{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Nitrogen fertilizer price trend"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to analyze how the nitrogen fertilizer price per ton evolved with years."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data import"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This data gives the long-term evolution of nitrogen fertilizer price in USD"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we import data from our gitlab repo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import io\n",
    "import pandas as pd\n",
    "df = pd.read_csv('https://app-learninglab.inria.fr/moocrr/gitlab/1d334d5105a1a432e18f2e361780bab5/mooc-rr/raw/master/module2/exo4/PCU325311325311.csv?inline=false', sep=',', decimal='.')\n",
    "df.columns = ['date', 'price']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we convert values as needed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1975-12-01'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.date[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('1975-01-01 00:12:00')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['date'] = pd.to_datetime(df['date'], format='%Y-%M-%d')\n",
    "df.date[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "df = df.replace('.', np.NaN)\n",
    "df['price'] = pd.to_numeric(df['price'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data viz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f8391afabe0>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.plot(df.date, df.price)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mean price before 2000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "prices_before_2000 = df[df['date']<datetime.datetime.strptime('2000-01-01', '%Y-%M-%d')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "price    120.05\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.around(prices_before_2000.mean(), decimals=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Std price before 2000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "price    21.58\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.around(prices_before_2000.std(ddof=1), decimals=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mean price after 2000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "prices_after_2000 = df[df['date']>=datetime.datetime.strptime('2000-01-01', '%Y-%M-%d')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "price    298.67\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.around(prices_after_2000.mean(), decimals=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Std price after 2000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "price    103.63\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.around(prices_after_2000.std(ddof=1), decimals=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion of the study"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We shown that after year 2000, the average price and variance of nitrogen fertilizer greatly increased compared to before year 2000."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}