Lang_Detection_Excel.ipynb

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            "Collecting langdetect\n",
            "  Downloading langdetect-1.0.9.tar.gz (981 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m981.5/981.5 kB\u001b[0m \u001b[31m6.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from langdetect) (1.16.0)\n",
            "Building wheels for collected packages: langdetect\n",
            "  Building wheel for langdetect (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for langdetect: filename=langdetect-1.0.9-py3-none-any.whl size=993227 sha256=9fb365e3e3161d30422b6c7327b3ef59c03d73117b80717140c5145def2f408a\n",
            "  Stored in directory: /root/.cache/pip/wheels/95/03/7d/59ea870c70ce4e5a370638b5462a7711ab78fba2f655d05106\n",
            "Successfully built langdetect\n",
            "Installing collected packages: langdetect\n",
            "Successfully installed langdetect-1.0.9\n"
          ]
        }
      ],
      "source": [
        "!pip install langdetect"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langdetect import detect\n",
        "detect(\"War doesn't show who's right, just who's left.\")"
      ],
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      "execution_count": 2,
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          "data": {
            "text/plain": [
              "'en'"
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      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
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        "id": "kE_xrRomPIcN"
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      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_excel('/content/Book.xlsx')"
      ],
      "metadata": {
        "id": "mBLq3xNQOfYY"
      },
      "execution_count": 4,
      "outputs": []
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      "source": [
        "df.head()"
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      "execution_count": 5,
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              "                        ANSWER\n",
              "0          Hello, How Are You?\n",
              "1               Olá, como vai?\n",
              "2  Bonjour comment allez-vous?\n",
              "3         नमस्ते, आप कैसे हैं?\n",
              "4           ¿Hola, cómo estás?"
            ],
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              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"ANSWER\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Ol\\u00e1, como vai?\",\n          \"\\u00bfHola, c\\u00f3mo est\\u00e1s?\",\n          \"Bonjour comment allez-vous?\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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      "cell_type": "code",
      "source": [
        "newList = df['ANSWER'].to_list()"
      ],
      "metadata": {
        "id": "VdbIX2lEPNIh"
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      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(newList)"
      ],
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          "height": 0
        },
        "id": "oXvY9MaSPViZ",
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      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Hello, How Are You?', 'Olá, como vai?', 'Bonjour comment allez-vous?', 'नमस्ते, आप कैसे हैं?', '¿Hola, cómo estás?']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(newList[0])"
      ],
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          "height": 0
        },
        "id": "FOIjABlOPkLE",
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      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Hello, How Are You?\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "l = len(newList)"
      ],
      "metadata": {
        "id": "BeC2DQC_PpOd"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(l)"
      ],
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        "id": "yF36zp5DPtTz",
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      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def fun(text):\n",
        "  return detect(text)\n",
        "for i in range(l):\n",
        "  print(fun(newList[i]))"
      ],
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      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "en\n",
            "pt\n",
            "fr\n",
            "hi\n",
            "es\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "thisdict = {\n",
        "    \"af\": \"AFRIKAANS\",\n",
        "    \"ar\": \"ARABIC\",\n",
        "    \"bg\": \"BULGARIAN\",\n",
        "    \"bn\": \"BENGALI\",\n",
        "    \"ca\": \"CATALAN\",\n",
        "    \"cs\": \"CZECH\",\n",
        "    \"cy\": \"WELSH\",\n",
        "    \"da\": \"DANISH\",\n",
        "    \"de\": \"GERMAN\",\n",
        "    \"el\": \"GREEK\",\n",
        "    \"en\": \"ENGLISH\",\n",
        "    \"es\": \"SPANISH\",\n",
        "    \"et\": \"ESTONIAN\",\n",
        "    \"fa\": \"PERSIAN\",\n",
        "    \"fi\": \"FINNISH\",\n",
        "    \"fr\": \"FRENCH\",\n",
        "    \"gu\": \"GUJARATI\",\n",
        "    \"he\": \"HEBREW\",\n",
        "    \"hi\": \"HINDI\",\n",
        "    \"hr\": \"CROATIAN\",\n",
        "    \"hu\": \"HUNGARIAN\",\n",
        "    \"id\": \"INDONESIAN\",\n",
        "    \"it\": \"ITALIAN\",\n",
        "    \"ja\": \"JAPANESE\",\n",
        "    \"kn\": \"KANNADA\",\n",
        "    \"ko\": \"KOREAN\",\n",
        "    \"lt\": \"LITHUANIAN\",\n",
        "    \"lv\": \"LATVIAN\",\n",
        "    \"mk\": \"MACEDONIAN\",\n",
        "    \"ml\": \"MALAYALAM\",\n",
        "    \"mr\": \"MARATHI\",\n",
        "    \"ne\": \"NEPALI\",\n",
        "    \"nl\": \"DUTCH\",\n",
        "    \"no\": \"NORWEGIAN\",\n",
        "    \"pa\": \"PUNJABI\",\n",
        "    \"pl\": \"POLISH\",\n",
        "    \"pt\": \"PORTUGUESE\",\n",
        "    \"ro\": \"ROMANIAN\",\n",
        "    \"ru\": \"RUSSIAN\",\n",
        "    \"sk\": \"SLOVAK\",\n",
        "    \"sl\": \"SLOVENIAN\",\n",
        "    \"so\": \"SOMALI\",\n",
        "    \"sq\": \"ALBANIAN\",\n",
        "    \"sv\": \"SWEDISH\",\n",
        "    \"sw\": \"SWAHILI\",\n",
        "    \"ta\": \"TAMIL\",\n",
        "    \"te\": \"TELUGU\",\n",
        "    \"th\": \"THAI\",\n",
        "    \"tl\": \"TAGALOG\",\n",
        "    \"tr\": \"TURKISH\",\n",
        "    \"uk\": \"UKRAINIAN\",\n",
        "    \"ur\": \"URDU\",\n",
        "    \"vi\": \"VIETNAMESE\",\n",
        "    \"zh-cn\": \"CHINESE (SIMPLIFIED)\",\n",
        "    \"zh-tw\": \"CHINESE (TRADITIONAL)\"\n",
        "}"
      ],
      "metadata": {
        "id": "9SR3NQcnQMZx"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def fun(text):\n",
        "  return thisdict[detect(text)]\n",
        "for i in range(l):\n",
        "  print(fun(newList[i]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "lYO1liLqShFH",
        "outputId": "b1ded153-5ead-4f87-d506-2aca4d6b8cb1"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ENGLISH\n",
            "PORTUGUESE\n",
            "FRENCH\n",
            "HINDI\n",
            "SPANISH\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "c1=[]\n",
        "c2=[]\n",
        "df['c1']=np.nan\n",
        "df['c2']=np.nan"
      ],
      "metadata": {
        "id": "nhR8IlylStuh"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def fun(text):\n",
        "  return thisdict[detect(text)]\n",
        "for i in range(l):\n",
        "  if(fun(newList[i])=='ENGLISH'):\n",
        "    df['c1'][i]='ENGLISH'\n",
        "  else:\n",
        "    df['c1'][i]='NON-ENGLISH'"
      ],
      "metadata": {
        "id": "rjjYS65uTh8N"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['c1']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "4BYaIOveUU9m",
        "outputId": "9d93e48e-05f8-4690-92b8-88dbdcc15550"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        ENGLISH\n",
              "1    NON-ENGLISH\n",
              "2    NON-ENGLISH\n",
              "3    NON-ENGLISH\n",
              "4    NON-ENGLISH\n",
              "Name: c1, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def fun(text):\n",
        "  return thisdict[detect(text)]\n",
        "for i in range(l):\n",
        "  df['c2'][i]=fun(newList[i])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "3XL-SAWYUq84",
        "outputId": "fe16c2d8-2728-4caa-8960-dc31d47a534f"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-28-a1694be2637f>:4: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['c2'][i]=fun(newList[i])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['c2']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "gr-sDqXrU5m2",
        "outputId": "6e0335b4-4443-4b79-d55a-8839122eaa16"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       ENGLISH\n",
              "1    PORTUGUESE\n",
              "2        FRENCH\n",
              "3         HINDI\n",
              "4       SPANISH\n",
              "Name: c2, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.to_excel('Output.xlsx')"
      ],
      "metadata": {
        "id": "2MiYZKljVL6L"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "pu40NU6AVQVd"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}