Cleaning_locust_hub_file.ipynb

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        ""
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          "ename": "SyntaxError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-63-268ff6b7ae3e>\"\u001b[0;36m, line \u001b[0;32m3\u001b[0m\n\u001b[0;31m    jupyter labextension install @jupyter-widgets/jupyterlab-manager@2\u001b[0m\n\u001b[0m                       ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
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    {
      "cell_type": "code",
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        "id": "UcvEKb09puMD"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "fn = pd.read_csv('/content/drive/MyDrive/final_points.csv')\n",
        "\n",
        "d_moist = pd.read_csv('/content/drive/MyDrive/moisture_data.csv')\n",
        "\n",
        "d_temp = pd.read_csv('/content/drive/MyDrive/temprature_data.csv')\n",
        "\n",
        "d_prep = pd.read_csv('/content/drive/MyDrive/precipitation_data.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "id": "FVwgILTwqnnZ",
        "outputId": "f1f11a01-3dee-40d1-f481-652d423bbc79"
      },
      "source": [
        "fn.head()"
      ],
      "execution_count": null,
      "outputs": [
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              "      <td>4</td>\n",
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              "   Unnamed: 0          X          Y  Label\n",
              "0           0  38.483333  27.633333    1.0\n",
              "1           1  38.200000  18.050000    1.0\n",
              "2           2  38.283333  18.183333    1.0\n",
              "3           3  38.350000  17.916667    1.0\n",
              "4           4  38.450000  17.816667    1.0"
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        "id": "1E1ZddCurNpk",
        "outputId": "e60c5cb7-0d0a-4401-fa37-daf78c119e80"
      },
      "source": [
        "fn.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1845.000000</td>\n",
              "      <td>1845.000000</td>\n",
              "      <td>1845.000000</td>\n",
              "      <td>1844.000000</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>460.750136</td>\n",
              "      <td>38.654415</td>\n",
              "      <td>17.660239</td>\n",
              "      <td>0.500000</td>\n",
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              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>266.375088</td>\n",
              "      <td>0.964445</td>\n",
              "      <td>3.525348</td>\n",
              "      <td>0.500136</td>\n",
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              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
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              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>230.000000</td>\n",
              "      <td>38.381667</td>\n",
              "      <td>15.381111</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>461.000000</td>\n",
              "      <td>38.681667</td>\n",
              "      <td>17.680000</td>\n",
              "      <td>0.500000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>691.000000</td>\n",
              "      <td>39.000000</td>\n",
              "      <td>18.166667</td>\n",
              "      <td>1.000000</td>\n",
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              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>922.000000</td>\n",
              "      <td>39.231111</td>\n",
              "      <td>29.867000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
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              "        Unnamed: 0            X            Y        Label\n",
              "count  1845.000000  1845.000000  1845.000000  1844.000000\n",
              "mean    460.750136    38.654415    17.660239     0.500000\n",
              "std     266.375088     0.964445     3.525348     0.500136\n",
              "min       0.000000     0.000000     1.000000     0.000000\n",
              "25%     230.000000    38.381667    15.381111     0.000000\n",
              "50%     461.000000    38.681667    17.680000     0.500000\n",
              "75%     691.000000    39.000000    18.166667     1.000000\n",
              "max     922.000000    39.231111    29.867000     1.000000"
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      "source": [
        "fn = fn.drop(labels=[1844], axis=0)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
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          "height": 297
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      "source": [
        "fn.describe()"
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          "output_type": "execute_result",
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              "      <td>1844.000000</td>\n",
              "      <td>1844.000000</td>\n",
              "      <td>1844.000000</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>460.500000</td>\n",
              "      <td>38.654586</td>\n",
              "      <td>17.660108</td>\n",
              "      <td>0.500000</td>\n",
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              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>266.230516</td>\n",
              "      <td>0.964679</td>\n",
              "      <td>3.526300</td>\n",
              "      <td>0.500136</td>\n",
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              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>230.000000</td>\n",
              "      <td>38.381667</td>\n",
              "      <td>15.380556</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>460.500000</td>\n",
              "      <td>38.684167</td>\n",
              "      <td>17.680000</td>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>691.000000</td>\n",
              "      <td>39.000000</td>\n",
              "      <td>18.166667</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>921.000000</td>\n",
              "      <td>39.231111</td>\n",
              "      <td>29.867000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Unnamed: 0            X            Y        Label\n",
              "count  1844.000000  1844.000000  1844.000000  1844.000000\n",
              "mean    460.500000    38.654586    17.660108     0.500000\n",
              "std     266.230516     0.964679     3.526300     0.500136\n",
              "min       0.000000     0.000000     1.000000     0.000000\n",
              "25%     230.000000    38.381667    15.380556     0.000000\n",
              "50%     460.500000    38.684167    17.680000     0.500000\n",
              "75%     691.000000    39.000000    18.166667     1.000000\n",
              "max     921.000000    39.231111    29.867000     1.000000"
            ]
          },
          "metadata": {
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          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "TzHplb7VuWw2",
        "outputId": "815122e8-988d-4e97-e7a7-56c88b11e0da"
      },
      "source": [
        "\n",
        "\n",
        "fn = fn.astype({'Label' : str})\n",
        "\n",
        "fn.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
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              "      <td>18.050000</td>\n",
              "      <td>1.0</td>\n",
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              "      <th>2</th>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>38.450000</td>\n",
              "      <td>17.816667</td>\n",
              "      <td>1.0</td>\n",
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            ],
            "text/plain": [
              "   Unnamed: 0          X          Y Label\n",
              "0           0  38.483333  27.633333   1.0\n",
              "1           1  38.200000  18.050000   1.0\n",
              "2           2  38.283333  18.183333   1.0\n",
              "3           3  38.350000  17.916667   1.0\n",
              "4           4  38.450000  17.816667   1.0"
            ]
          },
          "metadata": {
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          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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          "height": 204
        },
        "id": "rsK1vSUOUaQ8",
        "outputId": "ac77ab7d-08e6-453f-86a6-181fd9529939"
      },
      "source": [
        "fn = fn.rename(columns={'X': 'lat', 'Y' : 'lon'})\n",
        "\n",
        "fn.head()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>1</th>\n",
              "      <td>1</td>\n",
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              "      <th>3</th>\n",
              "      <td>3</td>\n",
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              "      <th>4</th>\n",
              "      <td>4</td>\n",
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            ],
            "text/plain": [
              "   Unnamed: 0        lat        lon Label\n",
              "0           0  38.483333  27.633333   1.0\n",
              "1           1  38.200000  18.050000   1.0\n",
              "2           2  38.283333  18.183333   1.0\n",
              "3           3  38.350000  17.916667   1.0\n",
              "4           4  38.450000  17.816667   1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kqCovguEulte"
      },
      "source": [
        "d_moist = pd.read_csv('/content/drive/MyDrive/moisture_data.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4OXn9tSn8qS8"
      },
      "source": [
        "d_temp = pd.read_csv('/content/drive/MyDrive/temprature_data.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lvuWz0oO9c09"
      },
      "source": [
        "d_prep = pd.read_csv('/content/drive/MyDrive/precipitation_data.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "9HoPss6lFhaR",
        "outputId": "241fbd24-b9dc-4a9b-b35e-b47f1cc89295"
      },
      "source": [
        "d_moist.head()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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>Unnamed: 0</th>\n",
              "      <th>lat</th>\n",
              "      <th>lon</th>\n",
              "      <th>soilw</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>35328</td>\n",
              "      <td>38.75</td>\n",
              "      <td>8.25</td>\n",
              "      <td>394.362520</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>35329</td>\n",
              "      <td>38.75</td>\n",
              "      <td>8.75</td>\n",
              "      <td>216.888950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>35330</td>\n",
              "      <td>38.75</td>\n",
              "      <td>14.75</td>\n",
              "      <td>61.184864</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>35331</td>\n",
              "      <td>38.75</td>\n",
              "      <td>15.25</td>\n",
              "      <td>58.990517</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>35332</td>\n",
              "      <td>38.75</td>\n",
              "      <td>15.75</td>\n",
              "      <td>574.778200</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0    lat    lon       soilw\n",
              "0       35328  38.75   8.25  394.362520\n",
              "1       35329  38.75   8.75  216.888950\n",
              "2       35330  38.75  14.75   61.184864\n",
              "3       35331  38.75  15.25   58.990517\n",
              "4       35332  38.75  15.75  574.778200"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y38W5G1YINFI",
        "outputId": "3bac3761-75f2-446c-a77b-95178902ff6c"
      },
      "source": [
        "main_df = pd.DataFrame(columns=['lat','lon','soilw','Label'])\n",
        "main_df\n",
        "count = 0\n",
        "for idx, fn_row in fn.iterrows():\n",
        "  for idx, d_moist_row in d_moist.iterrows():\n",
        "    if abs(d_moist_row['lat'] - fn_row['lat']) < 1e-4 and abs(d_moist_row['lon'] - fn_row['lon']) < 1e-4:\n",
        "      print(count)\n",
        "      count = count + 1\n",
        "      main_df.loc[len(main_df.index)] = [fn_row['lat'], fn_row['lon'], d_moist_row['soilw'], fn_row['Label'] ]\n",
        "         \n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "ei21f-MLReQT",
        "outputId": "7ed09f92-8789-44cd-c82e-124b98241afe"
      },
      "source": [
        "\n",
        "main_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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>lat</th>\n",
              "      <th>lon</th>\n",
              "      <th>soilw</th>\n",
              "      <th>Label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>38.75</td>\n",
              "      <td>23.75</td>\n",
              "      <td>215.12340</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>38.75</td>\n",
              "      <td>23.75</td>\n",
              "      <td>234.46460</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>38.75</td>\n",
              "      <td>23.75</td>\n",
              "      <td>250.24620</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>38.75</td>\n",
              "      <td>23.75</td>\n",
              "      <td>252.09668</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>38.75</td>\n",
              "      <td>23.75</td>\n",
              "      <td>228.53566</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     lat    lon      soilw Label\n",
              "0  38.75  23.75  215.12340   1.0\n",
              "1  38.75  23.75  234.46460   1.0\n",
              "2  38.75  23.75  250.24620   1.0\n",
              "3  38.75  23.75  252.09668   1.0\n",
              "4  38.75  23.75  228.53566   1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dVQmGlqrIQL4"
      },
      "source": [
        "main_df.to_csv('/content/drive/MyDrive/data.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b_fn5vSETcfy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1c55a5bd-4b46-4072-de97-b70210f3efda"
      },
      "source": [
        "main_df = pd.DataFrame(columns=['lat','lon','soilw','Label'])\n",
        "main_df\n",
        "count = 0\n",
        "for idx, fn_row in fn.iterrows():\n",
        "  for idx, d_moist_row in d_temp.iterrows():\n",
        "    if abs(d_moist_row['lat'] - fn_row['lat']) < 1e-6 and abs(d_moist_row['lon'] - fn_row['lon']) < 1e-6:\n",
        "      print(count)\n",
        "      count = count + 1\n",
        "      main_df.loc[len(main_df.index)] = [fn_row['lat'], fn_row['lat'], d_moist_row['soilw'], fn_row['Label'] ]\n",
        "         "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-e31e9605be26>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmain_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'lat'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'lon'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'soilw'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Label'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mmain_df\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mcount\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn_row\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miterrows\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m   \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md_moist_row\u001b[0m \u001b[0;32min\u001b[0m \u001b[0md_temp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miterrows\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'pd' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HoCOhLdeT2ch"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rg4mPeX5tgH1"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a2GLsZSevTg0"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}