEZf3jnc/6dd4Sk+Bj+fsNIvjOipw7Zl5DVYqBba2uNMbOB5dTvtrjQWrvVGDMXyLXWLgX+BMQCrzT8MO2z1l7pxbolhB0rr+bRD3bywqq9REeE8b/fGsiMr/WhXWS406WJOMqt/dCttcuAZU3uu6/R7Us9XJfIV5yurmPhJ7t5InsX5dW1TBmTyt2XDtC5V0Qa6EhR8Xu1dS5eXVvII+/v4MjJKi4dnMjPJg6kf2Kc06WJ+BUFuvgtay05h2uZ+8hHFBwtZ2RqPI/dOIrRaV2cLk3ELynQxe9Ya8neUczD/97OlgNV9O8eyfyb07lsSKI2eIqchQJd/Mqnu47yl3/vIHfvcZI7xzBzeBRzbrhY510RcYMCXfzCqoISHnlvB6t3HyOxYzQPXjWM72Wk8OnHHynMRdykQBfHWGtZVXCMRz/YyWcFJXSLi+b+K4Zww5hU7YIo0gYKdPE5ay0f5x/l7x/ks2bPMbrFRfOrbw9m6rjeCnKRc6BAF59xuSzvbzvCvBX5bCwspUfHdvzmyqFcPzpFQS7iAQp08bqaOhdLNxzkyY92seNIGald2vP7a4ZzzagkoiMU5CKeokAXrymrquXlnP0sWFnAwdJKBibG8dfrL+A7I3oSodPZinicAl087sjJSp75ZA8vrt7LqcpaxqR14bdXDydzYDftRy7iRQp08ZjNhaUs+LiAtzYdwmUtlw/rycyv92FkamenSxMJCQp0OSc1dS6Wbz3Ms5/uIWfPcWKjI7jlwjSmXZRGakJ7p8sTCSkKdGmT4lNVZK3Zx4ur93H4ZCUpXWL41bcHc/3oFOLaRTpdnkhIUqCL26y15Ow5zvOr9vLulkPU1Fm+3r8rD141jAmDuuuIThGHKdClRScqqnlt3QFeWrOP/KIy4tpFcPO4NG4al8p53ULvGpAi/kqBLs1yuSyfFZTwcs5+3t16mOpaFxekxPPQtSP4zvk9aR+lj46Iv9FPpfyX/ccqeH3dAV5dt5/9x07TsV0EU0anMGV0KkN6dXS6PBE5CwW6cKqyhnc2H+b19YWsKjiGMXBh3wTu/eZAvjW0hw7LFwkQCvQQVVVbx0c7jvKvDQd4L+8IVbUu0hLa85PLBnD1qCSSO2uXQ5FAo0APIbV1LlYVHOPNjQd5Z8shTlbW0rl9JNePTuHqkUlckBKvIzlFApgCPcjV1LlYVVDCss2HWL71CMfKq+kQFc63hvbgigt68bV+XYnUeVVEgoICPQhVVNeycudRlm89zPt5RzhZWUv7qHAuHZzIpOE9yRzYTfPiIkFIgR4kjpysZMXnRby/7Qgrdx6lqtZFx3YRXDokkYlDe3DxAIW4SLBToAeoOpdlw/4TfLi9iP9sL2LLgZMAJMXHcMOYVC4bksiYPl00nSISQhToAaS4wkXWmn2szD/KxzuPUnq6hjADI1M787/fGsglg7szMDFOGzZFQpQC3Y8Vnarks10lrCoo4bNdJewpOQ1sJrFjNJcNSSRzYDe+1q8r8e2jnC5VRPyAAt1PWGvZf+w0uXuPsWZ3/VfB0XIA4qIjGNu3Cxd1q2X65ePo1z1Wa+Ei8hUKdIecrq5j84FSNuw/zvp9J8jde5ziU1UAxLWLYExaF64fncKF5yUwtFcnwsMM2dnZ9E+Mc7hyEfFXCnQfOF1dx7bDJ9l68CSbC0+wqbCUnUVl1LksACldYhh/XgLpaV1IT+3MwB5xOhWtiLSaAt2DXC7LgROn2X74FNuPnOLzw6fYdugkBcVlNGQ3ndtHMjw5nksHJ3JBSjwXpMbTNTba2cJFJCgo0Nug9HQN+0oq2F1STkFxGQXF5ewqLmNXcRmVNa4vxyV3jmFQjzgmDe/J0F4dGdqrI0nxMZr/FhGvcCvQjTETgb8B4cDT1to/NHk8GngOSAdKgOuttXs8W6pv1LksJeVVHCmt4mDpaQ6XVnLwxGkKj59m//EK9h2r4ERFzZfjjYFenWLo260D4/r2pn/3WPonxjIgMU6XYhMRn2ox0I0x4cA84DKgEMgxxiy11uY1GjYDOG6t7WeMmQL8EbjeGwW7q6bORUVVHRU1tZRX1XGqsoZTlbWcqqzlxOlqTlTUcLy8mmMV1ZSUVVNSXkXRySpKyqu/nNv+QlREGMnxMSR1jmHS8J707tKe3gkdSOvanrSEDjoCU0T8gjtr6GOAfGttAYAxJguYDDQO9MnArxtuvwo8Zowx1tr/TkYPWJKzn7+urKDd2mysrV+jrq1zUeuy1NS5qKqt/2oays1pFxlGQodoEmKj6BobzZCeHeke147uHaPp0bEdPTvF0DO+HV3aRxGmjZQi4ufcCfQkYH+j5UJg7JnGWGtrjTGlQAJwtPEgY8wsYBZAYmIi2dnZrS648EgtiTEuIsMrCTNggPAwQ5iBCAOR4YbIsAiiwiE63BDd8G9MBHSINMREGDpE1t+OCv8ipGsbvirqF6uAYigprp8/8hdlZWVt+j8LZKHYM4Rm36HYM3i2b59uFLXWzgfmA2RkZNjMzMxWf49MYFR2Nm15bqDLDsG+Q7FnCM2+Q7Fn8Gzf7py56QCQ0mg5ueG+ZscYYyKATvjXyq2ISNBzJ9BzgP7GmD7GmChgCrC0yZilwK0Nt68D/uON+XMRETmzFqdcGubEZwPLqd9tcaG1dqsxZi6Qa61dCiwAnjfG5APHqA99ERHxIbfm0K21y4BlTe67r9HtSuC7ni1NRERaQ1c/EBEJEgp0EZEgoUAXEQkSCnQRkSBhnNq70BhTDOxt49O70uQo1BARin2HYs8Qmn2HYs/Q+r57W2u7NfeAY4F+LowxudbaDKfr8LVQ7DsUe4bQ7DsUewbP9q0pFxGRIKFAFxEJEoEa6POdLsAhodh3KPYModl3KPYMHuw7IOfQRUTkqwJ1DV1ERJpQoIuIBAm/DnRjzERjzHZjTL4x5ufNPB5tjHm54fHVxpg031fpWW70fI8xJs8Ys8kY84ExprcTdXpaS303GnetMcYaYwJ+9zZ3ejbGfK/h/d5qjFns6xq9wY3PeKoxZoUxZn3D53ySE3V6kjFmoTGmyBiz5QyPG2PMow3/J5uMMaPa9ELWWr/8omNiLSAAAALJSURBVP5UvbuAvkAUsBEY0mTMncATDbenAC87XbcPep4AtG+4/YNA79ndvhvGxQEfAauADKfr9sF73R9YD3RuWO7udN0+6ns+8IOG20OAPU7X7YG+LwZGAVvO8Pgk4B3qr6o5Dljdltfx5zX0Ly9Oba2tBr64OHVjk4FnG26/ClxijAnkqzm32LO1doW1tuHip6yi/gpSgc6d9xrgAeCPQKUvi/MSd3q+HZhnrT0OYK0t8nGN3uBO3xbo2HC7E3DQh/V5hbX2I+qvFXEmk4HnbL1VQLwxpmdrX8efA725i1MnnWmMtbYW+OLi1IHKnZ4bm0H9b/VA12LfDX+Cplhr3/ZlYV7kzns9ABhgjPnEGLPKGDPRZ9V5jzt9/xqYaowppP46DD/yTWmOau3PfrN8epFo8RxjzFQgA/iG07V4mzEmDPgLMM3hUnwtgvppl0zq/xL7yBgz3Fp7wtGqvO8GYJG19mFjzIXUXw1tmLXW5XRh/s6f19BD8eLU7vSMMeZS4JfAldbaKh/V5k0t9R0HDAOyjTF7qJ9jXBrgG0bdea8LgaXW2hpr7W5gB/UBH8jc6XsGsATAWvsZ0I76E1gFM7d+9lviz4EeihenbrFnY8xI4EnqwzwY5lShhb6ttaXW2q7W2jRrbRr12w6utNbmOlOuR7jz+X6D+rVzjDFdqZ+CKfBlkV7gTt/7gEsAjDGDqQ/0Yp9W6XtLgVsa9nYZB5Raaw+1+rs4vfW3hS3Dk6hfK9kF/LLhvrnU/zBD/Rv9CpAPrAH6Ol2zD3p+HzgCbGj4Wup0zb7ou8nYbAJ8Lxc332tD/VRTHrAZmOJ0zT7qewjwCfV7wGwAvul0zR7o+SXgEFBD/V9eM4A7gDsavdfzGv5PNrf1861D/0VEgoQ/T7mIiEgrKNBFRIKEAl1EJEgo0EVEgoQCXUQkSCjQRUSChAJdRCRI/H+bpMJabjPZHwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Tabulky, pandas"
      ],
      "metadata": {
        "id": "ei9hzP_BBXSf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = np.linspace(0,10,11)\n",
        "y = x**2\n",
        "df = pd.DataFrame()\n",
        "df[\"x\"] = x\n",
        "df[\"y\"] = y\n",
        "df[\"suma\"] = df[\"x\"] + df[\"y\"]\n",
        "df"
      ],
      "metadata": {
        "id": "rmR4okn39rhg",
        "outputId": "4ecfb1a2-dee9-4867-c930-e450d5011142",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        }
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       x      y   suma\n",
              "0    0.0    0.0    0.0\n",
              "1    1.0    1.0    2.0\n",
              "2    2.0    4.0    6.0\n",
              "3    3.0    9.0   12.0\n",
              "4    4.0   16.0   20.0\n",
              "5    5.0   25.0   30.0\n",
              "6    6.0   36.0   42.0\n",
              "7    7.0   49.0   56.0\n",
              "8    8.0   64.0   72.0\n",
              "9    9.0   81.0   90.0\n",
              "10  10.0  100.0  110.0"
            ],
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>x</th>\n",
              "      <th>y</th>\n",
              "      <th>suma</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
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              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>12.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>20.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>30.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>6.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>42.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>7.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>56.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>8.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>72.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>9.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>90.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>10.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>110.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3b1f998b-7153-423e-8552-7ecad01fedb8 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3b1f998b-7153-423e-8552-7ecad01fedb8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"soucin\"] = df.x * df.y\n",
        "df"
      ],
      "metadata": {
        "id": "Ub4ff822-CYF",
        "outputId": "86dd4a37-e715-425c-ea6a-6dbd4e176b72",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        }
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       x      y   suma  soucin\n",
              "0    0.0    0.0    0.0     0.0\n",
              "1    1.0    1.0    2.0     1.0\n",
              "2    2.0    4.0    6.0     8.0\n",
              "3    3.0    9.0   12.0    27.0\n",
              "4    4.0   16.0   20.0    64.0\n",
              "5    5.0   25.0   30.0   125.0\n",
              "6    6.0   36.0   42.0   216.0\n",
              "7    7.0   49.0   56.0   343.0\n",
              "8    8.0   64.0   72.0   512.0\n",
              "9    9.0   81.0   90.0   729.0\n",
              "10  10.0  100.0  110.0  1000.0"
            ],
            "text/html": [
              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "      <th>suma</th>\n",
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              "      <th>0</th>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>8.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>27.0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>64.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>125.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>6.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>216.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>7.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>343.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>8.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>72.0</td>\n",
              "      <td>512.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>9.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>90.0</td>\n",
              "      <td>729.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>10.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>1000.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-48073c95-fcbd-46f3-b1e0-e9ec1a6ba448')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-48073c95-fcbd-46f3-b1e0-e9ec1a6ba448 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-48073c95-fcbd-46f3-b1e0-e9ec1a6ba448');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.plot(x=\"x\")"
      ],
      "metadata": {
        "id": "fuiU1v9L-aGG",
        "outputId": "359a6621-982e-4303-d617-5712af8ee91e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0b6a0095d0>"
            ]
          },
          "metadata": {},
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " x = np.linspace(0,2*np.pi,100)\n",
        " df = pd.DataFrame()\n",
        " df[\"x\"]=x\n",
        " df[\"sin\"] = np.sin(df.x)\n",
        " df[\"cos\"] = np.cos(df.x)\n",
        " df[\"soucet mocnin\"] = df.sin**2 + df.cos**2\n",
        " df"
      ],
      "metadata": {
        "id": "gLPp7aolB2Tr",
        "outputId": "557a1c00-7df5-4a45-f7e7-83bac7b78f7a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           x           sin       cos  soucet mocnin\n",
              "0   0.000000  0.000000e+00  1.000000            1.0\n",
              "1   0.063467  6.342392e-02  0.997987            1.0\n",
              "2   0.126933  1.265925e-01  0.991955            1.0\n",
              "3   0.190400  1.892512e-01  0.981929            1.0\n",
              "4   0.253866  2.511480e-01  0.967949            1.0\n",
              "..       ...           ...       ...            ...\n",
              "95  6.029319 -2.511480e-01  0.967949            1.0\n",
              "96  6.092786 -1.892512e-01  0.981929            1.0\n",
              "97  6.156252 -1.265925e-01  0.991955            1.0\n",
              "98  6.219719 -6.342392e-02  0.997987            1.0\n",
              "99  6.283185 -2.449294e-16  1.000000            1.0\n",
              "\n",
              "[100 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c3f350c6-8066-4f8c-9894-246d26e17ada\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>x</th>\n",
              "      <th>sin</th>\n",
              "      <th>cos</th>\n",
              "      <th>soucet mocnin</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.063467</td>\n",
              "      <td>6.342392e-02</td>\n",
              "      <td>0.997987</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.126933</td>\n",
              "      <td>1.265925e-01</td>\n",
              "      <td>0.991955</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.190400</td>\n",
              "      <td>1.892512e-01</td>\n",
              "      <td>0.981929</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.253866</td>\n",
              "      <td>2.511480e-01</td>\n",
              "      <td>0.967949</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>6.029319</td>\n",
              "      <td>-2.511480e-01</td>\n",
              "      <td>0.967949</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>6.092786</td>\n",
              "      <td>-1.892512e-01</td>\n",
              "      <td>0.981929</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>6.156252</td>\n",
              "      <td>-1.265925e-01</td>\n",
              "      <td>0.991955</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>6.219719</td>\n",
              "      <td>-6.342392e-02</td>\n",
              "      <td>0.997987</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>6.283185</td>\n",
              "      <td>-2.449294e-16</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c3f350c6-8066-4f8c-9894-246d26e17ada')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c3f350c6-8066-4f8c-9894-246d26e17ada button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c3f350c6-8066-4f8c-9894-246d26e17ada');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Ukázka numerické simulace\n",
        "\n",
        "Rychlost s jakou se ochlazuje káva z teploty 100 stupňů je úměrná rozdílu teploty kávy a okolí. Koeficient úměrnosti `k` a teplota okolí `T0` jsou zadány. Pokusíme se proces namodelovat. Jedná se o numerické řešení diferenciální rovnice\n",
        "$$\\frac{\\mathrm dT}{\\mathrm dt}=-k(T-T_0).$$\n",
        "Toto řešení budeme aproximovat použitím dopředné diference, vztahu\n",
        "```\n",
        "T(t+dt) = T(t) - k*(T(t)-T0)*h\n",
        "```\n",
        "a zvoleného diferenčního kroku `h`."
      ],
      "metadata": {
        "id": "iHJ52XaQ4gF1"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oOBP_Z8QnvBv"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "n = 500\n",
        "tmin = 0 \n",
        "tmax = 10\n",
        "t = np.linspace(tmin,tmax,n)\n",
        "T = np.zeros(n)\n",
        "dt = t[1]-t[0]\n",
        "T0 = 18\n",
        "k = 0.5"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "T[0] = 100\n",
        "for i in range(n-1):\n",
        "  dT = - dt * k * (T[i]-T0)\n",
        "  T[i+1] = T[i] + dT"
      ],
      "metadata": {
        "id": "o0lEQF6m4u82"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(t,T)\n"
      ],
      "metadata": {
        "id": "4024_Oy05BcU",
        "outputId": "bcb90c50-b9d6-460a-8740-fe06f08c2a45",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fc73b252750>]"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Výše uvedenou úlohu se naučíme řešit i přesnými analytickými metodami. Ale numerickou simulaci můžeme použít například pro úlohy, kdy není jiná metoda k dispozici. Například stačí předpokládat, že systém reaguje na změnu teploty se zpožděním a že rychlost poklesu teploty souvisí s teplotou před nějakým definovaným časovým okamžikem."
      ],
      "metadata": {
        "id": "EUtzErjY9EG2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "zpozdeni = 10\n",
        "T_zpozdeni = np.zeros(n)\n",
        "T_zpozdeni[0] = 100\n",
        "for i in range(n-1):\n",
        "  dT = - dt * k * (T_zpozdeni[max(i-zpozdeni,0)]-T0)\n",
        "  T_zpozdeni[i+1] = T_zpozdeni[i] + dT"
      ],
      "metadata": {
        "id": "NDZj-WMe5C79"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(t,T,label=\"bez zpozdeni\")\n",
        "plt.plot(t,T_zpozdeni,label=\"se zpozdenim\")\n",
        "plt.legend()\n"
      ],
      "metadata": {
        "id": "gDFR1qr95E3s",
        "outputId": "02f4cc09-c0ca-48c0-a22f-287e23494dd6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fc73ad3edd0>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Následující kód je zopakování přechozího s mírnou modifikací: provádíme simulaci na delším intervalu a s větším zpožděním. Mění se proměnné `n`, `tmax` a `zpozdeni`. Ukazuje, že velké zpoždění může dramaticky změnit chování systému.\n",
        "\n",
        "Opisování kódu podobým stylem není dlouhodobě udržitelný způsob provádění výpočtů (**WET - write everything twice**). Hodí se jenom pro začátečníky, kteří se s prací seznamují. Pro netriviální problémy je vhodnější neopakovat kód, ale používat vlastní definované funkce (**DRY - don't repeat youself**). Ukázka DRY přístupu je níže, kdy budme kreslit grafy několika funkcí a jejich derivací pomocí cyklu přes všechny funkce. "
      ],
      "metadata": {
        "id": "ovXGMQ6o76ie"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "n = 5000\n",
        "tmin = 0 \n",
        "tmax = 100\n",
        "t = np.linspace(tmin,tmax,n)\n",
        "T = np.zeros(n)\n",
        "dt = t[1]-t[0]\n",
        "T0 = 18\n",
        "k = 0.5\n",
        "\n",
        "T[0] = 100\n",
        "for i in range(n-1):\n",
        "  dT = - dt * k * (T[i]-T0)\n",
        "  T[i+1] = T[i] + dT\n",
        "\n",
        "zpozdeni = 150\n",
        "T_zpozdeni = np.zeros(n)\n",
        "T_zpozdeni[0] = 100\n",
        "for i in range(n-1):\n",
        "  dT = - dt * k * (T_zpozdeni[max(i-zpozdeni,0)]-T0)\n",
        "  T_zpozdeni[i+1] = T_zpozdeni[i] + dT  \n",
        "\n",
        "plt.plot(t,T,label=\"bez zpozdeni\")\n",
        "plt.plot(t,T_zpozdeni,label=\"se zpozdenim\")\n",
        "plt.legend()\n"
      ],
      "metadata": {
        "id": "E2h3yt8h65cF",
        "outputId": "bf94a3b1-094f-4419-9b22-1fc11de759ab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fc73acde2d0>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E59ljJKTj5K3"
      },
      "source": [
        "# Ukázka práce s daty v tabulce\n",
        "\n",
        "Obrovské tabulky většinou načteme ze souboru. To může být soubor programu MS Excel, nebo datový soubor s hodnotami oddělenými čárkou (csv soubor). "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jxAfmzOej4VX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "outputId": "b6828bf0-09e2-431e-bafa-591d6b7b7eac"
      },
      "source": [
        "import pandas as pd\n",
        "soubor='http://user.mendelu.cz/marik/temp/Pamatne_stromy.csv'  # stazeno z https://data.brno.cz dne 4.12.2021\n",
        "df=pd.read_csv(soubor)\n",
        "df.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-5094786d-212b-4188-b48e-1cda2d20fdcf\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>ObjectId</th>\n",
              "      <th>ogcfid_bio</th>\n",
              "      <th>intenzitni_trida_tid</th>\n",
              "      <th>dendro_bio_tid</th>\n",
              "      <th>obvod_kmene</th>\n",
              "      <th>prumer_kmene</th>\n",
              "      <th>vyska_taxonu</th>\n",
              "      <th>vyska_koruny</th>\n",
              "      <th>sirka_koruny</th>\n",
              "      <th>polomer_koruny</th>\n",
              "      <th>spodni_okraj_koruny</th>\n",
              "      <th>nasazeni_koruny</th>\n",
              "      <th>ogcfid_pam_strom</th>\n",
              "      <th>cislo</th>\n",
              "      <th>rok_stari</th>\n",
              "      <th>sirka_ochranneho_pasma</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>180.000000</td>\n",
              "      <td>180.000000</td>\n",
              "      <td>180.000000</td>\n",
              "      <td>1.780000e+02</td>\n",
              "      <td>172.000000</td>\n",
              "      <td>172.000000</td>\n",
              "      <td>172.000000</td>\n",
              "      <td>172.000000</td>\n",
              "      <td>172.000000</td>\n",
              "      <td>172.000000</td>\n",
              "      <td>159.000000</td>\n",
              "      <td>159.0</td>\n",
              "      <td>180.000000</td>\n",
              "      <td>178.000000</td>\n",
              "      <td>159.000000</td>\n",
              "      <td>139.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>90.500000</td>\n",
              "      <td>103117.016667</td>\n",
              "      <td>1.227778</td>\n",
              "      <td>1.046032e+06</td>\n",
              "      <td>269.558140</td>\n",
              "      <td>14.715175</td>\n",
              "      <td>19.546512</td>\n",
              "      <td>1.718023</td>\n",
              "      <td>6.253488</td>\n",
              "      <td>1.122384</td>\n",
              "      <td>0.025157</td>\n",
              "      <td>0.0</td>\n",
              "      <td>99.400000</td>\n",
              "      <td>103157.426966</td>\n",
              "      <td>87.238994</td>\n",
              "      <td>9.238777</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>52.105662</td>\n",
              "      <td>41675.298333</td>\n",
              "      <td>0.789813</td>\n",
              "      <td>2.854449e+06</td>\n",
              "      <td>115.013765</td>\n",
              "      <td>40.583220</td>\n",
              "      <td>4.809134</td>\n",
              "      <td>5.328712</td>\n",
              "      <td>10.156077</td>\n",
              "      <td>3.391326</td>\n",
              "      <td>0.317221</td>\n",
              "      <td>0.0</td>\n",
              "      <td>58.599159</td>\n",
              "      <td>2401.931652</td>\n",
              "      <td>68.782767</td>\n",
              "      <td>3.515248</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>58970.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.740100e+04</td>\n",
              "      <td>61.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>101064.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>45.750000</td>\n",
              "      <td>72983.750000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.745525e+04</td>\n",
              "      <td>182.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>46.750000</td>\n",
              "      <td>101072.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.890000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>90.500000</td>\n",
              "      <td>103801.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.750050e+04</td>\n",
              "      <td>252.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>19.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>99.500000</td>\n",
              "      <td>101076.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>8.590000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>135.250000</td>\n",
              "      <td>103851.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.754675e+04</td>\n",
              "      <td>333.250000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>22.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>16.100000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>152.250000</td>\n",
              "      <td>105803.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>11.155000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>180.000000</td>\n",
              "      <td>242061.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>9.044691e+06</td>\n",
              "      <td>820.000000</td>\n",
              "      <td>154.320010</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>15.500000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>197.000000</td>\n",
              "      <td>106337.000000</td>\n",
              "      <td>430.000000</td>\n",
              "      <td>23.080000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5094786d-212b-4188-b48e-1cda2d20fdcf')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "      <script>\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5094786d-212b-4188-b48e-1cda2d20fdcf');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "         ObjectId     ogcfid_bio  ...   rok_stari  sirka_ochranneho_pasma\n",
              "count  180.000000     180.000000  ...  159.000000              139.000000\n",
              "mean    90.500000  103117.016667  ...   87.238994                9.238777\n",
              "std     52.105662   41675.298333  ...   68.782767                3.515248\n",
              "min      1.000000   58970.000000  ...    0.000000                0.500000\n",
              "25%     45.750000   72983.750000  ...    0.000000                6.890000\n",
              "50%     90.500000  103801.500000  ...  100.000000                8.590000\n",
              "75%    135.250000  103851.500000  ...  120.000000               11.155000\n",
              "max    180.000000  242061.000000  ...  430.000000               23.080000\n",
              "\n",
              "[8 rows x 16 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cdyeylYJlpb6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 565
        },
        "outputId": "be34ebb4-ae01-4199-d0d8-70e7a97e1cd8"
      },
      "source": [
        "df.sample(5) # několik náhodných řádků pro představu"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-deb39352-1fce-42dd-a88c-240a9d75b166\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ObjectId</th>\n",
              "      <th>ogcfid_bio</th>\n",
              "      <th>druh_bio_kod</th>\n",
              "      <th>vek_tid</th>\n",
              "      <th>svazitost_tid</th>\n",
              "      <th>intenzitni_trida_tid</th>\n",
              "      <th>sprava_tid</th>\n",
              "      <th>presnost_tid</th>\n",
              "      <th>last_date_bio</th>\n",
              "      <th>dendro_bio_tid</th>\n",
              "      <th>dendro_taxon_tid</th>\n",
              "      <th>obvod_kmene</th>\n",
              "      <th>prumer_kmene</th>\n",
              "      <th>vyska_taxonu</th>\n",
              "      <th>vyska_koruny</th>\n",
              "      <th>sirka_koruny</th>\n",
              "      <th>polomer_koruny</th>\n",
              "      <th>spodni_okraj_koruny</th>\n",
              "      <th>nasazeni_koruny</th>\n",
              "      <th>last_date_dendro</th>\n",
              "      <th>ogcfid_pam_strom</th>\n",
              "      <th>cislo</th>\n",
              "      <th>nazev</th>\n",
              "      <th>duvod_ochrany</th>\n",
              "      <th>rok_stari</th>\n",
              "      <th>pamatne_stromy_druh_tid</th>\n",
              "      <th>sirka_ochranneho_pasma</th>\n",
              "      <th>vyhlaseni_datum</th>\n",
              "      <th>lokalita</th>\n",
              "      <th>last_date_pam_strom</th>\n",
              "      <th>datum_exportu</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>24</td>\n",
              "      <td>103801</td>\n",
              "      <td>Stromy ve stromořadí - listnaté</td>\n",
              "      <td>St</td>\n",
              "      <td>Svah do 1:5</td>\n",
              "      <td>1</td>\n",
              "      <td>OŽP</td>\n",
              "      <td>ortofoto</td>\n",
              "      <td>2020/02/27 00:00:00+00</td>\n",
              "      <td>27436.0</td>\n",
              "      <td>Tilia sp.</td>\n",
              "      <td>190.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>21.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2019/11/21 00:00:00+00</td>\n",
              "      <td>25</td>\n",
              "      <td>105803.0</td>\n",
              "      <td>Maloměřická lipová alej na bývalém hřbitově</td>\n",
              "      <td>Vysoká estetická hodnota a historický význam.</td>\n",
              "      <td>100.0</td>\n",
              "      <td>Zvláště vyhlášené</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2011/12/29 00:00:00+00</td>\n",
              "      <td>Parková plocha bývalého hřbitova podél ulice P...</td>\n",
              "      <td>2021/09/15 00:00:00+00</td>\n",
              "      <td>2021/12/03 00:00:00+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>71</td>\n",
              "      <td>103849</td>\n",
              "      <td>Stromy ve stromořadí - listnaté</td>\n",
              "      <td>St</td>\n",
              "      <td>Svah do 1:5</td>\n",
              "      <td>1</td>\n",
              "      <td>OŽP</td>\n",
              "      <td>ÚMPS</td>\n",
              "      <td>2021/07/19 00:00:00+00</td>\n",
              "      <td>27409.0</td>\n",
              "      <td>Aesculus hippocastanum</td>\n",
              "      <td>220.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>14.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2019/11/21 00:00:00+00</td>\n",
              "      <td>77</td>\n",
              "      <td>101072.0</td>\n",
              "      <td>Stromořadí kaštanů na Malé Klajdovce</td>\n",
              "      <td>Jediné souvislé staré kaštanové stromořadí v B...</td>\n",
              "      <td>120.0</td>\n",
              "      <td>Ze zákona</td>\n",
              "      <td>7.00</td>\n",
              "      <td>1987/01/15 00:00:00+00</td>\n",
              "      <td>Oboustranně kolem staré silnice do zetoru Líše...</td>\n",
              "      <td>2021/09/15 00:00:00+00</td>\n",
              "      <td>2021/12/03 00:00:00+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>103787</td>\n",
              "      <td>Soliterní stromy - listnaté</td>\n",
              "      <td>St</td>\n",
              "      <td>Svah do 1:5</td>\n",
              "      <td>1</td>\n",
              "      <td>OŽP</td>\n",
              "      <td>geodeticky zaměřeno</td>\n",
              "      <td>2018/01/17 00:00:00+00</td>\n",
              "      <td>27428.0</td>\n",
              "      <td>Quercus robur</td>\n",
              "      <td>356.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2017/04/10 00:00:00+00</td>\n",
              "      <td>6</td>\n",
              "      <td>105711.0</td>\n",
              "      <td>Dub před kostelem sv. Jiljí</td>\n",
              "      <td>Dominanta, zajišťující zobytnění urbánního pro...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Ze zákona</td>\n",
              "      <td>11.33</td>\n",
              "      <td>2011/02/01 00:00:00+00</td>\n",
              "      <td>Před kostelem svatého Jiljí v Komárově</td>\n",
              "      <td>2017/04/10 00:00:00+00</td>\n",
              "      <td>2021/12/03 00:00:00+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138</th>\n",
              "      <td>139</td>\n",
              "      <td>103877</td>\n",
              "      <td>Stromy ve stromořadí - listnaté</td>\n",
              "      <td>St</td>\n",
              "      <td>Svah do 1:5</td>\n",
              "      <td>1</td>\n",
              "      <td>OŽP</td>\n",
              "      <td>ÚMPS</td>\n",
              "      <td>2017/04/10 00:00:00+00</td>\n",
              "      <td>27513.0</td>\n",
              "      <td>Tilia sp.</td>\n",
              "      <td>106.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2019/11/21 00:00:00+00</td>\n",
              "      <td>156</td>\n",
              "      <td>101069.0</td>\n",
              "      <td>Stromořadí 34 lip</td>\n",
              "      <td>Předmětné stromy dosahují nadprůměrného věku i...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Ze zákona</td>\n",
              "      <td>3.37</td>\n",
              "      <td>1999/03/01 00:00:00+00</td>\n",
              "      <td>Bosonožské náměstí</td>\n",
              "      <td>2021/09/15 00:00:00+00</td>\n",
              "      <td>2021/12/03 00:00:00+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>69</td>\n",
              "      <td>103846</td>\n",
              "      <td>Stromy ve stromořadí - listnaté</td>\n",
              "      <td>St</td>\n",
              "      <td>Svah do 1:5</td>\n",
              "      <td>1</td>\n",
              "      <td>OŽP</td>\n",
              "      <td>ortofoto</td>\n",
              "      <td>2021/07/19 00:00:00+00</td>\n",
              "      <td>27406.0</td>\n",
              "      <td>Aesculus hippocastanum</td>\n",
              "      <td>220.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>14.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2019/11/21 00:00:00+00</td>\n",
              "      <td>74</td>\n",
              "      <td>101072.0</td>\n",
              "      <td>Stromořadí kaštanů na Malé Klajdovce</td>\n",
              "      <td>Jediné souvislé staré kaštanové stromořadí v B...</td>\n",
              "      <td>120.0</td>\n",
              "      <td>Ze zákona</td>\n",
              "      <td>7.00</td>\n",
              "      <td>1987/01/15 00:00:00+00</td>\n",
              "      <td>Oboustranně kolem staré silnice do zetoru Líše...</td>\n",
              "      <td>2021/09/15 00:00:00+00</td>\n",
              "      <td>2021/12/03 00:00:00+00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-deb39352-1fce-42dd-a88c-240a9d75b166')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
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              "      \n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-deb39352-1fce-42dd-a88c-240a9d75b166 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-deb39352-1fce-42dd-a88c-240a9d75b166');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "     ObjectId  ogcfid_bio  ...     last_date_pam_strom           datum_exportu\n",
              "23         24      103801  ...  2021/09/15 00:00:00+00  2021/12/03 00:00:00+00\n",
              "70         71      103849  ...  2021/09/15 00:00:00+00  2021/12/03 00:00:00+00\n",
              "4           5      103787  ...  2017/04/10 00:00:00+00  2021/12/03 00:00:00+00\n",
              "138       139      103877  ...  2021/09/15 00:00:00+00  2021/12/03 00:00:00+00\n",
              "68         69      103846  ...  2021/09/15 00:00:00+00  2021/12/03 00:00:00+00\n",
              "\n",
              "[5 rows x 31 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RjspH7ztl3UM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 635
        },
        "outputId": "a06a698c-6c6d-4357-d3de-25c66e6eacc7"
      },
      "source": [
        "# Hodnoty serazene podle sloupce sestupne. Tiskne se prvnich par hodnot (funkce head)\n",
        "df.sort_values(by='vyska_taxonu', ascending=False).head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-c1606a74-31ee-4680-8acb-2151358a013c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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",
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              "\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>ObjectId</th>\n",
              "      <th>ogcfid_bio</th>\n",
              "      <th>druh_bio_kod</th>\n",
              "      <th>vek_tid</th>\n",
              "      <th>svazitost_tid</th>\n",
              "      <th>intenzitni_trida_tid</th>\n",
              "      <th>sprava_tid</th>\n",
              "      <th>presnost_tid</th>\n",
              "      <th>last_date_bio</th>\n",
              "      <th>dendro_bio_tid</th>\n",
              "      <th>dendro_taxon_tid</th>\n",
              "      <th>obvod_kmene</th>\n",
              "      <th>prumer_kmene</th>\n",
              "      <th>vyska_taxonu</th>\n",
              "      <th>vyska_koruny</th>\n",
              "      <th>sirka_koruny</th>\n",
              "      <th>polomer_koruny</th>\n",
              "      <th>spodni_okraj_koruny</th>\n",
              "      <th>nasazeni_koruny</th>\n",
              "      <th>last_date_dendro</th>\n",
              "      <th>ogcfid_pam_strom</th>\n",
              "      <th>cislo</th>\n",
              "      <th>nazev</th>\n",
              "      <th>duvod_ochrany</th>\n",
              "      <th>rok_stari</th>\n",
              "      <th>pamatne_stromy_druh_tid</th>\n",
              "      <th>sirka_ochranneho_pasma</th>\n",
              "      <th>vyhlaseni_datum</th>\n",
              "      <th>lokalita</th>\n",
              "      <th>last_date_pam_strom</th>\n",
              "      <th>datum_exportu</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>58</td>\n",
              "      <td>103836</td>\n",
              "      <td>Soliterní stromy - listnaté</td>\n",
              "      <td>St</td>\n",
              "      <td>Svah do 1:5</td>\n",
              "      <td>1</td>\n",
              "      <td>OŽP</td>\n",
              "      <td>ortofoto</td>\n",
              "      <td>2021/07/19 00:00:00+00</td>\n",
              "      <td>27483.0</td>\n",
              "      <td>Platanus x hispanica</td>\n",
              "      <td>575.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2017/04/10 00:00:00+00</td>\n",
              "      <td>60</td>\n",
              "      <td>101082.0</td>\n",
              "      <td>Platan javorolistý</td>\n",
              "      <td>Nejmohutnější brněnský platan.</td>\n",
              "      <td>170.0</td>\n",
              "      <td>Ze zákona</td>\n",
              "      <td>18.299999</td>\n",
              "      <td>1979/04/21 00:00:00+00</td>\n",
              "      <td>Dvorní trakt Fakultní nemocnice u sv. Anny, Pe...</td>\n",
              "      <td>2021/07/19 00:00:00+00</td>\n",
              "      <td>2021/12/03 00:00:00+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>79</td>\n",
              "      <td>103858</td>\n",
              "      <td>Stromy ve skupině - listnaté</td>\n",
              "      <td>St</td>\n",
              "      <td>Svah do 1:5</td>\n",
              "      <td>1</td>\n",
              "      <td>OŽP</td>\n",
              "      <td>ÚMPS</td>\n",
              "      <td>2021/09/15 00:00:00+00</td>\n",
              "      <td>27494.0</td>\n",
              "      <td>Platanus x hispanica</td>\n",
              "      <td>725.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2017/04/10 00:00:00+00</td>\n",
              "      <td>86</td>\n",
              "      <td>101070.0</td>\n",
              "      <td>Dva platany na Zvonařce</td>\n",
              "      <td>Stromy patřící k nejmohutnějším brněnským plat...</td>\n",
              "      <td>150.0</td>\n",
              "      <td>Ze zákona</td>\n",
              "      <td>23.080000</td>\n",
              "      <td>1987/01/15 00:00:00+00</td>\n",
              "      <td>Na Masné ul. u Autonovy před budovou průmyslov...</td>\n",
              "      <td>2021/09/15 00:00:00+00</td>\n",
              "      <td>2021/12/03 00:00:00+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>15</td>\n",
              "      <td>103793</td>\n",
              "      <td>Soliterní stromy - listnaté</td>\n",
              "      <td>St</td>\n",
              "      <td>Svah do 1:5</td>\n",
              "      <td>1</td>\n",
              "      <td>OŽP</td>\n",
              "      <td>geodeticky zaměřeno</td>\n",
              "      <td>2017/04/10 00:00:00+00</td>\n",
              "      <td>27477.0</td>\n",
              "      <td>Fagus sylvatica</td>\n",
              "      <td>376.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2017/04/10 00:00:00+00</td>\n",
              "      <td>16</td>\n",
              "      <td>106037.0</td>\n",
              "      <td>Buk lesní červenolistý v zámeckém parku v Medl...</td>\n",
              "      <td>Základní kompoziční prvek zámeckého parku, dop...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Ze zákona</td>\n",
              "      <td>11.970000</td>\n",
              "      <td>2015/01/27 00:00:00+00</td>\n",
              "      <td>V centrální části zámeckého parku v Medlánkách</td>\n",
              "      <td>2017/04/10 00:00:00+00</td>\n",
              "      <td>2021/12/03 00:00:00+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>16</td>\n",
              "      <td>71179</td>\n",
              "      <td>Soliterní stromy - listnaté</td>\n",
              "      <td>Ml</td>\n",
              "      <td>Svah do 1:5</td>\n",
              "      <td>1</td>\n",
              "      <td>OŽP</td>\n",
              "      <td>neuvedeno</td>\n",
              "      <td>2021/11/29 00:00:00+00</td>\n",
              "      <td>27478.0</td>\n",
              "      <td>Platanus x hispanica</td>\n",
              "      <td>553.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>29.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>32.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2017/04/10 00:00:00+00</td>\n",
              "      <td>17</td>\n",
              "      <td>106038.0</td>\n",
              "      <td>Platan nedaleko pítka v Lužánkách</td>\n",
              "      <td>Estetetický význam pro park Lužánky</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Ze zákona</td>\n",
              "      <td>17.600000</td>\n",
              "      <td>2015/01/24 00:00:00+00</td>\n",
              "      <td>V travnaté ploše nedaleko pítka v centrální čá...</td>\n",
              "      <td>2017/04/10 00:00:00+00</td>\n",
              "      <td>2021/12/03 00:00:00+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>103788</td>\n",
              "      <td>Soliterní stromy - listnaté</td>\n",
              "      <td>St</td>\n",
              "      <td>Svah do 1:5</td>\n",
              "      <td>1</td>\n",
              "      <td>OŽP</td>\n",
              "      <td>geodeticky zaměřeno</td>\n",
              "      <td>2017/04/10 00:00:00+00</td>\n",
              "      <td>27429.0</td>\n",
              "      <td>Tilia tomentosa</td>\n",
              "      <td>460.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>20.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2017/04/10 00:00:00+00</td>\n",
              "      <td>7</td>\n",
              "      <td>105704.0</td>\n",
              "      <td>Lípa na Jaselské</td>\n",
              "      <td>Estetická hodnota, mohutný kmen</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Ze zákona</td>\n",
              "      <td>14.640000</td>\n",
              "      <td>2011/02/08 00:00:00+00</td>\n",
              "      <td>Dvorní trakt domu Jaselská 15</td>\n",
              "      <td>2017/04/10 00:00:00+00</td>\n",
              "      <td>2021/12/03 00:00:00+00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c1606a74-31ee-4680-8acb-2151358a013c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
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              "    .colab-df-container {\n",
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              "    .colab-df-convert:hover {\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c1606a74-31ee-4680-8acb-2151358a013c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c1606a74-31ee-4680-8acb-2151358a013c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "    </div>\n",
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              "  "
            ],
            "text/plain": [
              "    ObjectId  ogcfid_bio  ...     last_date_pam_strom           datum_exportu\n",
              "57        58      103836  ...  2021/07/19 00:00:00+00  2021/12/03 00:00:00+00\n",
              "78        79      103858  ...  2021/09/15 00:00:00+00  2021/12/03 00:00:00+00\n",
              "14        15      103793  ...  2017/04/10 00:00:00+00  2021/12/03 00:00:00+00\n",
              "15        16       71179  ...  2017/04/10 00:00:00+00  2021/12/03 00:00:00+00\n",
              "5          6      103788  ...  2017/04/10 00:00:00+00  2021/12/03 00:00:00+00\n",
              "\n",
              "[5 rows x 31 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5R3Rc-yml_4p",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "outputId": "c45c81c2-4956-465f-acf0-851ffba1a756"
      },
      "source": [
        "# Graf zavislosti mezi dvema sloupci. Pouziva jenom data s kladnou hodnotou sloupce sirka_koruny\n",
        "df[df.sirka_koruny>0].plot.scatter(x='vyska_taxonu', y='sirka_koruny')\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": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Nakreslíme graf závislosti výšky na stáří. Z grafu je patrné, že v některé roky byla výsadba intenzivnější. Proto seskupíme data podle stáří a pro každou skupinu určíme její velikost."
      ],
      "metadata": {
        "id": "LvDbqqw2wCLq"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FgE5vPwJmajj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "outputId": "4676dc3a-017e-4f71-d59f-5d281c6c9fea"
      },
      "source": [
        "df[df.rok_stari>0].plot.scatter(y='vyska_taxonu', x='rok_stari')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc73a691210>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7s4esL1jncAb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "18198a7d-d208-483a-ca37-88321526f53e"
      },
      "source": [
        "# pocet stromu stejne vekove kategorie\n",
        "df.groupby(by='rok_stari').size()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "rok_stari\n",
              "0.0      47\n",
              "55.0      1\n",
              "76.0      1\n",
              "80.0      1\n",
              "100.0    43\n",
              "120.0    46\n",
              "130.0     1\n",
              "140.0     2\n",
              "150.0     7\n",
              "170.0     4\n",
              "200.0     1\n",
              "220.0     1\n",
              "250.0     2\n",
              "350.0     1\n",
              "430.0     1\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9aCn2S3sapGS"
      },
      "source": [
        "# Některé pokročilejší datové typy\n",
        "\n",
        "Kromě jednoduchých proměnných má Python i složitější datové typy umožňující udržovat data organizovaná. \n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Slovníky"
      ],
      "metadata": {
        "id": "ALuwqdYv4R7-"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "az1Irw_ba3pW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d271ffef-43e9-4d54-8838-e54538b29c01"
      },
      "source": [
        "slovnik = {1: 'jedna', 2: 'dva', 3: 'tri', 'skola':'LDF','univerzita':'MENDELU', 'fakulty':5}\n",
        "slovnik"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{1: 'jedna',\n",
              " 2: 'dva',\n",
              " 3: 'tri',\n",
              " 'fakulty': 5,\n",
              " 'skola': 'LDF',\n",
              " 'univerzita': 'MENDELU'}"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L4CHmIbhbLml",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "f9db95f6-f541-40e0-d300-af01d814070f"
      },
      "source": [
        "slovnik[1]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'jedna'"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kOxzWTKjbObb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "08edf506-84f1-4e7b-c49d-3a88d3dc2a5a"
      },
      "source": [
        "slovnik['skola']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'LDF'"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UKW70vPgboCu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5bac62be-0045-46c4-b98c-a8f05610f7c7"
      },
      "source": [
        "slovnik.keys()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys([1, 2, 3, 'skola', 'univerzita', 'fakulty'])"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lf2G7C2KbtvP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eb8a7ce9-4670-4cc1-d25e-200f296afe22"
      },
      "source": [
        "for i in slovnik.keys():\n",
        "  print(i,\" -> \",slovnik[i])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1  ->  jedna\n",
            "2  ->  dva\n",
            "3  ->  tri\n",
            "skola  ->  LDF\n",
            "univerzita  ->  MENDELU\n",
            "fakulty  ->  5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hT5IVnRzbaH6"
      },
      "source": [
        "## Seznamy\n",
        "\n",
        "Při zpracování dat pracujeme s řadami čísel. Takto umíme zadat posloupnost \n",
        "a v cyklu vytisknout druhou mocninu každého člene posloupnosti"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QrlzbNNtYToi",
        "outputId": "9a15c2bc-79b6-441f-a9ab-94d73807d27b"
      },
      "source": [
        "posloupnost = [1, 2, 3, 4, 5, 100]\n",
        "for i in posloupnost:\n",
        "  print(i**2)  \n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1\n",
            "4\n",
            "9\n",
            "16\n",
            "25\n",
            "10000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6FaGTTC9brGL"
      },
      "source": [
        "Posloupnost je možné zadat i následujícím způsobem (druhá mocnina prvních dvaceti čísel, počínaje od nuly). Zkuste, co udělá, pokud použijeme \n",
        "~~~\n",
        "range(1,10)\n",
        "~~~"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "818iE_cF77zi",
        "outputId": "0868ba6e-f329-4a37-9c9f-58cbdfa35eba"
      },
      "source": [
        "posloupnost = [i**2 for i in range(20)]  \n",
        "posloupnost"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0,\n",
              " 1,\n",
              " 4,\n",
              " 9,\n",
              " 16,\n",
              " 25,\n",
              " 36,\n",
              " 49,\n",
              " 64,\n",
              " 81,\n",
              " 100,\n",
              " 121,\n",
              " 144,\n",
              " 169,\n",
              " 196,\n",
              " 225,\n",
              " 256,\n",
              " 289,\n",
              " 324,\n",
              " 361]"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pro práci s numerickými daty, s grafy, s tabulkami a podobně se používají specializované knihovny. Ty si zpřístupníme standardním způsobem."
      ],
      "metadata": {
        "id": "QpO9syko2nhd"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pYTmzYhKcZpF"
      },
      "source": [
        "import matplotlib.pyplot as plt # knihovna pro kreslení\n",
        "import pandas as pd # knihovna pro práci s tabulkami\n",
        "import numpy as np # knihovna pro numerickou matematiku"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "-131t6HNTmaT",
        "outputId": "97fa8264-f3c2-43fe-ca61-2b596feccec9"
      },
      "source": [
        "plt.plot(posloupnost, color='red') # nakreslení grafu\n",
        "plt.title(\"Můj první graf\")\n",
        "plt.xlabel(\"vstup\")\n",
        "plt.ylabel(\"druhá mocnina vstupu\")\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": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BJvjreME8FGB",
        "outputId": "cde5a079-7f2c-44c5-a628-cea14403697f"
      },
      "source": [
        "posloupnost[0] # Python indexuje členy posloupnosti od nuly. Takto získáme první člen."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mMmIsqz08V8o",
        "outputId": "1113cde5-7d6c-4c40-9a69-64afd7b34fd6"
      },
      "source": [
        "posloupnost[4] # Pátý člen posloupnosti."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "16"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xteC1W-zdRIz",
        "outputId": "daf489ac-e630-438d-9941-fc114cf2d80f"
      },
      "source": [
        "posloupnost[:4] # První čtyři členy posloupnosti."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0, 1, 4, 9]"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VY6MWgYj8Xp3",
        "outputId": "bb8d2edc-1bb9-401f-b8d7-0dc3f828c6f6"
      },
      "source": [
        "posloupnost[-3:] # Poslední tři členy posloupnosti."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[289, 324, 361]"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I8afLTofHcCN",
        "outputId": "6e264c1c-6994-455a-e556-b6d1fca96533"
      },
      "source": [
        "A = np.array([[1,2,3,4],[10,20,30,40],[-1,-2,-3,-4]])\n",
        "A # matice"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1,  2,  3,  4],\n",
              "       [10, 20, 30, 40],\n",
              "       [-1, -2, -3, -4]])"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pz0pq2HKH4DM",
        "outputId": "6eb7d73a-1e6f-49a1-d878-d7e2872f2c97"
      },
      "source": [
        "mocniny = [i**2 for i in A[:,-1]] # druhé mocniny z posledního sloupce matice A (index -1) a všech řádků (dvojtečka)\n",
        "mocniny"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[16, 1600, 16]"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Jednoduché tabulky"
      ],
      "metadata": {
        "id": "DFLfg61T9ZsO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = {'polomer':[3,5,6,5.5,1,3.4],'vyska':[5,6,4,3.3,2,1.8]} # slovník se seznamem dat\n",
        "df = pd.DataFrame(data) # transforamce slovniku do tabulky se sloupci s polomerem a vyskou\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "FIqv8jKr83kZ",
        "outputId": "0919ba03-d46e-4379-a852-a4e6e9722c0c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>polomer</th>\n",
              "      <th>vyska</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5.0</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5.5</td>\n",
              "      <td>3.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>3.4</td>\n",
              "      <td>1.8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   polomer  vyska\n",
              "0      3.0    5.0\n",
              "1      5.0    6.0\n",
              "2      6.0    4.0\n",
              "3      5.5    3.3\n",
              "4      1.0    2.0\n",
              "5      3.4    1.8"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "lCDU43eIIUGL",
        "outputId": "db7f2e5b-f61d-4378-8fd1-452bdad42e7d"
      },
      "source": [
        "df['strana'] = np.sqrt(df['polomer']**2+df['vyska']**2) # pomoci sloupce vyska a polomer urcime stranu\n",
        "df['objem'] = 1/3*np.pi*df.strana**2*df.vyska  # kratsi zapis df.strana misto df['strana']\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>polomer</th>\n",
              "      <th>vyska</th>\n",
              "      <th>strana</th>\n",
              "      <th>objem</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.830952</td>\n",
              "      <td>178.023584</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>7.810250</td>\n",
              "      <td>383.274304</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>7.211103</td>\n",
              "      <td>217.817091</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5.5</td>\n",
              "      <td>3.3</td>\n",
              "      <td>6.414047</td>\n",
              "      <td>142.169634</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.236068</td>\n",
              "      <td>10.471976</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>3.4</td>\n",
              "      <td>1.8</td>\n",
              "      <td>3.847077</td>\n",
              "      <td>27.897343</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-acfcf594-7db0-47f7-a8d8-d0022f59fc17 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-acfcf594-7db0-47f7-a8d8-d0022f59fc17');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   polomer  vyska    strana       objem\n",
              "0      3.0    5.0  5.830952  178.023584\n",
              "1      5.0    6.0  7.810250  383.274304\n",
              "2      6.0    4.0  7.211103  217.817091\n",
              "3      5.5    3.3  6.414047  142.169634\n",
              "4      1.0    2.0  2.236068   10.471976\n",
              "5      3.4    1.8  3.847077   27.897343"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IoPeAalNiyEt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "de6c9363-4002-4771-9030-5686ef9ef381"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-82424dca-c1dc-41d2-82dc-26acba0a29d9\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "<style scoped>\n",
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              "\n",
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              "        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>polomer</th>\n",
              "      <th>vyska</th>\n",
              "      <th>strana</th>\n",
              "      <th>objem</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>6.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.983333</td>\n",
              "      <td>3.683333</td>\n",
              "      <td>5.558249</td>\n",
              "      <td>159.942322</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.876610</td>\n",
              "      <td>1.657005</td>\n",
              "      <td>2.124862</td>\n",
              "      <td>136.855197</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.800000</td>\n",
              "      <td>2.236068</td>\n",
              "      <td>10.471976</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>3.100000</td>\n",
              "      <td>2.325000</td>\n",
              "      <td>4.343046</td>\n",
              "      <td>56.465416</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>4.200000</td>\n",
              "      <td>3.650000</td>\n",
              "      <td>6.122499</td>\n",
              "      <td>160.096609</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>5.375000</td>\n",
              "      <td>4.750000</td>\n",
              "      <td>7.011839</td>\n",
              "      <td>207.868714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>6.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>7.810250</td>\n",
              "      <td>383.274304</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-82424dca-c1dc-41d2-82dc-26acba0a29d9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-82424dca-c1dc-41d2-82dc-26acba0a29d9 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-82424dca-c1dc-41d2-82dc-26acba0a29d9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "        polomer     vyska    strana       objem\n",
              "count  6.000000  6.000000  6.000000    6.000000\n",
              "mean   3.983333  3.683333  5.558249  159.942322\n",
              "std    1.876610  1.657005  2.124862  136.855197\n",
              "min    1.000000  1.800000  2.236068   10.471976\n",
              "25%    3.100000  2.325000  4.343046   56.465416\n",
              "50%    4.200000  3.650000  6.122499  160.096609\n",
              "75%    5.375000  4.750000  7.011839  207.868714\n",
              "max    6.000000  6.000000  7.810250  383.274304"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Obrázky"
      ],
      "metadata": {
        "id": "dX1q5vqC9fwj"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "id": "p6ga1NxT8t8f",
        "outputId": "f044a3a6-bc2f-4dc7-91aa-337b3d5a6215"
      },
      "source": [
        "# vypocet dat\n",
        "x = np.linspace(0,6*np.pi,1000) # data na vstupu, definicni obor funkce\n",
        "y = np.sin(x) # funkcni hodnoty\n",
        "\n",
        "# vykresleni dat do obrazku\n",
        "fig = plt.figure(figsize=(8,4))\n",
        "plt.plot(x, y, color='red')\n",
        "\n",
        "# kosmeticke upravy\n",
        "plt.title('Graf')\n",
        "plt.ylabel('Sinus')\n",
        "plt.xlabel('Nezávislá proměnná')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Nezávislá proměnná')"
            ]
          },
          "metadata": {},
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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5eFFaCl27hvENSefMM2HRIhsKcOLBE0/YZ2gBcOaZ9jl+fFg7nHqTFCfA/GPKFJtYI/RDDPYgv/aaO/TEhY0bYfJkKxu5DA2tjFQl/9RTYe1wdlNaapN6davU7zl3HH00HHKIl40E4wIgFKWlNnXn4MGhLYEzzrBPf5DjwaRJ1kMUB3F4+OHQvbv3EMWFjz+G55+PR9kQMYE4aRJs2RLaGqceuAAIgaoJgKFDzRM/NF26mJr3Sj4elJbCfvvZ+HscOPNMeO45nwc+DjzzjEXsxEEAgJWNTZs8l0hCcQEQgnnzzHEmm3O715Uzz4QXXoBPPgltSWGza5f1xAwfbol44sCZZ1qPhM8bEZ4nnoD27aFfv9CWGCefbD2Z3nhIJC4AQjBhgn0OGxbWjnTOPNNaFhMnhraksJk3zyZpOv300JbsZsAAmzfCK/mw7Nplz+ewYbmZNbQ2NG9uCaOefNITRiUQFwAhmDABjjsODjwwtCW7KS62sC+v5MOSEoennRbWjnSaNrXhqqee8ko+JC+/bDk7hgwJbcmenHmmRYksXhzaEqeOuADINRs2wIwZVqHGiSZN7KUzaZJX8iGZOBGOPx4OOCC0JXsybJglJ/JKPhxxFIewuyfTew8ThwuAXDN1qs2+FzcBAFaxrFnjlXwo4ioOYfdLZ9KksHYUMhMn2pThHTqEtmRPOnaEI4/0spFAXADkmgkTYO+9bTatuOGVfFimTDFxGLcuXoBOneCII7xshGL9+viKQ7C64/nnzVnUSQwuAHLNxInmORuH8L+KdO5scd9eyYdh4kTzqB4wILQlleOVfDimTDEn3TgLgM2bYebM0JY4dcAFQC55803LthfXhxi8kg/JhAkmDps1C21J5Zx2msV8l5XVvK+TWSZMMHF44omhLamck082PyJvPCQKFwC5JOUkE8cu3hRDhnglH4IVK2DZsniXDa/kwzFxos3ZEVdx2KoVlJR42UgYLgByyaRJNpZ65JGhLamak0+2GGP36M0tkyfbZ5wFwL77Wriol43csmIFLF8eP+//ipx2GsyZA+vWhbbEqSUuAHLFrl02jjd4cPgJXqojVcm7ks8tzz1neSHiLA7BK/kQTJlin6ecEtaOmjjtNAsh9oyRicEFQK5YtMgqzUGDQltSM6lK/sMPQ1tSGKhaJT9oULzFIeyu5J97LrQlhcOUKZb+N/TU0DVRVGRzWHjjITG4AMgVKRWfBAFwyilWyb/wQmhLCoNXXoH33ktG2ejbF1q29MlfckVKHJ58cvzFYZMmZqeXjcQQVACIyDAReVVElovImEq2Xyoia0VkXrRckbZttIgsi5bRubW8HkyZAoceaqF2cadvX8tV4A9ybkiSOGzSxGYp9LKRG15/3SYOS0LZABMAy5fDypWhLXFqQTABICKNgduA4UAP4AIR6VHJrg+pas9ouTM6ti3wM+AEoB/wMxFpkyPT687OnRZal5SHuFkzCzfySj43TJlizqGHHhraktpx8smwZIlNWuRklySJQ7CyAVbfObEnZA9AP2C5qr6hqtuAscDIWh47FJikqh+q6kfAJCBGU+tVYP58+Pjj5DzEYA/yggXuB5Btdu0yoZWE8f8UXsnnjilTbF6II44IbUntOO44aNPGGw8JIaQAOBh4J+33ymhdRb4kIgtEZJyIdKrjsYjIlSIyW0Rmr127NhN2152kqXiwSl4Vpk0LbUl+s3ixzfCWpLLRu7f7AeSCJDmHpmjUCE46yctGQoi7E+ATQBdVPQ5r5d9b1xOo6h2qWqSqRe3bt8+4gbViyhRLsXvQQWGuXx/69rV0xf4gZ5ckisMmTSxdsZeN7PLqq/Duu8kqGwCf/7z7ASSEkAJgFdAp7XfHaN3/UNV1qprKSXsn0Ke2x8aGHTvMmz5pD7H7AeSGKVOga1c45JDQltQN9wPIPkkUh+BDRAkipAB4CeguIl1FpCkwCihN30FEDkz7OQJYGn2fAAwRkTaR89+QaF38mDvXZvJK2kMM9iDPn+9+ANli165kOYem45V89pkyxaba7dYttCV147jjoHVrbzwkgGACQFV3AFdjL+6lwMOqulhEfikiI6Ldvi0ii0VkPvBt4NLo2A+B/8NExEvAL6N18SP1EKQqzCThfgDZZcEC+OijZAoA9wPILqq7xWFSxv9TNG7sfgAJoUnIi6vqeGB8hXU3pH2/Hri+imPvBu7OqoGZYNo08+Dt0CG0JXWnX7/dfgAjaxug4dSalLA66aSwdtSHvfZyP4Bs8tprNrySxLIB1ngoLTU/gI4dQ1vjVEHcnQCTza5dMGNGfOd3rwn3A8gu06db/H8SkkNVhvsBZI/p0+0zqXWHDxElAhcA2WTpUuviTepDDObRm8pj4GQOVavkk142wIeIssH06dCuXXLi/yty3HE2L4CnE481LgCySapiHDgwrB0NYcAAe1mVl4e2JL9YsQJWr0522ejd24aIZswIbUn+MW2aPXtJG/9P0bix9R562Yg1LgCyyfTplsUrKSleK+OEE+xhTnVJOpkh6V28AE2bmp+Il43MsmaNzQGQ5LIB0L+/JbryKKLY4gIgm6S6eJOq4gFatIBevVzJZ5rp062LNO5TvNbEgAHw8suwcWNoS/KH1LOW5N4h2C1gysrC2uFUiQuAbPHOO/DWW8lX8WD38OKLsG1baEvyh+nTrYXUKOGP4IABluxq1qzQluQP06fbbJy9eoW2pGH07WvRIt5DFFsSXvvEmFShT7qKB3tRbd5sLT2n4XzwgTmI5kPZKCmxHi6v5DPHtGlQXGwvzySzzz7mJ+JlI7a4AMgW06dbopTjjgttScPp398+fRggM6T+jvnQO9S6NRxzjJeNTLF+Pcyblx/iEKyMv/QSbN1a875OznEBkC2mT7fWUZOguZYyw4EHmiOjK/nMMH26OdAVFYW2JDP07w8zZ8LOnaEtST7l5ZY/JB/EIVjZ2LoV5swJbYlTCS4AssHHH8PChfmj4sEqpOnTLSTQaRjTpu3OspgPDBhgLdeFC0NbknymTTO/kOLi0JZkhlTvoTceYokLgGwwc6a9KPNFxYPdy9q1Ns2nU382bbLWUL6VDfBhgEwwfbo5/7VqFdqSzPC5z9lU6F42YokLgGwwfbp1/ffrF9qSzOFKPjPMmmVe8/kkADp3hoMP9rLRULZvtyGAfCobYHXHjBk2tOHEChcA2WDaNPN+bdEitCWZ48gjoW1br+QbyrRp5jV/4omhLckcIruHiJz6M3euRdvkmwAYMADWrYNXXw1tiVMBFwCZZssWa+Xl0/g/2Likp/ZsONOnm9d8mzahLcks/fvbzG9vvx3akuSSD9khK8OjiGKLC4BMM3euJcxJFfp8YsAAU/Fr14a2JJns3GlZ0fK1bID3AjSEGTOgWzdLH55PHH64TWzkZSN2BBUAIjJMRF4VkeUiMqaS7d8TkSUiskBEnhWRQ9K27RSRedFSmlvLqyE1aU6+ePGm485eDWPpUvOWLykJbUnmOfZYc1zzSr5+qJo4zMey4UNEsSWYABCRxsBtwHCgB3CBiPSosNvLQJGqHgeMA36btm2zqvaMlhE5Mbo2lJXBIYdY7Hy+0aePxa/7g1w/UjnR81EcNmli9+Vlo368/Ta8+25+lg2wXq/XX7d7dGJDyB6AfsByVX1DVbcBY4GR6Tuo6hRV3RT9LAc65tjGulNenp8qHixuvU8fn9yjvpSXmyNl9+6hLckOJ54IixbBp5+GtiR5pHoO87XuSDm9et0RK0IKgIOBd9J+r4zWVcXlwNNpv5uLyGwRKReRs7JhYJ1ZudKWfH2Iwe5tzhyfGKg+lJVZCy/Js0NWR0mJdWW/9FJoS5JHWZlNAHTssaEtyQ69e9vcBimh48SCRDgBishFQBFwc9rqQ1S1CLgQ+IOIdKvi2CsjoTB7bbad1/K5izdFSYml9pw3L7QlyeLjj80HIJ/F4Qkn2Ke38upOefnu2fPykebNTQR42YgVIQXAKqBT2u+O0bo9EJFTgR8DI1T1fzNKqOqq6PMNYCpQ6dyZqnqHqhapalH79u0zZ31llJdDs2bQs2d2rxOSlLhxJV83XnzRPvNZALRuDUcd5WWjrmzZYtFD+dxwALu/2bMt4ZETC0IKgJeA7iLSVUSaAqOAPbz5RaQXcDv28n8/bX0bEWkWfW8H9AeW5Mzyqigr2+0ol6907GiLK/m6UVZmXf99+4a2JLuUlJgA8Dkjas/cufZSzGdxCHZ/mzfDggWhLXEiggkAVd0BXA1MAJYCD6vqYhH5pYikvPpvBloC/6kQ7ncUMFtE5gNTgN+oalgBsHWrPcj5/hCDKXkXAHWjvNwSAO27b2hLsktxsWV9W7YstCXJIZ9Dh9NJ3Z/XHbEh6Fy1qjoeGF9h3Q1p30+t4riZQLy8ZebNMxFQCAKgpATGjYM1a/Iz3DHT7Npllfx554W2JPukyn95uSWAcWqmrAy6dMm/BEAV6dzZ6ovycrj66tDWOCTECTARFIIDYIr0St6pmVdfhU8+KQxx2KOH9XJ4K6/25HPocDoidp9eNmKDC4BMUV4OnTrZrGj5Tq9eHtJTF1IVXiFU8o0a2SyYXjZqRyp0uBAaDmD3+cYb8P77Ne/rZB0XAJkiX9N4VoaH9NSN8nLzkC+ULvGSEnP02rAhtCXxJ98TAFXEew9jhQuATLB6taXyLBQVDx7SUxdSCYAaFcjjVlxsfg+eEKhmyspMUB9/fGhLckOfPpY22hsPsaBAaqQsU2gqHjykp7Z8+iksXlx44hC8lVcbysvzP3Q4nb33tjwpXjZigQuATFBWZg9wr0pzEeUnKbHjSr56Zs2ymPhCEodt28IRR3jZqIlt2yytdiGJQ7BnYdYs2LEjtCUFjwuATFBebmPizZqFtiR3dOq0O6THqZrUS7Bfv7B25JriYk8IVBMvv1w4ocPpFBfDpk02cZQTFBcADWXbNhsLL7SH2EN6akd5uYXGtW4d2pLcUlICa9eax7dTOYU4dAjeexgjaiUARKS/iLSIvl8kIr8XkUOya1pCWLDAcnkXWjce2IPsIT1Vo1o4Md4V8Uq+ZsrKrCftoINCW5JbunSBDh28bMSA2vYA/A3YJCLHA98HXgfuy5pVSaKQYrwr4s5e1bNsGXz4YWGKw6OPhpYtvWxUR6GKQ5HdQ0ROUGorAHaoqgIjgb+o6m1Aq+yZlSDKyiz5T6dONe+bb3hIT/UUsjhs3Nj8HrxsVM6aNfDWW4UpDsGeiWXL4IMPQltS0NRWAKwXkeuBi4CnRKQRkKcTV9eR8vLCfYg9pKd6ysstLe5RR4W2JAzFxTB/vjl8OXtSqOP/KVJ1ZmqabCcItRUA5wNbgctV9V2gIzZTX2Hz3nuwYkXhPsTgIT3VUVYGJ5xQOAmAKlJSAjt3mpOssyeFGDqcTlGR9RJ5D1FQalUzqeq7qvp7VZ0W/X5bVd0HoFCm8ayOkhJr4S1cGNqSeLF+vf1NClkc+vSvVVNWVnihw+m0aGHZD71sBKW2UQDrReTTaNkiIjtF5JNsGxd7yspsUpw+fUJbEg6v5Ctn9mxLh1vI4rBdOzjsMC8bFdm+3cpHIZcNsPufNct6iZwg1LYHoJWq7quq+wJ7A1/CIgMKm7Iy68Jr3jy0JeFIhfS4H8CepF56J5wQ1o7QlJR4QqCKzJ9vocOF3DsEdv8bNliqbCcIdR6cVOO/wNCGXlxEhonIqyKyXETGVLK9mYg8FG1/UUS6pG27Plr/qog02JY6s2OHTXZS6CreQ3oqp7zc0uG2bRvakrAUF5uvzFtvhbYkPhS6A2AKDyMOTm2HAM5OW84Rkd8AWxpyYRFpDNwGDAd6ABeISI8Ku10OfKSqhwG3AjdFx/YARgFHA8OAv0bnyx0LFthkOIX+EIOH9FREtbCmh64OTwj0WcrKLPlPx46hLQlLt242TORlIxi17QH4QtoyFFiP5QRoCP2A5ar6hqpuA8ZWcs6RwL3R93HAKSIi0fqxqrpVVVcAy6Pz5Q53ANxNqpL3kB7jjTdMDHnZgGOPhX328VZeOqkEQCKhLQlLKp24l43dLF5sycNyRJPa7KSql2Xh2gcD76T9XglUHDD93z6quiNyPNw/Wl9e4diDs2Bj1ZSVwQEHwCGeEVidaZQAACAASURBVJk+fXaH9JxxRmhrwlPICYAq0qQJ9O3rrbwU779vAvHrXw9tSTwoLoYnnrCXXqEPlwFccIFNsjZhQk4uV9shgPYi8iMRuUNE7k4t2TYuE4jIlSIyW0Rmr127NnMnvvZa+Mc/XMWDh/RUpKzM0uAefXRoS+JBSYnNfLd5c2hLwuPicE9Sf4dZs8LaEQfWr7cegByWjdoOATwO7AdMBp5KWxrCKiA9f27HaF2l+4hIk8iGdbU8FgBVvUNVi1S1qH379g00OY3jj4czz8zc+ZKOh/Tsprzc0uA2zq1bSmwpLjan2blzQ1sSnvJy6xXp3Tu0JfGgb19LlOWNh92hwzmMHKqtANhHVX+oqg+r6iOppYHXfgnoLiJdRaQp5tRXWmGfUmB09P0c4LloToJSYFQUJdAV6A64hAyJh/QYGzdamJe38HbjuSJ2kwod3nvv0JbEg5YtzU/Ey8ZuX4gYCoAnReT0TF5YVXcAVwMTgKXAw6q6WER+KSIjot3uAvYXkeXA94Ax0bGLgYeBJcAzwDdV1ZueIfGQHmPOHOsFcQfA3XToAF27etnw0OHKKS42B+Jdu0JbEpayspyHDtdWAHwHEwGbo2yA60Xk04ZeXFXHq+rhqtpNVX8VrbtBVUuj71tU9VxVPUxV+6nqG2nH/io67ghVfbqhtjgNxEN6jNT9eyW/JyUl9rcp5IRACxda2mzvHdqTkhL49FNYujS0JeFQDTI9dF0yATZS1b2jjICtoqyAjmN4QiCjvNzS37ZrF9qSeFFcDKtXw8qVoS0Jh4cOV473HlpkyNq1OS8b1QoAETky+uxd2ZIbE53EUFICr7yS0zjWWBFIxScCTwhkZaNDB0uf7ezm8MOhTRsvG5DzuqOmPADfA64Efpe2Lr0Pb3DGLXKSS/oc38OHh7UlBG+9Be++6y28yjjuOJszo7wczjsvtDVhKCuzsuGhw3vivYfBQodrGgK4U0QOUNVBqjoIuAfYACzCvPIdZzf9+llIT6E+yJ7jvWqaNrU54Au1lbdunaXL9rJROSUlsGQJfFKgk8wGCh2uSQD8HdgGICInAb/GUvN+AtyRXdOcxFHoIT1lZRbedeyxoS2JJyUllgtg69bQluQeH/+vnpISG0IrxIRAmzZZ6HCAslGTAGisqqkB3fOBO6IcAD8FDsuuaU4iKeSQnvJyS2zSpFYZtguP4mLYts2yAhYa5eXWuisqCm1JPOnXz4YCCrHxMGeOhYgG6B2qUQBEGfgATgGeS9vmtZzzWQo1pGfLFnuxeRdv1RSyt3dZmflBtGgR2pJ4su++Nv5dqGUDcpoAKEVNAuBB4HkReRzYDEwDEJHDsGEAx9mTQq3k586F7du9i7c6DjoIOncuvFbezp3Wte1lo3pSjoCF1nuYCh3OZKr6WlKtAIiS83wfc/4bEKXhTR33reya5iSSQg3p8QRAtaMQp39dssQmevHeoeopKYGPPjJnyUJBdXd0SABqTASkquWq+piqbkxb95qq+swezmdJhfQUmgAoL7f47gMOCG1JvCkuhrfftqRAhYI7ANaOQpwz4u23LXQ4kDisbSpgx6k9qZCejz8ObUnuKCvzFl5tKMSEQGVlsP/+1s3rVM2RR8J++xVe2YD49gA4Tp1JFeZCCelZuRJWrfIWXm3o2dNyAhTSMEB5uScAqg2NGpkjXKGVjb33NgfRALgAcDLPCSdYZVcoD3JKxXsPQM00awZ9+hROK++jjywixstG7SgpgUWLzGeiECgrCxo67ALAyTypkJ5CqeTLyy3N7fHHh7YkGZSUWOzztm2hLck+qV4w7x2qHSUlFgXw0kuhLck+MQgddgHgZIdCSghUVmat2qZNQ1uSDIqLrfKbPz+0JdmnrMx6w/r1C21JMkj9nQqh8RCD0GEXAE52SIX0vPZaaEuyy9at9iB7C6/2pFo8hTBEVF4OxxwDrVqFtiQZtGkDRx1VOGUDCk8AiEhbEZkkIsuizzaV7NNTRMpEZLGILBCR89O23SMiK0RkXrT0zO0dODVSKCE98+aZCPAx3trTsSMcfHD+l41du3x66PqQSgikWvO+SaasLHjocKgegDHAs6raHXg2+l2RTcAlqno0MAz4g4i0Ttv+A1XtGS3zsm+yUydSIT35ruRjoOITSSEkBHr1VZvdzstG3SgpgQ8+gNdfD21JdomBOAwlAEZiswoSfZ5VcYco2dCy6Ptq4H0g97kSnfqRCunJ91ZeWdnuFq1Te4qLYcUKeO+90JZkD88OWT8Kofdw5UpbApeNUAKgg6quib6/C3SobmcR6Qc0BdIl4a+ioYFbRaRZNcdeKSKzRWT22rVrG2y4UwcKIaQnBio+kRSCH0B5ObRuDUccEdqSZNGjh/lM5HvZgOB1R9YEgIhMFpFFlSwj0/eL5heocrBHRA4E7gcuU9WUS/n1wJFAX6At8MOqjlfVO1S1SFWL2geYbKGgKS7O7zm+V6+Gt94KruITSe/esNde+d3KmznTesEaua91nWjc2KIB8r1sxCB0OGslU1VPVdVjKlkeB96LXuypF/z7lZ1DRPYFngJ+rKrlaedeo8ZW4J+Ax9jEkdT0lvmq5GfMsM8BA8LakUSaN4devfK3kv/oI1i82MtGfSkpgQULYOPGmvdNIjNmmMgJHDocSpqWAqOj76OBxyvuICJNgceA+1R1XIVtKfEgmP/Aoqxa69SPVEhPvlbyM2ZYGs9evUJbkkxKSizhy44doS3JPKky379/WDuSSkmJTaM8e3ZoSzLPpk0WOhyDshFKAPwGOE1ElgGnRr8RkSIRuTPa5zzgJODSSsL9HhCRhcBCoB1wY27Nd2pNPof0TJ9uvRx77RXakmRSXAybN1tLL9+YPt3Su3oCoPqR6j3Mx8bDrFkmemMgAIIkIFbVdcAplayfDVwRff8X8K8qjh+cVQOdzFFSAv/8p83xffjhoa3JHBs2WA6AMZVFsDq1In1mwN69w9qSaWbMsJ6hFi1CW5JM9t/f6ot8FACpocMTTwxrB54J0Mk2qUKeKvT5wqxZ1kUZAxWfWDp3hoMOyr+ysW2blQ8vGw3jxBPNWS7feg9nzLC5Utp8Jv9dznEB4GSXo46Ctm2tSzSfmD7dcrx7CGD9EYGBA2HatPyq5OfOtbkO3AGwYQwcaAmBXnkltCWZY9cuEzUxEYcuAJzs0qiRFfZp00JbkllmzLAc761b17yvUzUDBlhClLffDm1J5kj1aMSkkk8sKQGVT42HxYstO2RMxKELACf7DBxoPgD5kvVt504bm4zJQ5xoBg60z3wSiNOnQ7duQXO85wXdu8PnPpd/ZQNiIw5dADjZJ9+U/MKFlt0wJg9xojnmGNh33/wpG6rWA+Blo+GIWN2RL2UDrGwceCB07RraEsAFgJML+vSxxC/58iB7AqDM0bhxfg0RLV8Oa9d62cgUAwfanBGrVoW2JDOkxKFIaEsAFwBOLmja1OJ686WSnz7dJv/p3Dm0JfnBgAGwZAmsWxfakoYTsy7exJNPvYerVsGbb8aqbLgAcHLDwIHw8sv5MTHQjBlWMcVExSeelB9APoQDzphhUS9HHhnakvygZ0/LpZAPjYcY9hy6AHByw4ABFgKT9HkB3n4b3nknVio+8fTta71E+VLJn3iiTwCUKZo0sVDbfCkb++wTfAKgdLyUOrmhpMQqxaR35XmIV+Zp3txEQNLLRipm3ctGZhk40BxvP/44tCUNY/p0S38do9ThLgCc3LDvvtadl3QlP2MGtGwJxx0X2pL8YuBAm/hl06bQltSfmTPtM0ZdvHnBwIEWXZH6+yaRDRtg/vzYiUMXAE7uGDDAhgC2bw9tSf154QXrzWgSZBqN/GXAAJsgZdas0JbUnxdegGbNoKgotCX5xQkn2POW5B6imTMtf0jMxKELACd3DBxos7/NnRvakvqxbp11RZ58cmhL8o8TTzSnyiT3ED3/vHXxNm8e2pL8Yp99LJQ46WWjSZNYTACUjgsAJ3ckPaTnhRfs8/OfD2tHPtKmjSUFSmrZ+OQTE7ZeNrLDgAHWO7RlS2hL6sfUqdYz1LJlaEv2wAWAkzsOOAAOOyy5Sv7552Hvvc1hzck8AwdaV+mOHaEtqTszZliUi/cOZYeBA22WxdmzQ1tSdzZuNPESw7IRRACISFsRmSQiy6LPSudFFJGdIjIvWkrT1ncVkRdFZLmIPCQiTXNnvdMgUrO/7doV2pK6M3WqdeE19eKWFQYONGepl18ObUndmTrVykVxcWhL8pOU89zzz4e1oz6UlZmodQHwP8YAz6pqd+DZ6HdlbFbVntEyIm39TcCtqnoY8BFweXbNdTLGoEHw4YewYEFoS+pGyuYYPsR5Q+pvO2VKUDPqxdSp5qy2996hLclP2rWDY49Nbtlo3Dh24/8QTgCMBO6Nvt8LnFXbA0VEgMHAuPoc7wRm0CD7TNqDnJqz3sd4s8cBB8BRRyWvbHz6qY3/uzjMLoMH21DL1q2hLakbqfH/Vq1CW/IZQgmADqq6Jvr+LtChiv2ai8hsESkXkdRLfn/gY1VNDRSuBA7Ooq1OJunY0fwAklbJT51q3t39+oW2JL8ZNMjEVpJCRWfMsBAvF4fZZdAgcwJ88cXQltSeTZts/D+mZSNrAkBEJovIokqWken7qaoCWsVpDlHVIuBC4A8i0q0edlwZiYjZa9eurfuNOJln8GAby0uSs9fzz1v8f7NmoS3JbwYPNqepl14KbUntef55y+5WUhLakvzmpJMsVPS550JbUnvKykzMxrR3KGsCQFVPVdVjKlkeB94TkQMBos/3qzjHqujzDWAq0AtYB7QWkVQmlo5AlXNFquodqlqkqkXt27fP2P05DWDQIOs2TYqz10cfwbx5sX2I84pUSylJPUSp8f999gltSX7Tpg306pW8spGa8jqGhBoCKAVGR99HA49X3EFE2ohIs+h7O6A/sCTqMZgCnFPd8U6MSZqzl4//54527SzNclLKxvr1FprmZSM3DB5s2USTkjL6+eehd29LhR5DQgmA3wCnicgy4NToNyJSJCJ3RvscBcwWkfnYC/83qrok2vZD4HsishzzCbgrp9Y7DeOAA6BHj+R05U2dal3/J5wQ2pLCIEnOXj7+n1sGDbJ8AEmYF2DTJvNXiHHZCCIAVHWdqp6iqt2joYIPo/WzVfWK6PtMVT1WVY+PPu9KO/4NVe2nqoep6rmqmoCawtmDQYMs61sSnL0mTbJMZJ7iNTeknL2SMHX0pEkmDmOW4z1vGTjQutST0EM0bZqJlVNPDW1JlXgmQCcMgwYlw9nr3Xdh0SI47bTQlhQOJ51kU0cnoZKfPNle/h7/nxtatbKQuiSUjUmTLDnUwIGhLakSFwBOGFJ+AHEfBpg82T5jrOLzjtatbdw07mXjvfcsOZSXjdwyeLCF1q1fH9qS6pk82Zz/Yuwc6gLACcP++0PPnvDss6EtqZ5Jk8zWXr1CW1JYpJy9Nm4MbUnVpMSh9w7llsGDze8iNTlXHHnvPZg/P/ZlwwWAE47TTjMnqg0bQltSOapWyZ9yinVJO7njtNPMP2Tq1NCWVM3kydC2rYvDXNO/v/njTJwY2pKqSTVsXAA4ThUMHWqVfFzH85YuhdWrvYs3BKlx9WeeCW1J5aha75CLw9yz9942hBjXsgEmDlN5C2KMl1wnHAMG2PjYhAmhLamcSZPsM+YqPi9p3twq+biWjVdegVWrvGyEYuhQeO01ePPN0JZ8lnRx2LhxaGuqxQWAE45mzSwaIK6V/OTJNm9Bly6hLSlMhg2DZctgxYrQlnwWH/8Py9Ch9hnHuuO112DlykSUDRcATliGDoXly+H110Nbsifbttn4s3f/hyPOlfzEidCtm4vDUBx5JHTqFN+yAYmoO1wAOGGJayX/wgvmnDh8eGhLCpfDD4dDDolf2di82Zy8vGyEQ8R6iJ59Nn7JxJ56ysruoYeGtqRGXAA4Yene3VpRcavkn3rKhihOOSW0JYWLiAnEuFXyU6eaCDjjjNCWFDZDh9qkYnGaHnjjRisfCSkbLgCcsKQq+eees273uPDUU+af0KJFaEsKm6FDLeFLnNICP/WUOa/67JBhSTnZxanx8OyzNofFmWeGtqRWuABwwjN8uHW3T5sW2hLjtdfM+SwhKj6vOeUUaNLEXrpxQBWefNLGd31uiLC0bg0lJfEpG2Blo1WrxMwN4QLACU+qMi0tDW2JkapQXACEZ7/9rKX9eExm/F6yBN56y8tGXBgxAl5+Gd55J7QlJg7Hj4chQ2wOgATgAsAJT4sWFjLz+OP2EIXmqadsuuKuXUNb4gCMHGlx96+9FtqS3eLw9NPD2uEYI0bYZxwaD/PnW26IBIlDFwBOPBgxwlpWCxaEtePTT+H55xP1EOc9X/iCfcahkn/ySTj+eOjYMbQlDsARR9gShx6iJ5+0zwRFh7gAcOLBF75gDoGhH+RJk2DHjsQ48RQEhxxiE0eFLhsffQQzZ3rZiBsjR5rn/SefhLXjqaegb1844ICwdtSBIAJARNqKyCQRWRZ9tqlkn0EiMi9t2SIiZ0Xb7hGRFWnbeub+LpyM0qEDFBeHb+U99phN8HLiiWHtcPZk5Eh7+a5dG86GJ56wWehSPRJOPBgxwsJEQ84NsGqVRaokrGyE6gEYAzyrqt2BZ6Pfe6CqU1S1p6r2BAYDm4D06Z9+kNquqvNyYrWTXUaOhDlzLI1mCLZutUr+rLPM89yJDyNHwq5du7tZQzBunGWf69cvnA3OZykuhvbtw/YQPfaYfZ5zTjgb6kEoATASuDf6fi9wVg37nwM8raqbsmqVE5bQDj2TJ5sPQMIe4oKgZ097+Yaq5D/91FK8fulLNlTlxIfGjW1YZvz4cLlEHnnEHIePOirM9etJKAHQQVXXRN/fBTrUsP8o4MEK634lIgtE5FYRaZZxC53cc+SR5tAzblyY648bZ2Fnnv0vfohYz8yECfYyzjVPPWU9RF/6Uu6v7dTMF79oPgDPPpv7a7//vqUOT2DZyJoAEJHJIrKokmVk+n6qqkCVsV8iciBwLJCe7ul64EigL9AW+GE1x18pIrNFZPbakOOHTs2IwKhR5tCzZk2Nu2eU7dutdTliRGJieAuOUaNgy5YwPUSPPAIHHui+IXFlyBBLDDR2bO6v/d//2vBUAnsOsyYAVPVUVT2mkuVx4L3oxZ56wb9fzanOAx5T1f8lA1fVNWpsBf4JVDkop6p3qGqRqha1b98+MzfnZI/zz7dcAP/5T26vO2WKeXkn8CEuGIqLoXPn3FfyGzda9/LZZ0MjD5yKJc2a2f/nscdMJOaSceNsTpNjj83tdTNAqNJcCoyOvo8GqhvYu4AK3f9p4kEw/4FFWbDRCcFRR1mcda4r+QcftBSeQ4bk9rpO7WnUyATihAnw4Ye5u25pqU3+4+Iw3owaZfNGjB+fu2u++67NY3LOOYn0DQklAH4DnCYiy4BTo9+ISJGI3JnaSUS6AJ2A5ysc/4CILAQWAu2AG3Ngs5MrRo2CsjJ4883cXG/jRlPx553n+d3jzqhRlqfh0Udzd8377rOeh5NOyt01nbozaBB87nO5bTw8+KCFhl58ce6umUGCCABVXaeqp6hq92io4MNo/WxVvSJtvzdV9WBV3VXh+MGqemw0pHCRqm7I9T04WeT88+3zoYdyc73//tcmI0roQ1xQ9Opl3a0PVvQJzhJr1pj3/0UXefd/3GnSBM4910J516/PzTXvu8+S/yTM+z+Fl2gnfnTtas5W99yTm7kB7r/fss0NHJj9azkNQwS+/GXz2chFD9GDD5qDl4vDZPDlL5sPwMMPZ/9aCxfCvHlwySXZv1aWcAHgxJMrrrAJYGbMyO51Vq+29L8XX+wtvKRw2WX2effd2b/WffdZ4p8jj8z+tZyGU1xs8fj/+Ef2r3X//dbrMGpU9q+VJbzGc+LJeeeZU96dd9a8b0P417+8hZc0OneGYcNMAOzcmb3rzJtnM7x52UgOIvDVr8KLL1oLPVts3251x+mnQ7t22btOlnEB4MSTFi3gwgutKy9bk3zs2gV//7t1/R9+eHau4WSHK66w/OsTJtS8b33529/MKfTCC7N3DSfzXHSR5fK4667sXaO01PxDrrii5n1jjAsAJ75ccYWFX/3739k5/zPPwIoV8M1vZuf8TvY480zz+M5WV+/HH1sL78ILbXIoJzm0a2eZAe+7L3s5AW67zfyGTj89O+fPES4AnPjSp495ff/lL9lxBvzrX23qzi9+MfPndrJL06bmC1Bamh1nwPvug02bXBwmlSuvtMRe2Wg8LF1qTqhXXWXzECQYFwBOfBGB734XlizJ/FSfy5ZZwpCvftVT/yaVq682x80//jGz592500TnCSdA796ZPbeTGwYNguOOg9/9LvONhz//2eqMr3wls+cNgAsAJ96cfz4cdBDccktmz/vb39pD/I1vZPa8Tu7o2NHKx513Wpd9pnj0UROI3/9+5s7p5BYR+/9luvHw7rvmfHrJJTYElXBcADjxpmlT+M53LN3mnDmZOeeqVXDvvabgDzggM+d0wvD971sSp7/9LTPnU4Vf/9qcQs8+OzPndMIwapQ1Hn7728yd8w9/sAiA667L3DkD4gLAiT9f+xq0aQM//WlmznfLLRYB8IMfZOZ8Tjh69YLhw+HmmzMTLfLMM/DyyzBmTOLHdwuepk3tGZ861RoQDeWjj8xv6LzzLBtlHuACwIk/++1nFfLTT8O0aQ0715tv2kN88cWWcdBJPjfeaJXz737XsPPs3AnXXw9dulhGOSf5XHWVDRX9+McN9wX41a+st+lHP8qMbTHABYCTDK6+2uZjHzOmYQ/y9ddby+7//i9ztjlh6d3bcsD//vc2Rltf7rvPEv/85jfuGJovNG8OP/sZlJfD49VNOlsDr78Of/qTRZ4kcNrfqnAB4CSDffaxl/bMmfDPf9bvHGVlNlPY979vrQInf7jxRhubveaa+h2/fj385Cfm+X/eeZm1zQnLpZfC0UebL9GGes4bd911sNdeeddwcAHgJIfLLoMBA+Daa+H99+t27JYtcPnl9uLPEwceJ43DD7eu2YcesqGiunLddZbZ7Q9/SOS87k41NGkCt98Ob78NN9xQ9+MffdSWH/3InArzCBcATnJo1AjuuMNU/BVXmCNfbfnRjyyBx5132hwDTv4xZoxN2vPVr8LatbU/7umnLSX0975nk8k4+Uf//uZM/Mc/1s0hcPVq+PrXzdk0DxsOLgCcZHHUUebx/cQTNlZbG8aOhVtvNT+CoUOza58TjmbNLPPbBx9YCt/t22s+5vXXbd/jj8+77l2nAjffbD1FF1xgvQE1sXWr+ZZs3Ggz/+21V/ZtzDFBBICInCsii0Vkl4gUVbPfMBF5VUSWi8iYtPVdReTFaP1DIuIeO4XEt79tlfaPf2xde9UxfjyMHm1DBw31EnfiT69elhNg8mSL9Nixo+p933oLTj3VepYefRT23jt3djq5p1UreOQRGw489VQb8qmKbdvs5T9zpiX+Ofro3NmZQ0L1ACwCzgZeqGoHEWkM3AYMB3oAF4hIj2jzTcCtqnoY8BFweXbNdWKFiDkCnn66hflcd91nJ/3YudPGc0eONK/d0lL37C4ULrvMkr889JD1+Kxe/dl9pkyBkhLLIDhxIhx6aO7tdHJPjx425LNqFfTrZy/4irz9NgwebL2Mqbj/PKVJiIuq6lIAqd7Zph+wXFXfiPYdC4wUkaXAYCA1R+e9wM+BDKUCcxJB06bw2GPm2XvzzdbNf/HFlqBj5Up48EFLAzpyJNxzD7RuHdpiJ5f84AeWqvVrX4PDDrMpYvv1swl+xo+3aYQPP9w+8yisy6kFJ54I06fbJGD9+8MZZ8CwYTaEVF4ODzxg3f0PPmjZBPMY0WzMslbbi4tMBa5V1dmVbDsHGKaqV0S/LwZOwF725VHrHxHpBDytqsdUcY0rgSsBOnfu3Oett97Kwp04QXnuOQsDe+EFa/mL2EyCP/iBdeO5V3fh8vrr8ItfwH//a6F+AJ06mWPXt74FLVuGtc8Jx/r1Nix4113WaADYd1+rM37yE0sIlQeIyBxVrXSoPWs9ACIyGags0fqPVbUBGRnqhqreAdwBUFRUFE7tONlj8GBb1q+Hdessc2CbNqGtcuJAt26W4Gf7dhsK2GsvSyjlotBp1Qp+/nNLFLR6tZWRTp0KKgV01gSAqp7awFOsAjql/e4YrVsHtBaRJqq6I229U+i0auUhfk7l7LUXHHJIaCucOCICBx8c2oogxDkM8CWge+Tx3xQYBZSqjVlMAc6J9hsN5KxHwXEcx3HygVBhgF8UkZVACfCUiEyI1h8kIuMBotb91cAEYCnwsKoujk7xQ+B7IrIc2B+4K9f34DiO4zhJJqgTYK4pKirS2bM/42/oOI7jOHlJdU6AcR4CcBzHcRwnS7gAcBzHcZwCxAWA4ziO4xQgLgAcx3EcpwApKCdAEVkLZDIVYDvggwyeLy74fSWLfLyvfLwn8PtKGvlwX4eoavvKNhSUAMg0IjK7Ku/KJOP3lSzy8b7y8Z7A7ytp5Ot9pfAhAMdxHMcpQFwAOI7jOE4B4gKgYdwR2oAs4feVLPLxvvLxnsDvK2nk630B7gPgOI7jOAWJ9wA4juM4TgHiAsBxHMdxChAXALVARIaJyKsislxExlSyvZmIPBRtf1FEuuTeyrohIp1EZIqILBGRxSLynUr2OVlEPhGRedFyQwhb64qIvCkiCyObPzP7kxh/iv5fC0Skdwg7a4uIHJH2P5gnIp+KyDUV9knE/0pE7haR90VkUdq6tiIySUSWRZ9tqjh2dLTPMhEZnTura6aK+7pZRF6JythjItK6imOrLa8hqeK+fi4iq9LK2ulVHFttvRmSKu7robR7elNE5lVxbGz/X3VGVX2pZgEaA68DhwJNgflAjwr7fAP4e/R9FPBQaLtrcV8HAr2jmdoQbAAACIVJREFU762A1yq5r5OBJ0PbWo97exNoV83204GnAQGKgRdD21yHe2sMvIsl90jc/wo4CegNLEpb91tgTPR9DHBTJce1Bd6IPttE39uEvp8a7msI0CT6flNl9xVtq7a8xvC+fg5cW8NxNdabcbuvCtt/B9yQtP9XXRfvAaiZfsByVX1DVbcBY4GRFfYZCdwbfR8HnCIikkMb64yqrlHVudH39cBS4OCwVuWMkcB9apQDrUXkwNBG1ZJTgNdVNZMZLXOGqr4AfFhhdfrzcy9wViWHDgUmqeqHqvoRMAkYljVD60hl96WqE1V1R/SzHOiYc8MaSBX/r9pQm3ozGNXdV1R3nwc8mFOjAuACoGYOBt5J+72Sz74o/7dP9MB/AuyfE+syQDRk0Qt4sZLNJSIyX0SeFpGjc2pY/VFgoojMEZErK9lem/9pXBlF1RVTEv9XAB1UdU30/V2gQyX7JPl/BvAVrNepMmoqr3Hk6mho4+4qhmyS/P8aCLynqsuq2J7E/1eluAAocESkJfAIcI2qflph81ysq/l44M/Af3NtXz0ZoKq9geHAN0XkpNAGZQIRaQqMAP5Tyeak/q/2QK2PNa9ik0Xkx8AO4IEqdklaef0b0A3oCazBusvziQuovvWftP9XlbgAqJlVQKe03x2jdZXuIyJNgP2AdTmxrgGIyF7Yy/8BVX204nZV/VRVN0TfxwN7iUi7HJtZZ1R1VfT5PvAY1h2ZTm3+p3FkODBXVd+ruCGp/6uI91JDMNHn+5Xsk8j/mYhcCpwJfDkSN5+hFuU1Vqjqe6q6U1V3Af+gcnuT+v9qApwNPFTVPkn7f1WHC4CaeQnoLiJdoxbYKKC0wj6lQMor+Rzguaoe9rgQjXPdBSxV1d9Xsc8BKV8GEemHlZdYCxsRaSEirVLfMUesRRV2KwUuiaIBioFP0rqg40yVLZMk/q/SSH9+RgOPV7LPBGCIiLSJupyHROtii4gMA64DRqjqpir2qU15jRUV/GW+SOX21qbejCOnAq+o6srKNibx/1Utob0Qk7BgXuOvYV6tP47W/RJ7sAGaY92yy4FZwKGhba7FPQ3AuloXAPOi5XTgKuCqaJ+rgcWYB285cGJou2txX4dG9s6PbE/9v9LvS4Dbov/nQqAotN21uK8W2At9v7R1iftfYQJmDbAdGxe+HPOXeRZYBkwG2kb7FgF3ph37legZWw5cFvpeanFfy7Fx8NTzlYoUOggYX115jctSxX3dHz03C7CX+oEV7yv6/Zl6My5LZfcVrb8n9Uyl7ZuY/1ddF08F7DiO4zgFiA8BOI7jOE4B4gLAcRzHcQoQFwCO4ziOU4C4AHAcx3GcAsQFgOMUGCLydRHZN2nnroMNg0Skg4j0EZHuGThfYxG5JooRd5y8wQWA48QYEVER+V3a72tF5OcNON85wMH62ayPFfcrEpE/VbO9S/pManU5dw54DfgTcAOwIgPn+y6wQXfn9XecvMAVrePEm63A2SLya1X9IAPn2xt7MVaLqs4G6jrVaa3OXR0i0lhVdzbkHKq6SkT+Daxs6EtbRBpheeHvb8h5HCeOeA+A48SbHcAdWCt0D0SkvYg8IiIvRUv/aP34tHnNPxGR0VGLfVp0ntkicmK071gROSPtnPeIyDkicrKIPBmt+3za+V5OZUJLO6bSc1eyzysi8oCILBWRcSKyT7TtTRG5SUTmAueKyAVi860vEpGb0s6xQURuFpHFIjJZRPqJyFQReUNERkT7NBaRm4GfAPeIyNei9SdH+45Ls0PSrv8LEZkbXffIaH0/YAbwPRGZKSJH1Os/6DhxJXQmIl988aXqBdgA7IvNQb4fcC3w82jbv7GJSQA6Y2md04/tg2Vr2w/YB2gerT8SmBN9/yJwb/S9KZa5bm/gZODJaP0TQP/oe0us57AL0VzqVZ27gi1dsMyTqfPcTTSnfHRv10XfDwLeBtpH13kOOCvapsDw6PtjwERgL+B4YF60/krgp9H35tgkSYdG9/MJlpO+EVCW9rd7E/hW9P0bRNkHo797k+j7UOCR0OXBF18yufgQgOPEHFX9VETuA74NbE7bdCrQI2rIAuwrIi1VdYPYRED3A+ep6ieRY96tUet2O/aiBpui9o8i0gwYBrygqpvTzgnWCv69iDwAPKqqKytsb1LFuSvyjqrOiL7/K7qfW6LfqclX+gJTVXUtQHTNk7DZDbcBz0T7LQS2qup2EVmICQyw3OxdReSU6HdTTADsAGZplONdROZFx0yP9ktNhjUHmwwGoBXwDxE5GEsfnZgpvh2nNrgAcJxk8AesNfvPtHWNgGJV3ZK+o4g0BsYCv1TVlKPed4G1qnp55M2+BUBVt4jIVKyFe3503B6o6m9E5Ckst/sMERmaOr66c1dCxbzj6b83VnFMOttVNXXMLsw/AlXdleahL1h+9mfSDxSRk1P7R+xkz/pvayXrbwSmqOrfRaQrMKUWNjpOYnAfAMdJAKr6IfAwNhlLionAt1I/RKRn9PU3wAJVTX+ZtwHWRt8vBhqnbXsIuAwYyO4W9v8QkW6qulBVb8JmeavYwq/u3Ol0FpGS6PuF7G59pzML+LyItIuEzAXA81WcrzImAFeJTXWNiBwRzdpWH9Lv69J6nsNxYosLAMdJDr8D2qX9/jZQJCILRGQJNjsgmJ/AkDTHvRHA34BLRWQ+9gJPb3FPBD4PTFbVbZVc95rIIW8B1sX/dIXt1Z07nVeBb4rIUuzl+reKO6hNyzwGa23Px/wJKpseuCruBJYAc6Mwxb9T/57Om4Ffi8jLVC1qHCex+GyAjuNkHRHpgjkVHhPYFMdxIrwHwHEcx3EKEO8BcBzHcZwCxHsAHMdxHKcAcQHgOI7jOAWICwDHcRzHKUBcADiO4zhOAeICwHEcx3EKEBcAjuM4jlOA/D+Twehy0RN/IQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "HKSNfA-O9Arv",
        "outputId": "211ed24f-595a-4daa-9646-1d0b9de606f5"
      },
      "source": [
        "# vypocet dat\n",
        "dy = np.gradient(y, x) # výpočet derivace\n",
        "\n",
        "# vykrelseni dat do obrazku\n",
        "fig = plt.figure(figsize=(8,4)) # zalozeni obrazku\n",
        "plt.plot(x, y) # prvni graf - funkce\n",
        "plt.plot(x, dy) # druhy graf - derivace funkce\n",
        "\n",
        "# kosmeticke upravy obrazku\n",
        "plt.title('Graf derivace') # nadpis\n",
        "plt.ylabel('Sinus a jeho derivace') # pospisek svisle osy\n",
        "plt.xlabel('Nezávislá proměnná') # pospisek vodorovne osy\n",
        "plt.legend(['Funkce','Derivace']) # popisky do legendy\n",
        "plt.xticks([i*np.pi for i in range(6)],[0,'$\\pi$','$2\\pi$','$3\\pi$','$4\\pi$','$5\\pi$','$6\\pi$']) # nastaveni znacek\n",
        "plt.grid(axis='x') # vykresleni mrizky\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X8tdEUG9WDz0"
      },
      "source": [
        "Výpočet derivací je možné použít pro citlivostní analýzu, tj. posouzení, jak se změny na vstupu projevují na výstupu. \n",
        "\n",
        "\n",
        "\n",
        "*   Nulová derivace znamená, že veličina se nemění při změně vstupních dat.\n",
        "*   Konstantní derivace znamená, že veličina vykazuje lineární závislost na vstupních datech. Pokud vstup roste aritmetickou řadou (po stejných skocích), veličina lineárně závislá na vstupních datech roste nebo klesá také aritmetickou řadou.\n",
        "*   Nekonstantní derivace zamená nelineární závislost.\n",
        "*   Derivace zachovávající znaménko znamená monotonii.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "vo3-cljM_Fdy",
        "outputId": "bfbb3129-e1f6-422d-a025-9a3ae5b66e63"
      },
      "source": [
        "def konstanta(x): # ukazka definice konstantni funkce\n",
        "  return 5\n",
        "\n",
        "def primka(x): # definice linearni funkce\n",
        "  return 6*x-4\n",
        "\n",
        "def parabola(x,a=-2,b=4,c=10): # definice kvadraticke funkce, parametry je mozne nastavit pri volani funkce a maji defaultni hondoty\n",
        "  return a*x**2+b*x+c\n",
        "\n",
        "def exponenciela(x, A=3, k=0.2): # definice expoencialni funkce s parametry\n",
        "  return A*np.exp(k*x)  \n",
        "\n",
        "def sinusoida(x):\n",
        "  return np.sin(x)  \n",
        "\n",
        "x = np.linspace(0,10,100)\n",
        "\n",
        "# cyklus pres pet pojmenovanych funkci a jednu nepojmenovanou klesajici exponencialni funkci (lambda konstrukce)\n",
        "for f in [konstanta, primka, parabola, exponenciela, sinusoida, lambda x:exponenciela(x,A=1,k=-1)]:\n",
        "  fig, ax = plt.subplots(1, 2, sharey=False, figsize=(10,2)) # zalozeni obrazku se dvema grafy vedle sebe\n",
        "  y = [ f(i) for i in x] # vypocet funkcnich hodnot \n",
        "  dy = np.gradient(y,x) # vypocet derivace\n",
        "  m = np.min(dy) # minimum derivace\n",
        "  M = np.max(dy) # maximum derivace\n",
        "  ax[0].plot(x, y) \n",
        "  ax[0].set_title(\"Funkce\")\n",
        "  ax[1].plot(x, dy, color='red')\n",
        "  ax[1].set_title('Derivace')\n",
        "  plt.show(fig)\n",
        "  print (\"Derivace: minimum je %s a maximum %s\"%(m,M))\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x144 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Derivace: minimum je -3.552713678800501e-15 a maximum 3.552713678800501e-15\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x144 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Derivace: minimum je 5.999999999999943 a maximum 6.000000000000057\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x144 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Derivace: minimum je -35.79797979797995 a maximum 3.7979797979798\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x144 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Derivace: minimum je 0.6061016251802882 a maximum 4.388951547623518\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Wj/b7/WMiw3n9tos5npHFw5//qUPSoe7ll+2y/l69YN8+12lUXr36qp2wPnKk/e/obyIwdqydA3j77XZ/L+V1WmzlsHrXYV76cQ1/a1iBrj6cEH8+5UtEM+KWxqzZfYRXpq11lkPl3ex1e5kwbxN3tYynde3yznIkxhXniWvqMzcllY//0CHpkBYdDe+/b3tG+vVznUblxbp18PjjcM01vp0Qfz7lytltJlasgCeecJcjhGmx5ZGVbRg4aTklYiJ5/oZGzg/4vaJOebo2rcY7v25g2TbdfysYHEvPZOAXy0iMK86gq+q5jsNtl1anZUJZnp+6mu26pUhoa9QInnrKDkN9+qnrNCo3srOhZ09bLI8Z4/6Q6Kuugr59bU/pvHlus4QgLbY83pu3iaVbD/LktQ0oUyzKdRwABl9dn3LFo3nsi2Wc1KX8AW/ET2vZefgEL97YiJjIAu727AUiwos3NiLbGAZN0hWuIe+xx6BpU3uW3n49kSLgjR5tFzeMHAmVvb+HY778+99QrZrdSDUjw3WakKLFFrB1fxrDf1pDm7rlfbJxaX6VLBLJsOsuYPWuI7z96wbXcdQ5LNt2kAnzNvL3ZtW5pEZp13H+o1qZogzsWI/Z6/YxZdlO13GUL0VE2AOL9++HwYNdp1HnsmOHXUHasaOdHB8oihe3ReDKlTBihOs0IUWLLeDpKasAGHb9hc6HD0/XoWFFrrqgIq/9so5tB/QolkCUmZXN4MnLbS9kR/fDh6e7vXkNLqxSkmHfruKI7i4f2ho3hgcesFtC/Pab6zTqbB5+2PYcvf66++HD011zjT0A+5lnICXFdZqQUeiLrZlr9jDtr93c37Y2VUoVcR3njJ64pgEA//ruL8dJ1Jl8smArK3cc5slrGxIbE+k6zv8IDxOeve4C9h5NZ9S0da7jKF976im7Q3ifPnaHcBVYpk+3+1oNHmzPKAxEr74KkZEwYIDrJCGjUBdbGZnZPDNlFTXLFaPnZfGu45xVlVJFuK9NIt+v2MWcdbq0O5AcTMtgxE9raF6rDFdfWNF1nLO6qFopujatzvh5m3T/tlBXooSdB7RsmR1WVIHj5Em47z5bZA0c6DrN2VWuDEOHwrff6oHnXlKoi63xczeyYd8xhl7bgOgI9xOaz+We1rWoUbYoT01ZqZPlA8jIaes4fPwkT17bMOCGoE/32N/qUjw6gmHfrdLJ8qHuhhsgOdm+YB7U1cwB4623YPVqWwzHxLhOc279+0Pt2rZ3S4+DKjCvFFsi0lFE1ohIiogMOsPn7xKRvSLyp+ftHm/ctyBSj6bz2i8ptKsXR5u6vjsg2FtiIsN5olMDUvYcZeKCra7jKGDt7iN8MH8ztzWrTv1Ksa7jnFfpYlH0b1eb2ev2MXPtXtdxAkowtmHnJAKvvAKpqfY4FuXegQN2iLddO+jUyXWa84uKsttArF5tJ82rAilwsSUi4cBo4CqgAdBNRBqc4amfGmMu8ryNLeh9C+rV6es4fjKLwVfXdx0l19rXj+PSmmUYNW2tTnQOAC98v5qiUeE8dKX/zj4sqDua1yC+bFGe++4vMrWHFAjeNuy8LrrI7uP06qt280zl1rBhtuAaMSLwJsWfTadO0KGDnSx/4IDrNEHNGz1blwIpxpgNxpgMYCLQxQvX9ZkNe4/y0e9b6Nq0Golx/j+SJ79EhCFX12ff0QzGzNKtIFyavyGVX1bv4d7kxIDZly03oiLCGHRVPdZpD2lOQdeG5dqwYbaHQg+qdmv9enjtNVv8ujiSJ79E7N5bBw/Ciy+6ThPUvFFsVQFyttrbPI+d7kYRWSYiX4iIu7NwgJd+XENURBgPtK/tMka+NK5Wii4XVead2RvYeUh3BXfBGMPz36+mUskYerSKdx0nz/7WsCJN40szcto60jJ0tRpB2IblWsWKdpuBzz+HhQtdpym8hg61q/uefdZ1krxr3NiemThqFGzVP9Dyy18T5KcA8caYRsDPwHtnepKI9BaRhSKycO9e38wpWbLlAN+v2EXvy2sRVyLAJyiexSMd6pJtDK9O1z1QXJi6fBdLtx5kwJV1AmKn+LwSEQZ2rMe+o+mMn7vJdZxgETBtWJ49/LA9+27Q/0xFU/6wdCl8/LHd/6xSJddp8ufZZ+3xQk895TpJ0PJGsbUdyPlXXlXPY/9hjEk1xqR7PhwLXHKmCxlj3jbGJBljksqX980hviN+WkuZYlH0al3LJ9f3h2plinLbpdX5bOFWNu075jpOoZKZlc2In9ZQp0Jxbry4qus4+ZYUX4a29eIYM2s9h9IK/fy/oGrD8iw21g4jTp8OP//sOk3hM2QIlCoFjz7qOkn+1ahht6yYMAFWrXKdJih5o9haANQWkZoiEgV0Bb7J+QQRyVnOdwac7M752/pU5qTs497kBIpFR7iI4DX92iYSGS6MnLbWdZRC5csl29mw7xgPd6hLeFiQTHI9i0f/Vpcj6Zm89et611FcC5o2LN/69rUvmIMHg2774T9z5sB339lexdKBc4xXvgweDEWLau9WPhW42DLGZAL3AT9iG6DPjDErReQZEenseVp/EVkpIkuB/sBdBb1vPnIy/Kc1VIiN5vbmNfx9e6+LKxHDXS1r8vXSHbpJpZ9kZGYzavo6LqxSkg4NKriOU2D1K8XSuXFlxs/dyN4j6ef/ghAVLG1YgURH23lDixbBlCmu0xQOxsATT9h5c/ff7zpNwZUrBw8+aOf/LV3qOk3Q8cqcLWPMVGNMHWNMgjHmX57HhhpjvvG8P9gY09AY09gY08YYs9ob982LmWv3smjzAe5vWzso59mcSZ8ralE8KoKXf17jOkqh8PmirWw7cJyHOtQJ+A1Mc+vB9nXIyMxmzKzC3bsVDG1Ygd15p925fOhQO/9G+daMGTBrFjz+uO0RCgUPP2yHRIcOdZ0k6BSKHeSNMYz8eS1VSxfhlqTgWESUG6WKRtHzspr8uHI3q3Ycdh0npKVnZvH6LylcUqM0yXUCZC6OF9QsV4zrmlThw983s+fICddxlC9FRMCTT9peia++cp0mtBljf9ZVqkCvXq7TeE+pUrbg+uYbWLDAdZqgUiiKrZlr9rJ02yH6tUkkKiK0vuWel9WkREwEr07XTQt96bMFW9l56AQPXRk6vVqn9G9bm5NZhrdm6t5tIe+226BuXVsIaO+W70yfbudrDR4c+Mfy5NUDD0CZMvD0066TBJXQqjzOwBjDyOnrqFKqSFCvHjubkkUi6dmqJj+s3KW9Wz6SnpnFGzPXk1SjNC0TyrqO43Xx5Ypx3UVV+Oj3zew5rL1bIS083BZaK1bAl1+6ThOaTvVqVa0K9wT2qU75UqKE7d367jvduy0PQr7YmrV2L0u3HgzJXq1TTvVujZquKxN9YdKi7ew8dIL+7WqHXK/WKf3bJZKZbRjzq/ZuhbxbboE6dezeSboy0ft++QXmzbO9WtHRrtP4xn332SHFYNyk1ZHQrD48jDG86unVuumS0OvVOqVkkUh6tLJzt3RlondlZGYzekYKF1UrReva5VzH8ZkaZYvRpXFlPvp9M6lHC+/KxEIhPNzu/bR0qa5M9IVhw+zmpT17uk7iO7GxMGCAnbu1ZInrNEEhpIut39ansnjLQfokJ4Rsr9YpPVrGUywqnNEzdFd5b/pqyXa2HzzOAyHcq3XKvW0SSc/MZtycja6jKF+77TaoVUt7t7xtzhyYORMeeyz05mqdrn9/KFlSe7dyKaQrkNdnpBBXIpqbQ7hX65TSxaK4vUUNvl22g426q7xXZGUb3piZwoVVSpJcN3RWIJ5NYlxxrr6wEu//tpmDaRmu4yhfioiww1wLF8IPP7hOEzqGDYPy5aF3b9dJfK9UKbt/2JdfwsqVrtMEvJAtthZtPsC89an0vrxWyOyrdT73XFaLyPAw3pypvVve8N3ynWxKTaNfm4SQ79U65b42iRxNz9QzEwuDO++EatXguedcJwkNCxbAjz/ayeOhsq/W+TzwgP1eX3jBdZKAF7LF1ugZKZQuGsltzaq7juI35UtE0+3S6kxevJ1tB9Jcxwlq2dmGN2akkBhXnA4NKrqO4zf1K8VyZYMKTJi3iaPpma7jKF+KirLn9c2ZA7Nnu04T/J5/3vb23Huv6yT+U64c9OkDn3wCG3RxzbmEZLG1cschflm9h56talI0KrjPQMyr3pfXQgTe0VVlBTJ99R5W7zrCvckJhAX5GYh5dW9yAoeOn+ST37e4jqJ87Z57IC5Oe7cKatUqO5zWv7/dGqEwefhhu+jixRddJwloIVlsvTlzPcWjI7izZbzrKH5XuVQRrruoChMXbGWfrirLF2MMo2ekULV0ETo3ruw6jt81qW73E3tn9gbSM7Ncx1G+VKSIXVX2ww+weLHrNMHrhRegWDFbbBU2lSvblZcTJsD27a7TBKyQK7Y27TvG1OU7+Xvz6pQsEuk6jhN9khPIyMpm/FxdVZYf8zfs58+tB/nHFQlEhIfc/yK5cm9yInuOpDNpkTaeIa9vX7uqTHu38mfTJvj4YzspvmzobXqcK489BllZMHKk6yQBK+ReScb8uoGI8DDuvqym6yjOJJQvTseGFXn/t80cOXHSdZyg88bMFMoVjyoUq1jPplViWRpVLcmYX9eTmaXHuoS0kiWhXz+YPBnW6sbIeTZ8OISF2eG0wqpmTbj1VnjrLThwwHWagBRSxdbuwyeYtGgbN19SlbgSIb7HyXncm5zIkROZfDhf593kxYrth5i9bh89L6tZaFaxnomIcG9yAptT0/h+xS7XcZSv9e9vdzt/6SXXSYLLnj0wbpxd2Vmlius0bg0cCEePwujRrpMEpJAqtt6ds5HM7Gx6X17LdRTnLqxakta1yzFuzkZOnNR5N7n15sz1lIiO4PbmNVxHca5Dg4rUKl+MN2eux+jGl6GtQgU77+a993TeTV68+iqkp9thtMKuUSPo1AlGjYI0XQ1/upAptg6lneTD+Zvp1KgyNcoWcx0nIPS9IoF9R9OZtHib6yhBYeO+Y0xdsZO/N69BbEzhnO+XU1iY0OfyBFbtPMzsdftcx1G+9sgjkJ2t825y6/BheP11uPFGe9akgkGDYN8+29un/otXii0R6Sgia0QkRUQGneHz0SLyqefzv4tIvDfum9OHv2/mWEYWfa7QXq1TWiTYeTdv/7qBrGztmTift3/dQGR4GD0vi3cdJWB0aVKZirExvDlzvesoPhUIbZhzOu8mb8aMgUOH7PCZsi67DFq1ghEj4KTOF86pwMWWiIQDo4GrgAZANxFpcNrT7gYOGGMSgVcAr27IceJkFuPnbuSKOuVpWLmkNy8d1ESEvlecmnez03WcgLbHM9/vJp3v91+iI8K5+7Ka/LYhlSVbQvMFOBDasIBxat7NG2+4ThLY0tPhlVegXTtISnKdJrAMGgSbN8Onn7pOElC80bN1KZBijNlgjMkAJgJdTntOF+A9z/tfAO3Ei+effL5oG/uOZtDnigRvXTJkdGhYkVrlivHWLJ13cy7vzt1k5/u11p7R03VrVp3YmAjemhWyvVvO27CA0agRXHWVnXdz/LjrNIHrgw9g507t1TqTq6+Ghg3tJqf6mvMf3ii2qgBbc3y8zfPYGZ9jjMkEDgH/syGJiPQWkYUisnDv3r25urkxhvfnbeKiaqVoXqtMfvKHtPAwoffltVix/TBzUnTezZkcPnGSj+Zv5qoLKxFfTuf7na54dATdW8bz06rdrN971HUcX3DahgWcQYNg714YP951ksCUlQX//jdcfDG0b+86TeAJC7NF6IoVMHWq6zQBI6AmyBtj3jbGJBljksqXL5+rrxERPu7VnBdvbFRoDgvOq+svrkJciehQ7pkokI/mb+FIeiZ9tWf0rO5qGU90RBhvz9JjoM4lP21YwGndGlq0sNtAZOr5mP/jq69g3TpblOprzpl17QrVq9vzIhXgnWJrO1Atx8dVPY+d8TkiEgGUBFK9cG/AHsBct2IhO48qD6IjwrmndU3mpqSybNtB13ECyomTWbw7dyOta5fjgio63+9syhaP5pakakxeso1dh064juNtztuwgCJiC4lNm+Czz1ynCSzG2KN5EhPhhhtcpwlckZF2k9e5c+2b8kqxtQCoLSI1RSQK6Jp45ssAABfZSURBVAp8c9pzvgG6e96/CfjF6AQiv+p2aXVKhPa8m3yZvHg7e4+ka69WLvRqXYtsA+PmhFzvlrZhp7vmGmjQQOfdnG7GDFi4EB591B6+rM7u7rvt8UV6QDXghWLLM3/hPuBH4C/gM2PMShF5RkQ6e542DigrIinAQ8D/LK1WvlUiJpI7W9Tg+xW72BCa827yLCvbMObX9TSuWpIWCYX0TLM8qFamKNc2qsTHv2/hUFroLOvWNuwMwsLsRp3LltlDqpX1/PNQsaLdMV6d26mDuadMsfO3CjmvzNkyxkw1xtQxxiQYY/7leWyoMeYbz/snjDE3G2MSjTGXGmNC7k/jYNCjVU2iwsN4+1f98QN8v2Inm1PT6JucoPP9cqlPcgLHMrJ4/7dNrqN4lbZhZ9CtG1SrZofNlO3RmjYNBgyAGN0eJlf69YOiRe2CgkIuoCbIK98q55l3M2lxSM67yRNjDG/OXE+tcsW4skFF13GCRr2KsbStF8f4eZs4nqHHQIW0qCg77+bXX2HePNdp3HvxRXtod58+rpMEj7JloXdv+Phju/dWIabFViHT+3I772bs7ND/w/xcZq3dy8odh+mTnEB4mPZq5UXf5AT2H8vg0wV6yHnIu+ce+4JZ2Hu31qyBSZNsT01srOs0weWhh+yw9PDhrpM4pcVWIfOfeTd/bOFgWobrOM68MXM9lUrGcN1Fp2+npM6naXwZkmqU5p3ZGzmZle06jvKlnPNuli93ncadl16C6Gj7s1B5U60a3HEHjB0Lu3e7TuOMFluFUJ/kBNIyspgwb5PrKE4s2ryfPzbup1frWkRF6P8C+XFvmwS2HzzO13/ucB1F+dp990Hx4oW3d2vrVnj/fdvLV6GC6zTB6bHH7BFHo0a5TuKMvtIUQvUqxtK+fhwT5m3iWHrh27TwjRnrKVMsiq6XVjv/k9UZtakbR/1KsbwxM0UPOQ91ZcpA374wcSKsL4Rbxwwfbre/ePRR10mCV926cNNNMHq0Pby7ENJiq5Dq1yaRg2kn+ej3wjVpcdWOw0xfvYceLeMpGhXhOk7QEhH6tUlgw95j/Lhyl+s4ytcGDLAbVRa2VWV79sA779hhsOrVXacJboMHw+HDtuAqhLTYKqSaVC9Nq8SyvDN7IydOFp5VZaNnpFAiOoI7W8a7jhL0rrqgErXKFWP0jBQ95DzUVaoEPXvChAmwbZvrNP4zahScOKEHTntDkyb2kOpXXoFjx1yn8Tsttgqxfm0S2Xsknc8Xbj3/k0NAyp4jTF2xk+4t4ylZJNJ1nKAXHib0SU5g5Y7DzFwTpIcuq9x77DHIzraTxQuDgwfh9dft8Ffduq7ThIYnnoB9+2DMGNdJ/E6LrUKsRa2yXFy9FG/N2kBGZuivKntjxnpiIsLpeVlN11FCxvVNqlClVBFGTV+nvVuhLj7eDqe9/TbsKgRDx6++aoe9hgxxnSR0tGgBbdvagv1E4drrUYutQkxEuL9dbbYfPM7kxaE9NLAlNY2vl+7g9ubVKVMsynWckBEZHsa9bRL4c+tB5qTscx1H+drjj0NGBowY4TqJbx0+bIe7rrsOGjd2nSa0PPGELdbHjXOdxK+02CrkkuuUp3HVkrw+IyWk90x6fcY6wsOEXq1ruY4Scm66pCqVSsYwapr2boW8xER7jM+bb9rhoFD1+ut2GPGJJ1wnCT3JydCypd1KJD3ddRq/0WKrkBMRHmhfm20HjvPl4u2u4/jEltQ0Ji3ezt+bVScuVs8087boiHD6JiewcPMBftuQ6jqO8rUhQyAtLXR7t44ehZdfhk6d4JJLXKcJPSLw1FN2ocW777pO4zdabCna1I2jUQj3br32yzoiwoS+VyS4jhKybkmqRoXYaEb+rL1bIa9+fejaFV57zW6NEGpeew1SU+Gf/3SdJHS1b297t557rtD0bmmxpRAR+retzZb9aSHXu7U59RiTl2znNu3V8qmYyHD6tUnkj037mZuivVshb+hQOH489FYmHjpkv6dOnaBZM9dpQpcIPP207d0qJHO3tNhSALSrH0fjqiUZNX0d6Zmhs+/Wa7+kaK+Wn9zatBqVS8Yw4uc12rsV6urVg9tusxtUhtJ5dyNHwoED8MwzrpOEvnbt4LLLbO9WIViZWKBiS0TKiMjPIrLO82/pszwvS0T+9Lx9U5B7Kt8QER7uUJftB4/z6YLQ2HcrZc9RJi/exu3Na2ivlh9ER4RzX9vaLNlyMGj23dI2rACGDrUrE0PlzMT9++1creuvh4svdp0m9InYonb7drvgIsQVtGdrEDDdGFMbmO75+EyOG2Mu8rx1LuA9lY+0rl2OS+PL8NovKRzPCP7erZd/XkORyHDuTdZeLX+5Oakq1coUCabeLW3D8qt2bbjzTnjjDdiyxXWaghsxAo4cscNbyj/atLHzt557zv7sQ1hBi60uwHue998Drivg9ZRDtnerDnuPpPP+b5tcxymQ5dsOMXX5Lu5pXYuyxaNdxyk0IsPDeKBdHVZsP8zU5UGx8aW2YQXx1FP/v7osmO3caffV6toVLrzQdZrC5bnn7DYir7ziOolPFbTYqmCM2el5fxdQ4SzPixGRhSIyX0S0MQtgzWqV5Yo65Xlj5noOpZ10HSffhv+0htJFI7mnte4W72/XN6lC3QoleOnH1cGwulXbsIKoXh369YP33oNVq1ynyb+nn4aTJ+HZZ10nKXyaNrVDt8OHh/TebecttkRkmoisOMNbl5zPM3bM4GzjBjWMMUnAbcBIETnjuI6I9PY0aAv37g2OOR+haGDHehw+cZI3Zqa4jpIvc1P2MWvtXu5NTqREjJ6B6G/hYcLAq+qyKTWNiX+4H17SNszHBg+GYsWC91ibNWtg7Fjo0wcSdMqBE8OG2cOphw1zncRnzltsGWPaG2MuOMPb18BuEakE4Pn3jJuuGGO2e/7dAMwEmpzleW8bY5KMMUnly5fP57ekCqpB5Viub1KF8fM2sf3gcddx8iQr2/Cv7/6iauki3NGihus4hVabunFcWrMMo6av41h6ptMs2ob5WLly8Oij8NVXMHu26zR5N2QIFCmi+2q51KAB9Oxp5/+lBOcf+edT0GHEb4Dunve7A1+f/gQRKS0i0Z73ywGtgCDuby4cHu5gT7kf8eMax0nyZvLibazaeZjHOtYjJjLcdZxCS0QYfFU99h3NYNpfAb01gLZh3vDQQ1ClCgwYANkBP3T8/2bPhkmT4JFHIC7OdZrC7dlnISoKBg50ncQnClpsvQBcKSLrgPaejxGRJBEZ63lOfWChiCwFZgAvGGO0oQpwVUoVoWermkxesp0/tx50HSdXjmdkMfynNTSuVoprG1VyHafQa1K9NNMeuoIuF1VxHeVctA3zhmLF4PnnYdEi+PBD12lyJzsbHnwQqla1PXPKrYoVbaE1eXJw9pCehwTq8uykpCSzcOFC1zEKtSMnTtJ2xCwqlyrCl31bEhYmriOd08s/r+XV6ev47B8tuLRmGddxVD6IyCLP3KigV+jasOxsaN7c7pu0dq0twALZu+/C3XfDRx/ZDVqVe2lpUKeOLbx+/x3Cg2t04lztl+4gr86qREwkgzrWY+nWg0xeEtjH+GxOPcZbs9bTuXFlLbSUciEszC7f37ED/vUv12nO7fBhePxxaNECunVznUadUrSoPS5p0SK7aCGEaLGlzun6JlVoUr0UL3y/miMnAncriKenrCIyTBjSqb7rKEoVXq1a2Y1Ohw+H1atdpzm7oUPtIdqjRtl9wlTg6NoVkpPtKtcQ2gpCiy11TmFhwtOdG5J6LJ2XAnSy/LRVu/ll9R4ebF+HCnosj1JuvfSSHUK8914IxGkqixbBa69B3752jycVWETg9dftjvKDB7tO4zVabKnzalS1FN1bxPPB/M0s2nzAdZz/cuTESYZ+vYLaccW5q1W86zhKqbg4O1l+xgz4+GPXaf5bZib07m0zPvec6zTqbBo2tIsXxo6FX391ncYrtNhSufLI3+pSKTaGwZOXkZEZOEu7X/xhNTsPn+DFmxoRGa6/zkoFhF69oFkzeOAB2B1AW3+8/josXgwjR0LJkq7TqHN56imoWdMuYkhLc52mwPTVSeVK8egIhl1/AWt3H2X0jMDYdO639al8OH8LPVvV5OLqpV3HUUqdEh5uV/sdPWqH6wJhOHH1ajss1akT3HKL6zTqfIoVg3Hj7CanQ4e6TlNgWmypXGtbrwLXN6nC6zNSWLzF7XDi0fRMBk1eRo2yRXnEswGrUiqANGgAzzwDX34JEye6zZKZaSfuFy0K77yjk+KDRZs29hilV16BefNcpykQLbZUnjzdpSEVY2MY8OmfTo9hGfrVCrbuT+OlmxpTJCq49mJRqtB4+GG791a/frDF4TmZzz8PCxbAW29BJd3wOKi8+CLUqGH3QjsQWHOG80KLLZUnsTGRvHxLY7bsT+Opb1Y6yTBp0TYmL9lO/3a1dU8tpQJZeDh88IHtWbr1VsjI8H+GWbPg6aftflo33+z/+6uCiY2FTz6xm+X26hUYQ9L5oMWWyrNmtcrSLzmRzxdt45M//PvXasqeo/zz6xVcWrMM97et7dd7K6XyITHRzr2ZPx8GDfLvvXfssEVeYqLt1VLBqVkzu3p00iR4803XafJFiy2VLwOurEPr2uUY+vUKv20HcTAtg3veW0CRyHBGdb2I8AA/Pkgp5XHzzXDffXbujb/mb508aQutI0fsi3RsrH/uq3zj4Yfh6qvtCtcZM1ynyTMttlS+hIcJr3VrQqWSRej74SJ2HDzu0/udzMqm74eL2XHwBGPuuIRKJYv49H5KKS8bPhwuuwzuugvmzPHtvYyxWwbMmWP3amrY0Lf3U74XFmb3batTB2680Z6/GUS02FL5VqpoFG/feQnHM7K4fdzvpB5N98l9srMNgyYt57cNqbxw44Ukxes8LaWCTnQ0fPWVnezcpYtvXywHD7ZzxZ55Rs8+DCUlS8KUKXYu4DXXwK5dvrtXZia8/779nfUCLbZUgdSrGMu4u5qy4+Bx7nz3Dw4d9+75idnZhsGTlzNp8TYGtK/DDRdX9er1lVJ+VLYsTJ1qXyzbtoU1PjgC7MUX7VufPvDEE96/vnKrVi34+ms7H69NG+8XXMbA+PFQty50724LLi/QYksV2KU1y/DW7ZewdvcRur09n92HT3jluplZ2Tz+5XI+XbiV/m0T6d8u0SvXVUo5lJAA06bZOVWXXw7Ll3vnusbAwIF2En7Xrna3eN1PKzS1bGmL9q1bbcG1fbt3rpuRYQusnj2hTBlb1E2a5JVLa7GlvCK5bhzv3JnE5tRjXD96Lmt2HSnQ9Q4cy6D7+D+YuGAr97dNZMCVdRBtOJUKDY0a2S0ZIiJswTVlSsGul5YGPXrAv/9td6z/8EPbe6ZC1+WX24Jr2zZISir4pqcHD9oJ+B98AM8+C3/8AZ07e61gL1CxJSI3i8hKEckWkaRzPK+jiKwRkRQR8fPaX+UvyXXj+KxPC7KM4brRc5kwdyPZ2XnfE2XR5v10GT2XBRsP8O+bGvFwh7paaCmf0DbMoXr1YO5ce/5d5862Vyo/+3AtWwZNm8J779nz9EaP1kKrsLj8cvjtN3u0T3KyPfMyMx+bbU+fDo0b2z8AJkyww89efs0paM/WCuAG4KzHcotIODAauApoAHQTkQYFvK8KUA0rl+Sb+y6jWa0yPDVlFd3emZ/rrSH2HDnBY18s5cY3fyMzK5uJ/2jOLUnVfJxYFXLahrkUH297JP7xD9sr1aCBHbbJzcaVu3fDgAG20Nq/H376CZ58UocOC5sLLrCnA3ToYH8fkpJg9uzcfe2GDXaj1PbtISbGfl337j6JGVGQLzbG/AWcr9fhUiDFGLPB89yJQBdgVUHurQJXhdgYxt/VlM8XbuO57//ixjfncWl8Ga6/uArNa5UlvmxRRARjDHuOpLNs2yG+XLKNn1ftxhjoc0UC/dslUjSqQL+eSp2XtmEBICbGbjh63XXwyCNw001Qv75dRdi5M9Subc80NMYe1zJrlh12/PRTSE+3Zx6++CKUL+/6O1GulC5tfycmTbIF1+WXw0UX2e0/rrjCTnaPirK/Qzt32n26pkyBzz+3Q9kPPWSHDosW9VlEf7yaVQG25vh4G9DMD/dVDokItzStRqdGlfh0wVbenbuRwZPtRNiYyDBiIsPJzjYcPmG7fEsXjaR7i3j+3rwGNcsVcxldqdNpG+YPHTvaHoYPPrCrwYYOtW9gJysfOWIn1QOUKmU3LB082BZjSonYQr1jRzukPG4c3H+//VxEhC2kjh2DrCz7WOnStjB76CGoXNnn8c5bbInINKDiGT41xBjztTfDiEhvoDdA9erVvXlp5Uix6Ah6XlaTHq3i2bDvGPM3pLJp3zHSM7PJNoaE8sWpXymWi6qVIiZS51ko79M2LIhERNiJ7j162JVmc+bAxo12EnTJkrb36uKLoVUriIx0nVYFouLF7cHn/frB6tWwZIld8ZqWZguu8uVtb1fjxn6d23feYssY076A99gO5Jx4U9Xz2Jnu9TbwNkBSUlJwnjapzkhESChfnITyxV1HUYWMtmFBqlo13ZBUFUy9evYtAH6P/LH1wwKgtojUFJEooCvwjR/uq5RS3qBtmFKqQAq69cP1IrINaAF8JyI/eh6vLCJTAYwxmcB9wI/AX8BnxpiVBYutlFIFp22YUsofCroa8UvgyzM8vgO4OsfHU4GpBbmXUkp5m7ZhSil/EJOb/UwcEJG9wOY8fEk5YJ+P4viS5vYvze1/eclewxgTEmv489iGBet/32DNDcGbXXP7l1far4AttvJKRBYaY866A3Sg0tz+pbn9L5iz+0uw/oyCNTcEb3bN7V/eyq1nIyqllFJK+ZAWW0oppZRSPhRKxdbbrgPkk+b2L83tf8Gc3V+C9WcUrLkheLNrbv/ySu6QmbOllFJKKRWIQqlnSymllFIq4AR9sSUiHUVkjYikiMgg13lyS0SqicgMEVklIitF5AHXmfJCRMJFZImIfOs6S26JSCkR+UJEVovIXyLSwnWm3BCRAZ7fkRUi8omIxLjOdCYi8q6I7BGRFTkeKyMiP4vIOs+/pV1mDETB2IZp++V/2n75ni/bsKAutkQkHBgNXAU0ALqJSAO3qXItE3jYGNMAaA70C6LsAA9gd9MOJqOAH4wx9YDGBEF+EakC9AeSjDEXAOHY42IC0QSg42mPDQKmG2NqA9M9HyuPIG7DtP3yP22/fG8CPmrDgrrYAi4FUowxG4wxGcBEoIvjTLlijNlpjFnsef8I9n+cKm5T5Y6IVAU6AWNdZ8ktESkJXA6MAzDGZBhjDrpNlWsRQBERiQCKAjsc5zkjY8yvwP7THu4CvOd5/z3gOr+GCnxB2YZp++Vf2n75hy/bsGAvtqoAW3N8vI0g+R8+JxGJB5oAv7tNkmsjgceAbNdB8qAmsBcY7xk+GCsixVyHOh9jzHZgOLAF2AkcMsb85DZVnlQwxuz0vL8LqOAyTAAK+jZM2y+/0PbLHa+0YcFebAU9ESkOTAIeNMYcdp3nfETkGmCPMWaR6yx5FAFcDLxpjGkCHCMIhrQ88wO6YBvbykAxEbndbar8MXbpsy5/DiHafvmNtl8BoCBtWLAXW9uBajk+rup5LCiISCS2ofrIGDPZdZ5cagV0FpFN2CGPtiLyodtIubIN2GaMOfXX9xfYxivQtQc2GmP2GmNOApOBlo4z5cVuEakE4Pl3j+M8gSZo2zBtv/xK2y93vNKGBXuxtQCoLSI1RSQKO/HuG8eZckVEBDv+/pcx5mXXeXLLGDPYGFPVGBOP/Xn/YowJ+L9UjDG7gK0iUtfzUDtglcNIubUFaC4iRT2/M+0IgomxOXwDdPe83x342mGWQBSUbZi2X/6l7ZdTXmnDIrwWxwFjTKaI3Af8iF3l8K4xZqXjWLnVCrgDWC4if3oee9wYM9VhplB3P/CR50VtA9DDcZ7zMsb8LiJfAIuxK8CWEKA7MYvIJ0AyUE5EtgFPAi8An4nI3cBm4BZ3CQNPELdh2n75n7ZfPubLNkx3kFdKKaWU8qFgH0ZUSimllApoWmwppZRSSvmQFltKKaWUUj6kxZZSSimllA9psaWUUkop5UNabCmllFJK+ZAWW0oppZRSPqTFllJKKaWUD/0fsSsrCl7+nhgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x144 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Derivace: minimum je -0.9982476165298106 a maximum 0.9983003605552356\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x144 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Derivace: minimum je -0.9511533672767042 a maximum -4.777204802092623e-05\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fh3tjQPKEOWz"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    }
  ]
}