Data_cleaning_erp.ipynb

{
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   "cell_type": "code",
   "execution_count": 1,
   "id": "b72d8f3c-dbe3-4068-9544-f624eb972dba",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "import matplotlib.ticker as mticker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c8efc83c-5867-4171-a85b-77b69cfc31cc",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "erp4k = pd.read_csv('/home/sepal-user/CAFI_DDD/erp4k.csv', delimiter=';', encoding='latin-1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d5687e1e-630d-4391-ad5d-fbbac190bc3d",
   "metadata": {
    "tags": []
   },
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ï»¿53747</th>\n",
       "      <th>-7.23864009182039</th>\n",
       "      <th>7.52948470572773</th>\n",
       "      <th>0.0579206349206349</th>\n",
       "      <th>1</th>\n",
       "      <th>690</th>\n",
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       "      <th>40</th>\n",
       "      <th>Etablissement humain</th>\n",
       "      <th>...</th>\n",
       "      <th>4040.2</th>\n",
       "      <th>4040.3</th>\n",
       "      <th>Stable Non Forest</th>\n",
       "      <th>Stable Non Forest.1</th>\n",
       "      <th>Stable Non Forest.2</th>\n",
       "      <th>Stable Non Forest.3</th>\n",
       "      <th>NA</th>\n",
       "      <th>2</th>\n",
       "      <th>Doute pour CHG_20_21</th>\n",
       "      <th>FAUX</th>\n",
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       "      <td>1</td>\n",
       "      <td>2456.0</td>\n",
       "      <td>mesophile</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>Terres cultivees</td>\n",
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       "      <td>2121</td>\n",
       "      <td>2121</td>\n",
       "      <td>Stable Non Forest</td>\n",
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       "      <td>Stable Non Forest</td>\n",
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       "\n",
       "      690  mesophile  1.1  40 Etablissement humain  ... 4040.2  4040.3  \\\n",
       "0   326.0  mesophile    1  50  Terres gramineennes  ...   5050    5050   \n",
       "1  2456.0  mesophile    1  21     Terres cultivees  ...   2121    2121   \n",
       "\n",
       "   Stable Non Forest Stable Non Forest.1  Stable Non Forest.2  \\\n",
       "0  Stable Non Forest   Stable Non Forest    Stable Non Forest   \n",
       "1  Stable Non Forest   Stable Non Forest    Stable Non Forest   \n",
       "\n",
       "  Stable Non Forest.3   NA  2 Doute pour CHG_20_21  FAUX  \n",
       "0   Stable Non Forest  NaN  1                  NaN  FAUX  \n",
       "1   Stable Non Forest  NaN  2                  NaN  FAUX  \n",
       "\n",
       "[2 rows x 35 columns]"
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     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "erp4k.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7bd6edd1-64f4-4771-b7f9-1bbe9e1fe29f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4000"
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     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "len(pts4k)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "023c58dd-6d09-4fe7-b123-ea7db40545bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Freeze the id point_id into \n",
    "\n",
    "\n",
    "\n",
    "chg_yr_1520\n",
    "chg_1520\n",
    "ocs_2015\n",
    "ocs_2020\n",
    "point_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0ccdb28f-257f-4b0d-b76d-3f474b0bf556",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "point_id            float64\n",
       "LON                 float64\n",
       "LAT                 float64\n",
       "prob_chang          float64\n",
       "str_dal_ne            int64\n",
       "ID                  float64\n",
       "phyto                object\n",
       "str_dal_FA            int64\n",
       "INT1_2000_n1         object\n",
       "INT1_2000_n2         object\n",
       "INT1_2010_n1         object\n",
       "INT1_2010_n2         object\n",
       "INT1_2015_n1         object\n",
       "INT1_2015_n2         object\n",
       "INT1_2020_n1         object\n",
       "INT1_2020_n2         object\n",
       "INT1_2021_n1         object\n",
       "INT1_2021_n2         object\n",
       "CHG_00_10             int64\n",
       "CHG_10_15             int64\n",
       "CHG_15_20             int64\n",
       "CHG_20_21             int64\n",
       "Transition_00_10     object\n",
       "Transition_10_15     object\n",
       "chg_1520             object\n",
       "Transition_20_21     object\n",
       "Gain_Cohort          object\n",
       "IC                    int64\n",
       "COMMENT              object\n",
       "Wetlands               bool\n",
       "kmeans                int64\n",
       "chg_yr_1520         float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pts4k.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3271d78d-7026-439a-955e-09ecf84021a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create new columns for def_year and deg_year for each point_id using the column chg_yr_1520. def_year and deg_year should only be from 2015 to 2020\n",
    "\n",
    "# Function to set the year only if it is between 2015 and 2020\n",
    "def set_year_if_valid(year):\n",
    "    return year if 2015 <= year <= 2020 else None\n",
    "\n",
    "# Create the new columns based on the 'chg_yr_1520' column\n",
    "pts835['def_year'] = pts835['chg_yr_1520'].apply(set_year_if_valid)\n",
    "pts835['deg_year'] = pts835['chg_yr_1520'].apply(set_year_if_valid)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf36a0a5-e6a2-4606-ba07-14008c4f2bc7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Display the new columns along with the 'chg_yr_1520' column to verify the results\n",
    "pts4k[['point_id', 'chg_yr_1520', 'def_year', 'deg_year']].head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9a48f6d2-bc59-47b2-8335-524951ec76af",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "chg_yr_1520    Int64\n",
       "point_id       int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert 'chg_yr_1520' and 'point_id' columns to Int64\n",
    "pts835['chg_yr_1520'] = pts835['chg_yr_1520'].astype('Int64')\n",
    "pts835['deg_year2018'] = pts835['deg_year2018'].astype('Int64')\n",
    "pts835['def_year2019'] = pts835['def_year2019'].astype('Int64')\n",
    "pts835['deg_year2019'] = pts835['deg_year2019'].astype('Int64')\n",
    "pts835['def_year2020'] = pts835['def_year2020'].astype('Int64')\n",
    "pts835['deg_year2020'] = pts835['deg_year2020'].astype('Int64')\n",
    "   \n",
    "\n",
    "\n",
    "# Display the data types of the updated columns to verify the changes\n",
    "pts835[['chg_yr_1520', 'point_id']].dtypes\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2e72e537-9aea-4758-aa7f-45b701d25a9d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "point_id        int64\n",
       "fid             int64\n",
       "index           int64\n",
       "images          int64\n",
       "mon_images      int64\n",
       "                ...  \n",
       "deg_year2018    Int64\n",
       "def_year2019    Int64\n",
       "deg_year2019    Int64\n",
       "def_year2020    Int64\n",
       "deg_year2020    Int64\n",
       "Length: 180, dtype: object"
      ]
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     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
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    "pts835.dtypes"
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "70c38d5e-c8fa-4c41-8484-6006c3cfba7a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c288ff42-8413-466f-bf39-a0972a1900ae",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Define the columns to change data type\n",
    "new_type = ['def_year', 'deg_year', 'chg_yr_1520', 'point_id','kmeans' ]\n",
    "\n",
    "# change the data type to int64\n",
    "for column in new_type:\n",
    "    pts835[column] = pts835[column].astype('Int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c131eeb3-914a-48ca-8bec-76462ce4b3f0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Define the range of years to be considered\n",
    "years_range = range(2015, 2021)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5cd645eb-6a1c-4aca-94fc-3e51aebcc600",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Create new columns for def_year and deg_year\n",
    "for year in years_range:\n",
    "    pts835[f'def_year{year}'] = pts835['def_year'].apply(lambda y: 1 if str(year) in str(y).split(',') else 0)\n",
    "    pts835[f'deg_year{year}'] = pts835['deg_year'].apply(lambda y: 1 if str(year) in str(y).split(',') else 0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a95f886d-fe3d-431e-ab15-6bdaf594d036",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Create new columns for def_year and deg_year\n",
    "for year in years_range:\n",
    "    pts835[f'gain_year{year}'] = pts835['chg_yr_1520'].apply(lambda y: 1 if str(year) in str(y).split(',') else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "34158990-7937-4a65-b253-077525bad5d1",
   "metadata": {
    "tags": []
   },
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>point_id</th>\n",
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       "      <th>def_year2020</th>\n",
       "      <th>deg_year2020</th>\n",
       "      <th>CNC</th>\n",
       "      <th>gain_year2015</th>\n",
       "      <th>gain_year2016</th>\n",
       "      <th>gain_year2017</th>\n",
       "      <th>gain_year2018</th>\n",
       "      <th>gain_year2019</th>\n",
       "      <th>gain_year2020</th>\n",
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       "      <th>0</th>\n",
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       "      <td>81378</td>\n",
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       "      <td>27038</td>\n",
       "      <td>122</td>\n",
       "      <td>51</td>\n",
       "      <td>2019.290</td>\n",
       "      <td>555.863647</td>\n",
       "      <td>1.133991</td>\n",
       "      <td>2017.033</td>\n",
       "      <td>0.315303</td>\n",
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       "      <td>-1.000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>2020.937</td>\n",
       "      <td>0.384688</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>93956</td>\n",
       "      <td>35</td>\n",
       "      <td>26941</td>\n",
       "      <td>117</td>\n",
       "      <td>52</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2019.468</td>\n",
       "      <td>0.133678</td>\n",
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       "      <th>4</th>\n",
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       "      <td>36</td>\n",
       "      <td>25418</td>\n",
       "      <td>140</td>\n",
       "      <td>62</td>\n",
       "      <td>2016.315</td>\n",
       "      <td>-162.680954</td>\n",
       "      <td>0.369152</td>\n",
       "      <td>2018.066</td>\n",
       "      <td>0.593855</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 187 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   point_id  fid   index  images  mon_images  bfast_chan  bfast_magn  \\\n",
       "0    144804   16  141183     117          48      -1.000    0.000000   \n",
       "1     81378   33   27038     122          51    2019.290  555.863647   \n",
       "2     81984   34   26770     120          55      -1.000    0.000000   \n",
       "3     93956   35   26941     117          52      -1.000    0.000000   \n",
       "4    100390   36   25418     140          62    2016.315 -162.680954   \n",
       "\n",
       "   bfast_mean  cusum_chan  cusum_conf  ...  deg_year2019  def_year2020  \\\n",
       "0    0.000000    2018.110    0.016382  ...             0             0   \n",
       "1    1.133991    2017.033    0.315303  ...             0             0   \n",
       "2    0.000000    2020.937    0.384688  ...             0             0   \n",
       "3    0.000000    2019.468    0.133678  ...             0             0   \n",
       "4    0.369152    2018.066    0.593855  ...             0             0   \n",
       "\n",
       "   deg_year2020  CNC  gain_year2015  gain_year2016  gain_year2017  \\\n",
       "0             0    0              0              0              0   \n",
       "1             0    0              0              0              0   \n",
       "2             0    0              0              0              0   \n",
       "3             0    0              0              0              0   \n",
       "4             0    0              0              0              0   \n",
       "\n",
       "   gain_year2018  gain_year2019  gain_year2020  \n",
       "0              0              0              0  \n",
       "1              0              0              0  \n",
       "2              0              0              0  \n",
       "3              0              0              0  \n",
       "4              0              0              0  \n",
       "\n",
       "[5 rows x 187 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " pts835.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "98f22496-0b7a-4201-a732-5a7856517d75",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>kmeans</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>deg_year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "kmeans       0    1    2\n",
       "deg_year                \n",
       "2016       4.0  NaN  2.0\n",
       "2017       3.0  NaN  NaN\n",
       "2018       1.0  NaN  2.0\n",
       "2019       2.0  1.0  4.0\n",
       "2020      10.0  NaN  4.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "points_strata_deg = pd.pivot_table(pts835, values = 'point_id', index = 'deg_year',columns = 'kmeans', aggfunc =  \"count\")\n",
    "points_strata_deg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "948d00dd-f050-44ba-8193-7571800d06dc",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "points_strata_gain = pd.pivot_table(pts835, values = 'point_id', index = 'gain_year',columns = 'kmeans', aggfunc =  \"count\")\n",
    "points_strata_gain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5a4ce1ac-d7f9-4243-95a7-235fa807a70a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "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>kmeans</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chg_1520</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Deforestation</th>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Degradation</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gain</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stable</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "kmeans           0    1    2\n",
       "chg_1520                    \n",
       "Deforestation  8.0  1.0  3.0\n",
       "Degradation    NaN  NaN  3.0\n",
       "Gain           1.0  NaN  NaN\n",
       "Stable         NaN  7.0  NaN"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "points_strata_def = pd.pivot_table(pts835, values = 'point_id', index = 'chg_1520',columns = 'kmeans', aggfunc =  \"count\")\n",
    "points_strata_def"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a202e21-e8f3-4ced-8095-05f0eccd45bc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "15b9b311-93d8-40ee-b365-e8a5a6e629ea",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#select the values you want to plot\n",
    "values_to_plot = ['def_year2015', 'def_year2016','def_year2017','def_year2018','def_year2019','def_year2020']\n",
    "\n",
    "# Initialize a dictionary to hold the counts for each year\n",
    "def_value_counts = {year: (pts835[year] == 1).sum() for year in values_to_plot}\n",
    "\n",
    "# Convert to a Series for plotting\n",
    "def_value_counts_series = pd.Series(def_value_counts)\n",
    "\n",
    "# Plot the value counts as a bar graph\n",
    "plt.figure(figsize=(8, 6))  # Optional: Set the figure size\n",
    "def_value_counts_series.plot(kind='bar', color='skyblue')\n",
    "\n",
    "# Set plot labels and title\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Count')\n",
    "plt.title('# of deforestation points per year')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "45af1e8c-57e3-4147-b68d-c32489ce9cbd",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#select the values you want to plot\n",
    "values_to_plot = ['deg_year2015', 'deg_year2016','deg_year2017','deg_year2018','deg_year2019','deg_year2020']\n",
    "\n",
    "# Initialize a dictionary to hold the counts for each year\n",
    "def_value_counts = {year: (pts835[year] == 1).sum() for year in values_to_plot}\n",
    "\n",
    "# Convert to a Series for plotting\n",
    "def_value_counts_series = pd.Series(def_value_counts)\n",
    "\n",
    "# Plot the value counts as a bar graph\n",
    "plt.figure(figsize=(8, 6))  # Optional: Set the figure size\n",
    "def_value_counts_series.plot(kind='bar', color='skyblue')\n",
    "\n",
    "# Set plot labels and title\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Count')\n",
    "plt.title('# of degradation points per year')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "21aaf1bc-d802-4311-bc76-8d37fb7e920b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "pts835['CNC'] = pts835['chg_1520'].apply(lambda x: 1 if x in ['Gain', 'Degradation', 'Deforestation'] else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "5e3a0065-5308-4bb0-bb5e-700c8af9971e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Save the updated DataFrame to a new CSV file\n",
    "pts835.to_csv('/home/sepal-user/CAFI_DDD/835_test.csv', index=False, sep=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aab3fcc7-05e5-4a69-89be-adaa5bf221aa",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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