increse in temperature.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "data=pd.read_csv(r\"C:\\Users\\Febin\\Downloads\\new\\temp.csv\")\n",
    "\n",
    "print(type(data))\n",
    "u"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'plot' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-3-a379b91757f9>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      3\u001b[0m                                     'JUN-SEP', 'OCT-DEC'] ) \n\u001b[0;32m      4\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mplot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'plot' is not defined"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame(data, columns = ['YEAR', 'ANNUAL',  \n",
    "                                    'JAN-FEB', 'MAR-MAY', \n",
    "                                    'JUN-SEP', 'OCT-DEC'] ) \n",
    "df.hist()\n",
    "plot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#BAE CHART\n",
    "df.plot.bar()\n",
    "plt.bar(df['JAN-FEB'], df['MAR-MAY'],df['JUN-SEP'],df['OCT-DEC']) \n",
    "plt.xlabel(\"JAN-FEB\") \n",
    "plt.ylabel(\"OCT-DEC\") \n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#BOX CHART\n",
    "df.plot.box() \n",
    "plt.boxplot(df['JUN-SEP']) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "'label' must be of length 'x'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-15-26f0b0bf448b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m plt.pie(df['JAN-FEB'], labels = {\"X\",\"Y\"}, \n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m autopct ='% 1.1f %%', shadow = True) \n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\pyplot.py\u001b[0m in \u001b[0;36mpie\u001b[1;34m(x, explode, labels, colors, autopct, pctdistance, shadow, labeldistance, startangle, radius, counterclock, wedgeprops, textprops, center, frame, rotatelabels, hold, data)\u001b[0m\n\u001b[0;32m   3341\u001b[0m                      \u001b[0mradius\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mradius\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcounterclock\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcounterclock\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3342\u001b[0m                      \u001b[0mwedgeprops\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mwedgeprops\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtextprops\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtextprops\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcenter\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcenter\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3343\u001b[1;33m                      frame=frame, rotatelabels=rotatelabels, data=data)\n\u001b[0m\u001b[0;32m   3344\u001b[0m     \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3345\u001b[0m         \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_hold\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwashold\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\__init__.py\u001b[0m in \u001b[0;36minner\u001b[1;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1865\u001b[0m                         \u001b[1;34m\"the Matplotlib list!)\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mlabel_namer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1866\u001b[0m                         RuntimeWarning, stacklevel=2)\n\u001b[1;32m-> 1867\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1868\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1869\u001b[0m         inner.__doc__ = _add_data_doc(inner.__doc__,\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py\u001b[0m in \u001b[0;36mpie\u001b[1;34m(self, x, explode, labels, colors, autopct, pctdistance, shadow, labeldistance, startangle, radius, counterclock, wedgeprops, textprops, center, frame, rotatelabels)\u001b[0m\n\u001b[0;32m   2865\u001b[0m             \u001b[0mexplode\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2866\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2867\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"'label' must be of length 'x'\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2868\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mexplode\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2869\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"'explode' must be of length 'x'\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: 'label' must be of length 'x'"
     ]
    },
    {
     "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": [
    "#PIE CHART\n",
    "plt.pie(df['JAN-FEB'], labels = {\"A\", \"B\", \"C\", \n",
    "                             \"D\", \"E\", \"F\", \n",
    "                             \"G\", \"H\", \"I\", \"J\"}, \n",
    "                               \n",
    "autopct ='% 1.1f %%', shadow = True) \n",
    "plt.show() \n",
    "  \n",
    "plt.pie(df['MAR-MAY'], labels = {\"A\", \"B\", \"C\", \n",
    "                             \"D\", \"E\", \"F\", \n",
    "                             \"G\", \"H\", \"I\", \"J\"}, \n",
    "                                  \n",
    "autopct ='% 1.1f %%', shadow = True) \n",
    "plt.show() \n",
    "  \n",
    "plt.pie(df['JUN-SEP'], labels = {\"A\", \"B\", \"C\", \n",
    "                             \"D\", \"E\", \"F\", \n",
    "                             \"G\", \"H\", \"I\", \"J\" }, \n",
    "autopct ='% 1.1f %%', shadow = True) \n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "#scatter plot\n",
    "plt.scatter(df['JAN-FEB'], df['MAR-MAY']) \n",
    "plt.show() \n",
    "  \n",
    "# scatter plot between jan-feb jun-sep \n",
    "plt.scatter(df['JAN-FEB'], df['JUN-SEP']) \n",
    "plt.show() \n",
    "\n",
    "# scatter plot between jun-sep mar-may\n",
    "plt.scatter(df['JUN-SEP'], df['MAR-MAY']) \n",
    "plt.show() \n",
    "  \n",
    "# scatter plot between mar_may\n",
    "plt.scatter(df['MAR-MAY'], df['OCT-DEC']) \n",
    "plt.show() "
   ]
  },
  {
   "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}