stock_analysis.ipynb

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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data=pd.read_csv('/content/sample_data/train.csv')"
      ],
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      "outputs": []
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              "   Row ID        Order ID  Order Date   Ship Date       Ship Mode Customer ID  \\\n",
              "0       1  CA-2017-152156  08/11/2017  11/11/2017    Second Class    CG-12520   \n",
              "1       2  CA-2017-152156  08/11/2017  11/11/2017    Second Class    CG-12520   \n",
              "2       3  CA-2017-138688  12/06/2017  16/06/2017    Second Class    DV-13045   \n",
              "3       4  US-2016-108966  11/10/2016  18/10/2016  Standard Class    SO-20335   \n",
              "4       5  US-2016-108966  11/10/2016  18/10/2016  Standard Class    SO-20335   \n",
              "\n",
              "     Customer Name    Segment        Country             City       State  \\\n",
              "0      Claire Gute   Consumer  United States        Henderson    Kentucky   \n",
              "1      Claire Gute   Consumer  United States        Henderson    Kentucky   \n",
              "2  Darrin Van Huff  Corporate  United States      Los Angeles  California   \n",
              "3   Sean O'Donnell   Consumer  United States  Fort Lauderdale     Florida   \n",
              "4   Sean O'Donnell   Consumer  United States  Fort Lauderdale     Florida   \n",
              "\n",
              "   Postal Code Region       Product ID         Category Sub-Category  \\\n",
              "0      42420.0  South  FUR-BO-10001798        Furniture    Bookcases   \n",
              "1      42420.0  South  FUR-CH-10000454        Furniture       Chairs   \n",
              "2      90036.0   West  OFF-LA-10000240  Office Supplies       Labels   \n",
              "3      33311.0  South  FUR-TA-10000577        Furniture       Tables   \n",
              "4      33311.0  South  OFF-ST-10000760  Office Supplies      Storage   \n",
              "\n",
              "                                        Product Name     Sales  \n",
              "0                  Bush Somerset Collection Bookcase  261.9600  \n",
              "1  Hon Deluxe Fabric Upholstered Stacking Chairs,...  731.9400  \n",
              "2  Self-Adhesive Address Labels for Typewriters b...   14.6200  \n",
              "3      Bretford CR4500 Series Slim Rectangular Table  957.5775  \n",
              "4                     Eldon Fold 'N Roll Cart System   22.3680  "
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              "      <td>08/11/2017</td>\n",
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              "      <td>Second Class</td>\n",
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              "      <td>Claire Gute</td>\n",
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              "      <td>CA-2017-138688</td>\n",
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              "      <td>16/06/2017</td>\n",
              "      <td>Second Class</td>\n",
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              "      <td>14.6200</td>\n",
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              "      <td>US-2016-108966</td>\n",
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              "      <td>Sean O'Donnell</td>\n",
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              "      <td>Eldon Fold 'N Roll Cart System</td>\n",
              "      <td>22.3680</td>\n",
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\"20/04/2016\",\n          \"20/05/2017\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ship Date\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1326,\n        \"samples\": [\n          \"26/08/2015\",\n          \"14/12/2015\",\n          \"19/04/2018\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ship Mode\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Standard Class\",\n          \"Same Day\",\n          \"Second Class\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Customer ID\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 793,\n        \"samples\": [\n          \"DJ-13510\",\n          \"MD-17350\",\n          \"NF-18475\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Customer Name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 793,\n        \"samples\": [\n          \"Don Jones\",\n          \"Maribeth Dona\",\n          \"Neil Franz\\u00f6sisch\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Segment\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Consumer\",\n          \"Corporate\",\n          \"Home Office\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"United States\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 529,\n        \"samples\": [\n          \"Burlington\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"State\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 49,\n        \"samples\": [\n          \"Delaware\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Postal Code\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 32041.223412812957,\n        \"min\": 1040.0,\n        \"max\": 99301.0,\n        \"num_unique_values\": 626,\n        \"samples\": [\n          56301.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Region\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"West\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Product ID\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1861,\n        \"samples\": [\n          \"TEC-PH-10001580\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Category\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Furniture\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sub-Category\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 17,\n        \"samples\": [\n          \"Bookcases\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Product Name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1849,\n        \"samples\": [\n          \"Imation\\u00a032GB Pocket Pro USB 3.0\\u00a0Flash Drive\\u00a0- 32 GB - Black - 1 P ...\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sales\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 626.6518748388042,\n        \"min\": 0.444,\n        \"max\": 22638.48,\n        \"num_unique_values\": 5757,\n        \"samples\": [\n          35.34\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "44D3aof_sm6N",
        "outputId": "3ed7d9c7-e62b-4ce7-df81-61f2ea72d3c7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 9800 entries, 0 to 9799\n",
            "Data columns (total 18 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   Row ID         9800 non-null   int64  \n",
            " 1   Order ID       9800 non-null   object \n",
            " 2   Order Date     9800 non-null   object \n",
            " 3   Ship Date      9800 non-null   object \n",
            " 4   Ship Mode      9800 non-null   object \n",
            " 5   Customer ID    9800 non-null   object \n",
            " 6   Customer Name  9800 non-null   object \n",
            " 7   Segment        9800 non-null   object \n",
            " 8   Country        9800 non-null   object \n",
            " 9   City           9800 non-null   object \n",
            " 10  State          9800 non-null   object \n",
            " 11  Postal Code    9789 non-null   float64\n",
            " 12  Region         9800 non-null   object \n",
            " 13  Product ID     9800 non-null   object \n",
            " 14  Category       9800 non-null   object \n",
            " 15  Sub-Category   9800 non-null   object \n",
            " 16  Product Name   9800 non-null   object \n",
            " 17  Sales          9800 non-null   float64\n",
            "dtypes: float64(2), int64(1), object(15)\n",
            "memory usage: 1.3+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pHyolWTHs3uV",
        "outputId": "f09cd1e1-6caa-4d3f-c266-e61de7962e9d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(9800, 18)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aoYV8urZtCfF",
        "outputId": "d63ddceb-0926-4c0f-c420-a5613cf6d33d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#filling  0 to the empty spaces\n",
        "data['Postal Code'].fillna(0,inplace=True)\n",
        "#changing from float to integer\n",
        "data['Postal Code']=data['Postal Code'].astype(int)\n",
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EebzlfMCtHI2",
        "outputId": "4de11ba5-8caf-4bdf-be35-c90676584676"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 9800 entries, 0 to 9799\n",
            "Data columns (total 18 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   Row ID         9800 non-null   int64  \n",
            " 1   Order ID       9800 non-null   object \n",
            " 2   Order Date     9800 non-null   object \n",
            " 3   Ship Date      9800 non-null   object \n",
            " 4   Ship Mode      9800 non-null   object \n",
            " 5   Customer ID    9800 non-null   object \n",
            " 6   Customer Name  9800 non-null   object \n",
            " 7   Segment        9800 non-null   object \n",
            " 8   Country        9800 non-null   object \n",
            " 9   City           9800 non-null   object \n",
            " 10  State          9800 non-null   object \n",
            " 11  Postal Code    9800 non-null   int64  \n",
            " 12  Region         9800 non-null   object \n",
            " 13  Product ID     9800 non-null   object \n",
            " 14  Category       9800 non-null   object \n",
            " 15  Sub-Category   9800 non-null   object \n",
            " 16  Product Name   9800 non-null   object \n",
            " 17  Sales          9800 non-null   float64\n",
            "dtypes: float64(1), int64(2), object(15)\n",
            "memory usage: 1.3+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "1owUeOhbtnVS",
        "outputId": "0557009e-2af3-4f56-ca81-2b56068c2bad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Row ID   Postal Code         Sales\n",
              "count  9800.000000   9800.000000   9800.000000\n",
              "mean   4900.500000  55211.280918    230.769059\n",
              "std    2829.160653  32076.677954    626.651875\n",
              "min       1.000000      0.000000      0.444000\n",
              "25%    2450.750000  23223.000000     17.248000\n",
              "50%    4900.500000  57551.000000     54.490000\n",
              "75%    7350.250000  90008.000000    210.605000\n",
              "max    9800.000000  99301.000000  22638.480000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0949cfcf-fc65-47f2-b0c6-5369bc15ebaa\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
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              "        vertical-align: middle;\n",
              "    }\n",
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              "      <th></th>\n",
              "      <th>Row ID</th>\n",
              "      <th>Postal Code</th>\n",
              "      <th>Sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>9800.000000</td>\n",
              "      <td>9800.000000</td>\n",
              "      <td>9800.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>4900.500000</td>\n",
              "      <td>55211.280918</td>\n",
              "      <td>230.769059</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2829.160653</td>\n",
              "      <td>32076.677954</td>\n",
              "      <td>626.651875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.444000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2450.750000</td>\n",
              "      <td>23223.000000</td>\n",
              "      <td>17.248000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>4900.500000</td>\n",
              "      <td>57551.000000</td>\n",
              "      <td>54.490000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>7350.250000</td>\n",
              "      <td>90008.000000</td>\n",
              "      <td>210.605000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
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              "      <td>99301.000000</td>\n",
              "      <td>22638.480000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "</div>\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "  }\n",
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              "      border-color: transparent;\n",
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              "  }\n",
              "</style>\n",
              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Row ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3531.662618997046,\n        \"min\": 1.0,\n        \"max\": 9800.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          9800.0,\n          4900.5,\n          7350.25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Postal Code\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 36124.7415696294,\n        \"min\": 0.0,\n        \"max\": 99301.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          55211.280918367345,\n          57551.0,\n          9800.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sales\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8177.4368105571575,\n        \"min\": 0.444,\n        \"max\": 22638.48,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          230.7690594591837,\n          54.489999999999995,\n          9800.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ***DATA CLEANING***"
      ],
      "metadata": {
        "id": "lkRQpvCrv4CQ"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ZHxACHs3v1pU"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}