saglikta_veriH2.ipynb

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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "nLQUsDgbJkUp"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "outputId": "998b3128-6919-429f-cc45-0180633550a8"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "hello world 16\n"
          ]
        }
      ],
      "source": [
        "def MyFunction(number):\n",
        "  print(\"hello world \" + str(number))\n",
        "\n",
        "\n",
        "MyFunction(16)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class MyClass:\n",
        "  def __init__(self):\n",
        "    self.objectData=576\n",
        "\n",
        "  def printObjectData(self):\n",
        "    print(self.objectData)\n",
        "    return self.objectData"
      ],
      "metadata": {
        "id": "SOylrNzlHQGb"
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      "execution_count": 2,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "\n",
        "Object1= MyClass()\n",
        "print(\"Object1 veri üyesi:\", Object1.objectData)\n",
        "\n",
        "# MyClass örneğinin veri üyesini yazdırmak için fonksiyonu çağırın\n",
        "Object1.printObjectData()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4dtpszBqJKbu",
        "outputId": "036006ae-e92c-4388-c41c-2bb482ddd79d"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Object1 veri üyesi: 576\n",
            "576\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "576"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "dataframe = pd.read_csv('/saglikhizmetleri.csv')"
      ],
      "metadata": {
        "id": "Q7WQsA3dJRCo"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataframe.head()"
      ],
      "metadata": {
        "colab": {
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          "height": 2795
        },
        "id": "VmjzoUWcLm9K",
        "outputId": "ac366192-d28b-4f4f-b5f8-f97fd6d1fc74"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   _id                                           KONU                    ILCE  \\\n",
              "0    1                     İNAYET TOPÇUOĞLU HASTANESİ  Tüm İl baz alınmıştır.   \n",
              "1    2                             EVDE FİZİK TEDAVİ   Tüm İl baz alınmıştır.   \n",
              "2    3  MOBİL AĞIZ VE DİŞ SAĞLIĞI TEŞHİS TEDAVİ ARACI  Tüm İl baz alınmıştır.   \n",
              "3    4                      TÜP BEBEK MERKEZİ HİZMET   Tüm İl baz alınmıştır.   \n",
              "4    5                              ANNE OTELİ HİZMET  Tüm İl baz alınmıştır.   \n",
              "\n",
              "      MIKTAR BIRIM  \n",
              "0  6.683.612  KİŞİ  \n",
              "1       2319  KİŞİ  \n",
              "2       6712  KİŞİ  \n",
              "3     11.131  KİŞİ  \n",
              "4      7.223  KİŞİ  "
            ],
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              "      <td>Tüm İl baz alınmıştır.</td>\n",
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              "      <td>Tüm İl baz alınmıştır.</td>\n",
              "      <td>7.223</td>\n",
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              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "\n",
              "def value_plot(df, y, figscale=1):\n",
              "  from matplotlib import pyplot as plt\n",
              "  df[y].plot(kind='line', figsize=(8 * figscale, 4 * figscale), title=y)\n",
              "  plt.gca().spines[['top', 'right']].set_visible(False)\n",
              "  plt.tight_layout()\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = value_plot(_df_0, *['_id'], **{})\n",
              "chart"
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-758a1cad-649c-4496-9a80-7d7cb38bf18d\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-758a1cad-649c-4496-9a80-7d7cb38bf18d\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<google.colab._quickchart_helpers.SectionTitle at 0x7cc387d06ec0>"
            ],
            "text/html": [
              "<h4 class=\"colab-quickchart-section-title\">Distributions</h4>\n",
              "<style>\n",
              "  .colab-quickchart-section-title {\n",
              "      clear: both;\n",
              "  }\n",
              "</style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "\n",
              "def histogram(df, colname, num_bins=20, figscale=1):\n",
              "  from matplotlib import pyplot as plt\n",
              "  df[colname].plot(kind='hist', bins=num_bins, title=colname, figsize=(8*figscale, 4*figscale))\n",
              "  plt.gca().spines[['top', 'right',]].set_visible(False)\n",
              "  plt.tight_layout()\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = histogram(_df_1, *['_id'], **{})\n",
              "chart"
            ],
            "text/html": [
              "      <div class=\"colab-quickchart-chart-with-code\" id=\"chart-82499f3a-b12f-4d5d-b97a-ee60b12e2626\">\n",
              "        <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAYwAAADECAYAAACMRRb/AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90\n",
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-82499f3a-b12f-4d5d-b97a-ee60b12e2626\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-82499f3a-b12f-4d5d-b97a-ee60b12e2626\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<google.colab._quickchart_helpers.SectionTitle at 0x7cc387e58ee0>"
            ],
            "text/html": [
              "<h4 class=\"colab-quickchart-section-title\">Categorical distributions</h4>\n",
              "<style>\n",
              "  .colab-quickchart-section-title {\n",
              "      clear: both;\n",
              "  }\n",
              "</style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "\n",
              "def categorical_histogram(df, colname, figscale=1, mpl_palette_name='Dark2'):\n",
              "  from matplotlib import pyplot as plt\n",
              "  import seaborn as sns\n",
              "  df.groupby(colname).size().plot(kind='barh', color=sns.palettes.mpl_palette(mpl_palette_name), figsize=(8*figscale, 4.8*figscale))\n",
              "  plt.gca().spines[['top', 'right',]].set_visible(False)\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = categorical_histogram(_df_2, *['KONU'], **{})\n",
              "chart"
            ],
            "text/html": [
              "      <div class=\"colab-quickchart-chart-with-code\" id=\"chart-e767a139-1a95-40b2-8f31-755fa5e59cc7\">\n",
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-e767a139-1a95-40b2-8f31-755fa5e59cc7\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-e767a139-1a95-40b2-8f31-755fa5e59cc7\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "\n",
              "def categorical_histogram(df, colname, figscale=1, mpl_palette_name='Dark2'):\n",
              "  from matplotlib import pyplot as plt\n",
              "  import seaborn as sns\n",
              "  df.groupby(colname).size().plot(kind='barh', color=sns.palettes.mpl_palette(mpl_palette_name), figsize=(8*figscale, 4.8*figscale))\n",
              "  plt.gca().spines[['top', 'right',]].set_visible(False)\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = categorical_histogram(_df_3, *['MIKTAR'], **{})\n",
              "chart"
            ],
            "text/html": [
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-10f3b7b3-0ae5-4a3e-8103-2b0ee37362b1\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-10f3b7b3-0ae5-4a3e-8103-2b0ee37362b1\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<google.colab._quickchart_helpers.SectionTitle at 0x7cc387da9d50>"
            ],
            "text/html": [
              "<h4 class=\"colab-quickchart-section-title\">2-d categorical distributions</h4>\n",
              "<style>\n",
              "  .colab-quickchart-section-title {\n",
              "      clear: both;\n",
              "  }\n",
              "</style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "\n",
              "def heatmap(df, x_colname, y_colname, figscale=1, mpl_palette_name='viridis'):\n",
              "  from matplotlib import pyplot as plt\n",
              "  import seaborn as sns\n",
              "  import pandas as pd\n",
              "  plt.subplots(figsize=(8 * figscale, 8 * figscale))\n",
              "  df_2dhist = pd.DataFrame({\n",
              "      x_label: grp[y_colname].value_counts()\n",
              "      for x_label, grp in df.groupby(x_colname)\n",
              "  })\n",
              "  sns.heatmap(df_2dhist, cmap=mpl_palette_name)\n",
              "  plt.xlabel(x_colname)\n",
              "  plt.ylabel(y_colname)\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = heatmap(_df_4, *['KONU', 'MIKTAR'], **{})\n",
              "chart"
            ],
            "text/html": [
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-391313d9-8c7a-435c-8c6a-949f1fda32fb\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-391313d9-8c7a-435c-8c6a-949f1fda32fb\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<google.colab._quickchart_helpers.SectionTitle at 0x7cc37e161e10>"
            ],
            "text/html": [
              "<h4 class=\"colab-quickchart-section-title\">Faceted distributions</h4>\n",
              "<style>\n",
              "  .colab-quickchart-section-title {\n",
              "      clear: both;\n",
              "  }\n",
              "</style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "\n",
              "def violin_plot(df, value_colname, facet_colname, figscale=1, mpl_palette_name='Dark2', **kwargs):\n",
              "  from matplotlib import pyplot as plt\n",
              "  import seaborn as sns\n",
              "  figsize = (12 * figscale, 1.2 * figscale * len(df[facet_colname].unique()))\n",
              "  plt.figure(figsize=figsize)\n",
              "  sns.violinplot(df, x=value_colname, y=facet_colname, palette=mpl_palette_name, **kwargs)\n",
              "  sns.despine(top=True, right=True, bottom=True, left=True)\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = violin_plot(_df_5, *['_id', 'KONU'], **{'inner': 'stick'})\n",
              "chart"
            ],
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-36c0b82f-0f94-4646-82f2-3761d5b9ec99\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-36c0b82f-0f94-4646-82f2-3761d5b9ec99\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "\n",
              "def violin_plot(df, value_colname, facet_colname, figscale=1, mpl_palette_name='Dark2', **kwargs):\n",
              "  from matplotlib import pyplot as plt\n",
              "  import seaborn as sns\n",
              "  figsize = (12 * figscale, 1.2 * figscale * len(df[facet_colname].unique()))\n",
              "  plt.figure(figsize=figsize)\n",
              "  sns.violinplot(df, x=value_colname, y=facet_colname, palette=mpl_palette_name, **kwargs)\n",
              "  sns.despine(top=True, right=True, bottom=True, left=True)\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = violin_plot(_df_6, *['_id', 'MIKTAR'], **{'inner': 'stick'})\n",
              "chart"
            ],
            "text/html": [
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              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-eef0a4c4-5804-4544-aa14-da818d1583e2\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-eef0a4c4-5804-4544-aa14-da818d1583e2\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<google.colab._quickchart_helpers.SectionTitle at 0x7cc37e0bf550>"
            ],
            "text/html": [
              "<h4 class=\"colab-quickchart-section-title\">Time series</h4>\n",
              "<style>\n",
              "  .colab-quickchart-section-title {\n",
              "      clear: both;\n",
              "  }\n",
              "</style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "\n",
              "def time_series_multiline(df, timelike_colname, value_colname, series_colname, figscale=1, mpl_palette_name='Dark2'):\n",
              "  from matplotlib import pyplot as plt\n",
              "  import seaborn as sns\n",
              "  figsize = (10 * figscale, 5.2 * figscale)\n",
              "  palette = list(sns.palettes.mpl_palette(mpl_palette_name))\n",
              "  def _plot_series(series, series_name, series_index=0):\n",
              "    if value_colname == 'count()':\n",
              "      counted = (series[timelike_colname]\n",
              "                 .value_counts()\n",
              "                 .reset_index(name='counts')\n",
              "                 .rename({'index': timelike_colname}, axis=1)\n",
              "                 .sort_values(timelike_colname, ascending=True))\n",
              "      xs = counted[timelike_colname]\n",
              "      ys = counted['counts']\n",
              "    else:\n",
              "      xs = series[timelike_colname]\n",
              "      ys = series[value_colname]\n",
              "    plt.plot(xs, ys, label=series_name, color=palette[series_index % len(palette)])\n",
              "\n",
              "  fig, ax = plt.subplots(figsize=figsize, layout='constrained')\n",
              "  df = df.sort_values(timelike_colname, ascending=True)\n",
              "  if series_colname:\n",
              "    for i, (series_name, series) in enumerate(df.groupby(series_colname)):\n",
              "      _plot_series(series, series_name, i)\n",
              "    fig.legend(title=series_colname, bbox_to_anchor=(1, 1), loc='upper left')\n",
              "  else:\n",
              "    _plot_series(df, '')\n",
              "  sns.despine(fig=fig, ax=ax)\n",
              "  plt.xlabel(timelike_colname)\n",
              "  plt.ylabel(value_colname)\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = time_series_multiline(_df_7, *['_id', 'count()', 'KONU'], **{})\n",
              "chart"
            ],
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-70664ed6-a1a7-4fb8-aab8-7b758de212f4\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-70664ed6-a1a7-4fb8-aab8-7b758de212f4\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "\n",
              "def time_series_multiline(df, timelike_colname, value_colname, series_colname, figscale=1, mpl_palette_name='Dark2'):\n",
              "  from matplotlib import pyplot as plt\n",
              "  import seaborn as sns\n",
              "  figsize = (10 * figscale, 5.2 * figscale)\n",
              "  palette = list(sns.palettes.mpl_palette(mpl_palette_name))\n",
              "  def _plot_series(series, series_name, series_index=0):\n",
              "    if value_colname == 'count()':\n",
              "      counted = (series[timelike_colname]\n",
              "                 .value_counts()\n",
              "                 .reset_index(name='counts')\n",
              "                 .rename({'index': timelike_colname}, axis=1)\n",
              "                 .sort_values(timelike_colname, ascending=True))\n",
              "      xs = counted[timelike_colname]\n",
              "      ys = counted['counts']\n",
              "    else:\n",
              "      xs = series[timelike_colname]\n",
              "      ys = series[value_colname]\n",
              "    plt.plot(xs, ys, label=series_name, color=palette[series_index % len(palette)])\n",
              "\n",
              "  fig, ax = plt.subplots(figsize=figsize, layout='constrained')\n",
              "  df = df.sort_values(timelike_colname, ascending=True)\n",
              "  if series_colname:\n",
              "    for i, (series_name, series) in enumerate(df.groupby(series_colname)):\n",
              "      _plot_series(series, series_name, i)\n",
              "    fig.legend(title=series_colname, bbox_to_anchor=(1, 1), loc='upper left')\n",
              "  else:\n",
              "    _plot_series(df, '')\n",
              "  sns.despine(fig=fig, ax=ax)\n",
              "  plt.xlabel(timelike_colname)\n",
              "  plt.ylabel(value_colname)\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = time_series_multiline(_df_8, *['_id', 'count()', 'MIKTAR'], **{})\n",
              "chart"
            ],
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-9fe68ace-f5f6-4e98-b4eb-40783b0f8f97\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-9fe68ace-f5f6-4e98-b4eb-40783b0f8f97\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}