{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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male_mfemale_m
age
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" ], "text/plain": [ " male_m female_m\n", "age \n", "00-04 102314 99825\n", "05 -- 09 117662 117591\n", "10 -- 14 121934 121729\n", "15-19 93061 100093\n", "20-24 61507 78811\n", "25-29 55099 66047\n", "30-34 45378 54371\n", "35-39 44887 51227\n", "40-44 41656 46001\n", "45-49 38556 39807\n", "50-54 35511 34837\n", "55-59 29530 27519\n", "60-64 26327 29466\n", "65-69 19141 19999\n", "70-74 13859 14847\n", "75-79 8655 8986\n", "80-84 4854 5087\n", "85-89 1942 1941\n", "90-94 519 635\n", "95+ 200 381" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1 = pd.read_excel('data/mp2011.xlsx') #population in Moutain region in 2011\n", "df1 = df1.set_index('age')\n", "df1" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "group=np.arange(1,21)\n", "bar_width=0.3\n", "fig,ax = plt.subplots(figsize = [8,6])\n", "ax.bar(group, df1['male_m'],bar_width, facecolor='pink', edgecolor='k',label='Male')\n", "ax.bar(group+bar_width, df1['female_m'],bar_width, facecolor='orange', edgecolor='k',label='Female')\n", "plt.xlabel('Age')\n", "ax.set_xticks(group+bar_width/2)\n", "ax.set_xticklabels(df1.index)\n", "ax.set_ylabel('Population')\n", "ax.tick_params(axis='x', rotation= 45)\n", "plt.title('Population in Himalay region in 2011')\n", "plt.savefig(\"image/himal.png\", dpi = 600) # dpi dot per inch\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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\n", "
" ], "text/plain": [ " male_h female_h\n", "age \n", "00-04 540032 509291\n", "05 -- 09 657185 634246\n", "10 -- 14 742164 729226\n", "15-19 632209 682581\n", "20-24 464312 600570\n", "25-29 389744 510156\n", "30-34 324358 410277\n", "35-39 300292 371696\n", "40-44 277900 320467\n", "45-49 240867 266272\n", "50-54 222600 229230\n", "55-59 177381 179586\n", "60-64 154254 172310\n", "65-69 118494 125661\n", "70-74 87716 91604\n", "75-79 58420 61765\n", "80-84 32382 35651\n", "85-89 14056 14920\n", "90-94 4293 5706\n", "95+ 1408 2725" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2 = pd.read_excel('data/hp2011.xlsx')#population in Hill region in 2011\n", "df2 = df2.set_index('age')\n", "df2" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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6Bgfu/i7QvyBtCeHphcJpHTilRD6TgElF0mcB2xdJL7oMERHpmnr16sWSJUvo37+/AoQEd2fJkiX06tWrovnUQ6KIiHR6gwYNoqWlhcWLFze6KB1Or169GDRoUPkJExQciIhIp9ejR4+iXQtLdfTiJREREUlRcCDtIpdrwsyKfnK5pkYXT0REEnRbQdpFa+9Wr/Y96SIiUh+qORAREZEUBQciIiKSouBAREREUhQciIiISIqCAxEREUlRcCAiIiIpCg5EREQkRcGBiIiIpCg46ESacrmiPQw25XKNLpqIiHQh6iGxE1nU3EyxbgYXjRzZgNKIiEhXpZoDERERSVFwICIiIikKDkRERCRFwYGIiIikKDgQERGRFAUHIiIikqLgQERERFIUHIiIiEiKgoMuoEcPivacmMs1NbpoIiLSCamHxC5g+fKiHScycuSidi9LUy4XenIUEZFOS8GB1FSpLp5RF88iIp2GbiuIiIhIioIDERERSVFwICIiIikKDkRERCRFwYGIiIikKDgQERGRFAUHIiIiklLX4MDM+prZbWb2jJk9bWafMrMNzWyamc2Lf/vFac3MLjez+Wb2uJntkshnXJx+npmNS6TvamZz4zyXm5nF9KLLEBERkfLqXXNwGXCvu28L7AQ8DUwAHnD3ocADcRhgf2Bo/IwHJkI40QPnA7sDuwHnJ072E+O0+fnGxPRSyxAREZEy6hYcmNkGwH8B1wK4+wfu/gYwFpgcJ5sMHBy/jwVu8OBhoK+ZbQKMBqa5+1J3fx2YBoyJ4zZw94fc3YEbCvIqtgwREREpo541Bx8DFgPXmdlsM7vGzNYFBrr7KwDx78Zx+s2AZKf8LTGttfSWIum0sgwREREpo57BwVrALsBEd/8E8A6tV+9bkTSvIj0zMxtvZrPMbNbixYsrmVU6gFyuSW+jFBGpg3q+eKkFaHH3f8Th2wjBwSIz28TdX4m3Bv6dmH5wYv5BwMKYvndB+oyYPqjI9LSyjBR3vwq4CmD48OEVBRbSeM3NizrM2yhFRLqSutUcuPurQLOZbROT9gWeAqYC+ScOxgF3xe9TgaPiUwt7AG/GWwL3AaPMrF9siDgKuC+Oe9vM9ohPKRxVkFexZYiIiEgZ9X5l82nAzWbWE3gBOIYQkEwxs+OABcChcdq7gQOA+cC7cVrcfamZXQTMjNNd6O5L4/eTgeuB3sA98QNwaYlliIiISBl1DQ7cfQ4wvMiofYtM68ApJfKZBEwqkj4L2L5I+pJiyxAREZHy1EOiiIiIpCg4EBERkRQFByIiIpKi4EBERERSFByIiIhIioIDERERSVFwICIiIikKDkRERCRFwYGIiIikKDgQERGRFAUHIiIikqLgQERERFIUHIiIiEiKggMRERFJUXAgIiIiKQoOREREJEXBgYiIiKQoOBAREZEUBQciIiKSouBAREREUhQc1ElTLoeZrfZpyuUaXTQREZFWrdXoAnRVi5qbYfr01dNHjmxAaURERLJTzYGIiIikKDgQERGRFAUHIiIikqLgQERERFIUHIiIiEiKggMRERFJUXAgIiIiKQoOREREJEXBgYiIiKQoOBAREZEUBQciIiKSUtfgwMxeMrO5ZjbHzGbFtA3NbJqZzYt/+8V0M7PLzWy+mT1uZrsk8hkXp59nZuMS6bvG/OfHea21ZXQEPXpQ9IVMZkYu19To4q3xcrkmbRsRWeO1x4uXRrr7a4nhCcAD7n6pmU2Iw+cA+wND42d3YCKwu5ltCJwPDAcceNTMprr763Ga8cDDwN3AGOCeVpbRcMuXF30fEwAjRy5q38LIapqbFxXdPto2IrImacRthbHA5Ph9MnBwIv0GDx4G+prZJsBoYJq7L40BwTRgTBy3gbs/5O4O3FCQV7FliIiISBn1Dg4c+JOZPWpm42PaQHd/BSD+3TimbwY0J+ZtiWmtpbcUSW9tGSIiIlJGvW8r7OnuC81sY2CamT3TyrRWJM2rSM8sBizjAXK5XCWzioiIdFl1rTlw94Xx77+BO4DdgEXxlgDx77/j5C3A4MTsg4CFZdIHFUmnlWUUlu8qdx/u7sMHDBhQ7b8pIiLSpdQtODCzdc1s/fx3YBTwBDAVyD9xMA64K36fChwVn1rYA3gz3hK4DxhlZv3iUwejgPviuLfNbI/4lMJRBXkVW4aIiIiUUc/bCgOBO+LThWsBv3X3e81sJjDFzI4DFgCHxunvBg4A5gPvAscAuPtSM7sImBmnu9Ddl8bvJwPXA70JTyncE9MvLbEMERERKaNuwYG7vwDsVCR9CbBvkXQHTimR1yRgUpH0WcD2WZchIiIi5amHRBEREUlRcCAiIiIpCg5EREQkRcGBiIiIpCg4EBERkRQFByIiIpKi4EBERERSFByIiIhIioIDERERSVFwICIiIikKDqTDa8rlMLPVPiIiUh/1fPGSSE0sam6G6dNXHzFyZPsXRkRkDaCaAxEREUlRcCAiIiIpCg5EREQkRcGBiIiIpCg4EBERkRQFByIiIpKS6VFGM9sTuADYPM5jgLv7x+pXNBEREWmErP0cXAucATwKfFi/4oiIiEijZQ0O3nT3e+paEhEREekQsgYH083sx8D/Au/nE939n3UplYiIiDRM1uBg9/h3eCLNgX1qWxwRERFptEzBgburE3sREZE1RKZHGc2sj5n9zMxmxc9PzaxPvQsnIiIi7S9rPweTgLeBL8fPW8B19SqUiIiINE7WNgdbuvuXEsPfM7M59SiQiIiINFbWmoNlZrZXfiB2irSsPkUSERGRRspac3AyMDm2MzBgKXB0vQolIiIijZP1aYU5wE5mtkEcfquupRIREZGGaTU4MLMj3f0mMzuzIB0Ad/9ZHcsmIiIiDVCu5mDd+Hf9IuO8xmURERGRDqDV4MDdfxO/3u/uf0+Oi40SRUREpIvJ+rTCFRnTVmNm3c1stpn9IQ5vYWb/MLN5ZvY7M+sZ09eOw/Pj+CGJPM6N6c+a2ehE+piYNt/MJiTSiy5DpBFyuSbMbLVPLtfU6KKJiBRVrs3Bp4BPAwMK2h1sAHTPuIzTgafjPAA/BH7u7rea2ZXAccDE+Pd1d9/KzA6P0x1mZsOAw4GPA5sC95vZ1jGvXwGfBVqAmWY21d2famUZIu2uuXkR06evnj5y5KL2L4yISAblag56AusRgoj1E5+3gEPKZW5mg4DPAdfEYSO8rOm2OMlk4OD4fWwcJo7fN04/FrjV3d939xeB+cBu8TPf3V9w9w+AW4GxZZYhIiIiZZRrc/AX4C9mdr27/6uK/H8BnM2qBo39gTfcfUUcbgE2i983A5rjcleY2Ztx+s2AhxN5JudpLkjfvcwyREREpIysnSC9a2Y/JlTt98onunvJVzab2YHAv939UTPbO59cZFIvM65UerFaj9amL1bG8cB4gFwuV2wSERGRNU7WBok3A88AWwDfA14CZpaZZ0/gIDN7iVDlvw+hJqGvmeWDkkHAwvi9BRgMEMf3IfTEuDK9YJ5S6a+1sowUd7/K3Ye7+/ABAwaU+XdERETWDFmDg/7ufi2w3N3/4u7HAnu0NoO7n+vug9x9CKFB4Z/d/SvAdFa1VxgH3BW/T43DxPF/dneP6YfHpxm2AIYCjxCCk6HxyYSecRlT4zylliEiIiJlZA0Olse/r5jZ58zsE4Qr8mqcA5xpZvMJ7QOujenXAv1j+pnABAB3fxKYAjwF3Auc4u4fxjYFpwL3EZ6GmBKnbW0ZIiIiUkbWNgcXx5cunUXo32AD4IysC3H3GcCM+P0FwpMGhdO8BxxaYv5LgEuKpN8N3F0kvegyRPKacjkWNTeXn1BEZA2U9cVLf4hf3wRG1q84Iu1jUXMzJTofaP/CiIh0MOU6QbqCVt6h4O5fr3mJREREpKHK1RzMapdSiIiISIdRrhOkya2NFxERka4nU5sDM5tOkdsLrXWCJCIiIp1T1qcVvpn43gv4ErCixLQiIiLSiWV9WuHRgqS/m9lf6lAeERERabBMnSCZ2YaJz0ZmNhrQy+hFGiCXa8LMVvvkcvpJikhtZL2t8CirXmq0AngROK5ehRKR0pqbF5XoomFR+xdGRLqkrLcVtqh3QURERKRjyPq0Qi/ga8BehBqEB4GJsctjERER6UKy3la4AXib8F4FgCOAGynxLgQRERHpvLIGB9u4+06J4elm9lg9CiQiIiKNlfWVzbPNbI/8gJntDvy9PkUSERGRRspac7A7cJSZLYjDOeBpM5sLuLvvWJfSiYiISLvLGhyMqWspREREpMPI+ijjv8xsJ2BETPqbu6vNgYiISBeUtYfE04GbgY3j5yYzO62eBRMREZHGyHpb4Thgd3d/B8DMfgg8xKpHG0VERKSLyPq0ggEfJoY/jGkiIiLSxWStObgO+IeZ3RGHDwaurU+RREREpJGyNkj8mZnNIHSfbMAx7j67ngUTERGRxmg1OIjvVDgJ2AqYC/za3Ve0R8FERESkMcq1OZgMDCcEBvsDP6l7iURERKShyt1WGObuOwCY2bXAI/UvkoiIiDRSuZqD5fkvup0gIiKyZihXc7CTmb0VvxvQOw4b4Z0KG9S1dCIiItLuWg0O3L17exVEpDNryuVY1Nzc6GKIiNRE1n4ORKQVi5qbYfr04iNHjmzfwlQhl2uiuXnRaumDBw9kwYJXG1AiEWkkBQciQnPzoqKxzciRqwcMItL1Ze0+WURERNYQCg5EREQkRcGBiIiIpNQtODCzXmb2iJk9ZmZPmtn3YvoWZvYPM5tnZr8zs54xfe04PD+OH5LI69yY/qyZjU6kj4lp881sQiK96DJERESkvHrWHLwP7OPuOwE7A2PMbA/gh8DP3X0o8DpwXJz+OOB1d98K+HmcDjMbBhwOfBwYA/zazLqbWXfgV4RunYcBR8RpaWUZIiIiUkbdggMP/hMHe8SPA/sAt8X0yYTXPwOMjcPE8fuamcX0W939fXd/EZgP7BY/8939BXf/ALgVGBvnKbUMERERKaOubQ7iFf4c4N/ANOB54I1EV8wtwGbx+2ZAM6zsqvlNoH8yvWCeUun9W1mGiIiIlFHX4MDdP3T3nYFBhCv97YpNFv9aiXG1Sl+NmY03s1lmNmvx4sXFJhFpmKZcDjNb7SMiUm/t0gmSu79hZjOAPYC+ZrZWvLIfBCyMk7UAg4EWM1sL6AMsTaTnJecplv5aK8soLNdVwFUAw4cPLxpAiDRKyV4XO0GPiyLSudXzaYUBZtY3fu8N7Ac8DUwHDomTjQPuit+nxmHi+D+7u8f0w+PTDFsAQwmvjp4JDI1PJvQkNFqcGucptQwREREpo541B5sAk+NTBd2AKe7+BzN7CrjVzC4GZgPXxumvBW40s/mEGoPDAdz9STObAjwFrABOcfcPAczsVOA+oDswyd2fjHmdU2IZIiIiUkbdggN3fxz4RJH0FwjtDwrT3wMOLZHXJcAlRdLvBu7OugwREREpTz0kioiISIqCAxEREUlRcCAiIiIpCg5EREQkRcGByBpEHSuJSBbt0gmSiHQM6lhJRLJQzYGIiIikKDgQERGRFAUHIiIikqLgQETqIpdrKtr4MZdranTRRKQMNUgUkbpobl5Uou3jovYvjIhURDUHIiIikqLgQERERFIUHIiIiEiKggMRERFJUXAgIiIiKQoOREREJEXBgYiIiKQoOBAREZEUBQciIiKSouBARKrWlMsV7SLZzBpdNBFpA3WfLCJVW9TcTNE+kgFGjmzfwohIzajmQERERFIUHIiIiEiKggMR6RRKvQJar4EWqT21ORCRTqHUK6BBr4EWqTXVHIiIiEiKggMR6VBKPR4pIu1HtxVEpEMp+XikHo0UaTeqORAREZEUBQciIiKSouBAREREUhQciIiISErdggMzG2xm083saTN70sxOj+kbmtk0M5sX//aL6WZml5vZfDN73Mx2SeQ1Lk4/z8zGJdJ3NbO5cZ7LLTZpLrUMERERKa+eNQcrgLPcfTtgD+AUMxsGTAAecPehwANxGGB/YGj8jAcmQjjRA+cDuwO7AecnTvYT47T5+cbE9FLLEBERkTLqFhy4+yvu/s/4/W3gaWAzYCwwOU42GTg4fh8L3ODBw0BfM9sEGA1Mc/el7v46MA0YE8dt4O4PucPmVEUAACAASURBVLsDNxTkVWwZIiIiUka7tDkwsyHAJ4B/AAPd/RUIAQSwcZxsM6A5MVtLTGstvaVIOq0sQ0RERMqoe3BgZusBtwPfcPe3Wpu0SJpXkV5J2cab2Swzm7V48eJKZhUREemy6hocmFkPQmBws7v/b0xeFG8JEP/+O6a3AIMTsw8CFpZJH1QkvbVlpLj7Ve4+3N2HDxgwoLp/UkREpIup59MKBlwLPO3uP0uMmgrknzgYB9yVSD8qPrWwB/BmvCVwHzDKzPrFhoijgPviuLfNbI+4rKMK8iq2DBERESmjnu9W2BP4KjDXzObEtG8DlwJTzOw4YAFwaBx3N3AAMB94FzgGwN2XmtlFwMw43YXuvjR+Pxm4HugN3BM/tLIMERERKaNuwYG7P0jxdgEA+xaZ3oFTSuQ1CZhUJH0WsH2R9CXFliEiIiLlqYdEERERSVFwICIiIikKDkRERCRFwYGIrLFyuSbMbLVPLtfU6KKJNFQ9n1YQEekQmnI5FjU3Fx03ffrqaSNHLqpziUQ6NgUHItLlLWpuLhUFtH9hRDoB3VYQEakh3aqQrkA1ByIiNdTcvEi3KqTTU82BiIiIpCg4EBERkRQFByIiIpKi4EBERERSFByIiIhIioIDERERSVFwICJShaZcrmh/BiJdgfo5EBGpgnpdlK5MNQciIiKSouBAREREUhQciIiISIqCAxEREUlRcCAiIiIpCg5EREQkRcGBiIiIpCg4EBERkRQFByIiIpKi4EBERERSFByIiHRguVxT0Xc4mBm5XFOjiyddlN6tICLSgTU3Lyr6CgeAkSMXtW9hZI2hmgMRERFJUXAgItIB6BXQ0pHotoKISAegV0BLR6KaAxEREUlRcCAiIiIpdQsOzGySmf3bzJ5IpG1oZtPMbF782y+mm5ldbmbzzexxM9slMc+4OP08MxuXSN/VzObGeS63eHOu1DJEREQkm3rWHFwPjClImwA84O5DgQfiMMD+wND4GQ9MhHCiB84Hdgd2A85PnOwnxmnz840pswwRERHJoG7Bgbv/FVhakDwWmBy/TwYOTqTf4MHDQF8z2wQYDUxz96Xu/jowDRgTx23g7g+5uwM3FORVbBkiIiKSQXu3ORjo7q8AxL8bx/TNgObEdC0xrbX0liLprS1jNWY23sxmmdmsxYsXV/1PiYiIdCUdpUFisYd5vYr0irj7Ve4+3N2HDxgwoNLZRUREuqT2Dg4WxVsCxL//juktwODEdIOAhWXSBxVJb20ZIiIikkF7BwdTgfwTB+OAuxLpR8WnFvYA3oy3BO4DRplZv9gQcRRwXxz3tpntEZ9SOKogr2LLEBERkQzq+SjjLcBDwDZm1mJmxwGXAp81s3nAZ+MwwN3AC8B84GrgawDuvhS4CJgZPxfGNICTgWviPM8D98T0UssQERFKv+lRb3mUvLp1n+zuR5QYtW+RaR04pUQ+k4BJRdJnAdsXSV9SbBkiIhKUetOj3vIoeR2lQaKIiNSYXuYk1dKLl0REuii9zEmqpZoDERERSVFwICIiIikKDkRERCRFwYGIiIikKDgQERGRFAUHIiIikqLgQERERFIUHIiIiEiKggMREWkzva+ha1EPiSIi0mZ6X0PXopoDERHJTO9rWDOo5kBERDLT+xrWDKo5EBERkRQFByIiIpKi4EBERERSFByIiIhIioIDERERSVFwICIiIikKDkRERCRFwYGIiHRI6pK5cdQJkoiIdEjqkrlxVHMgIiINU6o7ZnXJ3FiqORARkYYp2R0zqEvmBlLNgYiIiKQoOBAREZEUBQciIiKSouBAREREUhQciIiISIqCAxER6VJKPR7Zu3f3ko9NqmOlND3KKCIiXUqpxyPfGzmylacm1bFSUpetOTCzMWb2rJnNN7MJjS6PiIhIZ9ElgwMz6w78CtgfGAYcYWbDGlsqERGRzqFLBgfAbsB8d3/B3T8AbgXGNrhMIiLSxXTVl0N11TYHmwHNieEWYPcGlUVERDq5plwutGUootYvh8rlmmhuXn3+wYMHsmDBq1XnWwlz93ZZUHsys0OB0e5+fBz+KrCbu59WMN14YHwc3AZ4th2KtxHw2hqYZ2coY2fJszOUsR55doYy1iPPzlDGzpJnZyhjvfIsZnN3H1BsRFetOWgBBieGBwELCydy96uAq9qrUABmNsvdh69peXaGMnaWPDtDGeuRZ2coYz3y7Axl7Cx5doYy1ivPSnXVNgczgaFmtoWZ9QQOB6Y2uEwiIiKdQpesOXD3FWZ2KnAf0B2Y5O5PNrhYIiIinUKXDA4A3P1u4O5Gl6OIetzG6Ax5doYydpY8O0MZ65FnZyhjPfLsDGXsLHl2hjLWK8+KdMkGiSIiIlK9rtrmQERERKqk4EBERERSFBx0cWa2tZn1NbON47DVIM99zGx3M+tw+4+Zrd3oMjRaR9wuhWpVxs6wvTtDGaHzlLOe1qT9spwOfxDp6sxsfzM7oV55A78D/h9wpZnt7+7elgAh7vSXAz8AdonvsegQzOwAYKKZbdfospRSj+1tZv9lZgeb2VgAd/+ojdu4w5cx5tkZtneHLyN0jnJqv2xfCg4aKJ5ojwN+FXt1rGXeg4AfAqcCZwN3Area2QFtDBCWAw8RnnT5DvDJuLyK8jOz3mbWu8oylLI1sCfwWTPbvq2ZmdloMzvLzL4dy9um30s9treZjQZ+DWwOHGVm95lZt2q3cWcoY0JNt3e+rLXc5nSOMkIHL+eatF8WLrtRNYEKDhrI3d8H/gD8D3CpmZ0IK98qWZXEjrUCmOvuf/fwSMr9wN+ASWa2r1f5mIq7fwT8Hjgv5vlNMzsJODHrThyj9CnA78zsKDPrX01ZiniR8E6NnYBDzawp3lLpWWlGZvYZ4ApCl9pbA5OBvdtSXVjr7W1mawHHABe5+2Xu/iVge0JtEdUc5DpDGRNqtr1jWWu+zTtJGTt8Odek/TJ/bDazLeLwR1WWo00UHDRI3DkBlhD6YxgLnGlmPwd+mRhfqR7x77+Bzc3st2a2OXAW8ED8O8rM1qpk5y8oT3fgJHf/JfAmIfpeP8tObGafAM4n3Or4LnA0cLGZbZO1LEXyzO/H/yAELucDfYAfAbcAfavI9gBC51l/cPejgX7AGYQ3flZTU5I/iNVye38ILAKWJdJuArY2s5UHuQrKmN93XqthGQFeBd6tRRkLPEwNtndiW9ZkmyfWYz3K+LlalDFOu24dypn/LdaynPk8a/bbcfcVhN/Oe4nkNu2XtTwOmdlxwDFmtnUc/oWZfaGS8rSVgoN2ZmYbwcqdE2AO8Hl3f4Jwkv0a0D0xvpK8RxHaFpwNfAL4rzjqHML7JX4JPA5s5O4rsuz8Fhoe9om9TuYPen8EnjKz4cAI4EZg3zhcTj9gnrs/7u5zgJ8TquAOqPQqxcw2iQeGXjHpXcKB42VCF9pjgbdYFTBV4mmgn5ltGYcfAT4AToLsB4541dCNVR2OzQYObMv2NrMh8cDeDZgOXGRmZ5vZZGAAMDxMZltlzG9Y/J+W17CMu8Y8VwBPAhe2pYwxz0+Z2Q6JpFpt73zg9hTQty3b3MxGAuPNbJ0alzF/C+7JtpYxlnNf4EYz27TG5Vw//n26reU0s61ireKGMakW++XHzWxLM+sH/Ina7JeD4m88XyuyDDiYtq3LfxJu4e5nZtcQXgx4VwXzt5mCg3ZkZgcR7vvfYmanmdkO7t4MLLHw5sjxhKvpL5jZURXmvTvhJD2NEKGeAnzT3f/b3b8GfCUe/HcB+hRcNZTKczShfcGTZtbf3ZfHK+DlwD6EH/wZ7j6OcIshyztKXwPettAIqDehKu8hwo/poAr+3wOAWwnR/gQzG+LubxHaVpxFaA8xAfgPcGSW6sx40u1jZhsQbsH0B35gZlOAj8eqx/XN7JAKyngLMAkYZ2brEQ4Sr1W7vS00Mp0CXAZc6u53AhcQrqqeBr4Wt/NHrDrptZbfMOBBM7shkfwfYLGZHVllGTcH/mZmMwDc/WrgkljGpyotY8xzNGH/Xrkd3f1twgHzm1SxvWO+ewH7xsGnCcHr96vZ5mY2JpbxGXd/N1HGqW0s477AeWbWq61ljPmNBq4BNia8/S9fzqp/OzHfUcB0C9Xhswkn9WrX5f7AbcBPCLcu+wFv07bfzhjCbYlzgKvd/Y/A94ClVPHbiXnmf+M/JwQau7v7m1S4zZM1KWbW3d1nE44bRwKfBs6tRWPJiri7Pu3wAYYA/4obeiyhkeBdwJaEHf194JA47e7AVhXm/xng+/H7BoQg4IZ8Wkw/Ange2DFDfusQdvgxwEXAC0D/xPj+hNdgZylbf2JvnHH4TOB6QhXh1Jj2eeDHGfPbh3Av85PAfsBPgTFx3NHA68DBcXgrYNMMee5PiPJvIhzgNyW82XNP4MtArzjdD4D9MuQ3hlArtCfhB34LsHbi/694e8f/+7GY56diOfskxud7PB0X/5cs//fGwP8C9wJ3JNLPrXafJFSlXkc4QcwuGFdNGUfE/Xa/OLxuYtznCQHXFyvZ3nHa0YSTwh6JtO2AvYDDsm5zwAhByzXAF2JaX8KJd0NC8FFtGUcRjhujE2nbxHWSuYyJeQ+M6/2TcRv/PTFuXDW/ncS6fIpwNX5gTPs44XhX0e8HGEb4fX8qfq4h1BBAqC2o5rezVSzfiLhdfh3307XasF9uCTyTLwPwrbjP7xC321uJ/aHkuozz71kk/ceEW8GXEGpcds2yLWr1abcFrekfYDPgxsRwE/B1woF5d2CzmL5WlfnvRTiBfzwO9wB2Ba4ERsS0/YEtKshzCLBB/P6LmP+AItN1ayWPLxJeob0/8eQY0/sSWgnnf/RnAj/PWK7zgRMTw98l3N/MD29aybqMB4xnCfdDt475n1xkupMJJ/xWD0aEk8UvgAPi8OB40PgZcBSh6nJgpdubcMWTP0FuSjhhTiQ0Dt0l8b/8H7Bzhvy6EaqrfwkMBa4lBEfDCQflLaooY/4ge27cxpOBB+P+OTyO2zNrGeP0346/k40JB+SbCMHHL+PvKh90da+gnLsDr7AqqOxLuPXTp+D/yLTN47QXE4LCTQi1apOA50gc1CssY3fg+8CX4/AAwm9yyzbsl78B9o3DPQhX0ockptmkinKOIlSD7xH375klpstazhHALfF7U1yHk4BfASOBDavYL4cRj79xn2khXDzdC+xd6W8nTr8dMCUxvBeh5vbhuA/0LbcuCUHVMxSc+OP++b/x+6aEC7VTgZ5Z/+e2ftplIfqEHTnuNN9LpG0aD3wnxOHuJK6wq1jGGXFn3yYOr0/ok+CUNuSZvOL/BfBi/D6C0FaitXk3A+6JB6A740FktZ0bOC3+QIZVsC4HseoAvg9wfWL8+oVlL5PficAxieHjgNsSwz0IgcwDwE6VrDdCLc5DhOrFIwiBxyUxz4q2dyLP9YDbCVcqewGnE2om1iWcQPtnzTPmdx7wpfh9OuGeaf4EslY1+yShKvWU+P1BQlXtQXG4qZIyAj2BCwkBwTPxIDmKUAV+A+HE3q3CdZmvzdmLcCV+O6tqsz4TpxlCuF2WdZufQ7giPRkYH9MOIdx73rqSfTKR57fj9h1EOAFfQ2hsnA8YtqiwjPl9aK3Eev1RYny3Cn87FvMYkUi7E/hWYrg7kMtazrgfv0a4bfg6oZb1U3E9/Db+pird3msTgoxbYt5nx/1wHOH3OSB+Ktkv1waeINZ4EmpFzibcTjguvz5LlTPuey8DI+PwevFvvpalZ2LabShyYVbPT7staE3+JH5w2xAa852WGHc4cEONlrMB4aA8Ddghpn2TECBUdZCPeXRPfD+fcKBvpvwVwIasqrU4IR4cUgFC/PF8nQy3OuL0q/0PhCvdafH7VwhXrZVcVXQHBiWGtyVG7XE4H2z0rnL9DUt8/1wttjeJq0dCNeaNQI8q98vjgOMJV0IvAn8mcYuhwjyTAdtZhJPXPOCvhEdrq/3t9CTcHz4pMW7H+H9XXNsWfw/HEt5+9zKhjc62hNtSf2NVzU7mbU6ohXmIUKOTvFXxG+BjVa7PL8Xf75mEe+IQaieWANtXu18mttPHgIXE4LCN+2SP+PerwBXF1k8F23t9wgXIVYlx28XtvU6V+1CvmMcvCrbZ5Pz2riDPfI1nDniUEKT+gXA8+xIwMUMeXyO0H9qRcPHx27iv/A8wNL+ftnW7VL09G7XgrvxJ/PCsIL0boVrsbkJjMghXML8nRo01WPZ6hAj7eUK160IyXpGXyTf/AzsIWJw1T2IUHL+vDBDicKY2CxmWsQvhKuO/CU9jbFPptipIGwrMit/HEZ7X7lls2nLbO/E9fzA5gtDWpKrtXSLPwwi1Gv0q3Sdj2qbADOANYGxMu5pEwFRFOfsQHulaBnwupt1ABbe1ivyf+ac+kv/3/cTq20q3OasChHGJcflbIX0qzDN/73qd+H9PItwCOZpwdZnp3n2RfNcm1GrMjceK/P/+a+JtmrZ+Yhl/SIUn3Vby24RwC/L4CrZFsoYy/z8aoWHfWXH4UEIt1EZVlGmtxPcprLraPwyYBTRVkWf+mNg9Hh96xuFjCbdziwbrhFuXwwhtsc4k3DJ4hXCRtBuhtuhPxIuSRn0atuCu/GHVVUc+kl4rsSMNJFRX/i0eLOeT8aq5zDILT0y7xk/FB+NWlrERoZX8DtWWjRAg3EkIXJ4pd9Ak3K/vSWyEVrAu8+00BhBaHP8fGYKWwnVVZHyOcI/7WMJVwXYVbu/uiTJulJjutJjf9hnKuCsFV5uJPPNtKroRrs7L5lmmjEPigSlZNVz2nnOJMuYP9psQAslPVbivtLa9N01Mdxqhmr3suiy2zRN5diPe4onDhwF/p5UTUJky9oz5/YRQ3X4/sR1QFWXMl6kXIfi9htAXw/GEKvJcW3878fsBhBrNdaspZ4lpPkc4CW9WZrrW9st1CO0Y5hIC6mdq8NtZjxAMzyMEHk9m2T4VrMujCe2Xih7TCU9lPUY4Bl5CCAS+CxyWmGYQIbhst/YFRcvayIV3xU/8Ufwfobry+8CQxLg9CVVH68QfQT8qvD9cZHlW7G8t8ywYt3Yr861WbVgijzsIHeO0GmTEdfkEoaptCokagbgupxGqrXsSgq0sT2Gs1jI48f+ubIBHaCH+KGWCjQzb+2ZCsDGQEAxmObiNpiBoTByIPh3/74/F/3tiuTzLlHEvwklnq/y6yLIPlSnjnoTasW1b2w+q3N5bxt/OZWQ/6ba2zTdPpJ1AaDhaMt8MZZxOCKJXnoxqUMY+cbucTLhldlvG/bLcutwqkVay1qmCcib3q08QTnAla2Ay/HZuiL+bAXE/bTXQyPjbeYBw3O1FuI20cY32y48RAs3jibcEiuTTn9AGa1hif5tBaMg6IDHdV2J6RTVitf40bMFd8RMPXC8QHiscQegF8B+EtgY9CVVHB9Zwefkf5U6EKreSTw3UO8/4o/wW6dsI+byGE+/BEqrsH6SVhknxQDiYcMWwdzxAnEW4RZJvS/FYcl2S/YSWahlccOB4jFDdtxbhSq3Vk0+l25sM9w8Jt53msaqRUu/4tychqEz93zXYJ1+lTMPSdihjxdu7gryzbvPuhBPvtq3ktRnhJFG2jFQQrGco45Okg5hW790Tam2ezFjOzA0Qs67LxLgNWslrq7hf7l1iv3ylyv1yfkfdLwlB3t+AfRJptxMaeh8Rh08iPNGRKfCt56ehC+9qH0JEenX8bvFzNiE67seqx4TafHWfWOanCY0DRzQqT8Jjio8RHwkqktdcVj2CtwEZrlIIB+urCAfk/IH264RHkJpIPHJGhqtdSrcMXifOfx/x3ngF66kvcGUttzfh3m/+yYMc4d5//i2YfVj1eFTWK/w+wLU1LuMPCFevvcuVscL1eRWhyrfs9s6YX822efxf1ybU1JQtY4W/tYVZykiGE3ks2/p1KOcehBN25nJm+O38qg6/nRsJtw5qsl9S4XEoQ34nxTJ+lXBb4SbC01LXJv6HVm9jttcn/89KG1h4L8CGhKj1HsIz97+O47oRWvh/QOhj+0Nv44s0LLw57KPYa9gwwk7/x0bkGXvY+yPwA3e/KnZ3uhGhBuExM5tAaKX+RzMzL7PDxW5L+xGuKn4NPOruP0qMP5fQoOcUYFm5/BLzfY0Q/V9MeB/EDwidlGxIaMC5xN0/yPdA1lq+Zvbx+D++SvihT3H3n8RxVW1vC29v6xnnO55w33pfQlXmQkKL5r6Ep0/eL5dn7PlvCOG2xl+BP7r7pW0s4wjC+rqX0DFWX0IX3VWVMeb5ecJV5C8J6/Jxd/9+YnxV2zvOeyJhHV5E2NZVbXMLLwobRWhHcGks4yVtLWPsifNowmN63y9Xxgy/ndGEJzpOIGyD+e5+UQ3KOYTwSOaubS2nme1BqD18LeZ1o7v/LI6rdr/8FOERXic8yfERoXO0tuyXQ+M8zxL6/3jQ3S9LjK92XfYhdES1H/CGu58R0+929wNi74gfZsmr7hodnXT2D6GTn2cI9xp/TDjIvQwclZhmNPDrGi93T+AvVPAIDuWvrqvJc1fCSfx4wg/zfsKbze4n3Ttjlqj6QMLTBn+J6/Eg4CVC16H5aYYAv6mgfFsRgp0m4BuEK6oWVrUM/g7hZLdBxjLuH8s4lXDQ2IdQhXtqtds75jmX8NTKTYT7tZeR6J+CcBK+OkNe3QhXTk8SDmwHE6qZ5wKnV1PGgjyfIzx+25PQqdPplZYxMf0oQhXq6MS2XQCcU+32jvMcRDhBrkM4cV1d7TYn3I55JlHGHKE9ypltLOPBhBPg1nG/rLqMBetyAaGBW79Yzrauy9GE+/S7EK56J7VhXR4Ufzs3EX7fYwm3ab7Wht9OMs9rCLVFPyfRiVkV+2W+0eAdcV3+lHBBkSxnxeuy8DeV+H4UocakJk+s1erT8AJ05g/h6u53xAY68aB5Zkx7ldifAeHq4AFCdV/WKtFNKNFaldAI70EqvAdLwT3vgh20ojyJnbrE73vGH+Tz8QCSv1f3ALFDmQz5fZpwEP5EHL6KcJW/aTzgfYdwoj+a8OhRllsT+WDjr4SD756EVu7jE9NkbhlMqHl4jvgIJuFk/klCgPR8PGBaJdu7SJ5TY55rk7i3TKiGvJtwks5ysjibcH/0t4RX025MaCeQ75iomn0yn+fNwLExbe1qyhi396LE/70Roa3HLoTq6zMJJ87M2zvmkz9J5ns+XI9w8j0+MU0l2/xMwjtKIAQGw+L/+TbhOfVtqihjPuDIl3HduO+Mq7KM+xFqLT9OCNr+TGhrshXhRF7xb6dgXS4ALolpXyd22lZJOQmN8e5jVd8MN8T894n5f43KfzuFeU6OeQ4h0Zivwv2ysNHgeMKTEjcSAoSzCDUfFa3LVpZ3LKFb54qeAGuPT7WvYJVVNiDsLH8ndF7xmZj+MuFd3jsT+xf38HKTsszsc4Te1g4h9IZWqJnQffCTWaobY577A8eZ2aNAi7vf6OE2QjcP1WyZ8zSzA4EpZjbV3Q9397+b2XLgr+5+R76MZtZM6Ac9q0s9vHAEQgOl6919oZntTTjAnUm4WjnG3V8v8/9+mlANfIS7zzazK4HD3f20gpeffIbQ0ngdQnVmaxYR1tEjZtZECArOI1xRTyH0YbA9oYFV1u1dmOcnY54LgYfN7CbCAep44Kvu/p8MeQKsIJzMriVcRQ8i1B4cZma7EdZj5n2yIM9JwAnx9soHwLlm9g3CFVDWMi4hvMBrk3gr6n9i/k8SrgB3JfyuhpNhe8PKbX4joSHbI2a2IaHW40bSr+atZJuvIJxwITRSXUgIBOcSTkTbEH7fmcoY7Qpc4+73mtkgwjp9jBAwVFPG7oSayifNrC9hHR7g7leY2WcIv51vxuVmXZf7EWoExxKCyrstvGlzIumXEmUt5wpCu41t43FhBOFE/DSh59jjCNX/nyH7fpnMcwGhhqA/4ST+opl9nxAYnEj2/XIFIYhoAp7ycKs0/7TCM4TgcFtgZyrb5qU8QDhuzm9jPrXX6Oiks3+AzxKu9vI9AXYndJzzM0IjmN5U0GkHoVrtMeC/SoyvqBe8OM9uhB/4VwgnsMdIV/lnzpNwlXMv4YR1PfDbxLjkle6XCC8w2Txjvt1Z9R6H7oST2WxWNUzanETf9xny+zRwdGJ4QNxOyd4Zj4vrouKWwYTg5Tvx+wmEhk9DCY9IVdxJS5E8jyHUQA2N6znT8/yJvLYEJsTvZxFOxN+Nwz2rKWORPN9lVaOy66oo406EtiUtcR12i/vVr4DBcZrMV2aEE3UL4YTWn3Cr7964Ho+J//fxVNAanBDsPUsIDI6JaVsT7rmPrbSMcfqvs6o24v9i3jcQqsY3JARZFbdYZ1WDxTGEmst8LVy+O97Mj8YRjkOfzs9H6Ags30Njvq3aCRWuy0MIjwc/nNgXRxFur+wVfzsVdRFckOd5MW2f+JsZUeVvp7DR4M2EtgU/SUzT0McM2+PT8AJ09k/coU8lVIP/VyL9L/kfZwV5DYzz5avw+hOqwb8CfLYNZdyLxD03QlT8Qn45VeS3KSG63ojQav3mgvHjCIFBRT/KxPxrxfwfiMNHEhoXVdKVbalgY0BM+xghgCv56FqFZb6XGr81LeZZbbe7mxJO2CcQAsPzCQ1HT2pDeQrz/G7M88tU+Rgt4UrslIK0+1j1IqlKn3goFnAcS7i9sgXhpF5Ra3DCmx9fBC5MpF1LbFdURRmLBRwfI/Sq98VYxjb1akrogOnbcd+vugteVg848o/wrUs4qVe6LvsR2mYlH/G9gxhoVVnGUnlW9QQX4aLuK3Ff/3ki/W7WgKAg/9FthTZy9/fM7GZCS9lzzWxbQlX6RoQqyEryWhSrkXeOretPInSG4cAWZraOu9+VJa/8rYHYEnsZMNDMNnT3pe7+aqyCvdvMHnP3KRWWM/9//Se2CL/KzG5y9yPNbDvCgeMwd3+hknwT+a+IeTeb2Q8IJ67QOAAABZ9JREFUVxdHu/uyCvL4kNCaGsK9zDeApe6+2MyOJLyP4AJ3f6tUHqUU3nYxsy8RaiZerjSvDHlm/p+TPNyOaSbcojjF3X9vZvnnwKtSIs99gHle5RM47v4U4Z4rsPL/3oi4LpPrJGN+j8XbXiPd/eqYPMnMDiOcxM+topj3EIKrC8zsXzFtJ0Jr+2rK+ISZ5d958lxMe8HMegArqixjoccIL2L7Yfw9VSW/XT3cArkKONDMnnL3d8xsQqXb3d1fN7M/A182sw8IF1ebx/JWW8ZSebZUmd+bwM1mdkv+/zOzowg1KMurLWen0+jopKt8CFWWIwlXA9dTQa0BoTp0r8TwYYRqsnw13jqEq4BTK8hznYLhicAjBWnHkXgJVBv+940IUfazhIPdJm3Mz+L6fJ7QWKloj2NV5Hs94arsUWrTZfXacR0+SZW1JPXMk9AodLUOazpanoltnm+cVdMOYAi3uP5JFf3nF+SzCyEg+CltbEBGqB07ilDLcVz8zCLDq6ErWMYUEr0O1mg9PkgbXwZEOMl+nVBLeh8Z3yjZ3nkm8u6wjQbr+VE/BzVmZt0JFxOZImoz+yLhgPMyodruQULL+sHAAo/PvJrZRYT75edkaDD4eUK14onu/kgi/beE+8ZjPdQeTCBU6x4dy1z1zmBmZxAaUX7W3edWm09BnkcT3g3/ZBvzMcKTJU/Hv/u6+7walK8Hoc3J8+7+bFvzq2OemRqtNjLPuI0+A7zq7s+Um76CPI8hNMY7tK37UT2Y2S6E++ZrExrgtvm3U4/tnch7CnC2u79Ug7zyTyRUXHvXznluTmiX1fEaDdaRgoMGiieCm4DLPbT4/xLhcbu3CY1f3o7THUk4wB1W7oRhoTOd2wgNnXYiPO+bDBCuIPT29UEcf0gNTr79CFcpZ7n7423JqyDfWp+AjqYGwYZ0DvUIONZU9Qw4pGNScNBAMTiYCvzO3a+30EPYCMKLPl4gNMIbQegQ58gsJzUzG0josOUGMzuZ0G7hhIIAYWtCo5vFtbgCiHn2cvf3yk/ZODrAiYhko+Cgwczss4QnEn7s7n+LtyUOIzynfGScZoC7L64gz7U8NkIys5MIb3Mb7+7/sNAV6kJ3L/fstIiIrKEUHDSYmfUiPHu9I/D/27ufF5viMI7j78+IlYWSppTYWRgbsxo2Gv+AH7MwxUIW/AP+AlnYKSVSFrJQks0kUsRG1BRlFmyklFFSysrmsThnpk7jZmjuD+b9Wt17z73nPLv76XzP831uVdWz9vPHwLmqml+Da5ylaS98TrN72KlqnsiVJGkFWxmHrHq3Qo7zl604v7jG1XbHs5M00xENBpKkngwHI6CaPt3rNO0yZ2i2ej1RVZ/X4vxJDtF0JUyvVSeBJOn/5bLCiPnTVshVnnNpiNOH335ZkrTuGQ4kSVLH2LALkCRJo8VwIEmSOgwHkiSpw3AgSZI6DAeSJKnDcCCpr5IcSVLtBl+S/gGGA0n9Nkszivz4sAuRtDqGA0l9k2QzzRjy07ThIMlYkitJFpLMJbmfZKY9NpnkaZL5JA/bDbwkDZjhQFI/HQYeVNU74GuSfcBRmgFge2mGjk3B8gjzy8BMVU0CN4ALwyhaWu+crSCpn2aBS+3r2+37jcCddovwxSRP2uO7gQngURKADcCnwZYrCQwHkvokyVZgGphIUjR/9gXc6/UTYKGqpgZUoqQeXFaQ1C8zwM2q2llVu6pqB/Ae+AIca589GAcOtt9/C2xLsrzMkGTPMAqX1jvDgaR+mWXlXYK7wHbgI/AGuAa8AL5V1Q+aQHExyWvgFbB/cOVKWuJURkkDl2RzVX1vlx5eAgeqanHYdUlq+MyBpGGYS7IF2AScNxhIo8U7B5IkqcNnDiRJUofhQJIkdRgOJElSh+FAkiR1GA4kSVKH4UCSJHX8BDQgCXmei7uhAAAAAElFTkSuQmCC\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig,ax = plt.subplots(figsize = [8,6])\n", "ax.bar(group, df2['male_h'],bar_width, facecolor='c', edgecolor='k',label='Male')\n", "ax.bar(group+bar_width, df2['female_h'],bar_width, facecolor='y', edgecolor='k',label='Female')\n", "plt.xlabel('Age')\n", "ax.set_xticks(group+bar_width/2)\n", "ax.set_xticklabels(df1.index)\n", "ax.set_ylabel('Population')\n", "ax.tick_params(axis='x', rotation= 45)\n", "plt.title('Population in Hill region in 2011')\n", "plt.savefig(\"image/Hill.png\", dpi = 600) # dpi dot per inch\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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\n", "
" ], "text/plain": [ " male_t female_t\n", "age \n", "00-04 672611 643890\n", "05 -- 09 860329 817846\n", "10 -- 14 900532 859839\n", "15-19 717921 706115\n", "20-24 518162 634709\n", "25-29 472400 585908\n", "30-34 400841 500080\n", "35-39 395021 441196\n", "40-44 340734 359363\n", "45-49 295678 291779\n", "50-54 247753 235545\n", "55-59 205981 198266\n", "60-64 187870 186600\n", "65-69 140147 131007\n", "70-74 98035 89092\n", "75-79 50283 47026\n", "80-84 25551 25252\n", "85-89 9812 9855\n", "90-94 4128 5054\n", "95+ 2593 3901" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df3 = pd.read_excel('data/tp2011.xlsx') #population in Tarai region in 2011\n", "df3 = df3.set_index('age')\n", "df3" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig,ax = plt.subplots(figsize = [8,6])\n", "ax.bar(group, df3['male_t'],bar_width, facecolor='b', edgecolor='k',label='Male')\n", "ax.bar(group+bar_width, df3['female_t'],bar_width, facecolor='g', edgecolor='k',label='Female')\n", "plt.xlabel('Age')\n", "ax.set_xticks(group+bar_width/2)\n", "ax.set_xticklabels(df1.index)\n", "ax.set_ylabel('Population')\n", "ax.tick_params(axis='x', rotation= 45)\n", "plt.title('Population in Tarai region in 2011')\n", "plt.savefig(\"image/Tarai.png\", dpi = 600) # dpi dot per inch\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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male_mfemale_mmale_hfemale_hmale_tfemale_t
age
00-04102314.099825.0NaNNaNNaNNaN
05 -- 09117662.0117591.0NaNNaNNaNNaN
10 -- 14121934.0121729.0NaNNaNNaNNaN
15-1993061.0100093.0NaNNaNNaNNaN
20-2461507.078811.0NaNNaNNaNNaN
25-2955099.066047.0NaNNaNNaNNaN
30-3445378.054371.0NaNNaNNaNNaN
35-3944887.051227.0NaNNaNNaNNaN
40-4441656.046001.0NaNNaNNaNNaN
45-4938556.039807.0NaNNaNNaNNaN
50-5435511.034837.0NaNNaNNaNNaN
55-5929530.027519.0NaNNaNNaNNaN
60-6426327.029466.0NaNNaNNaNNaN
65-6919141.019999.0NaNNaNNaNNaN
70-7413859.014847.0NaNNaNNaNNaN
75-798655.08986.0NaNNaNNaNNaN
80-844854.05087.0NaNNaNNaNNaN
85-891942.01941.0NaNNaNNaNNaN
90-94519.0635.0NaNNaNNaNNaN
95+200.0381.0NaNNaNNaNNaN
00-04NaNNaN540032.0509291.0NaNNaN
05 -- 09NaNNaN657185.0634246.0NaNNaN
10 -- 14NaNNaN742164.0729226.0NaNNaN
15-19NaNNaN632209.0682581.0NaNNaN
20-24NaNNaN464312.0600570.0NaNNaN
25-29NaNNaN389744.0510156.0NaNNaN
30-34NaNNaN324358.0410277.0NaNNaN
35-39NaNNaN300292.0371696.0NaNNaN
40-44NaNNaN277900.0320467.0NaNNaN
45-49NaNNaN240867.0266272.0NaNNaN
50-54NaNNaN222600.0229230.0NaNNaN
55-59NaNNaN177381.0179586.0NaNNaN
60-64NaNNaN154254.0172310.0NaNNaN
65-69NaNNaN118494.0125661.0NaNNaN
70-74NaNNaN87716.091604.0NaNNaN
75-79NaNNaN58420.061765.0NaNNaN
80-84NaNNaN32382.035651.0NaNNaN
85-89NaNNaN14056.014920.0NaNNaN
90-94NaNNaN4293.05706.0NaNNaN
95+NaNNaN1408.02725.0NaNNaN
00-04NaNNaNNaNNaN672611.0643890.0
05 -- 09NaNNaNNaNNaN860329.0817846.0
10 -- 14NaNNaNNaNNaN900532.0859839.0
15-19NaNNaNNaNNaN717921.0706115.0
20-24NaNNaNNaNNaN518162.0634709.0
25-29NaNNaNNaNNaN472400.0585908.0
30-34NaNNaNNaNNaN400841.0500080.0
35-39NaNNaNNaNNaN395021.0441196.0
40-44NaNNaNNaNNaN340734.0359363.0
45-49NaNNaNNaNNaN295678.0291779.0
50-54NaNNaNNaNNaN247753.0235545.0
55-59NaNNaNNaNNaN205981.0198266.0
60-64NaNNaNNaNNaN187870.0186600.0
65-69NaNNaNNaNNaN140147.0131007.0
70-74NaNNaNNaNNaN98035.089092.0
75-79NaNNaNNaNNaN50283.047026.0
80-84NaNNaNNaNNaN25551.025252.0
85-89NaNNaNNaNNaN9812.09855.0
90-94NaNNaNNaNNaN4128.05054.0
95+NaNNaNNaNNaN2593.03901.0
\n", "
" ], "text/plain": [ " male_m female_m male_h female_h male_t female_t\n", "age \n", "00-04 102314.0 99825.0 NaN NaN NaN NaN\n", "05 -- 09 117662.0 117591.0 NaN NaN NaN NaN\n", "10 -- 14 121934.0 121729.0 NaN NaN NaN NaN\n", "15-19 93061.0 100093.0 NaN NaN NaN NaN\n", "20-24 61507.0 78811.0 NaN NaN NaN NaN\n", "25-29 55099.0 66047.0 NaN NaN NaN NaN\n", "30-34 45378.0 54371.0 NaN NaN NaN NaN\n", "35-39 44887.0 51227.0 NaN NaN NaN NaN\n", "40-44 41656.0 46001.0 NaN NaN NaN NaN\n", "45-49 38556.0 39807.0 NaN NaN NaN NaN\n", "50-54 35511.0 34837.0 NaN NaN NaN NaN\n", "55-59 29530.0 27519.0 NaN NaN NaN NaN\n", "60-64 26327.0 29466.0 NaN NaN NaN NaN\n", "65-69 19141.0 19999.0 NaN NaN NaN NaN\n", "70-74 13859.0 14847.0 NaN NaN NaN NaN\n", "75-79 8655.0 8986.0 NaN NaN NaN NaN\n", "80-84 4854.0 5087.0 NaN NaN NaN NaN\n", "85-89 1942.0 1941.0 NaN NaN NaN NaN\n", "90-94 519.0 635.0 NaN NaN NaN NaN\n", "95+ 200.0 381.0 NaN NaN NaN NaN\n", "00-04 NaN NaN 540032.0 509291.0 NaN NaN\n", "05 -- 09 NaN NaN 657185.0 634246.0 NaN NaN\n", "10 -- 14 NaN NaN 742164.0 729226.0 NaN NaN\n", "15-19 NaN NaN 632209.0 682581.0 NaN NaN\n", "20-24 NaN NaN 464312.0 600570.0 NaN NaN\n", "25-29 NaN NaN 389744.0 510156.0 NaN NaN\n", "30-34 NaN NaN 324358.0 410277.0 NaN NaN\n", "35-39 NaN NaN 300292.0 371696.0 NaN NaN\n", "40-44 NaN NaN 277900.0 320467.0 NaN NaN\n", "45-49 NaN NaN 240867.0 266272.0 NaN NaN\n", "50-54 NaN NaN 222600.0 229230.0 NaN NaN\n", "55-59 NaN NaN 177381.0 179586.0 NaN NaN\n", "60-64 NaN NaN 154254.0 172310.0 NaN NaN\n", "65-69 NaN NaN 118494.0 125661.0 NaN NaN\n", "70-74 NaN NaN 87716.0 91604.0 NaN NaN\n", "75-79 NaN NaN 58420.0 61765.0 NaN NaN\n", "80-84 NaN NaN 32382.0 35651.0 NaN NaN\n", "85-89 NaN NaN 14056.0 14920.0 NaN NaN\n", "90-94 NaN NaN 4293.0 5706.0 NaN NaN\n", "95+ NaN NaN 1408.0 2725.0 NaN NaN\n", "00-04 NaN NaN NaN NaN 672611.0 643890.0\n", "05 -- 09 NaN NaN NaN NaN 860329.0 817846.0\n", "10 -- 14 NaN NaN NaN NaN 900532.0 859839.0\n", "15-19 NaN NaN NaN NaN 717921.0 706115.0\n", "20-24 NaN NaN NaN NaN 518162.0 634709.0\n", "25-29 NaN NaN NaN NaN 472400.0 585908.0\n", "30-34 NaN NaN NaN NaN 400841.0 500080.0\n", "35-39 NaN NaN NaN NaN 395021.0 441196.0\n", "40-44 NaN NaN NaN NaN 340734.0 359363.0\n", "45-49 NaN NaN NaN NaN 295678.0 291779.0\n", "50-54 NaN NaN NaN NaN 247753.0 235545.0\n", "55-59 NaN NaN NaN NaN 205981.0 198266.0\n", "60-64 NaN NaN NaN NaN 187870.0 186600.0\n", "65-69 NaN NaN NaN NaN 140147.0 131007.0\n", "70-74 NaN NaN NaN NaN 98035.0 89092.0\n", "75-79 NaN NaN NaN NaN 50283.0 47026.0\n", "80-84 NaN NaN NaN NaN 25551.0 25252.0\n", "85-89 NaN NaN NaN NaN 9812.0 9855.0\n", "90-94 NaN NaN NaN NaN 4128.0 5054.0\n", "95+ NaN NaN NaN NaN 2593.0 3901.0" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "frames = [df1, df2, df3]\n", "result1 = pd.concat(frames,axis=0, sort=False)\n", "result1" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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male_mfemale_mmale_hfemale_hmale_tfemale_t
age
00-0410231499825540032509291672611643890
05 -- 09117662117591657185634246860329817846
10 -- 14121934121729742164729226900532859839
15-1993061100093632209682581717921706115
20-246150778811464312600570518162634709
25-295509966047389744510156472400585908
30-344537854371324358410277400841500080
35-394488751227300292371696395021441196
40-444165646001277900320467340734359363
45-493855639807240867266272295678291779
50-543551134837222600229230247753235545
55-592953027519177381179586205981198266
60-642632729466154254172310187870186600
65-691914119999118494125661140147131007
70-74138591484787716916049803589092
75-798655898658420617655028347026
80-844854508732382356512555125252
85-8919421941140561492098129855
90-945196354293570641285054
95+2003811408272525933901
\n", "
" ], "text/plain": [ " male_m female_m male_h female_h male_t female_t\n", "age \n", "00-04 102314 99825 540032 509291 672611 643890\n", "05 -- 09 117662 117591 657185 634246 860329 817846\n", "10 -- 14 121934 121729 742164 729226 900532 859839\n", "15-19 93061 100093 632209 682581 717921 706115\n", "20-24 61507 78811 464312 600570 518162 634709\n", "25-29 55099 66047 389744 510156 472400 585908\n", "30-34 45378 54371 324358 410277 400841 500080\n", "35-39 44887 51227 300292 371696 395021 441196\n", "40-44 41656 46001 277900 320467 340734 359363\n", "45-49 38556 39807 240867 266272 295678 291779\n", "50-54 35511 34837 222600 229230 247753 235545\n", "55-59 29530 27519 177381 179586 205981 198266\n", "60-64 26327 29466 154254 172310 187870 186600\n", "65-69 19141 19999 118494 125661 140147 131007\n", "70-74 13859 14847 87716 91604 98035 89092\n", "75-79 8655 8986 58420 61765 50283 47026\n", "80-84 4854 5087 32382 35651 25551 25252\n", "85-89 1942 1941 14056 14920 9812 9855\n", "90-94 519 635 4293 5706 4128 5054\n", "95+ 200 381 1408 2725 2593 3901" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "result2 = pd.concat(frames, axis=1,sort=False)\n", "result2" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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age
00-04
05 -- 09
10 -- 14
15-19
20-24
25-29
30-34
35-39
40-44
45-49
50-54
55-59
60-64
65-69
70-74
75-79
80-84
85-89
90-94
95+
00-04
05 -- 09
10 -- 14
15-19
20-24
25-29
30-34
35-39
40-44
45-49
50-54
55-59
60-64
65-69
70-74
75-79
80-84
85-89
90-94
95+
00-04
05 -- 09
10 -- 14
15-19
20-24
25-29
30-34
35-39
40-44
45-49
50-54
55-59
60-64
65-69
70-74
75-79
80-84
85-89
90-94
95+
\n", "
" ], "text/plain": [ "Empty DataFrame\n", "Columns: []\n", "Index: [00-04, 05 -- 09, 10 -- 14, 15-19, 20-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55-59, 60-64, 65-69, 70-74, 75-79, 80-84, 85-89, 90-94, 95+, 00-04, 05 -- 09, 10 -- 14, 15-19, 20-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55-59, 60-64, 65-69, 70-74, 75-79, 80-84, 85-89, 90-94, 95+, 00-04, 05 -- 09, 10 -- 14, 15-19, 20-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55-59, 60-64, 65-69, 70-74, 75-79, 80-84, 85-89, 90-94, 95+]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "result3 = pd.concat(frames, sort=False, join = \"inner\")\n", "result3" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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female_hfemale_mfemale_tmale_hmale_mmale_t
age
00-04NaN99825.0NaNNaN102314.0NaN
05 -- 09NaN117591.0NaNNaN117662.0NaN
10 -- 14NaN121729.0NaNNaN121934.0NaN
15-19NaN100093.0NaNNaN93061.0NaN
20-24NaN78811.0NaNNaN61507.0NaN
25-29NaN66047.0NaNNaN55099.0NaN
30-34NaN54371.0NaNNaN45378.0NaN
35-39NaN51227.0NaNNaN44887.0NaN
40-44NaN46001.0NaNNaN41656.0NaN
45-49NaN39807.0NaNNaN38556.0NaN
50-54NaN34837.0NaNNaN35511.0NaN
55-59NaN27519.0NaNNaN29530.0NaN
60-64NaN29466.0NaNNaN26327.0NaN
65-69NaN19999.0NaNNaN19141.0NaN
70-74NaN14847.0NaNNaN13859.0NaN
75-79NaN8986.0NaNNaN8655.0NaN
80-84NaN5087.0NaNNaN4854.0NaN
85-89NaN1941.0NaNNaN1942.0NaN
90-94NaN635.0NaNNaN519.0NaN
95+NaN381.0NaNNaN200.0NaN
00-04509291.0NaNNaN540032.0NaNNaN
05 -- 09634246.0NaNNaN657185.0NaNNaN
10 -- 14729226.0NaNNaN742164.0NaNNaN
15-19682581.0NaNNaN632209.0NaNNaN
20-24600570.0NaNNaN464312.0NaNNaN
25-29510156.0NaNNaN389744.0NaNNaN
30-34410277.0NaNNaN324358.0NaNNaN
35-39371696.0NaNNaN300292.0NaNNaN
40-44320467.0NaNNaN277900.0NaNNaN
45-49266272.0NaNNaN240867.0NaNNaN
50-54229230.0NaNNaN222600.0NaNNaN
55-59179586.0NaNNaN177381.0NaNNaN
60-64172310.0NaNNaN154254.0NaNNaN
65-69125661.0NaNNaN118494.0NaNNaN
70-7491604.0NaNNaN87716.0NaNNaN
75-7961765.0NaNNaN58420.0NaNNaN
80-8435651.0NaNNaN32382.0NaNNaN
85-8914920.0NaNNaN14056.0NaNNaN
90-945706.0NaNNaN4293.0NaNNaN
95+2725.0NaNNaN1408.0NaNNaN
00-04NaNNaN643890.0NaNNaN672611.0
05 -- 09NaNNaN817846.0NaNNaN860329.0
10 -- 14NaNNaN859839.0NaNNaN900532.0
15-19NaNNaN706115.0NaNNaN717921.0
20-24NaNNaN634709.0NaNNaN518162.0
25-29NaNNaN585908.0NaNNaN472400.0
30-34NaNNaN500080.0NaNNaN400841.0
35-39NaNNaN441196.0NaNNaN395021.0
40-44NaNNaN359363.0NaNNaN340734.0
45-49NaNNaN291779.0NaNNaN295678.0
50-54NaNNaN235545.0NaNNaN247753.0
55-59NaNNaN198266.0NaNNaN205981.0
60-64NaNNaN186600.0NaNNaN187870.0
65-69NaNNaN131007.0NaNNaN140147.0
70-74NaNNaN89092.0NaNNaN98035.0
75-79NaNNaN47026.0NaNNaN50283.0
80-84NaNNaN25252.0NaNNaN25551.0
85-89NaNNaN9855.0NaNNaN9812.0
90-94NaNNaN5054.0NaNNaN4128.0
95+NaNNaN3901.0NaNNaN2593.0
\n", "
" ], "text/plain": [ " female_h female_m female_t male_h male_m male_t\n", "age \n", "00-04 NaN 99825.0 NaN NaN 102314.0 NaN\n", "05 -- 09 NaN 117591.0 NaN NaN 117662.0 NaN\n", "10 -- 14 NaN 121729.0 NaN NaN 121934.0 NaN\n", "15-19 NaN 100093.0 NaN NaN 93061.0 NaN\n", "20-24 NaN 78811.0 NaN NaN 61507.0 NaN\n", "25-29 NaN 66047.0 NaN NaN 55099.0 NaN\n", "30-34 NaN 54371.0 NaN NaN 45378.0 NaN\n", "35-39 NaN 51227.0 NaN NaN 44887.0 NaN\n", "40-44 NaN 46001.0 NaN NaN 41656.0 NaN\n", "45-49 NaN 39807.0 NaN NaN 38556.0 NaN\n", "50-54 NaN 34837.0 NaN NaN 35511.0 NaN\n", "55-59 NaN 27519.0 NaN NaN 29530.0 NaN\n", "60-64 NaN 29466.0 NaN NaN 26327.0 NaN\n", "65-69 NaN 19999.0 NaN NaN 19141.0 NaN\n", "70-74 NaN 14847.0 NaN NaN 13859.0 NaN\n", "75-79 NaN 8986.0 NaN NaN 8655.0 NaN\n", "80-84 NaN 5087.0 NaN NaN 4854.0 NaN\n", "85-89 NaN 1941.0 NaN NaN 1942.0 NaN\n", "90-94 NaN 635.0 NaN NaN 519.0 NaN\n", "95+ NaN 381.0 NaN NaN 200.0 NaN\n", "00-04 509291.0 NaN NaN 540032.0 NaN NaN\n", "05 -- 09 634246.0 NaN NaN 657185.0 NaN NaN\n", "10 -- 14 729226.0 NaN NaN 742164.0 NaN NaN\n", "15-19 682581.0 NaN NaN 632209.0 NaN NaN\n", "20-24 600570.0 NaN NaN 464312.0 NaN NaN\n", "25-29 510156.0 NaN NaN 389744.0 NaN NaN\n", "30-34 410277.0 NaN NaN 324358.0 NaN NaN\n", "35-39 371696.0 NaN NaN 300292.0 NaN NaN\n", "40-44 320467.0 NaN NaN 277900.0 NaN NaN\n", "45-49 266272.0 NaN NaN 240867.0 NaN NaN\n", "50-54 229230.0 NaN NaN 222600.0 NaN NaN\n", "55-59 179586.0 NaN NaN 177381.0 NaN NaN\n", "60-64 172310.0 NaN NaN 154254.0 NaN NaN\n", "65-69 125661.0 NaN NaN 118494.0 NaN NaN\n", "70-74 91604.0 NaN NaN 87716.0 NaN NaN\n", "75-79 61765.0 NaN NaN 58420.0 NaN NaN\n", "80-84 35651.0 NaN NaN 32382.0 NaN NaN\n", "85-89 14920.0 NaN NaN 14056.0 NaN NaN\n", "90-94 5706.0 NaN NaN 4293.0 NaN NaN\n", "95+ 2725.0 NaN NaN 1408.0 NaN NaN\n", "00-04 NaN NaN 643890.0 NaN NaN 672611.0\n", "05 -- 09 NaN NaN 817846.0 NaN NaN 860329.0\n", "10 -- 14 NaN NaN 859839.0 NaN NaN 900532.0\n", "15-19 NaN NaN 706115.0 NaN NaN 717921.0\n", "20-24 NaN NaN 634709.0 NaN NaN 518162.0\n", "25-29 NaN NaN 585908.0 NaN NaN 472400.0\n", "30-34 NaN NaN 500080.0 NaN NaN 400841.0\n", "35-39 NaN NaN 441196.0 NaN NaN 395021.0\n", "40-44 NaN NaN 359363.0 NaN NaN 340734.0\n", "45-49 NaN NaN 291779.0 NaN NaN 295678.0\n", "50-54 NaN NaN 235545.0 NaN NaN 247753.0\n", "55-59 NaN NaN 198266.0 NaN NaN 205981.0\n", "60-64 NaN NaN 186600.0 NaN NaN 187870.0\n", "65-69 NaN NaN 131007.0 NaN NaN 140147.0\n", "70-74 NaN NaN 89092.0 NaN NaN 98035.0\n", "75-79 NaN NaN 47026.0 NaN NaN 50283.0\n", "80-84 NaN NaN 25252.0 NaN NaN 25551.0\n", "85-89 NaN NaN 9855.0 NaN NaN 9812.0\n", "90-94 NaN NaN 5054.0 NaN NaN 4128.0\n", "95+ NaN NaN 3901.0 NaN NaN 2593.0" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "result4 = pd.concat(frames, sort=True, join = \"outer\")\n", "result4" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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male_mfemale_mmale_hfemale_hmale_tfemale_tmalefemaleratiototaldensity(per sq.km)
age
00-0410231499825540032509291672611643890131495712530061.049442256796317.447653
05 -- 09117662117591657185634246860329817846163517615696831.041724320485921.774951
10 -- 14121934121729742164729226900532859839176463017107941.031468347542423.613265
15-1993061100093632209682581717921706115144319114887890.969372293198019.920914
20-246150778811464312600570518162634709104398113140900.794452235807116.021572
25-29550996604738974451015647240058590891724311621110.789290207935414.127870
30-3445378543713243584102774008415000807705779647280.798751173530511.790279
35-3944887512273002923716963950214411967402008641190.856595160431910.900313
40-4441656460012779003204673407343593636602907258310.90970213861219.417798
45-4938556398072408672662722956782917795751015978580.96193611729597.969500
50-5435511348372226002292302477532355455058644996121.01251410054766.831561
55-5929530275191773811795862059811982664128924053711.0185538182635.559570
60-6426327294661542541723101878701866003684513883760.9486977568275.142152
65-6919141199991184941256611401471310072777822766671.0040305544493.767123
70-741385914847877169160498035890921996101955431.0207983951532.684810
75-7986558986584206176550283470261173581177770.9964422351351.597591
80-84485450873238235651255512525262787659900.9514621287770.874957
85-891942194114056149209812985525810267160.966088525260.356880
90-9451963542935706412850548940113950.784555203350.138163
95+2003811408272525933901420170070.599543112080.076151
\n", "
" ], "text/plain": [ " male_m female_m male_h female_h male_t female_t male \\\n", "age \n", "00-04 102314 99825 540032 509291 672611 643890 1314957 \n", "05 -- 09 117662 117591 657185 634246 860329 817846 1635176 \n", "10 -- 14 121934 121729 742164 729226 900532 859839 1764630 \n", "15-19 93061 100093 632209 682581 717921 706115 1443191 \n", "20-24 61507 78811 464312 600570 518162 634709 1043981 \n", "25-29 55099 66047 389744 510156 472400 585908 917243 \n", "30-34 45378 54371 324358 410277 400841 500080 770577 \n", "35-39 44887 51227 300292 371696 395021 441196 740200 \n", "40-44 41656 46001 277900 320467 340734 359363 660290 \n", "45-49 38556 39807 240867 266272 295678 291779 575101 \n", "50-54 35511 34837 222600 229230 247753 235545 505864 \n", "55-59 29530 27519 177381 179586 205981 198266 412892 \n", "60-64 26327 29466 154254 172310 187870 186600 368451 \n", "65-69 19141 19999 118494 125661 140147 131007 277782 \n", "70-74 13859 14847 87716 91604 98035 89092 199610 \n", "75-79 8655 8986 58420 61765 50283 47026 117358 \n", "80-84 4854 5087 32382 35651 25551 25252 62787 \n", "85-89 1942 1941 14056 14920 9812 9855 25810 \n", "90-94 519 635 4293 5706 4128 5054 8940 \n", "95+ 200 381 1408 2725 2593 3901 4201 \n", "\n", " female ratio total density(per sq.km) \n", "age \n", "00-04 1253006 1.049442 2567963 17.447653 \n", "05 -- 09 1569683 1.041724 3204859 21.774951 \n", "10 -- 14 1710794 1.031468 3475424 23.613265 \n", "15-19 1488789 0.969372 2931980 19.920914 \n", "20-24 1314090 0.794452 2358071 16.021572 \n", "25-29 1162111 0.789290 2079354 14.127870 \n", "30-34 964728 0.798751 1735305 11.790279 \n", "35-39 864119 0.856595 1604319 10.900313 \n", "40-44 725831 0.909702 1386121 9.417798 \n", "45-49 597858 0.961936 1172959 7.969500 \n", "50-54 499612 1.012514 1005476 6.831561 \n", "55-59 405371 1.018553 818263 5.559570 \n", "60-64 388376 0.948697 756827 5.142152 \n", "65-69 276667 1.004030 554449 3.767123 \n", "70-74 195543 1.020798 395153 2.684810 \n", "75-79 117777 0.996442 235135 1.597591 \n", "80-84 65990 0.951462 128777 0.874957 \n", "85-89 26716 0.966088 52526 0.356880 \n", "90-94 11395 0.784555 20335 0.138163 \n", "95+ 7007 0.599543 11208 0.076151 " ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "area=147181 # area of Nepal sq.km\n", "result2['male']=result2['male_m']+result2['male_h']+result2['male_t'] # sum of male\n", "result2['female']=result2['female_m']+result2['female_h']+result2['female_t'] # sum of female\n", "result2['ratio']=result2['male']/result2['female'] # male to female ratio\n", "result2['total']=result2['male']+result2['female']\n", "result2['density(per sq.km)'] = result2['total'].apply(lambda x: x/area) # population density\n", "result2" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "26494504" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x=result2.total.sum()\n", "x" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig,ax = plt.subplots(figsize = [8,6])\n", "ax.bar(group, result2['male'],bar_width, facecolor='g', edgecolor='k',label='Male')\n", "ax.bar(group+bar_width, result2['female'],bar_width, facecolor='r', edgecolor='k',label='Female')\n", "plt.xlabel('Age')\n", "ax.set_xticks(group+bar_width/2)\n", "ax.set_xticklabels(result2.index)\n", "ax.set_ylabel('Population')\n", "ax.tick_params(axis='x', rotation= 45)\n", "plt.title('Population in Nepal in 2011')\n", "plt.savefig(\"image/Nepal.png\", dpi = 600) # dpi dot per inch\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": 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Rbx5daJIkaTJtWqefTLku/neAzLwG2HcUQUmSpKm1SeIrZeb3x4y7d5jBSJKk/rVJ4ndExEOABIiIfYDfjCQqSZI0pb7vEwdeCZwEPCwifg38Ath/JFFJkqQp9Z3EM/Mm4GkRsTKwTGb+aXRhSZKkqUyZxCPi6AnGA5CZHxxyTJIkqQ/9nImvOvIoJElSa/109vIfiyIQSZLUTpsHoKwIHAxsDazYG5+ZLxtBXJIkaQptbjE7E1gP2A24BNgIsHGbJEkzpE0S3zwz3wL8v8w8HXgm8IjRhCVJkqbSJon/vf69MyK2AVYH5g49IkmS1Jc2nb2cFBFrAm8BzgNWAY4dSVSSJGlKbTp7OaW+vQTYbDThSJKkfrVpnb4G8FJKFfp9/5eZrxp+WJIkaSptqtPPBy4Hfgz8czThSJKkfrVJ4itm5rhdsEqSpEWv1X3iEXFoRKwfEWv1XiOLTJIkTarNmfg9wPuBf6c+U7z+tZGbJEkzoE0SP5rS4csdowpGkiT1r011+jzgL6MKRJIktdPmTPwfwFURcTHwt97IyW4xi4jdgY8As4BTMvM940zzAuBtlKr5qzPzRS1ikiRpqdUmiZ9bX32JiFnACcDTgQXADyLivMz8aWOaLYA3Adtn5h8i4sEt4pEkaanWpse20yPiQcCczLy+j395PHBDZt4EEBGfA/YGftqY5lDghMz8Q/2O3/YduSRJS7m+r4lHxLOBq4AL6vCjI+K8Sf5lQ2B+Y3hBHde0JbBlRHwnIi6v1e+SJKkPbRq2vY1ydn0nQGZeBWw6yfQxzrgcM7wssAWwE7AfcErt3vX+BUUcFhFXRMQVt99+e4uQJUkajTnrzSEiHvCas96cRRZDm2vi92bmXRH3y81jk3LTAmDjxvBGwC3jTHN5Zv4d+EVEXE9J6j+435dkngScBLDddttN9p2SJC0S82+bz8Vc/IDxO9+28yKLoc2Z+E8i4kXArIjYIiKOB747yfQ/ALaIiE0jYnlgX8ojTJvOBXYGiIh1KNXrN7WISZKkpVabJH4UsDXl9rKzgbuAf51o4sy8FzgSuBC4FjgnM+dFxNsjYq862YXA7yLip8DFwOsy83ftf4YkSUufKavTI+LMzHwJcGhm/jul29W+ZOb5lKefNccd23iflJ7gfLCKJEkt9XMmvm1EbAK8LCLWbD78xAegSJI0c/pp2PYJym1lmwFXcv9W5z4ARZKkGTLlmXhmfjQzHw6cmpmbZeamjZcJXJKkGdLPNfErgO8A/xMRK2bmX0cfliRJmko/18SfCHyJ0iHLJRFxfkS8OiK2HGlkkiRpUlOeiddbxb5ZX0TE+sAewDvqA0wuy8wjRhijJEkaR5se23r+mJmnAqdGxDLAk4YckyRJ6kObB6A8uXbKcm0dfhTwscz8zqiCkyRJE2vTY9uHgN2A3wFk5tXAU0YRlCRJmlqbJE5mzh8z6h9DjEWSJLXQ5pr4/Ih4MpD1gSavolatS5KkRa/NmfjhwCuBDSmPEH10HZYkSTOg7zPxzLwDePEIY5EkSS3002Pb8ZQ+0seVma8aakSSJKkv/ZyJXzHyKCRJUmv99Nh2+qIIRJIktdP3NfGImA28AdgKWLE3PjN3GUFckiRpCm1ap3+GckvZpsB/ADcDPxhBTJIkqQ9tkvjamfkp4O+ZeUlmvozyhDNJkjQD2nT28vf69zcR8UzgFmCj4YckSZL60SaJvyMiVgeOAY4HVgNeM5KoJEnSlNp09vLl+vYuYOfRhCNJkvrVpnX6psBRwNzm/2XmXsMPS5IkTaVNdfq5wKeA/wX+OZpwJElSv9ok8b9m5kdHFokkSWqlTRL/SES8Ffgq8LfeyMz84dCjkiRJU2qTxB8BvATYhYXV6VmHJUnSItYmiT8X2Cwz7xlVMJIkqX9temy7GlhjVIFIkqR22pyJrwtcFxE/4P7XxL3FTJKkGdAmib91ZFFIkqTWpkziERFZXDLVNMMNTZIkTaafa+IXR8RRETGnOTIilo+IXSLidOCA0YQnSZIm0k91+u7Ay4DP1q5X7wQeRDkA+Crwocy8anQhSpKk8UyZxDPzr8CJwIkRsRywDnB3Zt456uAkSdLE2jRsA9gK2BEgIr6VmdcMPyRJktSPvu8Tj4hXA58BHlxfn4mIo0YVmCRJmlybM/GDgSdk5v8DiIj3ApcBx48iMEmSNLk2PbYF8I/G8D/qOEmSNAPanIl/GvheRHypDj+H8nxxSZI0A/pO4pn5wYj4JrAD5Qz8oMz80agCkyRJk+unx7bVMvOPEbEWcHN99T5bKzN/P7rwJEnSRPo5Ez8beBZwJeX54T1RhzcbQVySJGkK/XT28qz6d9PRhyNJkvrV5j7xi/oZJ0mSFo1+romvCKwErBMRa7LwtrLVgA1GGJskSZpEP9fEXw78KyVhX8nCJP5H4IQRxSVJkqbQzzXxjwAfiYijMtPe2SRJWky0uU/8+IjYhvIQlBUb488YRWCSJGlyfSfxiHgrsBMliZ8P7AFcCpjEJUmaAW36Tt8H2BW4NTMPAh4FrDDZP0TE7hFxfUTcEBFvnGS6fSIiI2K7FvFIkrRUa5PE/5qZ/wTujYjVgN8ySUcvETGL0vBtD8rZ+34RsdU4060KvAr4XpvAJUla2vWVxCMigGsiYg3gZEor9R8C35/k3x4P3JCZN2XmPcDngL3Hme444H3AX9sELknS0q6vJJ6ZCTw6M+/MzE8ATwcOqNXqE9kQmN8YXlDH3SciHgNsnJlfbhe2JElqU51+eUQ8DiAzb87Ma6aYfrxnjd/X93pELAN8CDhmqi+OiMMi4oqIuOL2229vEbIkSUuuNkl8Z+CyiLgxIq6JiB9HxGSJfAGwcWN4I+CWxvCqwDbANyPiZuCJwHnjNW7LzJMyc7vM3G727NktQpYkacnV9y1mlAZqbfwA2CIiNgV+DewLvKj3YWbeBazTG67PKn9tZl7R8nskSVoqtens5ZdtCs7MeyPiSOBCYBZwambOi4i3A1dk5nntQpUkSU1tzsRby8zzKR3DNMcdO8G0O40yFkmSljRtrolLkqTFiElckqSOMolLktRRJnFJkjrKJC5JUkeZxCVJ6iiTuCRJHWUSlySpo0zikiR1lElckqSOMolLktRRJnFJkjrKJC5JUkeZxCVJ6iiTuCRJHWUSlySpo0zikiR1lElckqSOMolLktRRJnFJkjrKJC5JUkeZxCVJ6iiTuCRJHWUSlySpo0zikiR1lElckqSOMolLktRRJnFJkjrKJC5JUkeZxCVJ6iiTuCRJHWUSlySpo0zikiR1lElckqSOMolLktRRJnFJkjrKJC5JUkeZxCVJ6iiTuCRJHWUSlySpo0zikiR1lElckqSOMolLktRRJnFJkjrKJC5JUkeZxCVJ6iiTuCRJHWUSlySpo0zikiR1lElckqSOGmkSj4jdI+L6iLghIt44zudHR8RPI+KaiLgoIjYZZTySJC1JRpbEI2IWcAKwB7AVsF9EbDVmsh8B22XmI4EvAu8bVTySJC1pRnkm/njghsy8KTPvAT4H7N2cIDMvzsy/1MHLgY1GGI8kSUuUUSbxDYH5jeEFddxEDgb+b4TxSJK0RFl2hGXHOONy3Akj9ge2A546weeHAYcBzJkzZ1jxSZLUaaM8E18AbNwY3gi4ZexEEfE04N+BvTLzb+MVlJknZeZ2mbnd7NmzRxKsJEldM8ok/gNgi4jYNCKWB/YFzmtOEBGPAT5JSeC/HWEskiQtcUaWxDPzXuBI4ELgWuCczJwXEW+PiL3qZO8HVgG+EBFXRcR5ExQnSZLGGOU1cTLzfOD8MeOObbx/2ii/X5KkJZk9tkmS1FEmcUmSOsokLklSR5nEJUnqKJO4JEkdZRKXJKmjTOKSJHWUSVySpI4yiUuS1FEmcUmSOsokLklSR5nEJUnqKJO4JEkdZRKXJKmjTOKSJHWUSVySpI4yiUuS1FEmcUmSOsokLklSR5nEJUnqKJO4JEkdZRKXJKmjTOKSJHWUSVySpI4yiUuS1FEmcUmSOsokLklSR5nEJUnqKJO4JEkdZRKXJKmjTOKSJHWUSVySpI4yiUuS1FEmcUmSOsokLklSR5nEJUnqKJO4JEkdZRKXJKmjTOKSJHWUSVySpI4yiUuS1FEmcUmSOsokLklSR5nEJUnqKJO4JEkdZRKXJKmjTOKSJHWUSVySpI4yiUuS1FEmcUmSOmqkSTwido+I6yPihoh44zifrxARn6+ffy8i5o4yHkmSliQjS+IRMQs4AdgD2ArYLyK2GjPZwcAfMnNz4EPAe0cVjyRJS5pRnok/HrghM2/KzHuAzwF7j5lmb+D0+v6LwK4RESOMSZKkJcYok/iGwPzG8II6btxpMvNe4C5g7RHGJEnSEiMyczQFR/wLsFtmHlKHXwI8PjOPakwzr06zoA7fWKf53ZiyDgMOq4MPBa4fSdALrQPcYZmWaZmWaZmLTblLc5mbZObs8T5Ydshf1LQA2LgxvBFwywTTLIiIZYHVgd+PLSgzTwJOGlGcDxARV2TmdpZpmZZpmZa5eJS7NJc5mVFWp/8A2CIiNo2I5YF9gfPGTHMecEB9vw/wjRxV1YAkSUuYkZ2JZ+a9EXEkcCEwCzg1M+dFxNuBKzLzPOBTwJkRcQPlDHzfUcUjSdKSZpTV6WTm+cD5Y8Yd23j/V+BfRhnDgEZRdW+ZlmmZlrk0lDmqcpfmMic0soZtkiRptOx2VZKkjjKJS5LUUSbxxUREbBkRa0TEg+vwtHuui4hdIuIJEbHYLeeIWGGmY1gcLI7LZjzDirMry904u2VpWz+bOrEDmWkRsUdEHDrK8oHPA/8OfCIi9sjMnE4iryvjR4F3A4+tfdkvFiJiT+DjEfHwmY5lMqNY7hHxlIh4TkTsDZCZ/5zuAVuH4uzKcjfOIXL9HC2T+BRqMjwYOKH2Qjfs8jeiPPjlSOD1wLnA5yJiz2km8r8Dl1HuQHgz8Lj6fa3Ki4gHRcSDBoxhIlsC2wNPj4hthlFgROwWEcdExL/VmKe1bo9iuUfEbsCJwCbASyPiwohYZjrLuStxVov9cq+Mcynbjqppz8+x378oatpM4lPIzL8BXwa+ALwnIl4O9z2lbWCNhX0v8OPM/E7t6ObrwLeBUyNi10E7v8nMfwL/C7yllvnaiDgceHm/K1Y9yj0H+HxEvDQihtWv/S8ofeY/CviXiFivXkpYfpDCIuKpwPGU7ni3pDxUZ6fpVI0Ne7lH6ZHwIOC4zPxIZj4f2IZSA8OgO6CuxFkt9svdOJfO7aia9vzs7a8jYtM6/M8BY+mbSXwSdYUB+B3lfve9gaMj4kPAxxqfD2K5+ve3wCYRcXZEbAIcA1xU/z4jIpZts1KOiWkWcHhmfozycJkTgVX7WbEi4jHAWylV/McCBwLviIiH9hvLOGX21rfvUQ4w3krpavd9wGeBNQYsek9KZ0JfzswDgTWB11CepDdI7UNvBzPM5f4P4Dbg7sa4s4AtI+K+HVDLOHvr0B1DjBPgVuAvw4qz4XKGsNwby3Noy70xL0cR5zOHGOfKI4izt10OLc4x5Q5tO6oPyroN+Gtj9HS3o6HulyLiYOCgiNiyDn84Ip7bpow2TOLjiIh14L4VBuAq4NmZ+RNKIjwCmNX4vG35z6Bc+3498BjgKfWjN1D6mP8YcA2wTmbe289KGaUB2+q1p7zeDukrwE8jYjtgR+BMyuNe++nXd03g55l5TWZeRXne+/bAnm2P9CNi/brBrlhH/YWyQf+a0j3v3sAfWXhg09a1wJoR8ZA6/H3gHuBw6H+jrkfey7CwE6QfAc+aznKPiLl1x7sMcDFwXES8PiJOB2YD25XJYvMWZW5Vf9ffhxjntrXMe4F5wNuHEOeTIuIRjVHDWu69g6yfAmsMYbnvDBwWESsNOc7eZah5Q4pzV0oPlxsMOc5V699rhxTn5rXWbq06ahjr59YR8ZCIWBP4KsNZPzeq23uvpuFu4DlMf37+kHI582kRcQrloV3/07KMvpnEx4iIvSjXpD8bEUdFxCMycz7wuyhPYjuMcmb63Ih46QDlP4GSTL9GOcJ7JfDazHxRZh4BvLjunB8LrD7myHuiMnejXP+eFxFrZ+bf69nk34FdKBvjazLzAFm0F8cAABoESURBVErV+m19hHoH8KcojUceRKmyuoyyku/V4vfuSXmW/FnAGyNibmb+kXLt/xjK9fo3An8G9u+36q4mx9UjYjXK5Ye1gXdHxDnA1rWabdWI2KdFnJ8FTgUOiIhVKBvwHYMu9ygNFs8BPgK8JzPPBd5GOTO5FjiiLut/sjAxTVXmVsClEXFGY/SfgdsjYv8B49wE+HZEfBMgM08G3lnj/OmAce5GWc/vW56Z+SfKzuy1DL7cdwB2rYPXUg423zWN5b57jfO6zPxLI87zphnnrsBbImLFIcW5G3AK8GDKU7J6cU53O3oGcHGU6t8fURLvdOLcA/gi8AHKJbw1gT8xve1od0p1/BuAkzPzK8B/ULrqHnQ76m3vH6IcEDwhM+9igOXerKGIiFmZ+SPKfmR/4MnAm4bR8G5CmemrvoC5wC/rjN+b0tDsf4CHUFa+vwH71GmfAGw+wHc8FXhXfb8aJVmf0RtXx+8H3Ag8so/yVqKsiLsDxwE3AWs3Pl+b8njXfmJbm9qLXx0+GjiNUhV2Xh33bOD9fZa3C+X62uOApwH/CexePzsQ+APwnDq8ObBBn+XuQTlSPouyA96A8jS87YEXACvW6d4NPK2P8nan1LZsT9nwPgus0JgHrZd7/e1X1zKfVONcvfF5r7fEA+pv6fe3Pxj4b+AC4EuN8W8adP2kVB1+mrIT/9GYz1rHSan1ubE374GVG589m3Jw9LwBlvtulJ32ExvjHg7sALywzXIHgnKAcQrw3DpuDUqCXItyoDBonM+g7Ed2a4x7aJ0vreKs0z2rzvvH1eX8ncZnB0xjO9qNcpD2VcqZMsDWlP3fINvRVpTt/Un1dQrljBvK2fcg29HmNcYd67I5sa6vy05j/XwIcF0vBuB1dd1/RF12f2ysE5POz1rG9uOMfz/lsug7KTUZ2/azTAZ5jaTQrr6ADYEzG8PrAa+i7DSfAGxYxy87je/YgZJot67DywHbAp8Adqzj9gA2bVHmXGC1+v7DtfzZ40y3zCRlPI/yaNg9qAmsjl+D0gq0tzEeDXyoz7jeCry8MXws5Zpbb3iDtvOzbszXU67TbVm/4xXjTPcKSmKedEdB2Zl/GNizDm9cN+gPAi+lVNOtO0Ccb2BhEtuAktQ+Tmlo+NjGb/ku8Og+y1yGUkX7MWALygOEzqoxPqm3zrSMs7cTfFNd1qcDl9b1dLv62fYt4/y3us08mLLDPItykPCxuo31DpBmtYjzCcBvWHgQuAblssfqY35HX8u9Ue47KAdx61NqrE4FfkZjp9syzlnAu4AX1OHZlO3zIdNYPz8J7FqHl6Ocle7TmGb9AeJ8BqXa94l1Pf/BBNP1PT/r+vzZ+n69Oh9PBU4AdgbWGmD93Iq6T67rzgLKSc8FwE4DbkcPB85pDO9AqRm9vK4Ha/QzPykHQdcxJkHXdfW/6/sNKCdZRwLL9/u727yGXmCXX3WncDnwH41xG9Sd0qG9BUvjbHXA73lNXQkfWodXpdzT/cpplNk8g/4w8Iv6fkfK9fzJ/ndD4P/qzuHcuoE/YIUDjqor7VYt5udGLNzB7gKc1vh81bGx91Hmy4GDGsMHA19sDC9HOei4CHhUm3lHqRm5jFKVth/lAOGdtcxWy71R5irAf1GO9ncAXk0501+ZkuTW7rfMRtlvAZ5f319MuZbX28kvO8j6Sak6fGV9fymlanKvOrxemziB5YG3UxL3dXUH9gxKte8ZlAS8TMv52ash2YFyVvtfLKwlemqdZi7lclFfy73+zxsoZ3evAA6r4/ahXBfdsu36Waf/t7qcN6IkylMoDVh7iX3TNnE21qVlG/P2fY3Pl2kTJ+XA4O3Uk4Y67lzgdY3hWcCclnGuTLkM9zlK7cDrKQeXrwbOrttX2+W+AuVg4LO17NfX9fEAyrY6u77arJ8rAD+h1ihSahpeT6lGP7g3TyeLs66HvwZ2rsOr1L+92ovlG9M+lHFOqob1GkmhXXw1NoSHUhqEHdX4bF/gjCF+12qUnebXgEfUca+lJPKBdsK1jFmN92+l7IjnM/XR/losrAU4tG6490vkdaV+FX1U8dfpH/AbKGeMX6vvX0w5+2tVq1F3Lhs1hh9GPeqtw70DgwcNOA+3arx/5jCWO42zMEqV3ZnActNYRw8GDqGcUfwC+AaNqvWWZTYPsI6hJJifA9+i3Po4aIzLU65bHt747JH1t7euyarbxcsoT4j6NaUtycMol2W+zcLaklbLnVKzcRmlpqRZTf9JYLMB5+nz67Z8NOV6LZSz/d8B2wy6fjaW1WbALdQDuWmum8vVvy8Bjh9v/rRc7qtSThxOanz28LrcVxpwXVqxlvHhMcvt9N5yb1Fmr0ZxDnAl5aDyy5T92/OBj/dZzhGU9i6PpJw0nF3XmS8AW/TW2ekun75iWRRfsji+GhtEjBm/DKXq53xKYyQoZwH/Sz3aGtL3r0I5Qr2RUs14C32e4U5Rbm/F3wu4vd8yqUeQ9f19ibwO93VNvY/veCzlKP1FlNb3Dx1kmY0ZtwXl+fRQjs6PpySQcQ+EJlvujfe9DX0/SpuIgZb7BGW+kFJLsOYg62cdtwHwTeBOYO867mQaBzcDxLo65Rabu4Fn1nFn0OKyzji/tdfSv/nbv06trmy73FmYyA9ofNa7BLD6AHH2rquuVH/7qZTq/wMpZ2p9XVsep9wVKDUFP677jt7vP5F6iWK6rxrje2mZGCcpb33KZbhDWi6TZg1g73cGpYHYMXX4Xyi1O+sMuozq+3NYePb8QuAKYL0ByuztI2fVfcXydfhllMuaEx5gUy7hbUVpP3Q0par8N5QTnMdTamG+Sj2ZWBSvRfIli+OLhUfuvSPRZRsLd11K1dy3647sBvo8A+3je8cmj23rq/XOcpLvWIfSIvoRg8ZGSeTnUg4wrptqh0a5lrw8tRHTmPnZa0swm9Ki9Lv0f3Axaa0E5Yj6v+sGeCXw8JbLfVYjznUa0x1Vy9umjxi3ZcxZW6PM3nX/ZShnuv2WOVmcc+tOo1kdOuX10Ani7O2M16cc+D2p5Toz2XLfoDHdUZSq5Sl/+3jLvVHmMtTLG3X4hcB3mCJBTBHn8rXMD1Cqmb9ObbMyQJy9uFakHLCeQrmf/RBKtfCc6W5H9f2elBrDlQeJc4JpnklJlBv2Me1k6+dKlGvtP6YcBF83hO1oFcrB688pBwjz+llGLebngZS2NhPu5yl35lxN2S++k5KwjwVe2JhmI8rB4Eiuf48b16L6osXpVVfW71Kq5t4FzG18tj2lamSlumKuyQDXLcf5zhjv7zDLHPPZCpP83wOqyCYo40uUjj8mPRio8/MnlOqkc2icYdf5+TVKNe3ylAOjfqvkH9Dys/Gb72vERWkJfCVTHBj0sdw/QzkoWJdy8NbPjmc3xhzkNXYST66/fbP62z/eZ5mTxbkDJTFs3psf/axLU8S5PaXm6WGTrQ8DLveH1O3oI/SfGCdb7ps0xh1KaYQ4abl9xHkx5cD3voQxhDhXr8vmFZTLRl/sc/2can5u3hg3aW1On3E2163HUBLQpLUafWxHZ9RtaHZdX/s5KJhqO7qIsi9ekXIZ5cFDWj83oxwYHkKtBp+grLUp7Ya2aqx736Q0jJzdmO7FdXyr2qbpvBbJlyxOr7pTuYlyq9eOlB7Jvke5Fr48pWrkWUP+zt4G8yhK1dKErcRHXWbdWF7H/avPe2VtR70uSKmmvpRJGrXUndTGlCPuneqGewzl0kDvWv/VzflJnwcvjNPyc8xGfTWlWmtZyhnPVDvyVsudPq5nUS67/JyFjVseVP8uTzkIvN9vH9L6eStTNFQcdZyDLPcWZfe73GdRkuPDpihvQ8qOfMo4aXGA3Uec87j/Acek15YpNSHz+oyz74Zs/c7PxmerTVHe5nX93GmC9fM3A66fNyzO6yfloOzbwC6Ncf9FaUS8Xx0+nNKSv6+D1WG9FtkXLS4vytHcyY2FHZSWid+tn/Vu15j2mfKY730ypZHZjjNVJuX2saupt2aMU9aPWXhb1Gr0caRP2ZmeRNlZ9naCr6LcCrIejVuA6P+scaKWnyvVMi6kXrdtMa/WAD4xzOVOuSbZa2k+h3JduvfkuNVZeKtKX7+7Trs68Kkhx/luypngg6aKs+U8PYlSxTnlcu+zvKEu9/p7V6DUgEwZZ8vt7pZ+4qSPhFtjW3UEcT6RklT7jrPP7eiEEWxHZ1KqzIeyftJyv9RnmYfXOF9CqU4/i3K3zKcav2PSy3mjePV+3BIvSp/fa1GO+P6Pcr/yifWzZSitue+h9Jf7jxxCx/VRnqzzz9pr0VaUlfErM1Fm7enrK8C7M/Ok2i3iOpQz8qsj4o2U1shfiYjIKVaM2r3hmpSj8hOBKzPzfY3P30RpBPJK4O6pyhtT9hGUI+h3UPp8fzelA4a1KI0Bf5eZ9/R6QJqs7IjYuv7OWykb4DmZ+YH62UDLPcoTjpav/3cI5XrqrpRqu1soLVbXoNxx8Lc+y9yBcq37M5SW4V/JzPdMM84dKfPsAkpHQGtQuvidTpzPppyNfYwyP6/JzHc1Pp/Ocn85ZT4eR1ne01nue1PusPgA8J4a5zunG2ftIfBAyq1T75oqzj62o90orfgPpSyHGzLzuCHEOZdyq9y2Q4rziZTauTtqeWdm5gfrZ4Oun0+i3GaZlNb7/6R0CjWd9XOL+j/XU/pRuDQzP9L4fDrr5+qUjneeBtyZma+p48/PzD1rb23/6Le8oVnURw0z8aJ0ZHId5drX+yk7oF8DL21Msxtw4gi+e3vgElrcCsHUjbkGKXNbSrI9hLLBfJ3y5J+vc//e4vo5U34WpXX5JXVe7gXcTOlesDfNXOCTLefV5pQDk/WAf6WcmSxgYcvPN1MS0mp9xrlHjfM8yga9C6XK8shBl3st88eUuxXOolxH/AiNe/wpifLkPstbhnIGMo+y43kOpWr1x8CrB4lzTJk/o9wiuTylA5tXDxJnnf4ZlOrC3RrL+FfAG6a53PeiJLGVKMnl5Gku96dStvdenHMo7SaOnmacz6EkqS3r+jndOHvz81eUBlJr1jinOz93o1xDfizl7PHUaca5V92OzqJs73tTLlEcMY3tqFnmKZRamA/R6LhpgPWz1/DsS3V+/iflJKAZZ+v5Od721Xj/UkotxNDuXGodz0x98SL7geUs6fPUhh11h3Z0HXcr9X5wytH1RZRqrTbVNuszQUtESmOuS2l/XXTZMcPLDFomtcOK+n77uqHcWDfu3rWji6gdZvRR3pMpO8jH1OGTKGfMG9Sd0ZspyfhAyi0g/Ta+6R0YfIuyc9ye0qL5sMY0fbf8pJzJ/4x6exwl6T6OcjBzY92hRZvlPk6Z59UyV6BxzZNS3XY+JZH2W1X3esp1u7Mpj1p8MOU6dq8DltbrZ6PMzwAvq+NWGCTOutxva/z2dSjtER5LqbI9mpLc2i73XiLr9cS2CiVBHtKYplWL3xrLa+v7OZQDw5dQ+vA+gnL9tm2cvQODXpwr13XogAHXz6dRagW3phxgfYPSHmJzSsIddDtqHhi8s457FbWzqgHiXJtS7d67v/2M+h271O84gvbb0dgyT69lzqXRIKzl+jm24dlhlJbxZ1IS+TGUmoRW83OK73wZpUvYVncBDfs1nUdpdslqlAX4HcrN+E+t439NeW7so6n9BWd5qEBfIuKZlB6f9qH0yDTWfEq3o/P6qbKqZe4BHBwRVwILMvPMLNXny2SpTuq7zIh4FnBORJyXmftm5nci4u/AtzLzS70YI2I+pV/jfr0nSyf/UBq2nJaZt0TETpSdz9GUI/6DMvMPffzmJ1OqPffLzB9FxCeAfTPzqDEPHngqpTXpSpSqu8ncRplP34+I9SjJ+y2Us9NzKPeAb0NpnNPvch9b5uNqmbcAl0fEWZSdxyHASzLzz32U2XMvJeF8inJWuhHlbPyFEfF4yvxstX42yjwVOLReWrgHeFNE/CvlLKLfOH9HeaDO+vVSzBdq+fMoZ1LbUrax7Wi33M+kNIT6fkSsRalFOJP7P2qyzXKnxtV70t7nKMvnRsr8fAYliT+53zirbYFTMvOCiNiIMl+vpiT2QeKcRakJnBcRa1Dm456ZeXyU53q/mVKNvG2/cUbE0yi1bXtTDgDPj/J0uo9z/weDtInzXkrbgofV/cSOlIR5LaV3y4Mp1d5Ppf/1s1nmryhn3GtTku0vIuJdlAT+cvpfP++lJPv1gJ9muWTYa51+HeVA7mHAo2m33CdzEWVfesMQyhrcTB5BLKoX8HTKWVOvV7JZlI5BPkhpOPEgWnZEQKk+uhp4ygSfD9Ij1+MpG9+LKUnmau5f1d13mZQzhQsoSeU04OzGZ82zxudTHhywSZ/lzmJhP+2zKMnmRyxs0LIJjT6t+yzzycCBjeHZdXk1e4w7uM6P1i0/KQcab67vD6U0mtmCcrtK6w4oxinzIErNzhZ1Xvd1L/SY8h4CvLG+P4aSMI+tw8sPEuc4Zf6FhY2SPt02TsqdEDdRzhQPpSTcwyh9Y29cp+n7DIeSTBdQks7alMtdF9R5eVD93YfQssUv5eDsekoCP6iO25JyTXjvtnHW6V/FwrP779ayz6BUB69FOSBq3TKZhQ3fdqfUDPZquHrdd/Z9qxJln/Tk3v9ROj7q9RjXa/906ADzcx/KLZyXN9bJZ1AuLexQt6NW3YqOKfMtddwudfvZcZDtiAc2PPsM5dr3BxrTLLJbvxbVa8YDWCQ/sqxkR1Kqfp/SGH9Jb6NpWd669X971VVrU6p+Xww8fRpx7kDjGhDlqPKm3vcMUN4GlKPTdSitkz8z5vMDKAm8ddKp/79sLf+iOrw/pVFK264vJzowmF3HbUY54Jr0dqIW33cBQ36qUC1zoG46G8vq03Un+3NKQ6Gv0Oi2dAhlHlvLfAED3uZIOaN55ZhxF7LwoS5tW7iPd2DwMsplhU0pibd1i1/K09J+Aby9Me5T1HYwA8Q53oHBZpQevp5X45xWj4uUjmb+rW4D03nI0tgDg95tVStTEu8g83NNSnui5m2YX6IeFA0Y50RlDnQHD+WE7MV1nf9QY/z5LIHJ+77fN9MBLLIfWlaYV1KumxxWE9g8Wva92yjvUMoZyBGUa7kfpTRwOq/Nis3CI+SgVJ2dR33aTx2/HqWnqxdM8/evTbmv8aw6/PAa+8CJp1H2aXUndiXTvD7E+AcG72WK+1enmr+N4efXOFt319hHmetP87e/nXKd8dl1eGfqGe4Qy9xlumVO8NsH2o5qGRMdGEznoGhZytnxTZRanIMp10IfMo0yJzow2GuI8/JSWjyNrM/l/yYaXeFOo6w9aoJ8BqVh2g9pdPQyxDKn1Xsl4zc866tnuy6+ZjyARfpjS/XczpSj6dNoeRZOqf7boTH8Qkp1UK/KaiXKkfSRLcpcaczwx4Hvjxl3MI0Hskzj969TN5jrKQ20ppt0os7TG2uimLDHowHKPo2FBwbT7vKW0vjsYMqB20A1D6Muk9LI8AGdcixuZTaWfa9hz1A7t6jJ7IdM40CrUdZjKbdE/SfDOcAc78Cgr8ee9vkd50w3MY4zLy9lCA/joFTRv4pSC3khLZ4WtyjLbJS9WDQ8G/VrqblPvCkiZlFuM+37XvCIeB5lZ/BrShXVpZRW1BsDv8p6f2BEHEe5lvuGPhqePZtypPzyzPx+Y/zZlOuZe2fmrfUe7q0oLStzsjL7+B2voTTGe3pm/njQcsaUeSDlecTzhlBWUO4ouLb+3TUzfz6EcpejtI24MTOvn255oyqzlttXI8iZLLMup6cCt2bmdVNN36LMgygNuv5lGOvTKETEYynXdFegNOqc9nY0imXeKPsc4PWZefOQyuu1QP/jMMobYZmbUNoRzWzDsxFbKpN4W3VnfRbw0SwtvJ9PuQXqT5RGE3+q0+1P2QG9cKqdeu0w5IuUqp5HUe6PbCby4ym9Dd1TP99nuju12kHMOZSnC10znbLGlDuKpHMgQzowUDeM4sBgaTbKAwMtPkzifahJ/Dzg85l5Wu2haEdKB/s3URpz7Ui5Jr5/P4knItaldEZxRkS8gtKy8tAxiXxLSmON24d4FL1iZv516ilnljsgSZqaSbxPEfF0Sgv092fmt2uV/Asp93buX6eZnZm3tyhz2cy8t74/nPLEo8My83u128RbMrOf+2IlSUshk3ifImJFyj2rj6S08P5WHf8N4HWZeeUQvuNwSqv5yyi9Fx2UmXdNt1xJ0pJpaemxbdoy868R8RlKZ/1vioiHUXo5W5dyj+swvuMTtdell1CeJmYClyRNyCTeQmb+ISJOpty28HJK15D7Z+Ztwyg/InaltELfZVgtxyVJSy6r0wc0yG1qfZTZe5jKL4dVpiRpyWUSlySpo5aZ6QAkSdJgTOKSJHWUSVySpI4yiUuS1FEmcUmSOsokLuk+EfHciMjamZGkxZxJXFLTfpTH7O4704FImppJXBIAEbEK5RG7B1OTeEQsExEnRsS8iPhyRJwfEfvUz7aNiEsi4sqIuLB2ViRpETKJS+p5DnBBZv4M+H1EPBZ4HuVhPI+gPADoSXDf43mPpzznflvgVOCdMxG0tDSz73RJPfsBH67vP1eHlwO+ULsXvjUiLq6fPxTYBvhaRADMAn6zaMOVZBKXRESsDewCbBMRSUnKCXxpon8B5mXmkxZRiJLGYXW6JIB9gDMyc5PMnJuZGwO/AO4Anl+vja8L7FSnvx6YHRH3Va9HxNYzEbi0NDOJS4JSdT72rPu/gA2ABcBPgE8C3wPuysx7KIn/vRFxNXAV8ORFF64k8ClmkqYQEatk5p9rlfv3ge0z89aZjkuS18QlTe3LEbEGsDxwnAlcWnx4Ji5JUkd5TVySpI4yiUuS1FEmcUmSOsokLklSR5nEJUnqKJO4JEkd9f8BOG2Iq0N5VGcAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig,ax = plt.subplots(figsize = [8,6])\n", "ax.bar(group, result2['ratio'],bar_width, facecolor='m', edgecolor='k')\n", "plt.xlabel('Age')\n", "ax.set_xticks(group+bar_width/4)\n", "ax.set_xticklabels(df1.index)\n", "ax.set_ylabel('ratio(male/female)')\n", "ax.tick_params(axis='x', rotation= 45)\n", "plt.title('Population ratio in Nepal in 2011')\n", "plt.savefig(\"image/ratio.png\", dpi = 600) # dpi dot per inch\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig,ax = plt.subplots(figsize = [8,6])\n", "ax.bar(group, result2['density(per sq.km)'],bar_width, facecolor='g', edgecolor='k')\n", "plt.xlabel('Age')\n", "ax.set_xticks(group+bar_width/4)\n", "ax.set_xticklabels(df1.index)\n", "ax.set_ylabel('Population density(per sq.km)')\n", "ax.tick_params(axis='x', rotation= 45)\n", "plt.title('Population density in Nepal in 2011')\n", "plt.savefig(\"image/Nepald.png\", dpi = 600) # dpi dot per inch\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": "3.7.6" }, "widgets": { "state": {}, "version": "1.1.2" } }, "nbformat": 4, "nbformat_minor": 2 }