2_PedonDataStudy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "7ae93601-4ff6-45e1-9dd4-0c72f00cd9eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the Packages\n",
    "\n",
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "from shapely.geometry import Point\n",
    "from keplergl import KeplerGl\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "48af4107-9acf-44f7-82b1-ca410154c58f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reading the Data\n",
    "\n",
    "pedons_df = pd.read_csv(r'data\\RaCA_SOC_pedons.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "4393044b-ec2c-49e6-91a8-3d8da79e56c0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Making the List of Columns to Understand the Unique Identifier for both the datasets (Will be helpful for merging as well)\n",
    "\n",
    "identifiers_pedons = ['upedonid', 'rcasiteid', 'upedon']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "12f6d876-0b5d-40f3-a042-3bc7f28f71b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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></th>\n",
       "      <th>upedonid</th>\n",
       "      <th>rcasiteid</th>\n",
       "      <th>upedon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>C0101F01-1</td>\n",
       "      <td>C0101F01</td>\n",
       "      <td>C0101F011</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      upedonid rcasiteid     upedon\n",
       "43  C0101F01-1  C0101F01  C0101F011"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pedons_df.loc[pedons_df['upedon'] == 'C0101F011'][identifiers_pedons]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fbddaa6-32fc-44c3-b04d-68a2ebc104c2",
   "metadata": {},
   "source": [
    "* `upedon` - Unique RaCA pedon ID contains the site and pedon within the site\n",
    "* `upedonid` - This column has lots of typos, sometimes a hyphen '-' is used sometimes an underscore '_' \n",
    "* `rcasiteid` - This column is needed for the Mapping with the General Location Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "805a722f-c69b-41c5-87ea-d74a4fa38c3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "pedons_df.drop(columns = ['upedonid'], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6657a0b3-64c2-446c-a165-a7afc6b42b38",
   "metadata": {},
   "source": [
    "\n",
    "## Studying Pedons Dataset (Taking only the Columns to be focused on)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "8fb985fc-40f4-48d6-8ce7-c8f6f35dcdbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Taking the list of columns which are needed for data analysis - Along with the Identifiers. \n",
    "\n",
    "pedons_columns_needed = ['upedon', 'rcasiteid','SOCstock5','SOCstock30', 'SOCstock100']\n",
    "pedons_df = pedons_df[pedons_columns_needed]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "1c8afa87-cfab-460a-9b50-13505f97d33d",
   "metadata": {},
   "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></th>\n",
       "      <th>upedon</th>\n",
       "      <th>rcasiteid</th>\n",
       "      <th>SOCstock5</th>\n",
       "      <th>SOCstock30</th>\n",
       "      <th>SOCstock100</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A0108P921</td>\n",
       "      <td>A0108P92</td>\n",
       "      <td>15.126360</td>\n",
       "      <td>76.022073</td>\n",
       "      <td>100.113941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A0116P901</td>\n",
       "      <td>A0116P90</td>\n",
       "      <td>35.481720</td>\n",
       "      <td>95.965557</td>\n",
       "      <td>242.002573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A0116P941</td>\n",
       "      <td>A0116P94</td>\n",
       "      <td>36.827055</td>\n",
       "      <td>104.147733</td>\n",
       "      <td>293.079581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A0116X971</td>\n",
       "      <td>A0116X97</td>\n",
       "      <td>6.950620</td>\n",
       "      <td>29.854781</td>\n",
       "      <td>69.735244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A0120R931</td>\n",
       "      <td>A0120R93</td>\n",
       "      <td>13.072982</td>\n",
       "      <td>53.249971</td>\n",
       "      <td>116.996856</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      upedon rcasiteid  SOCstock5  SOCstock30  SOCstock100\n",
       "0  A0108P921  A0108P92  15.126360   76.022073   100.113941\n",
       "1  A0116P901  A0116P90  35.481720   95.965557   242.002573\n",
       "2  A0116P941  A0116P94  36.827055  104.147733   293.079581\n",
       "3  A0116X971  A0116X97   6.950620   29.854781    69.735244\n",
       "4  A0120R931  A0120R93  13.072982   53.249971   116.996856"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pedons_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "d93101ce-543e-4ed1-98e0-dae010f5954a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "# Checking if there are no duplicate entries for upedon and rcasiteid\n",
    "\n",
    "print(pedons_df['upedon'].nunique() == len(pedons_df))\n",
    "print(pedons_df['rcasiteid'].nunique() == len(pedons_df))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f1e6c70-0431-4bf7-a665-d77732a738a5",
   "metadata": {},
   "source": [
    "**`Conclusion:`** - There are no duplicate records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "56d0a07e-639b-4bd8-b328-f69d9c3f3424",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ForestLand', 'RangeLand', 'CropLand', 'PastureLand', 'Wetland', 'CRP']\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Fetching the Land Use from the RaCA Side Id from upedon column and storing in a new column\n",
    "# Taking the Land Use from the upedon column to ensure correct Land Use is entered for each data\n",
    "\n",
    "#Slicing and taking the Keyword from the unique pedon ID i.e the 6th Letter of the code.\n",
    "pedons_df.loc[:,'Land_Use'] = pedons_df['upedon'].apply(lambda x : x[5])\n",
    "\n",
    "# Creating a dictionary to decode the Letter for the Land Use\n",
    "land_use_dict = dict(C='Cropland', F='Forestland', P='Pastureland', R='Rangeland', W='Wetland', X='CRP')\n",
    "\n",
    "# Replacing the LandUse with the Letters keywords. \n",
    "pedons_df.replace({'Land_Use' : land_use_dict}, inplace=True)\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10,10))\n",
    "\n",
    "#Plotting the Land Use in Pie Plot.\n",
    "labels = ['ForestLand', 'RangeLand', 'CropLand', 'PastureLand', 'Wetland', 'CRP']\n",
    "explode = (0.1, 0, 0, 0, 0, 0)\n",
    "print(labels)\n",
    "plt.pie(pedons_df.loc[:,'Land_Use'].value_counts(), labels=labels, shadow=True, startangle=90, explode=explode)\n",
    "plt.title('Land Use distribution Pie Chart')\n",
    "plt.show()\n",
    "plt.savefig(r'output\\images\\2_landuse_distribution_pie.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acbd224c-2585-49a1-977e-46e947914a85",
   "metadata": {},
   "source": [
    "#### **`Conclusion:`** Based on the plot, we can say most of the Pedons (And Samples) are picked from the Forestland LandUse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "b5484458-ce8f-4dbb-8589-975f28a2069d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the correlation matrix of SOCStocks at 5, 30 and 100 cms.\n",
    "\n",
    "plt.figure(figsize=(16,7))\n",
    "sns.heatmap(pedons_df.corr(), annot=True, linewidths = 0.1, cmap=\"YlGnBu\")\n",
    "plt.title('Correlation matrix of the SOC for 5,30 and 100 cms')\n",
    "plt.show()\n",
    "plt.savefig(r'output\\images\\2_correlationmatrix_soc.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3ff7d89-fe2c-47a2-a814-b0c52595937d",
   "metadata": {},
   "source": [
    "#### **`Conclusion:`** SOCstock100 and SOCstock30 are highly co-related to each other"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "e3145776-0ed8-4cee-8996-3b9947f71d15",
   "metadata": {},
   "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></th>\n",
       "      <th>upedon</th>\n",
       "      <th>rcasiteid</th>\n",
       "      <th>SOCstock5</th>\n",
       "      <th>SOCstock30</th>\n",
       "      <th>SOCstock100</th>\n",
       "      <th>Land_Use</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A0108P921</td>\n",
       "      <td>A0108P92</td>\n",
       "      <td>15.126360</td>\n",
       "      <td>76.022073</td>\n",
       "      <td>100.113941</td>\n",
       "      <td>Pastureland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A0116P901</td>\n",
       "      <td>A0116P90</td>\n",
       "      <td>35.481720</td>\n",
       "      <td>95.965557</td>\n",
       "      <td>242.002573</td>\n",
       "      <td>Pastureland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A0116P941</td>\n",
       "      <td>A0116P94</td>\n",
       "      <td>36.827055</td>\n",
       "      <td>104.147733</td>\n",
       "      <td>293.079581</td>\n",
       "      <td>Pastureland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A0116X971</td>\n",
       "      <td>A0116X97</td>\n",
       "      <td>6.950620</td>\n",
       "      <td>29.854781</td>\n",
       "      <td>69.735244</td>\n",
       "      <td>CRP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A0120R931</td>\n",
       "      <td>A0120R93</td>\n",
       "      <td>13.072982</td>\n",
       "      <td>53.249971</td>\n",
       "      <td>116.996856</td>\n",
       "      <td>Rangeland</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      upedon rcasiteid  SOCstock5  SOCstock30  SOCstock100     Land_Use\n",
       "0  A0108P921  A0108P92  15.126360   76.022073   100.113941  Pastureland\n",
       "1  A0116P901  A0116P90  35.481720   95.965557   242.002573  Pastureland\n",
       "2  A0116P941  A0116P94  36.827055  104.147733   293.079581  Pastureland\n",
       "3  A0116X971  A0116X97   6.950620   29.854781    69.735244          CRP\n",
       "4  A0120R931  A0120R93  13.072982   53.249971   116.996856    Rangeland"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pedons_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "b648dc5c-b66b-437a-b6a0-73638d6372c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "pedons_df.to_csv(r'processed_data\\pedons_processed.csv', index=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8978ede0-410c-4928-aa8e-4b1ecc4e27e2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  }
 },
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}