Gaana_com_Data_Analysis.ipynb

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    "colab": {
      "name": "Gaana.com Data Analysis.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMOmijfh3amSOXwEvqrNFXN",
      "include_colab_link": true
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      "name": "python"
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        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Kranium2002/Gaana-Data-Analysis/blob/main/Gaana_com_Data_Analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3hGOxGl_BZ73",
        "outputId": "c68263a2-2ead-447a-b1aa-c8c084bc72a9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'Gaana-Data-Analysis'...\n",
            "remote: Enumerating objects: 24, done.\u001b[K\n",
            "remote: Counting objects: 100% (24/24), done.\u001b[K\n",
            "remote: Compressing objects: 100% (16/16), done.\u001b[K\n",
            "remote: Total 24 (delta 7), reused 24 (delta 7), pack-reused 0\u001b[K\n",
            "Unpacking objects: 100% (24/24), done.\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/Kranium2002/Gaana-Data-Analysis.git"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from tqdm import tqdm"
      ],
      "metadata": {
        "id": "q4E9JlHECa1L"
      },
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"Gaana-Data-Analysis/songs.csv\")"
      ],
      "metadata": {
        "id": "V8O0omyaKiz-"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
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      "execution_count": 6,
      "outputs": [
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              "                                      name                     singer  \\\n",
              "0                    Dil - E - Nadan Tujhe  Chitra Singh|Jagjit Singh   \n",
              "1        Agar Hum Kahen Aur Woh Muskara De  Chitra Singh|Jagjit Singh   \n",
              "2                            Unke Dekhe Se               Jagjit Singh   \n",
              "3  Yeh Na Thi Hamari Qismat - Chitra Singh               Chitra Singh   \n",
              "4                  Hazaron Khwahishen Aisi               Jagjit Singh   \n",
              "\n",
              "                                      singer_id duration  \\\n",
              "0  /artist/chitra-singh|/artist/jagjeet-singh-1    05:00   \n",
              "1  /artist/chitra-singh|/artist/jagjeet-singh-1    06:26   \n",
              "2                       /artist/jagjeet-singh-1    03:41   \n",
              "3                          /artist/chitra-singh    04:26   \n",
              "4                       /artist/jagjeet-singh-1    05:39   \n",
              "\n",
              "                                  link language  \n",
              "0                 /dil-e-nadan-tujhe-3     Urdu  \n",
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          "data": {
            "text/plain": [
              "name         0\n",
              "singer       0\n",
              "singer_id    0\n",
              "duration     0\n",
              "link         0\n",
              "language     0\n",
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          "metadata": {},
          "execution_count": 8
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    {
      "cell_type": "markdown",
      "source": [
        "2. Converting Duration from string to int(seconds)"
      ],
      "metadata": {
        "id": "qGAJimHmMUSo"
      }
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    {
      "cell_type": "code",
      "source": [
        "durationLst = []\n",
        "for i in df['duration']:\n",
        "  \n",
        "  sec = int(i.split(':')[0])*60+int(i.split(':')[1])\n",
        "  durationLst.append(sec)\n",
        "  \n",
        "df['duration'] = durationLst  "
      ],
      "metadata": {
        "id": "EOjPuTZULnJ-"
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      "execution_count": 21,
      "outputs": []
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              "                                                    name  \\\n",
              "0                                  Dil - E - Nadan Tujhe   \n",
              "1                      Agar Hum Kahen Aur Woh Muskara De   \n",
              "2                                          Unke Dekhe Se   \n",
              "3                Yeh Na Thi Hamari Qismat - Chitra Singh   \n",
              "4                                Hazaron Khwahishen Aisi   \n",
              "...                                                  ...   \n",
              "41350  Chaaridike Paaper Andhar (Manush Janam Diye Bi...   \n",
              "41351                                Ei Ki Go Shesh Daan   \n",
              "41352                                       Ei Je Bangla   \n",
              "41353                               Jatone Hridaye Rekho   \n",
              "41354                            Tomar Je Oi Haater Mala   \n",
              "\n",
              "                               singer  \\\n",
              "0           Chitra Singh|Jagjit Singh   \n",
              "1           Chitra Singh|Jagjit Singh   \n",
              "2                        Jagjit Singh   \n",
              "3                        Chitra Singh   \n",
              "4                        Jagjit Singh   \n",
              "...                               ...   \n",
              "41350                   Kishore Kumar   \n",
              "41351                    Feroza Begum   \n",
              "41352  Sandhya Mukherjee|Hemant Kumar   \n",
              "41353                Ajoy Chakrabarty   \n",
              "41354                     Asha Bhosle   \n",
              "\n",
              "                                               singer_id  duration  \\\n",
              "0           /artist/chitra-singh|/artist/jagjeet-singh-1       300   \n",
              "1           /artist/chitra-singh|/artist/jagjeet-singh-1       386   \n",
              "2                                /artist/jagjeet-singh-1       221   \n",
              "3                                   /artist/chitra-singh       266   \n",
              "4                                /artist/jagjeet-singh-1       339   \n",
              "...                                                  ...       ...   \n",
              "41350                              /artist/kishore-kumar       203   \n",
              "41351                               /artist/feroza-begum       236   \n",
              "41352  /artist/sandhya-mukherjee|/artist/hemanta-mukh...       456   \n",
              "41353                           /artist/ajoy-chakrabarty       269   \n",
              "41354                              /artist/asha-bhosle-1       202   \n",
              "\n",
              "                                                    link language  \n",
              "0                                   /dil-e-nadan-tujhe-3     Urdu  \n",
              "1                    /agar-hum-kahen-aur-woh-muskura-den     Urdu  \n",
              "2                                         /unke-dekhe-se     Urdu  \n",
              "3                            /yeh-na-thi-hamari-qismat-2     Urdu  \n",
              "4                             /hazaron-khwahishen-aisi-1     Urdu  \n",
              "...                                                  ...      ...  \n",
              "41350  /chaaridike-paaper-andhar-manush-janam-diye-bidhi  Bengali  \n",
              "41351                                 /ei-kigo-sesh-daan  Bengali  \n",
              "41352                                      /ei-je-bangla  Bengali  \n",
              "41353                              /jatone-hridaye-rekho  Bengali  \n",
              "41354                           /tomar-je-oi-haater-mala  Bengali  \n",
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              "      <td>339</td>\n",
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              "      <th>41351</th>\n",
              "      <td>Ei Ki Go Shesh Daan</td>\n",
              "      <td>Feroza Begum</td>\n",
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              "      <td>Jatone Hridaye Rekho</td>\n",
              "      <td>Ajoy Chakrabarty</td>\n",
              "      <td>/artist/ajoy-chakrabarty</td>\n",
              "      <td>269</td>\n",
              "      <td>/jatone-hridaye-rekho</td>\n",
              "      <td>Bengali</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41354</th>\n",
              "      <td>Tomar Je Oi Haater Mala</td>\n",
              "      <td>Asha Bhosle</td>\n",
              "      <td>/artist/asha-bhosle-1</td>\n",
              "      <td>202</td>\n",
              "      <td>/tomar-je-oi-haater-mala</td>\n",
              "      <td>Bengali</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>41355 rows × 6 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f7db76e9-1e77-4602-905d-aace7a2857c2')\"\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",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "      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",
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              "\n",
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              "      background-color: #434B5C;\n",
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              "  </style>\n",
              "\n",
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              "\n",
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              "          const element = document.querySelector('#df-f7db76e9-1e77-4602-905d-aace7a2857c2');\n",
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              "            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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "3. Average Duration of Song"
      ],
      "metadata": {
        "id": "IZL2nuLFOFP-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "avg = df['duration'].sum()/len(df['duration'])\n",
        "print(int(avg//60), \" min \",round(avg%60,2),\" sec\")\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hi74LwVFN84g",
        "outputId": "6e190706-6b4e-4807-bf03-50fce9d0a6e7"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4  min  55.31  sec\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "4. Number of languages included in df"
      ],
      "metadata": {
        "id": "UwGClEzKPVAP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['language'].nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-1JVJwbzOm9I",
        "outputId": "20676552-4563-409d-8864-57e2d9baef17"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "16"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['language'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1LLYbCchPemC",
        "outputId": "57ab876a-2d51-4546-e72a-65f66e2ff798"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Urdu', 'Gujarati', 'Tamil', 'Kannada', 'Rajasthani', 'Haryanvi',\n",
              "       'Punjabi', 'Hindi', 'Old', 'Malayalam', 'Marathi', 'Telugu',\n",
              "       'Odia', 'Bhojpuri', 'Assamese', 'Bengali'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "set(df['language'])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "88Uyw99DPiMK",
        "outputId": "e21ca6bb-13b9-44e5-b0e0-1dbe3f19a886"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Assamese',\n",
              " 'Bengali',\n",
              " 'Bhojpuri',\n",
              " 'Gujarati',\n",
              " 'Haryanvi',\n",
              " 'Hindi',\n",
              " 'Kannada',\n",
              " 'Malayalam',\n",
              " 'Marathi',\n",
              " 'Odia',\n",
              " 'Old',\n",
              " 'Punjabi',\n",
              " 'Rajasthani',\n",
              " 'Tamil',\n",
              " 'Telugu',\n",
              " 'Urdu'}"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "numL= set(df['language'])\n",
        "numL.remove('Old')"
      ],
      "metadata": {
        "id": "UusK_uNdQD3_"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Number of languages = \",len(numL))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "40gybFGSPnvC",
        "outputId": "2a26bd3a-ff37-4ca2-e3de-b4f52762810a"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of languages =  15\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "5. Number of songs per language"
      ],
      "metadata": {
        "id": "OEofAT05QfJn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for i in numL:\n",
        "  print(i,\" \",df.loc[df['language']==i].count()[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cO0Lu9JTP6Ay",
        "outputId": "6b054e62-da49-4690-d5fc-4166d326f074"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tamil   4677\n",
            "Rajasthani   541\n",
            "Punjabi   3818\n",
            "Bengali   958\n",
            "Hindi   4993\n",
            "Telugu   4996\n",
            "Assamese   724\n",
            "Gujarati   2115\n",
            "Odia   940\n",
            "Kannada   3559\n",
            "Bhojpuri   519\n",
            "Marathi   4699\n",
            "Haryanvi   228\n",
            "Malayalam   479\n",
            "Urdu   3116\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "6. Avg len of song in each language\n"
      ],
      "metadata": {
        "id": "ZYzvYz5IUh0N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for i in numL:\n",
        "  print(i,\" \",round(df.loc[df['language']==i].mean()[0],2),\"sec\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2t69oDw1RBdd",
        "outputId": "109035a5-d826-44bf-9d91-d51d319ecbb9"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
            "  \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tamil   270.59 sec\n",
            "Rajasthani   291.04 sec\n",
            "Punjabi   324.02 sec\n",
            "Bengali   255.18 sec\n",
            "Hindi   307.87 sec\n",
            "Telugu   279.23 sec\n",
            "Assamese   228.33 sec\n",
            "Gujarati   271.63 sec\n",
            "Odia   257.63 sec\n",
            "Kannada   250.59 sec\n",
            "Bhojpuri   357.14 sec\n",
            "Marathi   281.16 sec\n",
            "Haryanvi   320.0 sec\n",
            "Malayalam   242.61 sec\n",
            "Urdu   407.27 sec\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "7. How many singers are there?"
      ],
      "metadata": {
        "id": "V7SsBiNbU6DE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['singer'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gImce9hIUtB9",
        "outputId": "65a41a51-3f3b-4832-b825-e97c6913ee19"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Chitra Singh|Jagjit Singh', 'Jagjit Singh', 'Chitra Singh', ...,\n",
              "       'Bishnupada Das', 'Madhuri Chatterjee', 'Rabindranath Tagore'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "all_singers = []\n",
        "for i in df['singer']:\n",
        "  all_singers+=i.split('|')"
      ],
      "metadata": {
        "id": "pn96FfkjVCym"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(set(all_singers))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c1IySu33VWNk",
        "outputId": "3bfb3be4-14fc-40b3-ef7a-dd0a5f84c355"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3940"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "8. Number of songs per singer"
      ],
      "metadata": {
        "id": "t0baDBjnWrjv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "c=0\n",
        "lst = []\n",
        "for i in tqdm(list(set(all_singers))):\n",
        "  for m in df['singer']:\n",
        "    if(i in m):\n",
        "      c+=1\n",
        "  lst.append((i,c))\n",
        "  c=0    "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XhQYq4DYVetl",
        "outputId": "1bb870c0-e91f-4148-b30e-16d7dbf1336b"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 3940/3940 [00:31<00:00, 127.01it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "songs_freq_by_singer = pd.DataFrame(lst,columns={'singers','songs'})\n",
        "songs_freq_by_singer.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 214
        },
        "id": "zSQ-xFT-WaLt",
        "outputId": "54e9bf70-dfe8-4310-a5d0-5fa966931006"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 singers  songs\n",
              "0                  Ramya     11\n",
              "1           Nahid Akhter      2\n",
              "2      Lalitaben Trivedi      1\n",
              "3  CHANDRAMANI MUKHERJEE      8\n",
              "4             Sipra Basu      1"
            ],
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              "<style scoped>\n",
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              "          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": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "songs_freq_by_singer.sort_values(by='songs', ascending = False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "RtI8No2zaZhF",
        "outputId": "431ec58f-e963-4ff3-e61e-b01079221035"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                         singers  songs\n",
              "1548      S. P. Balasubrahmanyam   3659\n",
              "2751                       Kumar   3270\n",
              "3677                     Susheel   3216\n",
              "2159                    Susheela   3215\n",
              "2977                 P. Susheela   3213\n",
              "...                          ...    ...\n",
              "2953            Ram Niwas Sharma      1\n",
              "2952            M. Krishnamurthy      1\n",
              "641        Purshottam Das Jalota      1\n",
              "2949  Bangalore A.R. Ramani Amma      1\n",
              "2742             K.A. Thangavelu      1\n",
              "\n",
              "[3940 rows x 2 columns]"
            ],
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              "\n",
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              "      <td>Susheel</td>\n",
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              "      <td>Susheela</td>\n",
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              "      <th>2977</th>\n",
              "      <td>P. Susheela</td>\n",
              "      <td>3213</td>\n",
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              "      <td>Ram Niwas Sharma</td>\n",
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              "      <td>M. Krishnamurthy</td>\n",
              "      <td>1</td>\n",
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              "      <td>Purshottam Das Jalota</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>2949</th>\n",
              "      <td>Bangalore A.R. Ramani Amma</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>2742</th>\n",
              "      <td>K.A. Thangavelu</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3940 rows × 2 columns</p>\n",
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              "          const element = document.querySelector('#df-61a65277-118d-4631-ac9d-d96b84c4d4cb');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
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              "            + ' to learn more about interactive tables.';\n",
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            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "9. Singer who played most number of songs per language"
      ],
      "metadata": {
        "id": "pUc7NgaoczBU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for lang in df['language'].unique():\n",
        "  df_ = df[df['language']==lang]\n",
        "  all_singers = []\n",
        "  for i in df_['singer']:\n",
        "    all_singers+=i.split('|')\n",
        "  print (lang,len(set(all_singers)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pwoXS1Zscyrw",
        "outputId": "e558b530-b41a-4b76-cf07-fad8232f9ca4"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Urdu 385\n",
            "Gujarati 303\n",
            "Tamil 510\n",
            "Kannada 379\n",
            "Rajasthani 218\n",
            "Haryanvi 76\n",
            "Punjabi 636\n",
            "Hindi 515\n",
            "Old 515\n",
            "Malayalam 72\n",
            "Marathi 569\n",
            "Telugu 369\n",
            "Odia 243\n",
            "Bhojpuri 109\n",
            "Assamese 228\n",
            "Bengali 192\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "bz1_F9qmcS27"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}