indiranell/gender_wise_marks.ipynb

## gender_wise_marks.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "N=5\n",
    "ind = np.arange(N)  # the x locations for the groups\n",
    "width = 0.35       # the width of the bars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HZSr9UTM83QEY4M02mHfpPP6N7AuwH/BPNL2uAO+VvH+PD6/7203UaapeU/PN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\nWcH9L+9PRAqOfsEUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2462b424c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "boys_marks = (27, 35, 40, 35, 29)\n",
    "girls_marks = (25, 42, 34, 22, 30)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "rects1 = ax.bar(ind, boys_marks, width, color='b')\n",
    "rects2 = ax.bar(ind + width, girls_marks, width, color='g')\n",
    "ax.set_ylabel('Marks')\n",
    "ax.set_title('Gender wise marks')\n",
    "ax.set_xticks(ind + width / 1)\n",
    "ax.set_xticklabels(('English', 'Logical', 'Quant', 'Programming', 'Domain'))\n",
    "ax.legend((rects1[0], rects2[0]), ('Boys', 'Girls'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
	{
	"cells": [
	{
	"cell_type": "code",
	"execution_count": 9,
	"metadata": {
	"collapsed": true
	},
	"outputs": [],
	"source": [
	"import numpy as np\n",
	"import matplotlib.pyplot as plt"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 17,
	"metadata": {
	"collapsed": true
	},
	"outputs": [],
	"source": [
	"N=5\n",
	"ind = np.arange(N) # the x locations for the groups\n",
	"width = 0.35 # the width of the bars"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 16,
	"metadata": {},
	"outputs": [
	{
	"data": {
	"image/png": 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u3TvPMM3MNsvW/uzqXA8WR8SGiKgDegEDJfVrND7IWglNTTsmIuojor62tjbPMM3MCq0i\nZw1FxCrgMeBIYKmkngDp77JKxGBmZk3L86yhWkmfSMM1wBHAPGA8cFqqdhowLq8YzMysdXkeI+gJ\njE3HCToAd0fEg5KeAu6WdCawCDgxxxjMzKwVuSWCiJgN7NtE+UrgS3kt18ysKcrveC6MzHHeFeAr\ni83MCs6JwMys4JwIzMwKzonAzKzgnAjMzArOicDMrOCcCMzMCs6JwMys4JwIzMwKLtfbUG8J8rya\nMPK/O6yZWe7cIjAzKzgnAjOzgnMiMDMrOCcCM7OCcyIwMyu4PJ9QtoukxyS9IGmupPNS+UhJr0ia\nmV5H5xWDmZm1Ls/TR9cD34uIGZK6AtMlTUjjRkXElTku28zMypTnE8qWAEvS8GpJLwKfymt5Zma2\naSpyjEBSH7LHVj6TioZLmi3pJkndKhGDmZk1LfcriyVtD/wW+G5EvCHpWuDfgEh/fwac0cR0w4Bh\nAL179847zE2iS/K5bDku9iXLZlY5ubYIJHUmSwK/joj7ACJiaURsiIj3gOuBgU1NGxFjIqI+Iupr\na2vzDNPMrNDKSgSStpPUIQ1/RtKx6Uu+pWkE3Ai8GBE/LynvWVLteGDOxodtZmZtpdyuoceBA1N/\n/h+AqcDXgVNamGYQMBR4XtLMVHYRMERSHVnX0ELg25sQt5mZtZFyE4Ei4i1JZwK/jIgrSr7cmxQR\nTwJNdaI/tLFBmplZfso9RiBJB5C1AP4zlXXMJyQzM6ukchPBecCPgPsjYq6k3YHH8gvLzMwqpdyu\noZcj4tiGNxGxQNJtOcVkZmYVVG6L4F5J718VLOlg4KZ8QjIzs0oqNxGcBTwgaed0k7jRgG8WZ2bW\nDpTVNRQRUyWdS3bq6Drg8IhYnmtkZmZWES0mAkm/Izvfv8G2wOvAjZIoPW5gZmZbp9ZaBL5VtJlZ\nO9diIoiIyZI6Ao9ExKEVisnMzCqo1WMEEbFB0nuSdoiI1ysRlJltPfK6Cy/4TryVUu51BGvI7hk0\nAXizoTAizs0lKjMzq5hyE8F96WVmZu1MuaePjs07EDMzq46yEoGkPYCfAnsBXRrKI2L3nOIyM7MK\nKffK4puBa4H1wKHArcDteQVlZmaVU24iqImIiWTPJVgUESOBr+QXlpmZVUq5ieDt9KjK+ZLOkXQ8\nsH1LE0jaRdJjkl6QNFfSeal8R0kTJM1Pf7tt5jqYmdlm2JjnEWwLnAt8nuwRlKe1Ms164HsRsRew\nP3C2pL2AC4GJEbEHMDG9NzOzKin7pnNpcA3wzTKnWQIsScOrJb0IfAo4DjgkVRsLTAIuKDtiMzNr\nU63ddG58S+PLvemcpD7AvsAzQI+UJABeBXo0M80wYBhA7969y1mMmZltgtZaBAcALwN3kH2Jb/S1\n5JK2B34LfDci3pA+mEVEhKQmryGPiDHAGID6+npfZ25mlpPWEsHOwBHAEOBksgfX3xERc8uZuaTO\nZEng1xHRcGXyUkk9I2KJpJ7Ask0L3czM2kKLB4sjYkNEPBwRp5Ed8P0TMEnSOa3NWNlP/xuBFyPi\n5yWjxvPBgebTgHGbFLmZmbWJVg8WS/oY2TUDQ4A+ZI+pvL+MeQ8iO7voeUkzU9lFwGXA3ZLOBBYB\nJ2582GZm1lZaO1h8K9APeAi4JCLmlDvjiHiS5o8pfKnsCM3MLFettQi+QXbb6fOAc0sO9IrsWO/H\nc4zNzMwqoLUnlJV7wZmZmW2l/EVvZlZwTgRmZgXnRGBmVnBOBGZmBedEYGZWcE4EZmYF50RgZlZw\nTgRmZgXnRGBmVnBOBGZmBedEYGZWcE4EZmYF50RgZlZwTgRmZgWXWyKQdJOkZZLmlJSNlPSKpJnp\ndXReyzczs/Lk2SK4BTiyifJREVGXXg/luHwzMytDbokgIh4HXstr/mZm1jaqcYxguKTZqeuoW3OV\nJA2TNE3StOXLl1cyPjOzQql0IrgW2B2oA5YAP2uuYkSMiYj6iKivra2tVHxmZoVT0UQQEUsjYkNE\nvAdcDwys5PLNzOyjKpoIJPUseXs8MKe5umZmVhmd8pqxpDuAQ4CdJC0GLgYOkVQHBLAQ+HZeyzcz\ns/LklggiYkgTxTfmtTwzM9s0vrLYzKzgnAjMzArOicDMrOCcCMzMCs6JwMys4JwIzMwKzonAzKzg\nnAjMzArOicDMrOCcCMzMCs6JwMys4JwIzMwKzonAzKzgnAjMzArOicDMrOBySwTp4fTLJM0pKdtR\n0gRJ89PfZh9eb2ZmlZFni+AW4MhGZRcCEyNiD2Biem9mZlWUWyKIiMeB1xoVHweMTcNjgcF5Ld/M\nzMpT6WMEPSJiSRp+FejRXEVJwyRNkzRt+fLllYnOzKyAqnawOCKC7CH2zY0fExH1EVFfW1tbwcjM\nzIql0olgqaSeAOnvsgov38zMGql0IhgPnJaGTwPGVXj5ZmbWSJ6nj94BPAV8VtJiSWcClwFHSJoP\nHJ7em5lZFXXKa8YRMaSZUV/Ka5lmZrbxfGWxmVnBORGYmRWcE4GZWcE5EZiZFZwTgZlZwTkRmJkV\nnBOBmVnBORGYmRWcE4GZWcE5EZiZFZwTgZlZwTkRmJkVnBOBmVnBORGYmRWcE4GZWcE5EZiZFVxu\nD6ZpiaSFwGpgA7A+IuqrEYeZmVUpESSHRsSKKi7fzMxw15CZWeFVKxEE8Iik6ZKGNVVB0jBJ0yRN\nW758eYXDMzMrjmolgi9GRB1wFHC2pIMaV4iIMRFRHxH1tbW1lY/QzKwgqpIIIuKV9HcZcD8wsBpx\nmJlZFRKBpO0kdW0YBr4MzKl0HGZmlqnGWUM9gPslNSz/NxHxcBXiMDMzqpAIImIB0L/SyzUzs6b5\n9FEzs4JzIjAzKzgnAjOzgnMiMDMrOCcCM7OCcyIwMys4JwIzs4JzIjAzKzgnAjOzgnMiMDMrOCcC\nM7OCcyIwMys4JwIzs4JzIjAzKzgnAjOzgnMiMDMruKokAklHSnpJ0p8kXViNGMzMLFONZxZ3BK4B\njgL2AoZI2qvScZiZWaYaLYKBwJ8iYkFEvAPcCRxXhTjMzAxQRFR2gdJXgSMj4v+k90OBf4iIcxrV\nGwYMS28/C7xU0UDb3k7AimoHUWXeBt4GRV9/qOw22DUialurVPGH15crIsYAY6odR1uRNC0i6qsd\nRzV5G3gbFH39YcvcBtXoGnoF2KXkfa9UZmZmVVCNRDAV2EPSbpK2AU4CxlchDjMzowpdQxGxXtI5\nwO+BjsBNETG30nFUQbvp5toM3gbeBkVff9gCt0HFDxabmdmWxVcWm5kVnBOBmVnBORGUQdIGSTNL\nXpt8WwxJa9LfT0q6t4V6fSTN2dTltLWGuDdzHi2ucyvT3pKuQdkiSOolaZyk+ZIWSLpa0sfaeBmH\nSPpCG8+zYV+eI+keSdu25fwrTdKUCi6rYdvNlTRL0vck5fodKuksSafmuQxwIijX2oioK3ldtrkz\njIj/iYgt5outEtrLOksScB/wQETsAewB1ABXtPGiDgHaNBHwwb7cD3gHOKt0pDKb9b0gqWInoURE\nW2+fljRsu77AEWS3ybk4zwVGxHURcWueywAngs0iaaGkSyTNkPS8pM+l8lpJE9IvhxskLZK0U6Np\n3//FL6mvpGfTr43ZkvZI1TpKuj7N5w+Saiq8ii1K6/BoinmipN6p/O8lPZ22yU9KWkGl69xR0pXp\nl+lsScNT+b9KmprKx6Qv3S3NYcC6iLgZICI2ACOAUyWdI+nqhoqSHpR0SBq+VtK09P+8pKTOR/Yj\nSX3IvqRHpP3iwBzW4wng0+n/8pKkW4E5wC6ShqRY5ki6vCTWMyX9d9pfr29Y19Riu07SM8AVkgZK\nekrSc5KmSPpsqne6pAfS52Nh2l7np3pPS9ox1ZskaVTaXi9KGiDpvtQC+0lJPA371iFpmnslzZP0\n64Z9R9LRqWy6pNGSHtzcDRcRy8jufHBOSp5dJN2cttlzkg7dyPX9VtrvZ0n6rVJLTdJISd8v2SaX\np23/3225TzgRlKdGH+4a+nrJuBURsR9wLfD9VHYx8Gj65XAv0LuV+Z8F/CIi6oB6YHEq3wO4Js1n\nFXBCG61PW/kPYGxE7AP8Ghidyn9Btj5788G6NDYM6APUlUwPcHVEDEi/WGuAY/IKfjP0BaaXFkTE\nG8BCWj4l+/+mK0r3AQ6WtE/JuA/tRxGxELgOGJV+hT7RhvE3/Go/Cng+Fe0B/DLta+8Cl5MlvDpg\ngKTBkj4J/AuwPzAI+Fyj2fYCvhAR5wPzgAMjYl/gX4FLS+r1A/4ZGAD8O/BWqvcUUNoN8k7aXtcB\n44Cz07SnS+rexGrtC3yX7GaWuwODJHUBfgUcFRGfB1q93UK5ImIB2Snwf5dii7TPDwHGpmWXu773\npf2+P/AicGYzi+0UEQPTerZZa8SJoDyNu4buKhl3X/o7neyLDeCLZDfTIyIeBv7WyvyfAi6SdAHZ\nvUHWpvK/RMTMJua/pTgA+E0avo1svRvK70nDv2k8UXI48KuIWA8QEa+l8kMlPSPpebIvor5tHnX1\nnChpBvAc2XqV3nW3qf0oDzWSZgLTgL8CN6byRRHxdBoeAEyKiOXp//Nr4CCyG0ZOjojXIuJdPvgf\nN7gntY4AdgDuSS3AUXz4//hYRKyOiOXA68DvUvnzfHjdx5eUz42IJRHxNrCAD9+doMGzEbE4It4D\nZqZ5fQ5YEBF/SXXuaGnjbIYvArcDRMQ8YBHwmTSunPXtJ+mJtN+fQvP7fS77iRPB5ns7/d3AJl6g\nFxG/AY4F1gIPSTqs0bw3a/5bi/QL6pfAV9Mvq+uBLi1PVRUvAJ8vLZD0cWBnYCUf/lx1SeN3I2sx\nfim1gP6TD6/bZu9HZSr9UTM83QEY4M02mHfpPP6N7AuwH/BPNL2uAO+VvH+PD6/7203UaapeU/PN\n/fMiafe0nGWtVC1nfW8Bzkn7/SU0v9/nsp84EeTjj8CJAJK+DHRrqXLaoRZExGiyJvA+LdXfgkwh\nu0UIZL9iGrovnuaDbqyTGk+UTAC+nbooSH2lDTv/CknbA1vqgeWJwLZKZ3Moe8bGz4Crgb8AdZI6\nSNqF7Fc0wMfJvihfl9SDrFumNauBrm0dfBmeJeu62imt2xBgMtntYQ6W1C3931rqqtyBD+4hdnqe\nwbbiJWD3dMwF4OvNVy2fpFqyLqurI7sq9wmyzwCSPkPWHbwxd0zuCiyR1LlhPpXkRFCexscIWjtr\n6BLgy6lZ/DXgVbIPdXNOBOakJns/IPezBDbBtpIWl7zOB4YD35Q0GxgKnJfqfhc4P5V/mqw53NgN\nZF0TsyXNAk6OiFVkrYA5ZLcgmZrvKm2a9ME/HviqpPlkrYD3IuLfyX4E/IWs1TAamJGmmUXWJTSP\nrLvsj2Us6nfA8crvYHGTImIJcCHwGDALmB4R4yLiFbK+/mfJ4l9I0/9byM6g+qmk56hiSzZ1s34H\neFjSdLLPYXMxt6bhe2Au8AjwB7LPOmQt2Q6pa+cu4PTUjVWufwGeIduu8zYxvk3mW0zkQNn55BvS\nfZUOAK5NB4ILIZ3xsDYiQtJJwJCIaLcPH1J2rv8dwPERMaPa8eRJ0vYRsSa1CO4nu1fY/dWOqyUl\nMYvs6YjzI2JUtePakrTrPucq6g3crex87HeAb1U5nkr7PHB1+uCtAs6ocjy5iogpwK7VjqNCRko6\nnKwb7w/AA1WOpxzfknQasA1Zq+xXVY5ni+MWgZlZwfkYgZlZwTkRmJkVnBOBmVnBORGYmRWcE4GZ\nWcH9L+9PRAqOfsEUAAAAAElFTkSuQmCC\n",
	"text/plain": [
	"<matplotlib.figure.Figure at 0x2462b424c50>"
	]
	},
	"metadata": {},
	"output_type": "display_data"
	}
	],
	"source": [
	"boys_marks = (27, 35, 40, 35, 29)\n",
	"girls_marks = (25, 42, 34, 22, 30)\n",
	"\n",
	"fig, ax = plt.subplots()\n",
	"rects1 = ax.bar(ind, boys_marks, width, color='b')\n",
	"rects2 = ax.bar(ind + width, girls_marks, width, color='g')\n",
	"ax.set_ylabel('Marks')\n",
	"ax.set_title('Gender wise marks')\n",
	"ax.set_xticks(ind + width / 1)\n",
	"ax.set_xticklabels(('English', 'Logical', 'Quant', 'Programming', 'Domain'))\n",
	"ax.legend((rects1[0], rects2[0]), ('Boys', 'Girls'))\n",
	"plt.show()"
	]
	},
	{
	"cell_type": "code",
	"execution_count": null,
	"metadata": {
	"collapsed": true
	},
	"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": "3.6.1"
	}
	},
	"nbformat": 4,
	"nbformat_minor": 2
	}