assignment_1_Sonal_priya.ipynb

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    "colab": {
      "name": "assignment 1  Sonal priya",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
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        "id": "hvdUEDxJzwCQ",
        "colab_type": "code",
        "outputId": "74f156ea-3d24-4fad-f3eb-303a46b5be66",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "df_brand_details=pd.read_csv(\"/content/brand_details.csv\")\n",
        "df_brand_details.columns\n"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Rank', 'Brand', 'Brand_Value', 'Change_LastYr', 'Brand_Revenue',\n",
              "       'Company_Advertising', 'Industry'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7uCIgythTGgj",
        "colab_type": "code",
        "outputId": "ad774c15-acf3-4107-a54a-084dcaff8cac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "df_brand_details.head()"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "\n",
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              "    }\n",
              "\n",
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              "        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>Rank</th>\n",
              "      <th>Brand</th>\n",
              "      <th>Brand_Value</th>\n",
              "      <th>Change_LastYr</th>\n",
              "      <th>Brand_Revenue</th>\n",
              "      <th>Company_Advertising</th>\n",
              "      <th>Industry</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Apple</td>\n",
              "      <td>145.3</td>\n",
              "      <td>17</td>\n",
              "      <td>182.3</td>\n",
              "      <td>1.2</td>\n",
              "      <td>Technology</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Microsoft</td>\n",
              "      <td>69.3</td>\n",
              "      <td>10</td>\n",
              "      <td>93.3</td>\n",
              "      <td>2.3</td>\n",
              "      <td>Technology</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Google</td>\n",
              "      <td>65.6</td>\n",
              "      <td>16</td>\n",
              "      <td>61.8</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Technology</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Coca-Cola</td>\n",
              "      <td>56.0</td>\n",
              "      <td>0</td>\n",
              "      <td>23.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>Beverages</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>IBM</td>\n",
              "      <td>49.8</td>\n",
              "      <td>4</td>\n",
              "      <td>92.8</td>\n",
              "      <td>1.3</td>\n",
              "      <td>Technology</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Rank      Brand  Brand_Value  ...  Brand_Revenue  Company_Advertising    Industry\n",
              "0     1      Apple        145.3  ...          182.3                  1.2  Technology\n",
              "1     2  Microsoft         69.3  ...           93.3                  2.3  Technology\n",
              "2     3     Google         65.6  ...           61.8                  3.0  Technology\n",
              "3     4  Coca-Cola         56.0  ...           23.1                  3.5   Beverages\n",
              "4     5        IBM         49.8  ...           92.8                  1.3  Technology\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bWXLqInAY9c_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "cca4838e-d985-467b-bad4-d1285b3ba607"
      },
      "source": [
        "df_brand_details.tail()"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Rank</th>\n",
              "      <th>Brand</th>\n",
              "      <th>Brand_Value</th>\n",
              "      <th>Change_LastYr</th>\n",
              "      <th>Brand_Revenue</th>\n",
              "      <th>Company_Advertising</th>\n",
              "      <th>Industry</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>94</td>\n",
              "      <td>Hershey</td>\n",
              "      <td>6.3</td>\n",
              "      <td>10</td>\n",
              "      <td>4.6</td>\n",
              "      <td>0.570</td>\n",
              "      <td>Consumer Packaged Goods</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>97</td>\n",
              "      <td>KIA Motors</td>\n",
              "      <td>6.2</td>\n",
              "      <td>-11</td>\n",
              "      <td>42.9</td>\n",
              "      <td>0.992</td>\n",
              "      <td>Automotive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>98</td>\n",
              "      <td>Sprite</td>\n",
              "      <td>6.2</td>\n",
              "      <td>2</td>\n",
              "      <td>3.7</td>\n",
              "      <td>3.500</td>\n",
              "      <td>Beverages</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>99</td>\n",
              "      <td>MTV</td>\n",
              "      <td>6.2</td>\n",
              "      <td>6</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.000</td>\n",
              "      <td>Media</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>100</td>\n",
              "      <td>Estee Lauder</td>\n",
              "      <td>6.1</td>\n",
              "      <td>4</td>\n",
              "      <td>4.5</td>\n",
              "      <td>2.800</td>\n",
              "      <td>Consumer Packaged Goods</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Rank         Brand  ...  Company_Advertising                 Industry\n",
              "55    94       Hershey  ...                0.570  Consumer Packaged Goods\n",
              "56    97    KIA Motors  ...                0.992               Automotive\n",
              "57    98        Sprite  ...                3.500                Beverages\n",
              "58    99           MTV  ...                1.000                    Media\n",
              "59   100  Estee Lauder  ...                2.800  Consumer Packaged Goods\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z7O833NBZIN5",
        "colab_type": "text"
      },
      "source": [
        "**Problem 1 : Which industry has the maximum brands whose Brand_Revnue >80 million (Brand _Revenue is inn million)**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O04sGcxA3DEc",
        "colab_type": "code",
        "outputId": "28ba0e05-a381-4320-e53e-6b8f16f5e2d1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "df_brand_details.loc[(df_brand_details.groupby([\"Industry\"])[\"Brand\"].count().max())][[\"Industry\"]]"
      ],
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Industry    Technology\n",
              "Name: 13, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QN5tj2qlDglr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "outputId": "ffe67192-3955-436d-f0f8-c0c221d5500f"
      },
      "source": [
        "df_brand_details.loc[(df_brand_details.Industry==\"Technology\")&(df_brand_details.Brand_Revenue>80)][[\"Industry\",\"Brand\",\"Brand_Revenue\"]]"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    }\n",
              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Industry</th>\n",
              "      <th>Brand</th>\n",
              "      <th>Brand_Revenue</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Technology</td>\n",
              "      <td>Apple</td>\n",
              "      <td>182.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Technology</td>\n",
              "      <td>Microsoft</td>\n",
              "      <td>93.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Technology</td>\n",
              "      <td>IBM</td>\n",
              "      <td>92.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Technology</td>\n",
              "      <td>Samsung</td>\n",
              "      <td>187.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Technology</td>\n",
              "      <td>Amazon</td>\n",
              "      <td>87.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Technology</td>\n",
              "      <td>Hewlett-Packard</td>\n",
              "      <td>111.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Industry            Brand  Brand_Revenue\n",
              "0   Technology            Apple          182.3\n",
              "1   Technology        Microsoft           93.3\n",
              "4   Technology              IBM           92.8\n",
              "6   Technology          Samsung          187.8\n",
              "11  Technology           Amazon           87.5\n",
              "25  Technology  Hewlett-Packard          111.5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "80hXAFgfo0Pg",
        "colab_type": "text"
      },
      "source": [
        "problem 2: List the companised whose company_Advertsing budget is < 3 but  Brand_value is >20."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RAksMblEVCyF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "outputId": "9131a6c4-b4b8-4f3b-852c-2dfc98ab3e51"
      },
      "source": [
        "df_brand_details.loc[(df_brand_details[\"Company_Advertising\"]<3) & (df_brand_details[\"Brand_Value\"]>20)][[\"Brand\",\"Brand_Value\",\"Company_Advertising\"]]"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\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>Brand</th>\n",
              "      <th>Brand_Value</th>\n",
              "      <th>Company_Advertising</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Apple</td>\n",
              "      <td>145.3</td>\n",
              "      <td>1.200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Microsoft</td>\n",
              "      <td>69.3</td>\n",
              "      <td>2.300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>IBM</td>\n",
              "      <td>49.8</td>\n",
              "      <td>1.300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>McDonalds</td>\n",
              "      <td>39.5</td>\n",
              "      <td>0.808</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Facebook</td>\n",
              "      <td>36.5</td>\n",
              "      <td>0.135</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Disney</td>\n",
              "      <td>34.6</td>\n",
              "      <td>2.800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Cisco</td>\n",
              "      <td>27.6</td>\n",
              "      <td>0.196</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Oracle</td>\n",
              "      <td>26.8</td>\n",
              "      <td>0.079</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Intel</td>\n",
              "      <td>25.8</td>\n",
              "      <td>1.800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Wal-Mart</td>\n",
              "      <td>24.7</td>\n",
              "      <td>2.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Verizon</td>\n",
              "      <td>24.5</td>\n",
              "      <td>2.500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Honda</td>\n",
              "      <td>22.6</td>\n",
              "      <td>2.700</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Brand  Brand_Value  Company_Advertising\n",
              "0       Apple        145.3                1.200\n",
              "1   Microsoft         69.3                2.300\n",
              "4         IBM         49.8                1.300\n",
              "5   McDonalds         39.5                0.808\n",
              "8    Facebook         36.5                0.135\n",
              "9      Disney         34.6                2.800\n",
              "13      Cisco         27.6                0.196\n",
              "14     Oracle         26.8                0.079\n",
              "16      Intel         25.8                1.800\n",
              "17   Wal-Mart         24.7                2.400\n",
              "18    Verizon         24.5                2.500\n",
              "20      Honda         22.6                2.700"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IdyvvnUcy80r",
        "colab_type": "text"
      },
      "source": [
        "problem 3 : Find out top 5 technology Brand that showed maximum postive jump in thier postion(use change_LastYr)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TCE-lJNT3DIT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "5978119e-da39-465d-f2a7-004bc4a7018f"
      },
      "source": [
        "data=df_brand_details[(dataset['Industry']== 'Technology')]\n",
        "data.nlargest(5,['Change_LastYr'])[[\"Industry\",\"Change_LastYr\"]]\n",
        "\n"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Industry</th>\n",
              "      <th>Change_LastYr</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Technology</td>\n",
              "      <td>54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Technology</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>Technology</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>Technology</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Technology</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Industry  Change_LastYr\n",
              "8   Technology             54\n",
              "11  Technology             32\n",
              "33  Technology             21\n",
              "50  Technology             21\n",
              "0   Technology             17"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DXi-_8JczdoA",
        "colab_type": "text"
      },
      "source": [
        "problem 4 : Find out the average Brand_revenue by Industry."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GP22hx-kzZkO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "outputId": "61acd887-070d-4fed-e307-837b404cd15a"
      },
      "source": [
        "df_brand_details.groupby([\"Industry\"])['Brand_Revenue'].mean()"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Industry\n",
              "Apparel                     17.700000\n",
              "Automotive                  97.385714\n",
              "Beverages                   13.133333\n",
              "Business Services           32.800000\n",
              "Consumer Packaged Goods      7.175000\n",
              "Financial Services          51.622222\n",
              "Heavy Equipment             36.100000\n",
              "Leisure                     26.400000\n",
              "Luxury                       5.900000\n",
              "Media                        9.200000\n",
              "Restaurants                 51.900000\n",
              "Retail                     156.266667\n",
              "Technology                  80.238462\n",
              "Telecom                    129.750000\n",
              "Transportation              47.200000\n",
              "Name: Brand_Revenue, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 92
        }
      ]
    }
  ]
}