Different_Charts_Represent_Data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Histogram\n",
    "\n",
    "## Age of students in a remote school in India were listed as follows: 3, 11, 12, 19, 22, 23, 24, 25, 27, 29, 15, 6, 27, 17, 29."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "matplotlib.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "age = [3,11,12,19,22,23,24,25,27,29,15,6,27,17,29]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(age, bins=20,color='r',density= False,edgecolor= 'k',histtype='bar',alpha= 0.5)\n",
    "plt.title('My Own Histogram')\n",
    "plt.xlabel('Age of the students')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Why visualization is appropriate for the data: \n",
    "\n",
    "### I used Histogram to represent distribution of continuous data. Histogram plots quantitative data with ranges of the data into group.This data set represents a distribution of continuous data(numerical data) and I can summarize full data sets graphically by using Histogram. So, I think Histogram is the appropriate visualization for the data.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bar graph\n",
    "\n",
    "## 150 students of class were asked to state their favorite subjects and these were the results of survey."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt; plt.rcdefaults()\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    " \n",
    "objects = ('French', 'English', 'Maths', 'Geography', 'Science')\n",
    "y_sub = np.arange(len(objects))\n",
    "result = [30,20,26,38,34]\n",
    " \n",
    "plt.bar(y_sub, result, width=0.5, align='center', color='b',edgecolor= 'k', alpha=0.5)\n",
    "plt.xticks(y_sub, objects)\n",
    "plt.ylabel('Result of Survey')\n",
    "plt.title('150 students Favorite subjects')\n",
    " \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ** Why visualization is appropriate for the data:\n",
    "\n",
    "### I used Bar graph to  compare the result of several subjects since bar graph (each column) represents a group defined by a categorical variable. I've compared different subjects or to track the most favorite subject of 150 students by comparing different subject.I think Bar Plot is the appropriate visualization for the data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A company collected statistics about children to show that children liked TV more than other activities. \n",
    "These were the results of the survey"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Pie chart, where the slices will be ordered and plotted counter-clockwise:\n",
    "labels = 'School', 'Sleep', 'Playing', 'Study', 'T.V.', 'Others'\n",
    "sizes = [6,8,2,4,1,3]\n",
    "explode = (0, 0, 0, 0,0,0)  \n",
    "\n",
    "fig1, ax1 = plt.subplots()\n",
    "ax1.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',\n",
    "        shadow=True, startangle=90)\n",
    "ax1.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Why visualization is appropriate for the data:\n",
    "\n",
    "### Pie or Bar chart both can be used for this data. As I'm trying to compare parts of a whole activities, I used here Pie chart.The analysis of statistics about children shows that children liked TV more than other activities. However, If we see the result of the survey, we can see that children liked to sleep and go to school more than other activities. That is why visualization is important."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q.4: A clinal trial measurement of two different groups A and B were reported. Which group is performing better (high values are better)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tWxcb/v3vfy9JuvHGG7VixQonNw0AAFKMo6HkmmuuUXV1tZObAAAAgwTXlAAAACMQSgAAgBEIJQAAwAiEEgAAYARCCQAAMAKhBAAAGIFQAgAAjEAoAQAARiCUAAAAIxBKAACAEQglAADACIQSAABgBEIJAAAwAqEEAAAYgVACAACMQCgBAABGIJQAAAAjEEoAAIARCCUAAMAIhBIAAGAEQgkAADACoQQAABiBUAIAAIxAKAEAAEYglAAAACMQSgAAgBEIJQAAwAiEEgAAYARCCQAAMILH7QIA4Ktqa2t14MABffrpp0pPT9ekSZO0aNEijR071u3SADiMUALAKAcPHlRxcbEKCgp0/vx5vfjii3r44Yf12GOPKSMjw+3yADiIUALAKA888EC34fLyct11111qbGzU1Vdf3eMykUhEkUgkNmxZlnw+X+w93BPP59+1TKL2Hd+B1EEoAWC0s2fPSpKysrJ6nae2tlY1NTWx4by8PFVWVsrv9zteHy7U1NQkSfL7/crNzY17PYFAwPUakFyEEgDGsm1bzz33nCZPnqwJEyb0Ol9ZWZlKS0tjw11HxuFwWNFo1PE60V04HI69doWD/rAsS4FAQKFQSLZtu1IDBsbj8cR1UEAoAWCszZs36+jRo1q/fn2f83m9Xnm93h6nxftHDYkxkM/ftu2E7D++A6mDnwQDMNIzzzyjd955Rw899JBGjRrldjkAkoBQAsAotm1r8+bN2r9/v9asWaPRo0e7XRKAJCGUADDK5s2b9cYbb+hnP/uZfD6fWltb1draqnPnzrldGgCHcU0JAKPs2bNHkrR27dpu48vLyzVnzpzkFwQgaQglAIxSXV3tdgkAXMLpGwAAYAR6StCnxsZGff7553EvX19f3+01XpmZmcrPzx/QOgAAZiOUoFeNjY2aPXt2QtZVUVEx4HW88cYbBBMAGMQIJehVVw9JVVWVgsFgXOvo6OhQW1ubsrKy4n6YWn19vSoqKgbUYwMAMB+hBBcVDAZVVFQU17KWZSk3N1dNTU3cVREA0CcudAUAAEYglAAAACMQSgAAgBEIJQAAwAiEEgAAYARCCQAAMAKhBAAAGIFQAgAAjEAoAQAARiCUAAAAIxBKAACAEQglAADACIQSAABgBEIJAAAwAqEEAAAYwZOMjbz66qvavn27WltbNW7cOC1dulRTpkxJxqYBAECKcLyn5K233tKWLVu0YMECVVZWasqUKdqwYYOam5ud3jQAAEghjoeSV155RTfddJPmzp0b6yXJycnRnj17nN40AABIIY6evolGo2psbNTtt9/ebfy1116rQ4cOXTB/JBJRJBKJDVuWJZ/PF3sP98T7+Xctl6j9x/cAAAYvR0PJ6dOn1dnZqREjRnQbP2LECLW2tl4wf21trWpqamLDeXl5qqyslN/vd7JM9KKpqUmS5Pf7lZubO6B1BQIBI+oAAJgrKRe69nR029O4srIylZaWXjBPOBxWNBp1rkD0KBwOx167gkF/WZalQCCgUCgk27ZdqwPx83g8HBgASApHQ0l2drbS0tIu6BU5derUBb0nkuT1euX1entcV7x/0JAYA/38bdtOyD7kewAAg5ejF7p6PB7l5+errq6u2/i6ujpdddVVTm4aAACkGMdP35SWlqqqqkr5+fmaNGmS9u7dq+bmZs2bN8/pTQMAgBTieCiZOXOmzpw5o61bt+rkyZMaP368Vq9ezTlqABikznVKw/9PUMfPSsNbOvq9vCXpVNoZNf/fdsV7wvb42S9rONcZ5wrgiqRc6FpcXKzi4uJkbAoA4LLmDunqn/2vnmyQ1HAkzrXEu1wXr67+2f+quSNy8VlhjKSEEgDA0JGTIR18/G49UVWlwmCw38tbknL8fjWHw3H3lDTU1+ueigrl/K4qzjXADYQSAEBCpadJZz+t19jhUsHIjH4vb1mWcsd8TU2dbXH/4u7s8C9rSOexsymFUIJeDfS8sMS5YQDApSOUoFeJOS8scW4YAHApCCXo1UDPC0ucGwYAXDpCCXo10PPCEueGAQCXjmYeAAAYgVACAACMQCgBAABGIJQAAAAjcKErACO9+uqr2r59u1pbWzVu3DgtXbpUU6ZMcbssAA6ipwSAcd566y1t2bJFCxYsUGVlpaZMmaINGzaoubnZ7dIAOIieEgDGeeWVV3TTTTdp7ty5kqSlS5fqn//8p/bs2aOFCxdeMH8kElEk8v9vrmdZlnw+X+w93BPP59+1TKL2Hd+B1EEoAWCUaDSqxsZG3X777d3GX3vttTp06FCPy9TW1qqmpiY2nJeXp8rKSvn9fkdrRc+ampokSX6/X7m5uXGvJxAIuF4DkotQAsAop0+fVmdnp0aMGNFt/IgRI9Ta2trjMmVlZSotLY0Ndx0Zh8NhRaNR54pFj8LhcOy1Kxz0h2VZCgQCCoVCcd90caA1YGA8Hk9cBwWEEgBG6qnLvbdueK/XK6/X2+O0eP+oITEG8vnbtp2Q/cd3IHVwoSsAo2RnZystLe2CXpFTp05d0HsCYHAhlAAwisfjUX5+vurq6rqNr6ur01VXXeVSVQCSgdM3AIxTWlqqqqoq5efna9KkSdq7d6+am5s1b948t0sD4CBCCQDjzJw5U2fOnNHWrVt18uRJjR8/XqtXr+bXNMAgRygBYKTi4mIVFxe7XQaAJOKaEgAAYARCCQAAMAKhBAAAGIFQAgAAjEAoAQAARiCUAAAAIxBKAACAEQglAADACIQSAABgBEIJAAAwAqEEAAAYgVACAACMQCgBAABGIJQAAAAjEEoAAIARCCUAAMAIhBIAAGAEQgkAADACoQQAABiBUAIAAIxAKAEAAEYglAAAACMQSgAAgBEIJQAAwAiEEgAAYARCCQAAMAKhBAAAGIFQAgAAjEAoAQAARiCUAAAAIxBKAACAEQglAADACIQSAABgBEIJAAAwAqEEAAAYgVACAACMQCgBAABGIJQAAAAjeNwuAOZqb2+XJL3//vtxr6Ojo0NtbW3KyspSRkZGXOuor6+Pe/sAgNRBKEGvGhoaJEmrVq1yuZIvZWZmul0CAMBBjoaSbdu26d1339WRI0fk8Xi0ZcsWJzeHBCspKZEkFRYWyufzxbWO+vp6VVRUqKqqSsFgMO5aMjMzlZ+fH/fyAADzORpKotGorr/+ek2aNEmvvfaak5uCA0aOHKmFCxcmZF3BYFBFRUUJWRcGrxMnTmjr1q364IMP1NraqpEjR2r27NlasGCBPB46doHBztH/5XfccYck6fXXX3dyMwAGiePHj8u2bS1fvlyBQEDHjh3Tpk2b1NHRocWLF7tdHgCHGXXoEYlEFIlEYsOWZcVOG1iW5VZZGICu/WZZFvsQFzV16lRNnTo1NjxmzBgdP35ce/bs6TOU0HaYK57P/6vthls1wB1GhZLa2lrV1NTEhvPy8lRZWSm/3+9iVRiIpqYmSVJOTo5yc3Ndrgap6OzZs8rKyupzHtoOs3T9v/f7/QP6fx8IBFyvAcnV71BSXV3d7T9/TzZu3KiCgoJ+F1NWVqbS0tLYcFe6DYfDikaj/V4f3Nfc3Bx77WokkFo8Ho9rf9xDoZB27dp10VM3tB1mCYfDsdd4/t9blqVAIKBQKCTbtl2pAQMTb7vR71BSUlKiWbNm9TlPvA2Y1+uV1+vtcVq8X0y4q2u/2bbNPhzC4jmYaWlp0YYNG3TDDTdo7ty5fS5L22GugXz+iWo3+A6kjn6HkuzsbGVnZztRC4BBqr8HMy0tLVq3bp0mTZqk5cuXO10eAEM4ek1Jc3Oz2tra1NzcrM7OTh05ckTSl+cJ4727J4DU05+Dma5AkpeXp/LycqWl8TQMYKhwNJT84Q9/0F/+8pfY8H333SdJeuihh3TNNdc4uWkAKailpUVr165VTk6OFi9erNOnT8emXX755S5WBiAZHA0lK1as0IoVK5zcBIBBpK6uTqFQSKFQSHfffXe3adXV1S5VBSBZjPpJMIChbc6cOZozZ47bZQBwCSdrAQCAEQglAADACIQSAABgBEIJAAAwAqEEAAAYgVACAACMQCgBAABGIJQAAAAjEEoAAIARCCUAAMAIhBIAAGAEQgkAADACoQQAABiBUAIAAIxAKAEAAEYglAAAACMQSgAAgBEIJQAAwAiEEgAAYARCCQAAMAKhBAAAGMHjdgEAgMGlvb1dkvT+++/HtXxHR4fa2tqUlZWljIyMuNZRX18f13JwF6EEAJBQDQ0NkqRVq1a5XImUmZnpdgnoB0IJACChSkpKJEmFhYXy+Xz9Xr6+vl4VFRWqqqpSMBiMu47MzEzl5+fHvTySj1ACAEiokSNHauHChQNeTzAYVFFRUQIqQqrgQlcAAGAEQgkAADACoQQAABiBUAIAAIxAKAEAAEYglAAAACMQSgAAgBEIJQAAwAiEEgAAYARCCQAAMAKhBAAAGIFQAgAAjEAoAQAARiCUAAAAIxBKAACAEQglAADACIQSAEaKRCJatWqV7rjjDh05csTtcgAkAaEEgJGef/55jRw50u0yACSRx+0CAOC/vffee6qrq9O9996r995776LzRyIRRSKR2LBlWfL5fLH3SC1d+8yyLPbfEEMoAWCU1tZWbdq0SatWrVJ6evolLVNbW6uamprYcF5eniorK+X3+50qEw5qamqSJOXk5Cg3N9flapBMhBIAxrBtW08++aTmzZungoICnThx4pKWKysrU2lpaWy46+g6HA4rGo06Uiuc09zcHHvtCihILR6PJ66DAkIJAMdVV1d368noycaNG3Xo0CG1t7errKysX+v3er3yer09TrNtu1/rgvu69plt2+y/IYZQAsBxJSUlmjVrVp/z+P1+bd26Vf/+97+1cOHCbtPuv/9+ffOb39Q999zjZJkAXEYoAeC47OxsZWdnX3S+ZcuW6Xvf+15s+OTJk3rkkUf085//XMFg0MkSARiAUALAGDk5Od2GMzIyJEmBQECjRo1yoyQAScR9SgAAgBHoKQFgrNGjR6u6utrtMgAkCT0lAADACIQSAABgBEIJAAAwAqEEAAAYgVACAACMQCgBAABGIJQAAAAjEEoAAIARHLt52okTJ7R161Z98MEHam1t1ciRIzV79mwtWLBAHg/3bAMAAN05lg6OHz8u27a1fPlyBQIBHTt2TJs2bVJHR4cWL17s1GYBAECKciyUTJ06VVOnTo0NjxkzRsePH9eePXsIJQAA4AJJPY9y9uxZZWVl9To9EokoEonEhi3Lks/ni71H6unab5ZlsQ8BAH1KWigJhULatWtXn70ktbW1qqmpiQ3n5eWpsrJSfr8/GSXCAU1NTZK+fCR9bm6uy9UAAEzW71BSXV3dLTj0ZOPGjSooKIgNt7S0aMOGDbrhhhs0d+7cXpcrKytTaWlpbLjryDocDisajfa3VBigubk59toVUJBaPB4PBwYAkqLfoaSkpESzZs3qc56vNmAtLS1at26dJk2apOXLl/e5nNfrldfr7XGabdv9LRUG6Npvtm2zDwEAfep3KMnOzlZ2dvYlzdsVSPLy8lReXq60NG6LAgAAeubYNSUtLS1au3atcnJytHjxYp0+fTo27fLLL3dqswAAIEU5Fkrq6uoUCoUUCoV09913d5tWXV3t1GYBAECKciyUzJkzR3PmzHFq9QAAYJDhIg8AAGAEQgkAADACoQQAABiBUAIAAIxAKAEAAEYglAAAACMQSgAAgBEIJQAAwAiEEgAAYARCCQAAMAKhBAAAGIFQAgAAjEAoAQAARiCUAAAAIxBKAACAEQglAADACIQSAABgBEIJAAAwAqEEAAAYweN2AUhd7e3tamho6HOe+vr6bq+9KSwslM/nS1htAMx1sbbjUtsNibZjsLFs27bdLuJiwuGwIpGI22Xgv7z//vsqKSlJyLp2796toqKihKwLieX1euX3+90uIy60HWai7Rj84m03CCWI26X0lHR0dKitrU1ZWVnKyMjodT6OdsxFKEGiXaztuNR2Q6LtMBWhBEayLEu5ublqampSCnzV0AM3Qsm7776rmpoaffLJJ8rIyNCUKVO0cuXKfq+HtiM10W6kvnjbDa4pAWCUv/3tb9q0aZO+//3v6+tf/7ok6ejRoy5XBSAZCCUAjHH+/Hlt2bJFd97gUi9KAAADOUlEQVR5p2666abY+LFjx/a5XCQS6dYjYllWrEvfsixnioVjuvYZ+27oIZQAMMbhw4fV0tIiy7J03333qbW1VRMnTtSdd96p8ePH97pcbW2tampqYsN5eXmqrKxM2Wth8KVAIOB2CUgyrimBozg3nPqSeU3Jm2++qccff1w5OTlavHixRo8erR07dqiurk6PP/64srKyelyut56ScDisaDSalNqROJZlKRAIKBQK0W6kKI/HwzUlAMxUXV3drSejJxs3boz9AVqwYIGuv/56SVJ5ebnuvvtu/fWvf9W8efN6XNbr9crr9fY4jT9qqcu2bfbfEEMoAeC4kpISzZo1q895/H6/2tvbJUnjxo2Ljfd6vRozZoyam5sdrRGA+wglAByXnZ2t7Ozsi86Xn58vr9er48ePa/LkyZKkaDSqcDjM9SHAEEAoAWCM4cOHa968eaqurtaoUaPk9/u1fft2SYqdzgEweBFKABhl0aJFSktL0xNPPKFz586psLBQa9as6fUiVwCDB6EEgFE8Ho8WL16sxYsXu10KgCRLc7sAAAAAiVACAAAMQSgBAABGSIlrSjyelCgTfWAfpq5U3nepXDvYf6ks3n2XEreZBwAAgx+nb+Co9vZ2/eIXv4jdqRMALoZ2Y+gilMBRtm3r8OHDPL8CwCWj3Ri6CCUAAMAIhBIAAGAEQgkc5fV69d3vfrfXx8oDwH+j3Ri6+PUNAAAwAj0lAADACIQSAABgBEIJAAAwAqEEAAAYgVACAACMwNOO4IiDBw9q+/btOnz4sE6ePKmVK1dqxowZbpcFwHC0HUMbPSVwxBdffKGJEydq2bJlbpcCIIXQdgxt9JTAEdOmTdO0adPcLgNAiqHtGNroKQEAAEYglAAAACMQSgAAgBEIJQAAwAiEEgAAYAR+fQNHdHR0KBQKxYZPnDihI0eOKCsrSzk5OS5WBsBktB1Dm2Xbtu12ERh8/vWvf2ndunUXjL/xxhu1YsUKFyoCkApoO4Y2QgkAADAC15QAAAAjEEoAAIARCCUAAMAIhBIAAGAEQgkAADACoQQAABiBUAIAAIxAKAEAAEYglAAAACMQSgAAgBEIJQAAwAj/D/0u/kw73AHhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "matplotlib.style.use('ggplot')\n",
    "\n",
    "A = {0.22, -0.87, -2.39, -1.79, 0.37, -1.54, 1.28, -0.31, -0.74, 1.72, 0.38, -0.17, -1.62, -1.10, 0.30, 0.15, 2.30, 0.19, -0.50, -0.09}\n",
    "B = {-5.13, -2.15, -3.43, -3.83, 0.50, -3.25, 4.32, 0.63, 5.18, -0.43, 7.11, 4.87, -3.10, -5.81, 3.76, 6.31, 2.58, 0.07, 5.76, 3.50}\n",
    "\n",
    "list_a=[]\n",
    "\n",
    "for item in A:\n",
    "    list_a.append(item)\n",
    "#print(list_a)\n",
    "#print(len(list_a))\n",
    "list_b=[]\n",
    "for item in B:\n",
    "    list_b.append(item)\n",
    "#print(len(list_b))\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(121)\n",
    "ax1.title.set_text('A')\n",
    "ax1.boxplot(list_a)\n",
    "\n",
    "\n",
    "ax2 = fig.add_subplot(122)\n",
    "ax2.title.set_text('B')\n",
    "ax2.boxplot(list_b)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Why visualization is appropriate for the data\n",
    "\n",
    "### I used Box Plot to represent the measurements of the two groups since boxplot shows the distribution of data in different quartiles. In our case we have same amount of data for group A and B; and high values are considered better. As we can see in the above boxplots, the median for B is above 0 that is higher than the median of A. It means 50% of the measurements of group B are higher than A, and hence 50% of the measurements of group B are better than A. Furthermore, the Q3 and Q4 measures in the boxplot for group B are significantly higher than the A. However, the Q1 measurements are better in boxplot for group A than B, i.e., the smallest 25% of the measurements of group A data is better (higher value) than the group B. Therefore, except Q1, the other quartiles in the boxplot for B are better than A given that higher values represent better performance. Considering all the above, we can conclude group B is performing better than group A.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# # PART 2\n",
    "\n",
    "### Look at the visualization in this link http://www.weeklystandard.com/over-100-million-now-receiving-federal-welfare/article/649589¶\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. What is the objective of the design? \n",
    "\n",
    "### The design shows that the number of people receiving federal welfare in the US is rising. Now, It has been reached over 100 million."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. What elements of the design are related to that objective?\n",
    "\n",
    "### The charts X axis shows the different year (or time), Y-axis is count of people.The chart bars displaying the number of people receiving federal welfare for a certain time and has now reached over 100 million. Moreover, I see there is statement at the bottom of figure that states that \"it includes anyone in a household in which atleast one person received a program benefit\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# (3) Are those elements effective in achieving the objective (4) Why or why not?\n",
    "\n",
    "### If we take a look at the chart, the number has been rising steadily since 2009. Now, it has been reached over 100 million. So,it is supporting the objective. However,there are some obvious concerns. For example, 1. The chart's y-axis is 94,000,000, not 0. This makes the difference (between bars) much more. 2. The article says that, \"this is not just Americans\". It included not only citizens but also non-citizens as well. However, the Y axis labeled as \"Number of Americans\". So, The chart Y axis should be Number of people (citizen and non_citizen).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Based on my own critique I fixed the visualisation: Here's a more realistic chart."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt; \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "from textwrap import wrap\n",
    " \n",
    "objects = ('2009\\nQ1', '2009\\nQ2', '2009\\nQ3', '2009\\nQ4', '2010\\nQ1', '2010\\nQ2','2010\\nQ3', '2010\\nQ4','2011\\nQ1','2011\\nQ2')\n",
    "y_sub = np.arange(len(objects))\n",
    "result = [97,98.5,99,101,103,104.5,105,106,107,107.5]\n",
    "#result= [97500000,98999000,99999000,101500000,103999000,104999000,105999000,106500000,106999000,1007500000]\n",
    "\n",
    "\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.bar(y_sub, result,width= 0.5, align='center', color='r',edgecolor= 'k', alpha=0.5)\n",
    "plt.xticks(y_sub, objects) \n",
    "\n",
    "plt.ylabel('Number of people (In Million)')\n",
    "plt.title(\"\\n\".join(wrap(\"Over 100 Million People in U.S. now Receiving Some Form Of Federal Welfare (Figures Count Means-Tested Welfare, Not Social Security or Medicare)\", 80)))\n",
    "#plt.yticks(np.arange(0, 108000000, 20000000))\n",
    "#plt.title('Over 100 Million People In U.S. Now Receving Some Form Of Federal Welfare((Figures Count Means-Tested Welfare excluding the Earned Income Tax Credit or the health insurance premium subsidies included in the President’s health care law, Social Security and/or Medicare))')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}