Generate_Your_Own_Data_with_These_3_Python_Libraries.ipynb

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    },
    {
      "cell_type": "markdown",
      "source": [
        "#Faker\n"
      ],
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      }
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          "text": [
            "William Bryan\n",
            "7045 Vaughn Brook\n",
            "Fordmouth, IL 36011\n",
            "1930-10-18\n",
            "Office manager\n",
            "Powellfurt\n",
            "Moccasin\n"
          ]
        }
      ],
      "source": [
        "#import module\n",
        "from faker import Faker\n",
        "\n",
        "#create an instance of Faker()\n",
        "fake = Faker()\n",
        "\n",
        "#generate data by accessing different properties\n",
        "print(fake.name())\n",
        "print(fake.address())\n",
        "print(fake.date_of_birth(minimum_age = 18))\n",
        "print(fake.job())\n",
        "print(fake.city())\n",
        "print(fake.color_name())"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#specify locales\n",
        "fake = Faker(['it_IT', 'de_DE', 'zh_CN'])\n",
        "for _ in range(10):\n",
        "    print(fake.name())"
      ],
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      "execution_count": 6,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "Birgit Krebs\n",
            "Prof. Vinzenz Ortmann\n",
            "Ing. Marie Fritsch\n",
            "梁云\n",
            "聂涛\n",
            "Liliana Pelli\n",
            "Jost Gorlitz\n",
            "Catrin Christoph\n",
            "Ing. Reginald Adolph\n",
            "杭岩\n"
          ]
        }
      ]
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              "                        Full Name  \\\n",
              "0                     Marc Allain   \n",
              "1  Édouard Marchand de la Meunier   \n",
              "2               Christine Bernier   \n",
              "3        Aurélie Turpin Le Lenoir   \n",
              "4                   Céline Launay   \n",
              "\n",
              "                                        Address       Phone Number  \\\n",
              "0         3, avenue Danielle Barbe\\n24060 Barre  +33 4 30 19 42 64   \n",
              "1  rue Isabelle Blanc\\n15513 Laroche-sur-Diallo     02 96 43 20 30   \n",
              "2  253, chemin Dupuis\\n43878 Jacquot-sur-Dijoux  +33 1 76 06 67 60   \n",
              "3            279, boulevard Lopez\\n72048 Aubert     04 83 53 43 44   \n",
              "4      8, rue Rémy Mathieu\\n89082 Roy-les-Bains         0364086747   \n",
              "\n",
              "  Date of Birth                                Job Title  \n",
              "0    1926-05-10                 chef de projet packaging  \n",
              "1    1947-01-19                       pisteur secouriste  \n",
              "2    1959-09-23                                   trader  \n",
              "3    1949-01-29       rédacteur territorial territoriale  \n",
              "4    1917-05-03  technicien en engins de travaux publics  "
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              "      <td>Christine Bernier</td>\n",
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              "      <td>1959-09-23</td>\n",
              "      <td>trader</td>\n",
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              "      <td>1917-05-03</td>\n",
              "      <td>technicien en engins de travaux publics</td>\n",
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          },
          "metadata": {},
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        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "#set locale\n",
        "fake = Faker('fr_FR')\n",
        "\n",
        "#create an empty list for each column\n",
        "name, address, phone_number, dob, job = [[] for k in range(5)]\n",
        "\n",
        "#create 200 rows of data by appending properties to the above lists\n",
        "for row in range(200):\n",
        "  name.append(fake.name())\n",
        "  address.append(fake.address())\n",
        "  phone_number.append(fake.phone_number())\n",
        "  dob.append(fake.date_of_birth(minimum_age=25))\n",
        "  job.append(fake.job())\n",
        "\n",
        "#create a dictionary and map column names to corresponding lists\n",
        "data = {\"Full Name\":name, \"Address\":address, \"Phone Number\":phone_number, \"Date of Birth\":dob, \"Job Title\":job}\n",
        "\n",
        "#create dataframe from dictionary\n",
        "df = pd.DataFrame(data)\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#create dataframe from profiles\n",
        "fake = Faker()\n",
        "df = pd.DataFrame()\n",
        "for i in range(1000):\n",
        "    df = df.append(fake.profile(), ignore_index=True)\n",
        "\n",
        "df.head()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
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        "outputId": "6bb48402-7661-4c4e-a31c-7efd5343c51a"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     job                   company          ssn  \\\n",
              "0    Art gallery manager         Williams and Sons  348-87-6935   \n",
              "1          Stage manager     Joseph, Mack and Snow  364-28-6395   \n",
              "2  Clinical psychologist    Clark, Moran and Reyes  752-67-0273   \n",
              "3     Materials engineer  Harmon, Stewart and Webb  662-95-4785   \n",
              "4       Textile designer             Vincent-Green  533-23-0383   \n",
              "\n",
              "                                           residence  \\\n",
              "0                   PSC 5328, Box 9151\\nAPO AP 97163   \n",
              "1  9677 Anthony Mountain Apt. 215\\nStephanieland,...   \n",
              "2         493 Kyle Park\\nNorth Gregoryfurt, SC 57719   \n",
              "3  98559 Timothy Spur Apt. 301\\nAndersonburgh, IN...   \n",
              "4               468 Brown Lake\\nJamesmouth, LA 33176   \n",
              "\n",
              "             current_location blood_group  \\\n",
              "0   (-61.5671345, 141.730102)          A+   \n",
              "1  (-48.4197575, -126.279874)          B+   \n",
              "2   (85.0020595, -160.908183)          B+   \n",
              "3   (-35.784314, -100.531758)          A+   \n",
              "4     (-9.2456155, 85.986861)         AB-   \n",
              "\n",
              "                                             website       username  \\\n",
              "0  [https://collier-roy.biz/, http://dyer-dunn.in...  richardhoward   \n",
              "1  [https://www.holmes.net/, https://dawson.com/,...    acostafrank   \n",
              "2        [http://www.perez.biz/, https://dixon.com/]   williamperez   \n",
              "3  [http://www.cortez.net/, http://day.com/, http...    codyfreeman   \n",
              "4  [http://www.juarez.com/, https://jones.com/, h...     ndavenport   \n",
              "\n",
              "                  name sex                                            address  \\\n",
              "0    Kirsten Hernandez   F  683 Christopher Manor Apt. 642\\nCarolinetown, ...   \n",
              "1  Dr. Charles Perkins   M          13249 Abigail Road\\nSouth Brian, KY 75453   \n",
              "2      Catherine Burch   F           302 Rodriguez Path\\nNorth Mark, ND 10853   \n",
              "3           Cesar King   M              498 Moore Fords\\nLake David, RI 67385   \n",
              "4       Kelly Gonzales   F            67248 Barton Knoll\\nWongmouth, GA 08129   \n",
              "\n",
              "                       mail   birthdate  \n",
              "0    patricia68@hotmail.com  1940-02-29  \n",
              "1   josephgross@hotmail.com  2015-04-26  \n",
              "2      ryanjeremy@gmail.com  1944-05-17  \n",
              "3  clinejeffrey@hotmail.com  1921-08-20  \n",
              "4   cookgregory@hotmail.com  1944-09-22  "
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              "      <th>0</th>\n",
              "      <td>Art gallery manager</td>\n",
              "      <td>Williams and Sons</td>\n",
              "      <td>348-87-6935</td>\n",
              "      <td>PSC 5328, Box 9151\\nAPO AP 97163</td>\n",
              "      <td>(-61.5671345, 141.730102)</td>\n",
              "      <td>A+</td>\n",
              "      <td>[https://collier-roy.biz/, http://dyer-dunn.in...</td>\n",
              "      <td>richardhoward</td>\n",
              "      <td>Kirsten Hernandez</td>\n",
              "      <td>F</td>\n",
              "      <td>683 Christopher Manor Apt. 642\\nCarolinetown, ...</td>\n",
              "      <td>patricia68@hotmail.com</td>\n",
              "      <td>1940-02-29</td>\n",
              "    </tr>\n",
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              "      <th>1</th>\n",
              "      <td>Stage manager</td>\n",
              "      <td>Joseph, Mack and Snow</td>\n",
              "      <td>364-28-6395</td>\n",
              "      <td>9677 Anthony Mountain Apt. 215\\nStephanieland,...</td>\n",
              "      <td>(-48.4197575, -126.279874)</td>\n",
              "      <td>B+</td>\n",
              "      <td>[https://www.holmes.net/, https://dawson.com/,...</td>\n",
              "      <td>acostafrank</td>\n",
              "      <td>Dr. Charles Perkins</td>\n",
              "      <td>M</td>\n",
              "      <td>13249 Abigail Road\\nSouth Brian, KY 75453</td>\n",
              "      <td>josephgross@hotmail.com</td>\n",
              "      <td>2015-04-26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Clinical psychologist</td>\n",
              "      <td>Clark, Moran and Reyes</td>\n",
              "      <td>752-67-0273</td>\n",
              "      <td>493 Kyle Park\\nNorth Gregoryfurt, SC 57719</td>\n",
              "      <td>(85.0020595, -160.908183)</td>\n",
              "      <td>B+</td>\n",
              "      <td>[http://www.perez.biz/, https://dixon.com/]</td>\n",
              "      <td>williamperez</td>\n",
              "      <td>Catherine Burch</td>\n",
              "      <td>F</td>\n",
              "      <td>302 Rodriguez Path\\nNorth Mark, ND 10853</td>\n",
              "      <td>ryanjeremy@gmail.com</td>\n",
              "      <td>1944-05-17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Materials engineer</td>\n",
              "      <td>Harmon, Stewart and Webb</td>\n",
              "      <td>662-95-4785</td>\n",
              "      <td>98559 Timothy Spur Apt. 301\\nAndersonburgh, IN...</td>\n",
              "      <td>(-35.784314, -100.531758)</td>\n",
              "      <td>A+</td>\n",
              "      <td>[http://www.cortez.net/, http://day.com/, http...</td>\n",
              "      <td>codyfreeman</td>\n",
              "      <td>Cesar King</td>\n",
              "      <td>M</td>\n",
              "      <td>498 Moore Fords\\nLake David, RI 67385</td>\n",
              "      <td>clinejeffrey@hotmail.com</td>\n",
              "      <td>1921-08-20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Textile designer</td>\n",
              "      <td>Vincent-Green</td>\n",
              "      <td>533-23-0383</td>\n",
              "      <td>468 Brown Lake\\nJamesmouth, LA 33176</td>\n",
              "      <td>(-9.2456155, 85.986861)</td>\n",
              "      <td>AB-</td>\n",
              "      <td>[http://www.juarez.com/, https://jones.com/, h...</td>\n",
              "      <td>ndavenport</td>\n",
              "      <td>Kelly Gonzales</td>\n",
              "      <td>F</td>\n",
              "      <td>67248 Barton Knoll\\nWongmouth, GA 08129</td>\n",
              "      <td>cookgregory@hotmail.com</td>\n",
              "      <td>1944-09-22</td>\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",
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              "  "
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          "metadata": {},
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      "cell_type": "markdown",
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        "#Pandas DataReader"
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      "metadata": {
        "id": "rbJ8wRkuhkfL"
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              "              Open      High       Low   Close    Volume\n",
              "Date                                                    \n",
              "2022-05-31  773.84  778.8000  734.2300  758.26  33971457\n",
              "2022-05-27  723.25  759.8000  720.5311  759.63  29764994\n",
              "2022-05-26  661.42  718.6699  653.6600  707.73  35334448\n",
              "2022-05-25  623.85  669.3200  623.0101  658.80  30713108\n",
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              "\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "#import modules\n",
        "import pandas_datareader.data as web\n",
        "import pandas as pd\n",
        "\n",
        "#fetch stock prices for Tesla\n",
        "df = web.DataReader(name='TSLA',\n",
        "                   data_source='stooq',\n",
        "                   start='2022-01-01',\n",
        "                   end='2022-05-31')\n",
        "\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "lBkR4E0ofxpl",
        "colab": {
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          "height": 551
        },
        "outputId": "b9e46ebc-bb00-40a4-d44d-83e7b4bcca89"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    NY.GDP.PCAP.KD\n",
              "country       year                \n",
              "Canada        2020    42258.691017\n",
              "              2019    45109.244486\n",
              "              2018    44917.483728\n",
              "              2017    44325.488337\n",
              "              2016    43536.913403\n",
              "Mexico        2020     8909.677654\n",
              "              2019     9820.448553\n",
              "              2018     9945.776845\n",
              "              2017     9842.400712\n",
              "              2016     9751.569083\n",
              "United States 2020    58203.379006\n",
              "              2019    60836.771454\n",
              "              2018    59821.592274\n",
              "              2017    58387.775808\n",
              "              2016    57418.933846"
            ],
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              "      <th rowspan=\"5\" valign=\"top\">Canada</th>\n",
              "      <th>2020</th>\n",
              "      <td>42258.691017</td>\n",
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              "    <tr>\n",
              "      <th>2019</th>\n",
              "      <td>45109.244486</td>\n",
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              "    <tr>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019</th>\n",
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              "      <th>2018</th>\n",
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              "      <td>9751.569083</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">United States</th>\n",
              "      <th>2020</th>\n",
              "      <td>58203.379006</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019</th>\n",
              "      <td>60836.771454</td>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "from pandas_datareader import wb\n",
        "gdp_per_capita = wb.download(indicator='NY.GDP.PCAP.KD', \n",
        "                              start='2016-01-01', end='2020-12-31', \n",
        "                              country=['US', 'CA', 'MX'])\n",
        "gdp_per_capita"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Sk-Learn"
      ],
      "metadata": {
        "id": "E_fcA69bhuD2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Make blobs"
      ],
      "metadata": {
        "id": "lOtAzg8MhzyW"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "mR8wBu4Vf75j",
        "outputId": "3aad0d3c-58e8-4db1-e0b9-954d0f9aa628"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   feature_1  feature_2  label\n",
              "0 -10.002670  -7.235981      2\n",
              "1  -9.647147  -6.056309      2\n",
              "2  -4.591598  11.399327      1\n",
              "3   2.201340  -7.923151      0\n",
              "4  -3.994572  10.173265      1\n",
              "5  -7.658666  -4.710454      2"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cdbecdc3-8ef9-4090-b580-1c2e6857442f\">\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>feature_1</th>\n",
              "      <th>feature_2</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-10.002670</td>\n",
              "      <td>-7.235981</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-9.647147</td>\n",
              "      <td>-6.056309</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-4.591598</td>\n",
              "      <td>11.399327</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.201340</td>\n",
              "      <td>-7.923151</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-3.994572</td>\n",
              "      <td>10.173265</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>-7.658666</td>\n",
              "      <td>-4.710454</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cdbecdc3-8ef9-4090-b580-1c2e6857442f')\"\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-cdbecdc3-8ef9-4090-b580-1c2e6857442f 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-cdbecdc3-8ef9-4090-b580-1c2e6857442f');\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": 19
        }
      ],
      "source": [
        "from sklearn.datasets import make_blobs\n",
        "import pandas as pd\n",
        "# generate classification dataset with 2 features and 3 labels\n",
        "X, y = make_blobs(n_samples=200, centers=3, n_features=2)\n",
        "\n",
        "#create dataframe\n",
        "df = pd.DataFrame(dict(feature_1=X[:,0], feature_2=X[:,1], label=y))\n",
        "\n",
        "df.head(6)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "#create scatter plots\n",
        "fig, ax = plt.subplots()\n",
        "colors = {0:'blue', 1:'red', 2:'green'}\n",
        "groups = df.groupby('label')\n",
        "for i, group in groups:\n",
        "    group.plot(ax=ax, kind='scatter', x='feature_1', y='feature_2', label=i, color=colors[i])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "-kRruNZYGi8D",
        "outputId": "c71d5736-d5e1-43e6-e8e9-5a36445bbf6a"
      },
      "execution_count": 20,
      "outputs": [
        {
          "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": [
        "##Make moons"
      ],
      "metadata": {
        "id": "7aJzR51DiFhT"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "6yBMkMdxgT4z",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "dfbc2ef2-8658-4dce-9f58-c449d9d48163"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   feature_1  feature_2  label\n",
              "0   1.402227  -0.680245      1\n",
              "1   0.848533   0.270902      0\n",
              "2   1.660789  -0.243025      1\n",
              "3   0.652651  -0.348914      1\n",
              "4  -0.473509   0.874162      0\n",
              "5  -0.110606   0.427848      1"
            ],
            "text/html": [
              "\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>feature_1</th>\n",
              "      <th>feature_2</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.402227</td>\n",
              "      <td>-0.680245</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.848533</td>\n",
              "      <td>0.270902</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.660789</td>\n",
              "      <td>-0.243025</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.652651</td>\n",
              "      <td>-0.348914</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.473509</td>\n",
              "      <td>0.874162</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>-0.110606</td>\n",
              "      <td>0.427848</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-248e8727-30d8-4da5-9658-a3854066d932')\"\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",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      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-248e8727-30d8-4da5-9658-a3854066d932 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-248e8727-30d8-4da5-9658-a3854066d932');\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": 23
        }
      ],
      "source": [
        "from sklearn.datasets import make_moons\n",
        "import pandas as pd\n",
        "\n",
        "# generate binary classification dataset\n",
        "X, y = make_moons(n_samples=200, noise=0.1)\n",
        "\n",
        "# create dataframe\n",
        "df = pd.DataFrame(dict(feature_1=X[:,0], feature_2=X[:,1], label=y))\n",
        "df.head(6)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# create scatter plots\n",
        "fig, ax = plt.subplots()\n",
        "colors = {0:'blue', 1:'red'}\n",
        "groups = df.groupby('label')\n",
        "for i, group in groups:\n",
        "    group.plot(ax=ax, kind='scatter', x='feature_1', y='feature_2', label=i, color=colors[i])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "9bDCZ31wNaas",
        "outputId": "ae073a7b-ae53-4657-af73-cb25e4791f51"
      },
      "execution_count": 26,
      "outputs": [
        {
          "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": [
        "##Make regression"
      ],
      "metadata": {
        "id": "kUJ7Joj4iKVZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import make_regression\n",
        "import pandas as pd\n",
        "\n",
        "# generate regression dataset\n",
        "X, y = make_regression(n_samples=200, n_features=1, noise=0.1)\n",
        "df = pd.DataFrame(X,y)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "lPLE1Ev6N8uY",
        "outputId": "ece1754b-3294-4f9a-d209-1813bf1411a3"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    0\n",
              "-166.615707 -1.996269\n",
              " 21.932287   0.263835\n",
              " 155.211447  1.857451\n",
              "-71.070157  -0.850061\n",
              " 47.928942   0.573002"
            ],
            "text/html": [
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "        vertical-align: middle;\n",
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              "      <th>-166.615707</th>\n",
              "      <td>-1.996269</td>\n",
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              "    <tr>\n",
              "      <th>21.932287</th>\n",
              "      <td>0.263835</td>\n",
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              "    <tr>\n",
              "      <th>155.211447</th>\n",
              "      <td>1.857451</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>-71.070157</th>\n",
              "      <td>-0.850061</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47.928942</th>\n",
              "      <td>0.573002</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-888af2a0-6f2c-4037-80a2-bb6506147509')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\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",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-888af2a0-6f2c-4037-80a2-bb6506147509 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-888af2a0-6f2c-4037-80a2-bb6506147509');\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": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# create scatter plot\n",
        "plt.scatter(X,y)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "AFmCBD_mFv-f",
        "outputId": "57221625-054e-4639-cb74-328afe4f5f7d"
      },
      "execution_count": 14,
      "outputs": [
        {
          "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": {
    "colab": {
      "name": "Generate Your Own Data with These 3 Python Libraries.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyODMvT2OXzun2IPQzso1mQt",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}