JPX_EDA.ipynb

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      "name": "JPX_EDA.ipynb",
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        "id": "ZaKq0ZcBzV30"
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      "source": [
        "!mv kaggle.json /root/.kaggle/\n",
        "!kaggle competitions download -c jpx-tokyo-stock-exchange-prediction\n",
        "!unzip ./jpx-tokyo-stock-exchange-prediction.zip -d jpx-tokyo-stock-exchange-prediction\n",
        "!rm -r sample_data"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "BSKx1Z2DZhz6"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dframe = pd.read_csv('jpx-tokyo-stock-exchange-prediction/train_files/stock_prices.csv', parse_dates=['Date'])\n",
        "dframe.info()"
      ],
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        "outputId": "62816456-4c15-4dc6-9b14-f542556a2725"
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      "execution_count": 38,
      "outputs": [
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2332531 entries, 0 to 2332530\n",
            "Data columns (total 12 columns):\n",
            " #   Column            Dtype         \n",
            "---  ------            -----         \n",
            " 0   RowId             object        \n",
            " 1   Date              datetime64[ns]\n",
            " 2   SecuritiesCode    int64         \n",
            " 3   Open              float64       \n",
            " 4   High              float64       \n",
            " 5   Low               float64       \n",
            " 6   Close             float64       \n",
            " 7   Volume            int64         \n",
            " 8   AdjustmentFactor  float64       \n",
            " 9   ExpectedDividend  float64       \n",
            " 10  SupervisionFlag   bool          \n",
            " 11  Target            float64       \n",
            "dtypes: bool(1), datetime64[ns](1), float64(7), int64(2), object(1)\n",
            "memory usage: 198.0+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Quite a few nulls, note 238 missing targets\n",
        "dframe.isnull().sum()"
      ],
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        "outputId": "5871aa4b-2805-4ef1-d1eb-9395c441e10f"
      },
      "execution_count": 10,
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          "data": {
            "text/plain": [
              "RowId                     0\n",
              "Date                      0\n",
              "SecuritiesCode            0\n",
              "Open                   7608\n",
              "High                   7608\n",
              "Low                    7608\n",
              "Close                  7608\n",
              "Volume                    0\n",
              "AdjustmentFactor          0\n",
              "ExpectedDividend    2313666\n",
              "SupervisionFlag           0\n",
              "Target                  238\n",
              "dtype: int64"
            ]
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    {
      "cell_type": "code",
      "source": [
        "#the 0 volume entries correspond to the missing dates\n",
        "(dframe['Close'].isnull() == (dframe['Volume'] == 0)).sum() #same as len(dframe)"
      ],
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      "cell_type": "code",
      "source": [
        "#yet some of them have a target\n",
        "dframe[dframe['Close'].isnull()]"
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              "                 RowId       Date  SecuritiesCode  Open  High  Low  Close  \\\n",
              "401      20170104_3540 2017-01-04            3540   NaN   NaN  NaN    NaN   \n",
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              "2332462  20211203_9733 2021-12-03            9733   NaN   NaN  NaN    NaN   \n",
              "\n",
              "         Volume  AdjustmentFactor  ExpectedDividend  SupervisionFlag    Target  \n",
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "dframe['SecuritiesCode'].value_counts().sort_values()"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "jbyoFZvbbjx6",
        "outputId": "919c8c9f-4057-4450-aff6-df8a7007236f"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4169     232\n",
              "7342     233\n",
              "4168     233\n",
              "7358     234\n",
              "4167     235\n",
              "        ... \n",
              "9613    1202\n",
              "9616    1202\n",
              "9619    1202\n",
              "9663    1202\n",
              "2780    1202\n",
              "Name: SecuritiesCode, Length: 2000, dtype: int64"
            ]
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          "metadata": {},
          "execution_count": 29
        }
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    },
    {
      "cell_type": "code",
      "source": [
        "#Grouping by Close values\n",
        "dframe.pivot(columns='SecuritiesCode', index='Date', values='Close')"
      ],
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      "execution_count": 31,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SecuritiesCode    1301   1332    1333    1375    1376    1377    1379    1381  \\\n",
              "Date                                                                            \n",
              "2017-01-04      2742.0  571.0  3210.0     NaN  1550.0  3330.0  2143.0  1960.0   \n",
              "2017-01-05      2738.0  568.0  3250.0     NaN  1538.0  3305.0  2128.0  1930.0   \n",
              "2017-01-06      2740.0  575.0  3270.0     NaN  1555.0  3315.0  2139.0  1912.0   \n",
              "2017-01-10      2748.0  562.0  3235.0     NaN  1563.0  3330.0  2120.0  1950.0   \n",
              "2017-01-11      2745.0  553.0  3185.0     NaN  1563.0  3220.0  2109.0  1935.0   \n",
              "...                ...    ...     ...     ...     ...     ...     ...     ...   \n",
              "2021-11-29      2951.0  567.0  2269.0  1248.0  1381.0  3125.0  1889.0  3160.0   \n",
              "2021-11-30      2900.0  573.0  2277.0  1217.0  1348.0  3125.0  1842.0  3135.0   \n",
              "2021-12-01      2911.0  574.0  2299.0  1231.0  1353.0  3135.0  1889.0  3185.0   \n",
              "2021-12-02      2933.0  573.0  2303.0  1212.0  1327.0  3110.0  1867.0  3150.0   \n",
              "2021-12-03      2982.0  585.0  2358.0  1235.0  1332.0  3170.0  1891.0  3200.0   \n",
              "\n",
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              "2017-01-11       857.0     NaN  ...   471.0  38560.0  8495.0  3885.0  8590.0   \n",
              "...                ...     ...  ...     ...      ...     ...     ...     ...   \n",
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              "2021-12-01      6290.0  2123.0  ...  1722.0  67080.0  5900.0  3090.0  2937.0   \n",
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              "2021-12-03      6190.0  2197.0  ...  1746.0  66650.0  5559.0  3175.0  3015.0   \n",
              "\n",
              "SecuritiesCode    9990    9991    9993    9994   9997  \n",
              "Date                                                   \n",
              "2017-01-04      1223.0  1129.0  1773.0  1754.0  778.0  \n",
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              "2021-12-01       522.0   770.0  1671.0  2340.0  685.0  \n",
              "2021-12-02       507.0   778.0  1650.0  2362.0  684.0  \n",
              "2021-12-03       528.0   794.0  1645.0  2389.0  696.0  \n",
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              "      <td>465.0</td>\n",
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              "      <td>1555.0</td>\n",
              "      <td>3315.0</td>\n",
              "      <td>2139.0</td>\n",
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              "      <td>882.0</td>\n",
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              "      <td>473.0</td>\n",
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              "      <th>2017-01-10</th>\n",
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              "      <td>1563.0</td>\n",
              "      <td>3330.0</td>\n",
              "      <td>2120.0</td>\n",
              "      <td>1950.0</td>\n",
              "      <td>874.0</td>\n",
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              "      <td>471.0</td>\n",
              "      <td>38690.0</td>\n",
              "      <td>8423.0</td>\n",
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              "      <th>2021-11-29</th>\n",
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              "      <th>2021-11-30</th>\n",
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              "      <td>3085.0</td>\n",
              "      <td>2989.0</td>\n",
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              "      <td>768.0</td>\n",
              "      <td>1680.0</td>\n",
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              "      <td>1231.0</td>\n",
              "      <td>1353.0</td>\n",
              "      <td>3135.0</td>\n",
              "      <td>1889.0</td>\n",
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              "      <td>2123.0</td>\n",
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              "      <td>1722.0</td>\n",
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              "      <td>5900.0</td>\n",
              "      <td>3090.0</td>\n",
              "      <td>2937.0</td>\n",
              "      <td>522.0</td>\n",
              "      <td>770.0</td>\n",
              "      <td>1671.0</td>\n",
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              "      <td>1212.0</td>\n",
              "      <td>1327.0</td>\n",
              "      <td>3110.0</td>\n",
              "      <td>1867.0</td>\n",
              "      <td>3150.0</td>\n",
              "      <td>6070.0</td>\n",
              "      <td>2112.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1680.0</td>\n",
              "      <td>67140.0</td>\n",
              "      <td>5599.0</td>\n",
              "      <td>3105.0</td>\n",
              "      <td>2947.0</td>\n",
              "      <td>507.0</td>\n",
              "      <td>778.0</td>\n",
              "      <td>1650.0</td>\n",
              "      <td>2362.0</td>\n",
              "      <td>684.0</td>\n",
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              "      <th>2021-12-03</th>\n",
              "      <td>2982.0</td>\n",
              "      <td>585.0</td>\n",
              "      <td>2358.0</td>\n",
              "      <td>1235.0</td>\n",
              "      <td>1332.0</td>\n",
              "      <td>3170.0</td>\n",
              "      <td>1891.0</td>\n",
              "      <td>3200.0</td>\n",
              "      <td>6190.0</td>\n",
              "      <td>2197.0</td>\n",
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              "      <td>66650.0</td>\n",
              "      <td>5559.0</td>\n",
              "      <td>3175.0</td>\n",
              "      <td>3015.0</td>\n",
              "      <td>528.0</td>\n",
              "      <td>794.0</td>\n",
              "      <td>1645.0</td>\n",
              "      <td>2389.0</td>\n",
              "      <td>696.0</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Some quick plots\n",
        "\n",
        "#One of the lowest value_counts \n",
        "stock_1 = dframe[dframe['SecuritiesCode'] == 4169]\n",
        "stock_1.plot('Date', 'Volume', kind='line')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "id": "U7AhJe2eJD5j",
        "outputId": "2f382512-3cf8-4415-952e-998221bbe0f5"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2d6759d750>"
            ]
          },
          "metadata": {},
          "execution_count": 50
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#One of the Hhghest value_counts \n",
        "stock_1 = dframe[dframe['SecuritiesCode'] == 2780]\n",
        "stock_1.plot('Date', 'Volume', kind='line')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "BZtTVMnXn7wu",
        "outputId": "4b9e5781-b74a-44d2-dc13-9d99e8f255ae"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2d6707b850>"
            ]
          },
          "metadata": {},
          "execution_count": 51
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "mqU7tn-TMIFm"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}