ischurov/lesson27.ipynb Secret

## lesson27.ipynb
{
  "cells": [
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Работа с геоданными, часть 2"
    },
    {
      "metadata": {
        "trusted": true
      },
      "id": "useful-mercury",
      "cell_type": "code",
      "source": "import geopandas as gpd\n%matplotlib inline",
      "execution_count": 22,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# download and unzip: https://mydata.biz/ru/catalog/databases/borders_ru\n# Адм-территориальные границы РФ в формате SHP.zip",
      "execution_count": 23,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "id": "stable-inventory",
      "cell_type": "code",
      "source": "russia_adm4 = gpd.read_file(\"admin_level_4.shp\", encoding='CP1251')",
      "execution_count": 24,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "id": "superb-button",
      "cell_type": "code",
      "source": "russia_adm4.crs",
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 25,
          "data": {
            "text/plain": "<Geographic 2D CRS: EPSG:4326>\nName: WGS 84\nAxis Info [ellipsoidal]:\n- Lat[north]: Geodetic latitude (degree)\n- Lon[east]: Geodetic longitude (degree)\nArea of Use:\n- name: World.\n- bounds: (-180.0, -90.0, 180.0, 90.0)\nDatum: World Geodetic System 1984\n- Ellipsoid: WGS 84\n- Prime Meridian: Greenwich"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "id": "arranged-onion",
      "cell_type": "code",
      "source": "russia_adm4.plot()",
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 26,
          "data": {
            "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7ff571374f28>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "id": "unusual-hundred",
      "cell_type": "code",
      "source": "russia_adm4_good = russia_adm4.to_crs(\"ESRI:102012\")",
      "execution_count": 28,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "id": "purple-greensboro",
      "cell_type": "code",
      "source": "russia_adm4_good.plot()",
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 29,
          "data": {
            "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7ff57178f780>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "russia_adm4_good",
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 31,
          "data": {
            "text/plain": "                    name               name_ru        boundary admin_leve  \\\n0                 Сумска                Сумска  administrative          4   \n1        Камчатский край       Камчатский край  administrative          4   \n2     Мурманская область    Мурманская область  administrative          4   \n3          Пермский край         Пермский край  administrative          4   \n4   Свердловская область  Свердловская область  administrative          4   \n..                   ...                   ...             ...        ...   \n83     Красноярский край     Красноярский край  administrative          4   \n84               Бурятия               Бурятия  administrative          4   \n85     Иркутская область     Иркутская область  administrative          4   \n86    Кабардино-Балкария    Кабардино-Балкария  administrative          4   \n87    Карачаево-Черкесия    Карачаево-Черкесия  administrative          4   \n\n       ref int_ref                   name_ca                   name_de  \\\n0     None    None                      None                      None   \n1   RU-KAM  RU-KAM    Territori de Kamtxatka        Region Kamtschatka   \n2   RU-MUR  RU-MUR     Provincia de Murmansk           Oblast Murmansk   \n3   RU-PER  RU-PER         Territori de Perm               Region Perm   \n4   RU-SVE  RU-SVE   Provincia de Sverdlovsk         Oblast Swerdlowsk   \n..     ...     ...                       ...                       ...   \n83  RU-KYA  RU-KYA  Territori de Krasnoiarsk        Region Krasnojarsk   \n84   RU-BU   RU-BU                  Buriatia                 Burjatien   \n85  RU-IRK  RU-IRK       Provincia d'Irkutsk            Oblast Irkutsk   \n86   RU-KB   RU-KB        Kabardino-Balkaria       Kabardino-Balkarien   \n87   RU-KC   RU-KC       Karatxai-Txerkessia  Karatschai-Tscherkessien   \n\n                name_en             name_es  ... alt_name_n name_sq name_su  \\\n0                  None                None  ...       None    None    None   \n1        Kamchatka Krai   Krai de Kamchatka  ...       None    None    None   \n2       Murmansk Oblast  Oblast de Murmansk  ...       None    None    None   \n3             Perm Krai                None  ...       None    None    None   \n4     Sverdlovsk Oblast                None  ...       None    None    None   \n..                  ...                 ...  ...        ...     ...     ...   \n83     Krasnoyarsk Krai                None  ...       None    None    None   \n84             Buryatia            Buriatia  ...       None    None    None   \n85       Irkutsk Oblast   Oblast de Irkutsk  ...       None    None    None   \n86   Kabardino-Balkaria   Kabardia-Balkaria  ...       None    None    None   \n87  Karachay-Cherkessia                None  ...       None    None    None   \n\n   name_ug name_tyv         official14                  name_aba  \\\n0     None     None               None                      None   \n1     None     None               None                      None   \n2     None     None               None                      None   \n3     None     None               None                      None   \n4     None     None               None                      None   \n..     ...      ...                ...                       ...   \n83    None     None               None                      None   \n84    None     None  Буряад Республика                      None   \n85    None     None               None                      None   \n86    None     None               None                      None   \n87    None     None               None  Къарча-Черкес Республика   \n\n                       name_nog         alt_name_s  \\\n0                          None               None   \n1                          None               None   \n2                          None               None   \n3                          None               None   \n4                          None               None   \n..                          ...                ...   \n83                         None               None   \n84                         None               None   \n85                         None               None   \n86                         None               None   \n87  Карашай-Шеркеш Республикасы  Karacaj-Cerkezija   \n\n                                             geometry  \n0   POLYGON ((-4123423.055 7919889.717, -4123435.4...  \n1   MULTIPOLYGON (((3567201.230 7875234.620, 35674...  \n2   POLYGON ((-2536043.819 8802067.896, -2552654.2...  \n3   POLYGON ((-2325088.221 7733871.539, -2325578.7...  \n4   POLYGON ((-2160737.596 7275962.490, -2160777.6...  \n..                                                ...  \n83  MULTIPOLYGON (((-1002003.075 6540178.296, -100...  \n84  POLYGON ((741520.403 6438465.437, 741504.720 6...  \n85  POLYGON ((810075.252 6835214.413, 810077.881 6...  \n86  POLYGON ((-4253219.551 6904886.143, -4257937.0...  \n87  POLYGON ((-4342239.182 7013040.046, -4342631.5...  \n\n[88 rows x 194 columns]",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>name_ru</th>\n      <th>boundary</th>\n      <th>admin_leve</th>\n      <th>ref</th>\n      <th>int_ref</th>\n      <th>name_ca</th>\n      <th>name_de</th>\n      <th>name_en</th>\n      <th>name_es</th>\n      <th>...</th>\n      <th>alt_name_n</th>\n      <th>name_sq</th>\n      <th>name_su</th>\n      <th>name_ug</th>\n      <th>name_tyv</th>\n      <th>official14</th>\n      <th>name_aba</th>\n      <th>name_nog</th>\n      <th>alt_name_s</th>\n      <th>geometry</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Сумска</td>\n      <td>Сумска</td>\n      <td>administrative</td>\n      <td>4</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>...</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>POLYGON ((-4123423.055 7919889.717, -4123435.4...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Камчатский край</td>\n      <td>Камчатский край</td>\n      <td>administrative</td>\n      <td>4</td>\n      <td>RU-KAM</td>\n      <td>RU-KAM</td>\n      <td>Territori de Kamtxatka</td>\n      <td>Region Kamtschatka</td>\n      <td>Kamchatka Krai</td>\n      <td>Krai de Kamchatka</td>\n      <td>...</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>MULTIPOLYGON (((3567201.230 7875234.620, 35674...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Мурманская область</td>\n      <td>Мурманская область</td>\n      <td>administrative</td>\n      <td>4</td>\n      <td>RU-MUR</td>\n      <td>RU-MUR</td>\n      <td>Provincia de Murmansk</td>\n      <td>Oblast Murmansk</td>\n      <td>Murmansk Oblast</td>\n      <td>Oblast de Murmansk</td>\n      <td>...</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>POLYGON ((-2536043.819 8802067.896, -2552654.2...</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Пермский край</td>\n      <td>Пермский край</td>\n      <td>administrative</td>\n      <td>4</td>\n      <td>RU-PER</td>\n      <td>RU-PER</td>\n      <td>Territori de Perm</td>\n      <td>Region Perm</td>\n      <td>Perm Krai</td>\n      <td>None</td>\n      <td>...</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>POLYGON ((-2325088.221 7733871.539, -2325578.7...</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Свердловская область</td>\n      <td>Свердловская область</td>\n      <td>administrative</td>\n      <td>4</td>\n      <td>RU-SVE</td>\n      <td>RU-SVE</td>\n      <td>Provincia de Sverdlovsk</td>\n      <td>Oblast Swerdlowsk</td>\n      <td>Sverdlovsk Oblast</td>\n      <td>None</td>\n      <td>...</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>POLYGON ((-2160737.596 7275962.490, -2160777.6...</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>83</th>\n      <td>Красноярский край</td>\n      <td>Красноярский край</td>\n      <td>administrative</td>\n      <td>4</td>\n      <td>RU-KYA</td>\n      <td>RU-KYA</td>\n      <td>Territori de Krasnoiarsk</td>\n      <td>Region Krasnojarsk</td>\n      <td>Krasnoyarsk Krai</td>\n      <td>None</td>\n      <td>...</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>MULTIPOLYGON (((-1002003.075 6540178.296, -100...</td>\n    </tr>\n    <tr>\n      <th>84</th>\n      <td>Бурятия</td>\n      <td>Бурятия</td>\n      <td>administrative</td>\n      <td>4</td>\n      <td>RU-BU</td>\n      <td>RU-BU</td>\n      <td>Buriatia</td>\n      <td>Burjatien</td>\n      <td>Buryatia</td>\n      <td>Buriatia</td>\n      <td>...</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>Буряад Республика</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>POLYGON ((741520.403 6438465.437, 741504.720 6...</td>\n    </tr>\n    <tr>\n      <th>85</th>\n      <td>Иркутская область</td>\n      <td>Иркутская область</td>\n      <td>administrative</td>\n      <td>4</td>\n      <td>RU-IRK</td>\n      <td>RU-IRK</td>\n      <td>Provincia d'Irkutsk</td>\n      <td>Oblast Irkutsk</td>\n      <td>Irkutsk Oblast</td>\n      <td>Oblast de Irkutsk</td>\n      <td>...</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>POLYGON ((810075.252 6835214.413, 810077.881 6...</td>\n    </tr>\n    <tr>\n      <th>86</th>\n      <td>Кабардино-Балкария</td>\n      <td>Кабардино-Балкария</td>\n      <td>administrative</td>\n      <td>4</td>\n      <td>RU-KB</td>\n      <td>RU-KB</td>\n      <td>Kabardino-Balkaria</td>\n      <td>Kabardino-Balkarien</td>\n      <td>Kabardino-Balkaria</td>\n      <td>Kabardia-Balkaria</td>\n      <td>...</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>POLYGON ((-4253219.551 6904886.143, -4257937.0...</td>\n    </tr>\n    <tr>\n      <th>87</th>\n      <td>Карачаево-Черкесия</td>\n      <td>Карачаево-Черкесия</td>\n      <td>administrative</td>\n      <td>4</td>\n      <td>RU-KC</td>\n      <td>RU-KC</td>\n      <td>Karatxai-Txerkessia</td>\n      <td>Karatschai-Tscherkessien</td>\n      <td>Karachay-Cherkessia</td>\n      <td>None</td>\n      <td>...</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>Къарча-Черкес Республика</td>\n      <td>Карашай-Шеркеш Республикасы</td>\n      <td>Karacaj-Cerkezija</td>\n      <td>POLYGON ((-4342239.182 7013040.046, -4342631.5...</td>\n    </tr>\n  </tbody>\n</table>\n<p>88 rows × 194 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "id": "involved-poland",
      "cell_type": "code",
      "source": "russia_adm4_good.dropna(axis=0, subset=[\"population\"]).assign(\n    population=lambda x: x[\"population\"].astype(\"int64\")\n).plot(column=\"population\", legend=True)",
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 30,
          "data": {
            "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7ff571797f98>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 2 Axes>",
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "id": "signed-satisfaction",
      "cell_type": "code",
      "source": "(russia_adm4[['name', 'population']]\n .rename(columns={'name': 'Russian Name'})\n .to_csv(\"population.csv\", index=False))",
      "execution_count": 32,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Немножко математики"
    },
    {
      "metadata": {
        "trusted": true
      },
      "id": "governing-carbon",
      "cell_type": "code",
      "source": "import sympy",
      "execution_count": 33,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "id": "moved-tucson",
      "cell_type": "code",
      "source": "from sympy import sin, cos, Symbol, solve, expand",
      "execution_count": 34,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "id": "mathematical-baseball",
      "cell_type": "code",
      "source": "x = Symbol('x')",
      "execution_count": 35,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "id": "metallic-failure",
      "cell_type": "code",
      "source": "expand((x + 1) ** 10)",
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 36,
          "data": {
            "text/plain": "x**10 + 10*x**9 + 45*x**8 + 120*x**7 + 210*x**6 + 252*x**5 + 210*x**4 + 120*x**3 + 45*x**2 + 10*x + 1",
            "text/latex": "$\\displaystyle x^{10} + 10 x^{9} + 45 x^{8} + 120 x^{7} + 210 x^{6} + 252 x^{5} + 210 x^{4} + 120 x^{3} + 45 x^{2} + 10 x + 1$"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "id": "gross-pressing",
      "cell_type": "code",
      "source": "sin(x ** 2 + 3*x).series(n=10)",
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 37,
          "data": {
            "text/plain": "3*x + x**2 - 9*x**3/2 - 9*x**4/2 + 21*x**5/40 + 77*x**6/24 + 1017*x**7/560 - 21*x**8/80 - 3733*x**9/4480 + O(x**10)",
            "text/latex": "$\\displaystyle 3 x + x^{2} - \\frac{9 x^{3}}{2} - \\frac{9 x^{4}}{2} + \\frac{21 x^{5}}{40} + \\frac{77 x^{6}}{24} + \\frac{1017 x^{7}}{560} - \\frac{21 x^{8}}{80} - \\frac{3733 x^{9}}{4480} + O\\left(x^{10}\\right)$"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "id": "small-lawsuit",
      "cell_type": "code",
      "source": "solve(x ** 2 - 5 * x + 6, x)",
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 38,
          "data": {
            "text/plain": "[2, 3]"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "id": "duplicate-shopper",
      "cell_type": "code",
      "source": "y = Symbol('y')",
      "execution_count": 39,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "id": "productive-exhaust",
      "cell_type": "code",
      "source": "solve([x + y - 7, x - y - 1], [x, y])",
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 40,
          "data": {
            "text/plain": "{x: 4, y: 3}"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "id": "rolled-dakota",
      "cell_type": "code",
      "source": "sin(x ** 2 + x * 5).diff(x)",
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 41,
          "data": {
            "text/plain": "(2*x + 5)*cos(x**2 + 5*x)",
            "text/latex": "$\\displaystyle \\left(2 x + 5\\right) \\cos{\\left(x^{2} + 5 x \\right)}$"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "id": "white-legislature",
      "cell_type": "code",
      "source": "from sympy import exp",
      "execution_count": 42,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "id": "electrical-diving",
      "cell_type": "code",
      "source": "exp(-1/x**2).diff(x, 20)",
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 43,
          "data": {
            "text/plain": "1024*(-49893498214560000 + 2053949009832720000/x**2 - 18959529321532800000/x**4 + 67915742533919280000/x**6 - 120547245458039400000/x**8 + 121653594182932812000/x**10 - 76139814558271464000/x**12 + 31338553356145035000/x**14 - 8837505902735259750/x**16 + 1757713032599398875/x**18 - 251567964083135700/x**20 + 26245648977174900/x**22 - 2009019262723200/x**24 + 112847919055200/x**26 - 4618435086720/x**28 + 135455967360/x**30 - 2760952320/x**32 + 36990720/x**34 - 291840/x**36 + 1024/x**38)*exp(-1/x**2)/x**22",
            "text/latex": "$\\displaystyle \\frac{1024 \\left(-49893498214560000 + \\frac{2053949009832720000}{x^{2}} - \\frac{18959529321532800000}{x^{4}} + \\frac{67915742533919280000}{x^{6}} - \\frac{120547245458039400000}{x^{8}} + \\frac{121653594182932812000}{x^{10}} - \\frac{76139814558271464000}{x^{12}} + \\frac{31338553356145035000}{x^{14}} - \\frac{8837505902735259750}{x^{16}} + \\frac{1757713032599398875}{x^{18}} - \\frac{251567964083135700}{x^{20}} + \\frac{26245648977174900}{x^{22}} - \\frac{2009019262723200}{x^{24}} + \\frac{112847919055200}{x^{26}} - \\frac{4618435086720}{x^{28}} + \\frac{135455967360}{x^{30}} - \\frac{2760952320}{x^{32}} + \\frac{36990720}{x^{34}} - \\frac{291840}{x^{36}} + \\frac{1024}{x^{38}}\\right) e^{- \\frac{1}{x^{2}}}}{x^{22}}$"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "id": "sudden-xerox",
      "cell_type": "code",
      "source": "from sympy import dsolve, Derivative, Function",
      "execution_count": 44,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "id": "elect-disco",
      "cell_type": "code",
      "source": "f = Function(\"f\")\nf_ = Derivative(f(x), x)",
      "execution_count": 45,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "id": "graduate-desktop",
      "cell_type": "code",
      "source": "x",
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 46,
          "data": {
            "text/plain": "x",
            "text/latex": "$\\displaystyle x$"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "id": "metallic-opportunity",
      "cell_type": "code",
      "source": "dsolve(f_ - f(x), f(x))",
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 47,
          "data": {
            "text/plain": "Eq(f(x), C1*exp(x))",
            "text/latex": "$\\displaystyle f{\\left(x \\right)} = C_{1} e^{x}$"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "id": "imported-purchase",
      "cell_type": "code",
      "source": "dsolve(Derivative(f(x), x) + 9*f(x), f(x))",
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 48,
          "data": {
            "text/plain": "Eq(f(x), C1*exp(-9*x))",
            "text/latex": "$\\displaystyle f{\\left(x \\right)} = C_{1} e^{- 9 x}$"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "id": "partial-basement",
      "cell_type": "code",
      "source": "dsolve(Derivative(f(x), x) - f(x) ** 2, f(x))",
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 49,
          "data": {
            "text/plain": "Eq(f(x), -1/(C1 + x))",
            "text/latex": "$\\displaystyle f{\\left(x \\right)} = - \\frac{1}{C_{1} + x}$"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "id": "adjacent-hungary",
      "cell_type": "code",
      "source": "solve(x ** 3 - 5 * x ** 2 + 5 * x - 2, x)[0]",
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 50,
          "data": {
            "text/plain": "5/3 + (-1/2 - sqrt(3)*I/2)*(sqrt(249)/18 + 79/54)**(1/3) + 10/(9*(-1/2 - sqrt(3)*I/2)*(sqrt(249)/18 + 79/54)**(1/3))",
            "text/latex": "$\\displaystyle \\frac{5}{3} + \\left(- \\frac{1}{2} - \\frac{\\sqrt{3} i}{2}\\right) \\sqrt[3]{\\frac{\\sqrt{249}}{18} + \\frac{79}{54}} + \\frac{10}{9 \\left(- \\frac{1}{2} - \\frac{\\sqrt{3} i}{2}\\right) \\sqrt[3]{\\frac{\\sqrt{249}}{18} + \\frac{79}{54}}}$"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "id": "collaborative-butterfly",
      "cell_type": "code",
      "source": "from sympy import Matrix",
      "execution_count": 51,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "id": "emotional-technical",
      "cell_type": "code",
      "source": "Matrix([[1, 2, x],\n        [x, 2, 5],\n        [8, 3, 1]])",
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 52,
          "data": {
            "text/plain": "Matrix([\n[1, 2, x],\n[x, 2, 5],\n[8, 3, 1]])",
            "text/latex": "$\\displaystyle \\left[\\begin{matrix}1 & 2 & x\\\\x & 2 & 5\\\\8 & 3 & 1\\end{matrix}\\right]$"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "id": "imported-irish",
      "cell_type": "code",
      "source": "solve(Matrix([[1, 2, x],\n        [x, 2, 5],\n        [8, 3, 1]]).det(), x)",
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 53,
          "data": {
            "text/plain": "[3 - 2*sqrt(30)*I/3, 3 + 2*sqrt(30)*I/3]"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "id": "chief-spelling",
      "cell_type": "code",
      "source": "Matrix([[1, 2, x],\n        [x, 2, 5],\n        [8, 3, 1]]) ** (-1)",
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 54,
          "data": {
            "text/plain": "Matrix([\n[   -169/(39*x**2 - 234*x + 871),  (3*x - 2)/(3*x**2 - 18*x + 67), -(2*x - 10)/(3*x**2 - 18*x + 67)],\n[ -(x - 40)/(3*x**2 - 18*x + 67), -(8*x - 1)/(3*x**2 - 18*x + 67),  (x**2 - 5)/(3*x**2 - 18*x + 67)],\n[(3*x - 16)/(3*x**2 - 18*x + 67),       -13/(-3*x**2 + 18*x - 67),  -(2*x - 2)/(3*x**2 - 18*x + 67)]])",
            "text/latex": "$\\displaystyle \\left[\\begin{matrix}- \\frac{169}{39 x^{2} - 234 x + 871} & \\frac{3 x - 2}{3 x^{2} - 18 x + 67} & - \\frac{2 x - 10}{3 x^{2} - 18 x + 67}\\\\- \\frac{x - 40}{3 x^{2} - 18 x + 67} & - \\frac{8 x - 1}{3 x^{2} - 18 x + 67} & \\frac{x^{2} - 5}{3 x^{2} - 18 x + 67}\\\\\\frac{3 x - 16}{3 x^{2} - 18 x + 67} & - \\frac{13}{- 3 x^{2} + 18 x - 67} & - \\frac{2 x - 2}{3 x^{2} - 18 x + 67}\\end{matrix}\\right]$"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "id": "coral-firewall",
      "cell_type": "code",
      "source": "from sympy import integrate",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "id": "atomic-accommodation",
      "cell_type": "code",
      "source": "integrate(sin(x) ** 10, x)",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "id": "spectacular-tribute",
      "cell_type": "code",
      "source": "from sympy import pi",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "id": "pretty-germany",
      "cell_type": "code",
      "source": "pi",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "id": "sporting-operator",
      "cell_type": "code",
      "source": "print(pi.evalf(n=50000))",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "id": "consolidated-reality",
      "cell_type": "code",
      "source": "from sympy import solveset",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "id": "taken-pulse",
      "cell_type": "code",
      "source": "solveset(sin(x), x)",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "willing-angel",
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3",
      "language": "python"
    },
    "language_info": {
      "name": "python",
      "version": "3.6.10",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "gist": {
      "id": "",
      "data": {
        "description": "Untitled13.ipynb",
        "public": false
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}