Pandas_in_Python.ipynb

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        "<a href=\"https://colab.research.google.com/github/joynaomi81/Python-Programming/blob/main/Pandas_in_Python.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": "markdown",
      "source": [
        "Pandas is a Python library. It is used for data mainpulation,analysis and cleaning\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "iVI7UAHex92j"
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      "cell_type": "markdown",
      "source": [
        "Pandas Series\n",
        "* A Pandas Series is like a column in a table. It can hold data of any type such as string, integer, float, dictionaries, lists, booleans etc."
      ],
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      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a = pd.Series([1, 2, 3, 4, 5, 6, 7, 8, np.nan, 97, 90, 100])\n",
        "\n",
        "print(a)\n"
      ],
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            "0       1.0\n",
            "1       2.0\n",
            "2       3.0\n",
            "3       4.0\n",
            "4       5.0\n",
            "5       6.0\n",
            "6       7.0\n",
            "7       8.0\n",
            "8       NaN\n",
            "9      97.0\n",
            "10     90.0\n",
            "11    100.0\n",
            "dtype: float64\n"
          ]
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "# create labels\n",
        "a = ([1, 2, 3, 4, 5, 6, 7, 8, 45, 97, 90, 100])\n",
        "\n",
        "a = pd.Series(a, index = [\"apple\", \"mango\", \" rice\", \" yam\", \" egg\", \"cat\", \"dog\", \" car\", \"goat\", \"comb\", \"water\", \"soup\"])\n",
        "\n",
        "print(a)"
      ],
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        "outputId": "32e06b48-05cd-40d6-8ce0-2fb20f22986f"
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          "text": [
            "apple      1\n",
            "mango      2\n",
            " rice      3\n",
            " yam       4\n",
            " egg       5\n",
            "cat        6\n",
            "dog        7\n",
            " car       8\n",
            "goat      45\n",
            "comb      97\n",
            "water     90\n",
            "soup     100\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# creating a DataFrame\n",
        "dates = pd.date_range(\"20240101\", periods=12)\n",
        "\n",
        "dates"
      ],
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              "DatetimeIndex(['2024-01-01', '2024-01-02', '2024-01-03', '2024-01-04',\n",
              "               '2024-01-05', '2024-01-06', '2024-01-07', '2024-01-08',\n",
              "               '2024-01-09', '2024-01-10', '2024-01-11', '2024-01-12'],\n",
              "              dtype='datetime64[ns]', freq='D')"
            ]
          },
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          "execution_count": 8
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      "source": [
        "df = pd.DataFrame(np.random.randn(12, 4), index=dates, columns=list(\"ABCD\"))\n",
        "\n",
        "df"
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              "                   A         B         C         D\n",
              "2024-01-01  0.038195  0.886886  1.159848  1.338778\n",
              "2024-01-02 -0.669470 -1.691862  0.936321  0.696655\n",
              "2024-01-03 -1.354818  0.715870  0.936225 -0.940082\n",
              "2024-01-04  1.472123  1.231717  0.242657  0.074355\n",
              "2024-01-05  2.144935 -0.326182  0.245334 -0.382203\n",
              "2024-01-06 -0.676995 -0.019045 -0.071780  0.199774\n",
              "2024-01-07  0.772477  2.438046 -0.168106  1.362038\n",
              "2024-01-08  0.289345  0.236277  1.783896  0.952557\n",
              "2024-01-09 -2.620529  1.481910  1.896794  0.263055\n",
              "2024-01-10  1.068900  0.831577 -1.417558 -0.311569\n",
              "2024-01-11 -0.445423 -0.345471 -0.451918  0.368663\n",
              "2024-01-12 -0.608301 -0.963390 -0.027305 -0.370412"
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              "      <th>2024-01-01</th>\n",
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              "type": "dataframe",
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 12,\n  \"fields\": [\n    {\n      \"column\": \"A\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.3052698083520622,\n        \"min\": -2.6205288178784762,\n        \"max\": 2.144935106756515,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          -0.4454231921603798,\n          1.0689002899488407,\n          0.03819515008178421\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"B\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1301835747381292,\n        \"min\": -1.691861719748459,\n        \"max\": 2.4380461608002535,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          -0.345470688253029,\n          0.8315773816799598,\n          0.8868855600086835\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"C\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9586046423456083,\n        \"min\": -1.4175581961203787,\n        \"max\": 1.896794307342246,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          -0.4519177974723839,\n          -1.4175581961203787,\n          1.1598482017154252\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"D\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7181890402011403,\n        \"min\": -0.9400822460890853,\n        \"max\": 1.3620383046211768,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          0.3686632288161783,\n          -0.3115689762778537,\n          1.3387779371681883\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a dataframe\n",
        "data = {\n",
        "  \"Age\": [13, 18, 20, 21, 25, 24],\n",
        "  \"Name\": ['Ayo',  'John', 'Gift','Naomi', 'James', 'Joy']\n",
        "}\n",
        "\n",
        "#load data into a DataFrame object:\n",
        "df = pd.DataFrame(data)\n",
        "\n",
        "print(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4UmnF-Wi5W_B",
        "outputId": "0a2bd7a1-cc64-487b-f3be-5169ae4c4478"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Age   Name\n",
            "0   13    Ayo\n",
            "1   18   John\n",
            "2   20   Gift\n",
            "3   21  Naomi\n",
            "4   25  James\n",
            "5   24    Joy\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.loc[3]) # locate row"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bZIuyd5z6edk",
        "outputId": "75e4d82b-4749-4843-911a-337b72e282f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Age        21\n",
            "Name    Naomi\n",
            "Name: 3, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.loc[[0, 5]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NaR-Oe9-62ms",
        "outputId": "f66e0fa0-b987-4df2-b7d2-7fc62bd09b9b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Age Name\n",
            "0   13  Ayo\n",
            "5   24  Joy\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vfO2uSxa7a4W",
        "outputId": "0f8ad5c6-d4b0-44f3-ef0a-26d15b423a19"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Age   Name\n",
            "0   13    Ayo\n",
            "1   18   John\n",
            "2   20   Gift\n",
            "3   21  Naomi\n",
            "4   25  James\n",
            "5   24    Joy\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame(data, index = [\"female\", \"male\", \"female\", \"female\", \"male\", \"female\"])\n",
        "\n",
        "print(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5N_z-9E87Wrr",
        "outputId": "531f51ac-2c6a-4829-fb20-0e173fd52ac8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        Age   Name\n",
            "female   13    Ayo\n",
            "male     18   John\n",
            "female   20   Gift\n",
            "female   21  Naomi\n",
            "male     25  James\n",
            "female   24    Joy\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Analyzing Data in Pandas"
      ],
      "metadata": {
        "id": "PQc6Au6c9qt_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"/content/drive/MyDrive/Sales.csv\")\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 330
        },
        "id": "sCoBke-B91Vm",
        "outputId": "5c89a4bb-c85d-4ecf-baa1-f575f14531f8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Brands        Models         Colors Memory Storage Camera  Rating  \\\n",
              "0  SAMSUNG  GALAXY M31S    Mirage Black   8 GB  128 GB    Yes     4.3   \n",
              "1    Nokia           3.2          Steel   2 GB   16 GB    Yes     3.8   \n",
              "2   realme           C2   Diamond Black   2 GB     NaN    Yes     4.4   \n",
              "3  Infinix        Note 5       Ice Blue   4 GB   64 GB    Yes     4.2   \n",
              "4    Apple    iPhone 11           Black    4GB   64 GB    Yes     4.6   \n",
              "\n",
              "   Selling Price  Original Price                Mobile  Discount  \\\n",
              "0          19330           20999  SAMSUNG GALAXY M31S       1669   \n",
              "1          10199           10199             Nokia 3.2         0   \n",
              "2           6999            7999            realme C2       1000   \n",
              "3          12999           12999        Infinix Note 5         0   \n",
              "4          49900           49900      Apple iPhone 11          0   \n",
              "\n",
              "   discount percentage  \n",
              "0             7.947998  \n",
              "1             0.000000  \n",
              "2            12.501563  \n",
              "3             0.000000  \n",
              "4             0.000000  "
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              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ff2c5f82-0a0f-453f-9e82-d6e34f124b2f button');\n",
              "      quickchartButtonEl.style.display =\n",
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              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 3114,\n  \"fields\": [\n    {\n      \"column\": \"Brands\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 17,\n        \"samples\": [\n          \"SAMSUNG\",\n          \"Nokia\",\n          \"GIONEE\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Models\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 914,\n        \"samples\": [\n          \"E7 Plus \",\n          \"Galaxy Grand Prime 4g \",\n          \"Galaxy M12 \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Colors\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 639,\n        \"samples\": [\n          \"Obsidian Black\",\n          \"Dashing Blue\",\n          \"Mystic Green\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Memory\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 26,\n        \"samples\": [\n          \"1 GB\",\n          \"16 MB\",\n          \"8 GB\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Storage\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 26,\n        \"samples\": [\n          \"2 MB\",\n          \"128 MB\",\n          \"128 GB\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Camera\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.27199071621684595,\n        \"min\": 2.3,\n        \"max\": 5.0,\n        \"num_unique_values\": 25,\n        \"samples\": [\n          3.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Selling Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 30066,\n        \"min\": 1000,\n        \"max\": 179900,\n        \"num_unique_values\": 844,\n        \"samples\": [\n          2533\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Original Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31525,\n        \"min\": 1000,\n        \"max\": 189999,\n        \"num_unique_values\": 819,\n        \"samples\": [\n          18990\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mobile\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 928,\n        \"samples\": [\n          \"OPPO F19s\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Discount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5337,\n        \"min\": -8000,\n        \"max\": 75000,\n        \"num_unique_values\": 267,\n        \"samples\": [\n          26901\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"discount percentage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.106776414882027,\n        \"min\": -160.32064128256513,\n        \"max\": 70.61030515257629,\n        \"num_unique_values\": 539,\n        \"samples\": [\n          2.8628571428571425\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.tail() # last 5 rows"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "id": "ZHW-idVp91ls",
        "outputId": "282a9fdc-af12-4dd1-bf77-93edd077f8c6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Brands          Models     Colors Memory                Storage Camera  \\\n",
              "3109    POCO       M4 Pro 5G  Cool Blue   6 GB                 128 GB    Yes   \n",
              "3110   Nokia             225      Black    NaN  Expandable Upto 32 GB    Yes   \n",
              "3111   Apple      iPhone SE       White   2 GB                 128 GB    Yes   \n",
              "3112   Apple  iPhone 13 Pro        Gold   6 GB                 128 GB    Yes   \n",
              "3113  GIONEE             F9        Blue   3 GB                  32 GB    Yes   \n",
              "\n",
              "      Rating  Selling Price  Original Price                Mobile  Discount  \\\n",
              "3109     4.4          16999           19999        POCO M4 Pro 5G      3000   \n",
              "3110     3.6           3499            3499             Nokia 225         0   \n",
              "3111     4.5          44900           44900      Apple iPhone SE          0   \n",
              "3112     NaN         119900          119900  Apple iPhone 13 Pro          0   \n",
              "3113     4.2           7900            7900            GIONEE F9          0   \n",
              "\n",
              "      discount percentage  \n",
              "3109             15.00075  \n",
              "3110              0.00000  \n",
              "3111              0.00000  \n",
              "3112              0.00000  \n",
              "3113              0.00000  "
            ],
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              "      <th></th>\n",
              "      <th>Brands</th>\n",
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              "      <td>POCO</td>\n",
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              "      <td>Cool Blue</td>\n",
              "      <td>6 GB</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>Expandable Upto 32 GB</td>\n",
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              "      <td>3499</td>\n",
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              "    <tr>\n",
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              "      <td>2 GB</td>\n",
              "      <td>128 GB</td>\n",
              "      <td>Yes</td>\n",
              "      <td>4.5</td>\n",
              "      <td>44900</td>\n",
              "      <td>44900</td>\n",
              "      <td>Apple iPhone SE</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3112</th>\n",
              "      <td>Apple</td>\n",
              "      <td>iPhone 13 Pro</td>\n",
              "      <td>Gold</td>\n",
              "      <td>6 GB</td>\n",
              "      <td>128 GB</td>\n",
              "      <td>Yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>119900</td>\n",
              "      <td>119900</td>\n",
              "      <td>Apple iPhone 13 Pro</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3113</th>\n",
              "      <td>GIONEE</td>\n",
              "      <td>F9</td>\n",
              "      <td>Blue</td>\n",
              "      <td>3 GB</td>\n",
              "      <td>32 GB</td>\n",
              "      <td>Yes</td>\n",
              "      <td>4.2</td>\n",
              "      <td>7900</td>\n",
              "      <td>7900</td>\n",
              "      <td>GIONEE F9</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00000</td>\n",
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              "                                                    [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",
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              "      --disabled-fill-color: #666;\n",
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              "\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Brands\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Nokia\",\n          \"GIONEE\",\n          \"POCO\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Models\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"225\",\n          \"F9 \",\n          \"iPhone SE \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Colors\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Black\",\n          \"Blue\",\n          \"White\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Memory\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"6 GB\",\n          \"2 GB\",\n          \"3 GB\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Storage\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"128 GB\",\n          \"Expandable Upto 32 GB\",\n          \"32 GB\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Camera\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4031128874149275,\n        \"min\": 3.6,\n        \"max\": 4.5,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Selling Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 48193,\n        \"min\": 3499,\n        \"max\": 119900,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3499\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Original Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 47874,\n        \"min\": 3499,\n        \"max\": 119900,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3499\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mobile\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Nokia 225\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Discount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1341,\n        \"min\": 0,\n        \"max\": 3000,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"discount percentage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.708539359467342,\n        \"min\": 0.0,\n        \"max\": 15.000750037501875,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape # number of rows and columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f9-EOyS9ACwB",
        "outputId": "a6b0323b-50fc-43d3-9ab2-fbfd03e63c0b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3114, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info() # Information about the data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ENkQlE5-91iw",
        "outputId": "e78185db-1e7a-4dc0-efcd-dc11712e123a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 3114 entries, 0 to 3113\n",
            "Data columns (total 12 columns):\n",
            " #   Column               Non-Null Count  Dtype  \n",
            "---  ------               --------------  -----  \n",
            " 0   Brands               3114 non-null   object \n",
            " 1   Models               3114 non-null   object \n",
            " 2   Colors               3114 non-null   object \n",
            " 3   Memory               3071 non-null   object \n",
            " 4   Storage              3075 non-null   object \n",
            " 5   Camera               3114 non-null   object \n",
            " 6   Rating               2970 non-null   float64\n",
            " 7   Selling Price        3114 non-null   int64  \n",
            " 8   Original Price       3114 non-null   int64  \n",
            " 9   Mobile               3114 non-null   object \n",
            " 10  Discount             3114 non-null   int64  \n",
            " 11  discount percentage  3114 non-null   float64\n",
            "dtypes: float64(2), int64(3), object(7)\n",
            "memory usage: 292.1+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Data Cleaning"
      ],
      "metadata": {
        "id": "mOT8LBMjAKpa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum() # check for null values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 460
        },
        "id": "sehEtbmK91gE",
        "outputId": "b64d0c84-80c5-452a-d73f-6da48f9f525b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Brands                   0\n",
              "Models                   0\n",
              "Colors                   0\n",
              "Memory                  43\n",
              "Storage                 39\n",
              "Camera                   0\n",
              "Rating                 144\n",
              "Selling Price            0\n",
              "Original Price           0\n",
              "Mobile                   0\n",
              "Discount                 0\n",
              "discount percentage      0\n",
              "dtype: int64"
            ],
            "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>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Brands</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Models</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Colors</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Memory</th>\n",
              "      <td>43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Storage</th>\n",
              "      <td>39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Camera</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Rating</th>\n",
              "      <td>144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Selling Price</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Original Price</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mobile</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Discount</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>discount percentage</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.dropna(inplace=True) # drop null values"
      ],
      "metadata": {
        "id": "E2HVFA2Z91dR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5B5fQfmOAiy0",
        "outputId": "0fbf67bd-dfb0-4b4e-81f4-d8ca20610ee3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 2897 entries, 0 to 3113\n",
            "Data columns (total 12 columns):\n",
            " #   Column               Non-Null Count  Dtype  \n",
            "---  ------               --------------  -----  \n",
            " 0   Brands               2897 non-null   object \n",
            " 1   Models               2897 non-null   object \n",
            " 2   Colors               2897 non-null   object \n",
            " 3   Memory               2897 non-null   object \n",
            " 4   Storage              2897 non-null   object \n",
            " 5   Camera               2897 non-null   object \n",
            " 6   Rating               2897 non-null   float64\n",
            " 7   Selling Price        2897 non-null   int64  \n",
            " 8   Original Price       2897 non-null   int64  \n",
            " 9   Mobile               2897 non-null   object \n",
            " 10  Discount             2897 non-null   int64  \n",
            " 11  discount percentage  2897 non-null   float64\n",
            "dtypes: float64(2), int64(3), object(7)\n",
            "memory usage: 294.2+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df = pd.read_csv('/content/drive/MyDrive/Sales.csv')\n",
        "\n",
        "df = df.dropna()\n",
        "print(df)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qB9H9Sv891ay",
        "outputId": "2d14fabc-a976-404d-c387-6a8aafa4fe0f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       Brands            Models        Colors  Memory Storage Camera  Rating  \\\n",
            "0     SAMSUNG      GALAXY M31S   Mirage Black    8 GB  128 GB    Yes     4.3   \n",
            "1       Nokia               3.2         Steel    2 GB   16 GB    Yes     3.8   \n",
            "3     Infinix            Note 5      Ice Blue    4 GB   64 GB    Yes     4.2   \n",
            "4       Apple        iPhone 11          Black     4GB   64 GB    Yes     4.6   \n",
            "5      GIONEE              L800         Black    8 MB   16 MB    Yes     4.0   \n",
            "...       ...               ...           ...     ...     ...    ...     ...   \n",
            "3107  SAMSUNG  Galaxy S Duos 2          Black  512 MB    4 GB    Yes     4.0   \n",
            "3108  SAMSUNG         Metro 313         Black    4 MB    2 MB    Yes     4.2   \n",
            "3109     POCO         M4 Pro 5G     Cool Blue    6 GB  128 GB    Yes     4.4   \n",
            "3111    Apple        iPhone SE          White    2 GB  128 GB    Yes     4.5   \n",
            "3113   GIONEE               F9           Blue    3 GB   32 GB    Yes     4.2   \n",
            "\n",
            "      Selling Price  Original Price                    Mobile  Discount  \\\n",
            "0             19330           20999      SAMSUNG GALAXY M31S       1669   \n",
            "1             10199           10199                 Nokia 3.2         0   \n",
            "3             12999           12999            Infinix Note 5         0   \n",
            "4             49900           49900          Apple iPhone 11          0   \n",
            "5              2199            2199               GIONEE L800         0   \n",
            "...             ...             ...                       ...       ...   \n",
            "3107           5999            5999  SAMSUNG Galaxy S Duos 2          0   \n",
            "3108           2299            2299         SAMSUNG Metro 313         0   \n",
            "3109          16999           19999            POCO M4 Pro 5G      3000   \n",
            "3111          44900           44900          Apple iPhone SE          0   \n",
            "3113           7900            7900                GIONEE F9          0   \n",
            "\n",
            "      discount percentage  \n",
            "0                7.947998  \n",
            "1                0.000000  \n",
            "3                0.000000  \n",
            "4                0.000000  \n",
            "5                0.000000  \n",
            "...                   ...  \n",
            "3107             0.000000  \n",
            "3108             0.000000  \n",
            "3109            15.000750  \n",
            "3111             0.000000  \n",
            "3113             0.000000  \n",
            "\n",
            "[2897 rows x 12 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.duplicated())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oub1ZZlzBdjZ",
        "outputId": "5f056945-1667-4b1f-803c-fa2ee9cd1c7e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0       False\n",
            "1       False\n",
            "3       False\n",
            "4       False\n",
            "5       False\n",
            "        ...  \n",
            "3107    False\n",
            "3108    False\n",
            "3109    False\n",
            "3111     True\n",
            "3113    False\n",
            "Length: 2897, dtype: bool\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop_duplicates(inplace = True) # remove duplicates"
      ],
      "metadata": {
        "id": "3BRE2VJCBjrj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "df = pd.read_csv('/content/drive/MyDrive/Sales.csv')\n",
        "\n",
        "df.plot()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "10zQqIpxCq9U",
        "outputId": "3112a62e-6224-4a62-ea08-8be62a197bba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}