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Agrover112/viz (1).ipynb

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viz (1).ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Q</th>\n",
" <th>Ans</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>6</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>7</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>8</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>9</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>10</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>11</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>12</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>13</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>14</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>15</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>16</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>17</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>18</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>19</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>20</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>21</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>22</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>23</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>24</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>25</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>26</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>27</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>28</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>29</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>30</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>60</th>\n",
" <td>61</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>61</th>\n",
" <td>62</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>62</th>\n",
" <td>63</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>63</th>\n",
" <td>64</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>64</th>\n",
" <td>65</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65</th>\n",
" <td>66</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>66</th>\n",
" <td>67</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>67</th>\n",
" <td>68</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68</th>\n",
" <td>69</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>69</th>\n",
" <td>70</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>70</th>\n",
" <td>71</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>71</th>\n",
" <td>72</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>72</th>\n",
" <td>73</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>73</th>\n",
" <td>74</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>74</th>\n",
" <td>75</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75</th>\n",
" <td>76</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>76</th>\n",
" <td>77</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>77</th>\n",
" <td>78</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>78</th>\n",
" <td>79</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>79</th>\n",
" <td>80</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>80</th>\n",
" <td>81</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>81</th>\n",
" <td>82</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>82</th>\n",
" <td>83</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>83</th>\n",
" <td>84</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>84</th>\n",
" <td>85</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>85</th>\n",
" <td>86</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>86</th>\n",
" <td>87</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87</th>\n",
" <td>88</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>88</th>\n",
" <td>89</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>89</th>\n",
" <td>90</td>\n",
" <td>4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>90 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" Q Ans\n",
"0 1 3\n",
"1 2 2\n",
"2 3 3\n",
"3 4 3\n",
"4 5 2\n",
"5 6 3\n",
"6 7 2\n",
"7 8 1\n",
"8 9 3\n",
"9 10 1\n",
"10 11 3\n",
"11 12 2\n",
"12 13 2\n",
"13 14 1\n",
"14 15 3\n",
"15 16 2\n",
"16 17 4\n",
"17 18 3\n",
"18 19 1\n",
"19 20 2\n",
"20 21 3\n",
"21 22 4\n",
"22 23 2\n",
"23 24 1\n",
"24 25 4\n",
"25 26 2\n",
"26 27 1\n",
"27 28 3\n",
"28 29 3\n",
"29 30 1\n",
".. .. ...\n",
"60 61 1\n",
"61 62 3\n",
"62 63 3\n",
"63 64 4\n",
"64 65 3\n",
"65 66 2\n",
"66 67 1\n",
"67 68 2\n",
"68 69 2\n",
"69 70 3\n",
"70 71 1\n",
"71 72 4\n",
"72 73 3\n",
"73 74 4\n",
"74 75 3\n",
"75 76 4\n",
"76 77 3\n",
"77 78 4\n",
"78 79 3\n",
"79 80 3\n",
"80 81 4\n",
"81 82 4\n",
"82 83 4\n",
"83 84 3\n",
"84 85 1\n",
"85 86 1\n",
"86 87 4\n",
"87 88 1\n",
"88 89 1\n",
"89 90 4\n",
"\n",
"[90 rows x 2 columns]"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"d10 = pd.read_csv('2010.txt', sep=\"-\", header=None)\n",
"d10.columns=[\"Q\",\"Ans\"]\n",
"d10.set_index(\"Q\")\n",
"d10\n",
" \n",
"d11 = pd.read_csv('2011.txt', sep=\"-\", header=None)\n",
"d11.columns=[\"Q\",\"Ans\"]\n",
"d11.set_index(\"Q\")\n",
"d11\n",
"d12 = pd.read_csv('2012.txt', sep=\"-\", header=None)\n",
"d12.columns=[\"Q\",\"Ans\"]\n",
"d12.set_index(\"Q\")\n",
"d12\n",
"d13 = pd.read_csv('2013.txt', sep=\"-\", header=None)\n",
"d13.columns=[\"Q\",\"Ans\"]\n",
"d13.set_index(\"Q\")\n",
"d13\n",
"d14 = pd.read_csv('2014.txt', sep=\"-\", header=None)\n",
"d14.columns=[\"Q\",\"Ans\"]\n",
"d14.set_index(\"Q\")\n",
"d14\n",
"d15 = pd.read_csv('2015.txt', sep=\"-\", header=None)\n",
"d15.columns=[\"Q\",\"Ans\"]\n",
"d15.set_index(\"Q\")\n",
"d15"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"([<matplotlib.axis.YTick at 0x185661440b8>,\n",
" <matplotlib.axis.YTick at 0x18566122048>,\n",
" <matplotlib.axis.YTick at 0x18566152d30>,\n",
" <matplotlib.axis.YTick at 0x1856616f208>,\n",
" <matplotlib.axis.YTick at 0x18566176198>,\n",
" <matplotlib.axis.YTick at 0x18566176668>,\n",
" <matplotlib.axis.YTick at 0x185641e2908>,\n",
" <matplotlib.axis.YTick at 0x1856617e128>,\n",
" <matplotlib.axis.YTick at 0x1856617e588>,\n",
" <matplotlib.axis.YTick at 0x1856617ea58>],\n",
" <a list of 10 Text yticklabel objects>)"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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7a0nS3TdV1flJ3ldVD8vS8wWW3dnd30ry9ar6r+6+LUm6+xtVdXSTZ2N69iS5NMmrkvxOdx+qqm+IqPUTUyffGUmenuR/jrm/kvzryR+HCftyVe3u7kNJMnuF6plJrkjyQ5s7GhPzzar6nu7+epLzlu+sqvsnEVN8m+4+muQtVfXe2e9bogeG+J938l2V5L7L/0CuVFXXnPxxmLAXJLlz5R3dfWeSF1TV2zZnJCbqJ7v7juSufyiX3TvJJZszElPX3V9McnFV/UyS2zZ7nq3MNVMAAAOsRgAAGCCmAAAGiCkAgAFiCgBggJgCABjw/6jnOgAjx+YKAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# plt.rcParams['figure.figsize']=10,5\n",
"l_2010_ans=d10['Ans'].tolist()\n",
"a10={x:l_2010_ans.count(x) for x in l_2010_ans}\n",
"a10\n",
"df10=pd.Series(a10)\n",
"df10.sort_index().plot(kind=\"bar\",color=['r','g','c','m'])\n",
"plt.yticks(np.arange(20,30,step=1))\n",
"# df10.plot(kind=\"bar\",color=['r','g','c','m'])\n",
"# plt.yticks(np.arange(20,30,step=1))\n"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x18566187b38>"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"l_2011_ans=d11['Ans'].tolist()\n",
"a11= {x:l_2010_ans.count(x) for x in l_2011_ans}\n",
"df11=pd.Series(a11)\n",
"df11.plot.pie()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x185661caef0>"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"l_2012_ans=d12['Ans'].tolist()\n",
"a12={x:l_2012_ans.count(x) for x in l_2012_ans}\n",
"df12=pd.Series(a12)\n",
"df12.sort_index().plot.bar()"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{3: 27, 4: 20, 2: 24, 1: 19}"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l_2013_ans=d13['Ans'].tolist()\n",
"a13= {x:l_2013_ans.count(x) for x in l_2013_ans}\n",
"a13"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{1: 24, 3: 22, 4: 17, 2: 27}"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l_2014_ans=d14['Ans'].tolist()\n",
"a14= {x:l_2014_ans.count(x) for x in l_2014_ans}\n",
"a14\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{1: 22, 2: 23, 3: 22, 4: 23}"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l_2015_ans=d10['Ans'].tolist()\n",
"a15= {x:l_2015_ans.count(x) for x in l_2015_ans}\n",
"a15"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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