food_analysis.ipynb

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        "The dataset consists of about **255** Indian dishes and **9** columns associated with each of them.\n",
        "\n",
        "The **9** columns are as follows:-\n",
        "\n",
        "**name** : name of the dish\n",
        "\n",
        "**ingredients** : main ingredients used\n",
        "\n",
        "**diet** : type of diet - either vegetarian or non vegetarian\n",
        "\n",
        "**prep_time** : preparation time\n",
        "\n",
        "**cook_time** : cooking time\n",
        "\n",
        "**flavor_profile** : flavor profile includes whether the dish is spicy, sweet, bitter, etc\n",
        "\n",
        "**course** : course of meal - starter, main course, dessert, etc\n",
        "\n",
        "**state** : state where the dish is famous or is originated\n",
        "\n",
        "**region** : region where the state belongs\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "7DKpHR0fDbZw"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import plotly.express as px\n",
        "from plotly.offline import init_notebook_mode\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "from wordcloud import WordCloud , ImageColorGenerator\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "O7I1EuVdjsap"
      },
      "outputs": [],
      "source": [
        "data = pd.read_csv(\"indian_food.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
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        "id": "uLhcBjVnCV7k",
        "outputId": "0e95541f-7755-424b-966a-134a0044be38"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>ingredients</th>\n",
              "      <th>diet</th>\n",
              "      <th>prep_time</th>\n",
              "      <th>cook_time</th>\n",
              "      <th>flavor_profile</th>\n",
              "      <th>course</th>\n",
              "      <th>state</th>\n",
              "      <th>region</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Balu shahi</td>\n",
              "      <td>Maida flour, yogurt, oil, sugar</td>\n",
              "      <td>vegetarian</td>\n",
              "      <td>45</td>\n",
              "      <td>25</td>\n",
              "      <td>sweet</td>\n",
              "      <td>dessert</td>\n",
              "      <td>West Bengal</td>\n",
              "      <td>East</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Boondi</td>\n",
              "      <td>Gram flour, ghee, sugar</td>\n",
              "      <td>vegetarian</td>\n",
              "      <td>80</td>\n",
              "      <td>30</td>\n",
              "      <td>sweet</td>\n",
              "      <td>dessert</td>\n",
              "      <td>Rajasthan</td>\n",
              "      <td>West</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Gajar ka halwa</td>\n",
              "      <td>Carrots, milk, sugar, ghee, cashews, raisins</td>\n",
              "      <td>vegetarian</td>\n",
              "      <td>15</td>\n",
              "      <td>60</td>\n",
              "      <td>sweet</td>\n",
              "      <td>dessert</td>\n",
              "      <td>Punjab</td>\n",
              "      <td>North</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Ghevar</td>\n",
              "      <td>Flour, ghee, kewra, milk, clarified butter, su...</td>\n",
              "      <td>vegetarian</td>\n",
              "      <td>15</td>\n",
              "      <td>30</td>\n",
              "      <td>sweet</td>\n",
              "      <td>dessert</td>\n",
              "      <td>Rajasthan</td>\n",
              "      <td>West</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Gulab jamun</td>\n",
              "      <td>Milk powder, plain flour, baking powder, ghee,...</td>\n",
              "      <td>vegetarian</td>\n",
              "      <td>15</td>\n",
              "      <td>40</td>\n",
              "      <td>sweet</td>\n",
              "      <td>dessert</td>\n",
              "      <td>West Bengal</td>\n",
              "      <td>East</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>250</th>\n",
              "      <td>Til Pitha</td>\n",
              "      <td>Glutinous rice, black sesame seeds, gur</td>\n",
              "      <td>vegetarian</td>\n",
              "      <td>5</td>\n",
              "      <td>30</td>\n",
              "      <td>sweet</td>\n",
              "      <td>dessert</td>\n",
              "      <td>Assam</td>\n",
              "      <td>North East</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>251</th>\n",
              "      <td>Bebinca</td>\n",
              "      <td>Coconut milk, egg yolks, clarified butter, all...</td>\n",
              "      <td>vegetarian</td>\n",
              "      <td>20</td>\n",
              "      <td>60</td>\n",
              "      <td>sweet</td>\n",
              "      <td>dessert</td>\n",
              "      <td>Goa</td>\n",
              "      <td>West</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>252</th>\n",
              "      <td>Shufta</td>\n",
              "      <td>Cottage cheese, dry dates, dried rose petals, ...</td>\n",
              "      <td>vegetarian</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>sweet</td>\n",
              "      <td>dessert</td>\n",
              "      <td>Jammu &amp; Kashmir</td>\n",
              "      <td>North</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>253</th>\n",
              "      <td>Mawa Bati</td>\n",
              "      <td>Milk powder, dry fruits, arrowroot powder, all...</td>\n",
              "      <td>vegetarian</td>\n",
              "      <td>20</td>\n",
              "      <td>45</td>\n",
              "      <td>sweet</td>\n",
              "      <td>dessert</td>\n",
              "      <td>Madhya Pradesh</td>\n",
              "      <td>Central</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>254</th>\n",
              "      <td>Pinaca</td>\n",
              "      <td>Brown rice, fennel seeds, grated coconut, blac...</td>\n",
              "      <td>vegetarian</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>sweet</td>\n",
              "      <td>dessert</td>\n",
              "      <td>Goa</td>\n",
              "      <td>West</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>255 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "               name                                        ingredients  \\\n",
              "0        Balu shahi                    Maida flour, yogurt, oil, sugar   \n",
              "1            Boondi                            Gram flour, ghee, sugar   \n",
              "2    Gajar ka halwa       Carrots, milk, sugar, ghee, cashews, raisins   \n",
              "3            Ghevar  Flour, ghee, kewra, milk, clarified butter, su...   \n",
              "4       Gulab jamun  Milk powder, plain flour, baking powder, ghee,...   \n",
              "..              ...                                                ...   \n",
              "250       Til Pitha            Glutinous rice, black sesame seeds, gur   \n",
              "251         Bebinca  Coconut milk, egg yolks, clarified butter, all...   \n",
              "252          Shufta  Cottage cheese, dry dates, dried rose petals, ...   \n",
              "253       Mawa Bati  Milk powder, dry fruits, arrowroot powder, all...   \n",
              "254          Pinaca  Brown rice, fennel seeds, grated coconut, blac...   \n",
              "\n",
              "           diet  prep_time  cook_time flavor_profile   course  \\\n",
              "0    vegetarian         45         25          sweet  dessert   \n",
              "1    vegetarian         80         30          sweet  dessert   \n",
              "2    vegetarian         15         60          sweet  dessert   \n",
              "3    vegetarian         15         30          sweet  dessert   \n",
              "4    vegetarian         15         40          sweet  dessert   \n",
              "..          ...        ...        ...            ...      ...   \n",
              "250  vegetarian          5         30          sweet  dessert   \n",
              "251  vegetarian         20         60          sweet  dessert   \n",
              "252  vegetarian         -1         -1          sweet  dessert   \n",
              "253  vegetarian         20         45          sweet  dessert   \n",
              "254  vegetarian         -1         -1          sweet  dessert   \n",
              "\n",
              "               state      region  \n",
              "0        West Bengal        East  \n",
              "1          Rajasthan        West  \n",
              "2             Punjab       North  \n",
              "3          Rajasthan        West  \n",
              "4        West Bengal        East  \n",
              "..               ...         ...  \n",
              "250            Assam  North East  \n",
              "251              Goa        West  \n",
              "252  Jammu & Kashmir       North  \n",
              "253   Madhya Pradesh     Central  \n",
              "254              Goa        West  \n",
              "\n",
              "[255 rows x 9 columns]"
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              "      <td>Balu shahi</td>\n",
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              "      <td>vegetarian</td>\n",
              "      <td>45</td>\n",
              "      <td>25</td>\n",
              "      <td>sweet</td>\n",
              "      <td>dessert</td>\n",
              "      <td>West Bengal</td>\n",
              "      <td>East</td>\n",
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              "      <td>Boondi</td>\n",
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              "      <td>vegetarian</td>\n",
              "      <td>80</td>\n",
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              "      <td>60</td>\n",
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              "      <th>3</th>\n",
              "      <td>Ghevar</td>\n",
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              "      <td>vegetarian</td>\n",
              "      <td>15</td>\n",
              "      <td>30</td>\n",
              "      <td>sweet</td>\n",
              "      <td>dessert</td>\n",
              "      <td>Rajasthan</td>\n",
              "      <td>West</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Gulab jamun</td>\n",
              "      <td>Milk powder, plain flour, baking powder, ghee,...</td>\n",
              "      <td>vegetarian</td>\n",
              "      <td>15</td>\n",
              "      <td>40</td>\n",
              "      <td>sweet</td>\n",
              "      <td>dessert</td>\n",
              "      <td>West Bengal</td>\n",
              "      <td>East</td>\n",
              "    </tr>\n",
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              "             name                                        ingredients  \\\n",
              "0      Balu shahi                    Maida flour, yogurt, oil, sugar   \n",
              "1          Boondi                            Gram flour, ghee, sugar   \n",
              "2  Gajar ka halwa       Carrots, milk, sugar, ghee, cashews, raisins   \n",
              "3          Ghevar  Flour, ghee, kewra, milk, clarified butter, su...   \n",
              "4     Gulab jamun  Milk powder, plain flour, baking powder, ghee,...   \n",
              "\n",
              "         diet  prep_time  cook_time flavor_profile   course        state  \\\n",
              "0  vegetarian         45         25          sweet  dessert  West Bengal   \n",
              "1  vegetarian         80         30          sweet  dessert    Rajasthan   \n",
              "2  vegetarian         15         60          sweet  dessert       Punjab   \n",
              "3  vegetarian         15         30          sweet  dessert    Rajasthan   \n",
              "4  vegetarian         15         40          sweet  dessert  West Bengal   \n",
              "\n",
              "  region  \n",
              "0   East  \n",
              "1   West  \n",
              "2  North  \n",
              "3   West  \n",
              "4   East  "
            ]
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              "Index(['name', 'ingredients', 'diet', 'prep_time', 'cook_time',\n",
              "       'flavor_profile', 'course', 'state', 'region'],\n",
              "      dtype='object')"
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 255 entries, 0 to 254\n",
            "Data columns (total 9 columns):\n",
            " #   Column          Non-Null Count  Dtype \n",
            "---  ------          --------------  ----- \n",
            " 0   name            255 non-null    object\n",
            " 1   ingredients     255 non-null    object\n",
            " 2   diet            255 non-null    object\n",
            " 3   prep_time       255 non-null    int64 \n",
            " 4   cook_time       255 non-null    int64 \n",
            " 5   flavor_profile  255 non-null    object\n",
            " 6   course          255 non-null    object\n",
            " 7   state           255 non-null    object\n",
            " 8   region          254 non-null    object\n",
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              "name              False\n",
              "ingredients       False\n",
              "diet              False\n",
              "prep_time         False\n",
              "cook_time         False\n",
              "flavor_profile    False\n",
              "course            False\n",
              "state             False\n",
              "region             True\n",
              "dtype: bool"
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              "ingredients       0\n",
              "diet              0\n",
              "prep_time         0\n",
              "cook_time         0\n",
              "flavor_profile    0\n",
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              "dtype: int64"
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        "data=data.replace(-1,np.nan)\n",
        "data=data.replace('-1',np.nan)\n"
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              "      <th>0</th>\n",
              "      <td>Balu shahi</td>\n",
              "      <td>Maida flour, yogurt, oil, sugar</td>\n",
              "      <td>vegetarian</td>\n",
              "      <td>45.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>sweet</td>\n",
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              "      <td>Boondi</td>\n",
              "      <td>Gram flour, ghee, sugar</td>\n",
              "      <td>vegetarian</td>\n",
              "      <td>80.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>sweet</td>\n",
              "      <td>dessert</td>\n",
              "      <td>Rajasthan</td>\n",
              "      <td>West</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Gajar ka halwa</td>\n",
              "      <td>Carrots, milk, sugar, ghee, cashews, raisins</td>\n",
              "      <td>vegetarian</td>\n",
              "      <td>15.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>sweet</td>\n",
              "      <td>dessert</td>\n",
              "      <td>Punjab</td>\n",
              "      <td>North</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Ghevar</td>\n",
              "      <td>Flour, ghee, kewra, milk, clarified butter, su...</td>\n",
              "      <td>vegetarian</td>\n",
              "      <td>15.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>sweet</td>\n",
              "      <td>dessert</td>\n",
              "      <td>Rajasthan</td>\n",
              "      <td>West</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Gulab jamun</td>\n",
              "      <td>Milk powder, plain flour, baking powder, ghee,...</td>\n",
              "      <td>vegetarian</td>\n",
              "      <td>15.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>sweet</td>\n",
              "      <td>dessert</td>\n",
              "      <td>West Bengal</td>\n",
              "      <td>East</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             name                                        ingredients  \\\n",
              "0      Balu shahi                    Maida flour, yogurt, oil, sugar   \n",
              "1          Boondi                            Gram flour, ghee, sugar   \n",
              "2  Gajar ka halwa       Carrots, milk, sugar, ghee, cashews, raisins   \n",
              "3          Ghevar  Flour, ghee, kewra, milk, clarified butter, su...   \n",
              "4     Gulab jamun  Milk powder, plain flour, baking powder, ghee,...   \n",
              "\n",
              "         diet  prep_time  cook_time flavor_profile   course        state  \\\n",
              "0  vegetarian       45.0       25.0          sweet  dessert  West Bengal   \n",
              "1  vegetarian       80.0       30.0          sweet  dessert    Rajasthan   \n",
              "2  vegetarian       15.0       60.0          sweet  dessert       Punjab   \n",
              "3  vegetarian       15.0       30.0          sweet  dessert    Rajasthan   \n",
              "4  vegetarian       15.0       40.0          sweet  dessert  West Bengal   \n",
              "\n",
              "  region  \n",
              "0   East  \n",
              "1   West  \n",
              "2  North  \n",
              "3   West  \n",
              "4   East  "
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "blQSxn-TCniJ",
        "outputId": "30d5ef1d-d70b-454d-e910-8081441f4f7a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "name               0\n",
              "ingredients        0\n",
              "diet               0\n",
              "prep_time         30\n",
              "cook_time         28\n",
              "flavor_profile    29\n",
              "course             0\n",
              "state             24\n",
              "region            14\n",
              "dtype: int64"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J6wx-HEJDRsS",
        "outputId": "3def4e7e-65d2-47ed-c83a-97683039b6cb"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(255, 9)"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pKw91Gy_HABN"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "ZxO5HA05sRkt"
      },
      "outputs": [],
      "source": [
        "pie_data = data.diet.value_counts().reset_index()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "R_SyV71Sr-0L"
      },
      "outputs": [],
      "source": [
        "sweet_data = data[data['flavor_profile']=='sweet']\n",
        "final_sweet_data = sweet_data[sweet_data['course']!='dessert']\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "satl2duEExBq",
        "outputId": "bcdaaa77-ab43-45e3-a655-81e97b9c6fe0"
      },
      "outputs": [
        {
          "data": {
            "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>ingredients</th>\n",
              "      <th>diet</th>\n",
              "      <th>prep_time</th>\n",
              "      <th>cook_time</th>\n",
              "      <th>flavor_profile</th>\n",
              "      <th>course</th>\n",
              "      <th>state</th>\n",
              "      <th>region</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>Obbattu holige</td>\n",
              "      <td>Maida flour, turmeric, coconut, chickpeas, jag...</td>\n",
              "      <td>vegetarian</td>\n",
              "      <td>180.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>sweet</td>\n",
              "      <td>main course</td>\n",
              "      <td>Karnataka</td>\n",
              "      <td>South</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85</th>\n",
              "      <td>Dal makhani</td>\n",
              "      <td>Red kidney beans, urad dal, cream, garam masal...</td>\n",
              "      <td>vegetarian</td>\n",
              "      <td>10.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>sweet</td>\n",
              "      <td>main course</td>\n",
              "      <td>Punjab</td>\n",
              "      <td>North</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>243</th>\n",
              "      <td>Mishti Chholar Dal</td>\n",
              "      <td>Chana dal, fresh coconut, ginger, cinnamon, ra...</td>\n",
              "      <td>vegetarian</td>\n",
              "      <td>10.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>sweet</td>\n",
              "      <td>main course</td>\n",
              "      <td>West Bengal</td>\n",
              "      <td>East</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   name                                        ingredients  \\\n",
              "46       Obbattu holige  Maida flour, turmeric, coconut, chickpeas, jag...   \n",
              "85         Dal makhani   Red kidney beans, urad dal, cream, garam masal...   \n",
              "243  Mishti Chholar Dal  Chana dal, fresh coconut, ginger, cinnamon, ra...   \n",
              "\n",
              "           diet  prep_time  cook_time flavor_profile       course  \\\n",
              "46   vegetarian      180.0       60.0          sweet  main course   \n",
              "85   vegetarian       10.0       60.0          sweet  main course   \n",
              "243  vegetarian       10.0       30.0          sweet  main course   \n",
              "\n",
              "           state region  \n",
              "46     Karnataka  South  \n",
              "85        Punjab  North  \n",
              "243  West Bengal   East  "
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "final_sweet_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "selz2QOutc2q"
      },
      "outputs": [],
      "source": [
        "cooking_time= data[['cook_time','name']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "c5QJV2y0F6OM",
        "outputId": "05fa3ae6-62a0-4f32-c3e6-6571018d96bb"
      },
      "outputs": [
        {
          "data": {
            "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>cook_time</th>\n",
              "      <th>name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>25.0</td>\n",
              "      <td>Balu shahi</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>30.0</td>\n",
              "      <td>Boondi</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>60.0</td>\n",
              "      <td>Gajar ka halwa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>30.0</td>\n",
              "      <td>Ghevar</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>40.0</td>\n",
              "      <td>Gulab jamun</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   cook_time            name\n",
              "0       25.0      Balu shahi\n",
              "1       30.0          Boondi\n",
              "2       60.0  Gajar ka halwa\n",
              "3       30.0          Ghevar\n",
              "4       40.0     Gulab jamun"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "cooking_time.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "znlvs5E7F9p3"
      },
      "outputs": [],
      "source": [
        "cooking_time=cooking_time.sort_values(['cook_time'],ascending=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "SOuuZca3IFx-"
      },
      "outputs": [],
      "source": [
        "ten_cook_quickly=cooking_time.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "wrSyvwJKGCKZ"
      },
      "outputs": [],
      "source": [
        "#cook_data = ten_cook_quickly.cook_time.value_counts().reset_index()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GHjNHEwdIFCa"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VdS86OrsH4EF",
        "outputId": "a0d8e3b6-0734-41aa-ee9a-e4a8bba7464e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['name', 'ingredients', 'diet', 'prep_time', 'cook_time',\n",
              "       'flavor_profile', 'course', 'state', 'region'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "y4QCKnAkI0CZ"
      },
      "outputs": [],
      "source": [
        "cooking_time_longest=cooking_time.sort_values(['cook_time'],ascending=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "2jSfTouKJDfD"
      },
      "outputs": [],
      "source": [
        "tencooking_time_longest=cooking_time_longest.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "u80tEPATLD6h"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "on8rUdw8KWqn"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "PDDL_VApJIT1",
        "outputId": "cb37fe8d-e744-4251-c73a-6cc36ea2b1b5"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "y=tencooking_time_longest['cook_time']\n",
        "x=tencooking_time_longest['name']\n",
        "plt.plot(x,y)\n",
        "plt.title('dishes based on cooking time')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eTKHGCX1KM1Y"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "Indian_food_analysis",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.12.2"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}