Data Source -- Department of
Consumer Affairs (Price Monitoring Cell)
Note -- All the prices are in Rs./Kg. except Milk whose price is in Rs./Litre
import pandas as pd
def str_to_datetime(improper_str):
month, _, year = improper_str.partition(" ")
day = "1"
return pd.to_datetime("{0} {1}, {2}".format(month, day, year))
We need to read HTML tables and convert them into Series objects:
def table_to_series(table_str):
df = pd.read_html(table_str)[0].T.iloc[1:-1,[0,2]]
df[0] = df[0].map(str_to_datetime)
df.rename(index = df[0], inplace=True)
del df[0]
df[2] = pd.to_numeric(df[2])
return df[2]
Let's aggregate all the Series objects in a DataFrame object:
data = pd.DataFrame()
data["Rice"] = table_to_series("""<table cellspacing="0" align="Center" rules="all" border="1" style="border-collapse:collapse;">
<tbody><tr>
<th scope="col">Zone</th><th scope="col">Jan 2012</th><th scope="col">Feb 2012</th><th scope="col">Mar 2012</th><th scope="col">Apr 2012</th><th scope="col">May 2012</th><th scope="col">Jun 2012</th><th scope="col">Jul 2012</th><th scope="col">Aug 2012</th><th scope="col">Sep 2012</th><th scope="col">Oct 2012</th><th scope="col">Nov 2012</th><th scope="col">Dec 2012</th><th scope="col">Jan 2013</th><th scope="col">Feb 2013</th><th scope="col">Mar 2013</th><th scope="col">Apr 2013</th><th scope="col">May 2013</th><th scope="col">Jun 2013</th><th scope="col">Jul 2013</th><th scope="col">Aug 2013</th><th scope="col">Sep 2013</th><th scope="col">Oct 2013</th><th scope="col">Nov 2013</th><th scope="col">Dec 2013</th><th scope="col">Jan 2014</th><th scope="col">Feb 2014</th><th scope="col">Mar 2014</th><th scope="col">Apr 2014</th><th scope="col">May 2014</th><th scope="col">Jun 2014</th><th scope="col">Jul 2014</th><th scope="col">Aug 2014</th><th scope="col">Sep 2014</th><th scope="col">Oct 2014</th><th scope="col">Nov 2014</th><th scope="col">Dec 2014</th><th scope="col">Jan 2015</th><th scope="col">Feb 2015</th><th scope="col">Mar 2015</th><th scope="col">Apr 2015</th><th scope="col">May 2015</th><th scope="col">Jun 2015</th><th scope="col">Jul 2015</th><th scope="col">Aug 2015</th><th scope="col">Sep 2015</th><th scope="col">Oct 2015</th><th scope="col">Nov 2015</th><th scope="col">Dec 2015</th><th scope="col">Jan 2016</th><th scope="col">Feb 2016</th><th scope="col">Mar 2016</th><th scope="col">Apr 2016</th><th scope="col">May 2016</th><th scope="col">Jun 2016</th><th scope="col">Jul 2016</th><th scope="col">Aug 2016</th><th scope="col">Sep 2016</th><th scope="col">Oct 2016</th><th scope="col">Nov 2016</th><th scope="col">Dec 2016</th><th scope="col">Jan 2017</th><th scope="col">Feb 2017</th><th scope="col">Mar 2017</th><th scope="col">Apr 2017</th><th scope="col">May 2017</th><th scope="col">Jun 2017</th><th scope="col">Jul 2017</th><th scope="col">Average</th>
</tr><tr align="left">
<td>NORTH ZONE</td><td>20.73</td><td>20.87</td><td>21.02</td><td>20.77</td><td>21.08</td><td>21.27</td><td>21.44</td><td>22.29</td><td>22.56</td><td>23.01</td><td>23.08</td><td>23</td><td>23.03</td><td>23.33</td><td>23.99</td><td>24.36</td><td>24.46</td><td>24.99</td><td>25.47</td><td>25.63</td><td>26.05</td><td>26.17</td><td>26.71</td><td>26.08</td><td>25.61</td><td>25.85</td><td>26.71</td><td>26.64</td><td>26.77</td><td>26.77</td><td>27.31</td><td>27.22</td><td>27.3</td><td>26.93</td><td>26.57</td><td>26.52</td><td>26.42</td><td>26.24</td><td>26.11</td><td>26.09</td><td>25.91</td><td>25.51</td><td>25.6</td><td>25.78</td><td>26.03</td><td>26.09</td><td>26.48</td><td>25.92</td><td>25.45</td><td>25.31</td><td>25.55</td><td>25.79</td><td>25.21</td><td>25.34</td><td>25.69</td><td>26.04</td><td>26.2</td><td>25.98</td><td>25.98</td><td>26.41</td><td>26.4</td><td>27.01</td><td>26.85</td><td>26.59</td><td>26.7</td><td>27.06</td><td>27.8</td><td>25.55</td>
</tr><tr align="left">
<td>All India Average</td><td>20.51</td><td>20.59</td><td>20.77</td><td>20.88</td><td>21.05</td><td>21.76</td><td>22.48</td><td>23.02</td><td>23.36</td><td>24.22</td><td>24.41</td><td>24.44</td><td>24.65</td><td>25.21</td><td>25.1</td><td>25.25</td><td>25.4</td><td>25.85</td><td>26.32</td><td>26.65</td><td>26.9</td><td>27.02</td><td>27.53</td><td>27.43</td><td>27.23</td><td>27.44</td><td>27.57</td><td>27.44</td><td>27.57</td><td>27.79</td><td>28.26</td><td>28.38</td><td>28.78</td><td>28.54</td><td>28.15</td><td>27.86</td><td>27.64</td><td>27.66</td><td>27.43</td><td>27.56</td><td>27.5</td><td>27.58</td><td>27.52</td><td>27.34</td><td>27.4</td><td>27.55</td><td>27.52</td><td>27.43</td><td>27.06</td><td>27.04</td><td>26.95</td><td>26.85</td><td>26.83</td><td>27.03</td><td>27.38</td><td>27.53</td><td>27.51</td><td>27.45</td><td>27.73</td><td>28.15</td><td>28.34</td><td>28.87</td><td>28.85</td><td>28.64</td><td>28.84</td><td>29.07</td><td>29.47</td><td> </td>
</tr>
</tbody></table>""")
data["Wheat"] = table_to_series("""<table cellspacing="0" align="Center" rules="all" border="1" style="border-collapse:collapse;">
<tbody><tr>
<th scope="col">Zone</th><th scope="col">Jan 2012</th><th scope="col">Feb 2012</th><th scope="col">Mar 2012</th><th scope="col">Apr 2012</th><th scope="col">May 2012</th><th scope="col">Jun 2012</th><th scope="col">Jul 2012</th><th scope="col">Aug 2012</th><th scope="col">Sep 2012</th><th scope="col">Oct 2012</th><th scope="col">Nov 2012</th><th scope="col">Dec 2012</th><th scope="col">Jan 2013</th><th scope="col">Feb 2013</th><th scope="col">Mar 2013</th><th scope="col">Apr 2013</th><th scope="col">May 2013</th><th scope="col">Jun 2013</th><th scope="col">Jul 2013</th><th scope="col">Aug 2013</th><th scope="col">Sep 2013</th><th scope="col">Oct 2013</th><th scope="col">Nov 2013</th><th scope="col">Dec 2013</th><th scope="col">Jan 2014</th><th scope="col">Feb 2014</th><th scope="col">Mar 2014</th><th scope="col">Apr 2014</th><th scope="col">May 2014</th><th scope="col">Jun 2014</th><th scope="col">Jul 2014</th><th scope="col">Aug 2014</th><th scope="col">Sep 2014</th><th scope="col">Oct 2014</th><th scope="col">Nov 2014</th><th scope="col">Dec 2014</th><th scope="col">Jan 2015</th><th scope="col">Feb 2015</th><th scope="col">Mar 2015</th><th scope="col">Apr 2015</th><th scope="col">May 2015</th><th scope="col">Jun 2015</th><th scope="col">Jul 2015</th><th scope="col">Aug 2015</th><th scope="col">Sep 2015</th><th scope="col">Oct 2015</th><th scope="col">Nov 2015</th><th scope="col">Dec 2015</th><th scope="col">Jan 2016</th><th scope="col">Feb 2016</th><th scope="col">Mar 2016</th><th scope="col">Apr 2016</th><th scope="col">May 2016</th><th scope="col">Jun 2016</th><th scope="col">Jul 2016</th><th scope="col">Aug 2016</th><th scope="col">Sep 2016</th><th scope="col">Oct 2016</th><th scope="col">Nov 2016</th><th scope="col">Dec 2016</th><th scope="col">Jan 2017</th><th scope="col">Feb 2017</th><th scope="col">Mar 2017</th><th scope="col">Apr 2017</th><th scope="col">May 2017</th><th scope="col">Jun 2017</th><th scope="col">Jul 2017</th><th scope="col">Average</th>
</tr><tr align="left">
<td>NORTH ZONE</td><td>13.5</td><td>13.58</td><td>13.84</td><td>13.67</td><td>13.79</td><td>13.79</td><td>13.76</td><td>14.57</td><td>15.82</td><td>15.84</td><td>16</td><td>16.46</td><td>16.51</td><td>16.65</td><td>17.22</td><td>17.06</td><td>16.45</td><td>16.86</td><td>17.09</td><td>17.12</td><td>17.34</td><td>17.1</td><td>16.93</td><td>17.21</td><td>17.43</td><td>17.5</td><td>17.5</td><td>17.45</td><td>17.25</td><td>17.07</td><td>17.11</td><td>17.26</td><td>17.26</td><td>17.35</td><td>17.66</td><td>17.65</td><td>17.95</td><td>18.04</td><td>17.9</td><td>17.76</td><td>17.45</td><td>17.46</td><td>17.47</td><td>17.47</td><td>17.63</td><td>17.88</td><td>18.28</td><td>18.51</td><td>18.58</td><td>18.64</td><td>18.78</td><td>18.72</td><td>18.73</td><td>18.82</td><td>18.99</td><td>19.11</td><td>19.32</td><td>19.37</td><td>20.45</td><td>21.24</td><td>21.42</td><td>21.29</td><td>20.77</td><td>20.26</td><td>19.95</td><td>19.72</td><td>19.56</td><td>18.13</td>
</tr><tr align="left">
<td>All India Average</td><td>16.22</td><td>16.16</td><td>16.28</td><td>16.39</td><td>16.51</td><td>16.73</td><td>17.02</td><td>17.73</td><td>18.8</td><td>19.21</td><td>19.53</td><td>19.96</td><td>19.9</td><td>20.28</td><td>20.57</td><td>20.45</td><td>20.09</td><td>20.52</td><td>20.66</td><td>20.87</td><td>20.6</td><td>21.06</td><td>21.2</td><td>21.58</td><td>21.84</td><td>21.76</td><td>21.61</td><td>21.29</td><td>21.03</td><td>21</td><td>21.49</td><td>21.52</td><td>22.07</td><td>22.16</td><td>22.12</td><td>21.89</td><td>22.06</td><td>22.9</td><td>22.72</td><td>22.61</td><td>22.33</td><td>22.56</td><td>22.64</td><td>22.5</td><td>22.7</td><td>23.14</td><td>23.54</td><td>23.41</td><td>23.35</td><td>23.82</td><td>23.69</td><td>23.27</td><td>23.34</td><td>23.39</td><td>23.31</td><td>23.31</td><td>23.32</td><td>23.42</td><td>24.05</td><td>24.56</td><td>24.51</td><td>24.65</td><td>24.41</td><td>23.96</td><td>23.73</td><td>23.68</td><td>23.67</td><td> </td>
</tr>
</tbody></table>""")
data["Atta (Wheat)"] = table_to_series("""<table cellspacing="0" align="Center" rules="all" border="1" style="border-collapse:collapse;">
<tbody><tr>
<th scope="col">Zone</th><th scope="col">Jan 2012</th><th scope="col">Feb 2012</th><th scope="col">Mar 2012</th><th scope="col">Apr 2012</th><th scope="col">May 2012</th><th scope="col">Jun 2012</th><th scope="col">Jul 2012</th><th scope="col">Aug 2012</th><th scope="col">Sep 2012</th><th scope="col">Oct 2012</th><th scope="col">Nov 2012</th><th scope="col">Dec 2012</th><th scope="col">Jan 2013</th><th scope="col">Feb 2013</th><th scope="col">Mar 2013</th><th scope="col">Apr 2013</th><th scope="col">May 2013</th><th scope="col">Jun 2013</th><th scope="col">Jul 2013</th><th scope="col">Aug 2013</th><th scope="col">Sep 2013</th><th scope="col">Oct 2013</th><th scope="col">Nov 2013</th><th scope="col">Dec 2013</th><th scope="col">Jan 2014</th><th scope="col">Feb 2014</th><th scope="col">Mar 2014</th><th scope="col">Apr 2014</th><th scope="col">May 2014</th><th scope="col">Jun 2014</th><th scope="col">Jul 2014</th><th scope="col">Aug 2014</th><th scope="col">Sep 2014</th><th scope="col">Oct 2014</th><th scope="col">Nov 2014</th><th scope="col">Dec 2014</th><th scope="col">Jan 2015</th><th scope="col">Feb 2015</th><th scope="col">Mar 2015</th><th scope="col">Apr 2015</th><th scope="col">May 2015</th><th scope="col">Jun 2015</th><th scope="col">Jul 2015</th><th scope="col">Aug 2015</th><th scope="col">Sep 2015</th><th scope="col">Oct 2015</th><th scope="col">Nov 2015</th><th scope="col">Dec 2015</th><th scope="col">Jan 2016</th><th scope="col">Feb 2016</th><th scope="col">Mar 2016</th><th scope="col">Apr 2016</th><th scope="col">May 2016</th><th scope="col">Jun 2016</th><th scope="col">Jul 2016</th><th scope="col">Aug 2016</th><th scope="col">Sep 2016</th><th scope="col">Oct 2016</th><th scope="col">Nov 2016</th><th scope="col">Dec 2016</th><th scope="col">Jan 2017</th><th scope="col">Feb 2017</th><th scope="col">Mar 2017</th><th scope="col">Apr 2017</th><th scope="col">May 2017</th><th scope="col">Jun 2017</th><th scope="col">Jul 2017</th><th scope="col">Average</th>
</tr><tr align="left">
<td>NORTH ZONE</td><td>15.66</td><td>15.72</td><td>15.94</td><td>15.81</td><td>15.89</td><td>15.92</td><td>15.94</td><td>16.6</td><td>17.75</td><td>18.06</td><td>18.52</td><td>18.78</td><td>19.1</td><td>19.17</td><td>19.54</td><td>19.63</td><td>19.07</td><td>19.49</td><td>19.83</td><td>19.88</td><td>20.13</td><td>20.13</td><td>20.12</td><td>20.64</td><td>20.86</td><td>20.85</td><td>20.75</td><td>20.63</td><td>20.43</td><td>20.46</td><td>20.56</td><td>20.85</td><td>20.65</td><td>20.61</td><td>20.63</td><td>20.72</td><td>20.95</td><td>21.01</td><td>20.96</td><td>21</td><td>20.89</td><td>20.87</td><td>20.74</td><td>20.77</td><td>20.79</td><td>20.91</td><td>21.16</td><td>21.3</td><td>21.27</td><td>21.4</td><td>21.5</td><td>21.57</td><td>21.57</td><td>21.67</td><td>21.89</td><td>21.98</td><td>22.12</td><td>22.15</td><td>23.3</td><td>24.81</td><td>24.95</td><td>24.69</td><td>24.29</td><td>23.78</td><td>23.41</td><td>23.05</td><td>23.05</td><td>21.11</td>
</tr><tr align="left">
<td>All India Average</td><td>17.95</td><td>17.87</td><td>17.84</td><td>17.79</td><td>17.88</td><td>18.15</td><td>18.44</td><td>19.1</td><td>20.13</td><td>21.03</td><td>21.56</td><td>21.95</td><td>22.02</td><td>22.39</td><td>22.49</td><td>22.51</td><td>22.29</td><td>22.65</td><td>22.78</td><td>22.87</td><td>22.81</td><td>23.19</td><td>23.35</td><td>23.41</td><td>23.8</td><td>23.55</td><td>23.47</td><td>23.4</td><td>22.92</td><td>23.12</td><td>23.51</td><td>23.69</td><td>23.96</td><td>24.12</td><td>24.06</td><td>23.98</td><td>24.19</td><td>24.97</td><td>24.97</td><td>24.95</td><td>24.53</td><td>24.61</td><td>24.67</td><td>24.49</td><td>24.62</td><td>24.78</td><td>24.74</td><td>24.71</td><td>24.58</td><td>24.98</td><td>24.95</td><td>24.58</td><td>24.48</td><td>24.61</td><td>24.76</td><td>24.97</td><td>25.07</td><td>25.38</td><td>26.14</td><td>27.12</td><td>27.12</td><td>26.85</td><td>26.64</td><td>26.17</td><td>25.98</td><td>25.7</td><td>25.97</td><td> </td>
</tr>
</tbody></table>""")
data["Gram"] = table_to_series("""<table cellspacing="0" align="Center" rules="all" border="1" style="border-collapse:collapse;">
<tbody><tr>
<th scope="col">Zone</th><th scope="col">Jan 2012</th><th scope="col">Feb 2012</th><th scope="col">Mar 2012</th><th scope="col">Apr 2012</th><th scope="col">May 2012</th><th scope="col">Jun 2012</th><th scope="col">Jul 2012</th><th scope="col">Aug 2012</th><th scope="col">Sep 2012</th><th scope="col">Oct 2012</th><th scope="col">Nov 2012</th><th scope="col">Dec 2012</th><th scope="col">Jan 2013</th><th scope="col">Feb 2013</th><th scope="col">Mar 2013</th><th scope="col">Apr 2013</th><th scope="col">May 2013</th><th scope="col">Jun 2013</th><th scope="col">Jul 2013</th><th scope="col">Aug 2013</th><th scope="col">Sep 2013</th><th scope="col">Oct 2013</th><th scope="col">Nov 2013</th><th scope="col">Dec 2013</th><th scope="col">Jan 2014</th><th scope="col">Feb 2014</th><th scope="col">Mar 2014</th><th scope="col">Apr 2014</th><th scope="col">May 2014</th><th scope="col">Jun 2014</th><th scope="col">Jul 2014</th><th scope="col">Aug 2014</th><th scope="col">Sep 2014</th><th scope="col">Oct 2014</th><th scope="col">Nov 2014</th><th scope="col">Dec 2014</th><th scope="col">Jan 2015</th><th scope="col">Feb 2015</th><th scope="col">Mar 2015</th><th scope="col">Apr 2015</th><th scope="col">May 2015</th><th scope="col">Jun 2015</th><th scope="col">Jul 2015</th><th scope="col">Aug 2015</th><th scope="col">Sep 2015</th><th scope="col">Oct 2015</th><th scope="col">Nov 2015</th><th scope="col">Dec 2015</th><th scope="col">Jan 2016</th><th scope="col">Feb 2016</th><th scope="col">Mar 2016</th><th scope="col">Apr 2016</th><th scope="col">May 2016</th><th scope="col">Jun 2016</th><th scope="col">Jul 2016</th><th scope="col">Aug 2016</th><th scope="col">Sep 2016</th><th scope="col">Oct 2016</th><th scope="col">Nov 2016</th><th scope="col">Dec 2016</th><th scope="col">Jan 2017</th><th scope="col">Feb 2017</th><th scope="col">Mar 2017</th><th scope="col">Apr 2017</th><th scope="col">May 2017</th><th scope="col">Jun 2017</th><th scope="col">Jul 2017</th><th scope="col">Average</th>
</tr><tr align="left">
<td>NORTH ZONE</td><td>48.83</td><td>49.73</td><td>50.92</td><td>51.36</td><td>53.5</td><td>55.77</td><td>59.27</td><td>63.55</td><td>64.17</td><td>63.75</td><td>63.71</td><td>63</td><td>60.98</td><td>59.2</td><td>55.61</td><td>54.39</td><td>55.34</td><td>55.38</td><td>54.67</td><td>51.51</td><td>51.36</td><td>50.87</td><td>51.19</td><td>50.91</td><td>48.87</td><td>48.17</td><td>49.14</td><td>48.64</td><td>49.08</td><td>48.36</td><td>46.95</td><td>46.54</td><td>46.33</td><td>45.95</td><td>46.41</td><td>46.69</td><td>47.68</td><td>48.25</td><td>48.93</td><td>50.95</td><td>55.74</td><td>58.08</td><td>59.31</td><td>60.66</td><td>62.04</td><td>68.13</td><td>71.51</td><td>70.36</td><td>68.06</td><td>67.08</td><td>66.19</td><td>69.01</td><td>74.21</td><td>80.22</td><td>93.68</td><td>99.4</td><td>96.87</td><td>111.3</td><td>123.18</td><td>124.03</td><td>112.58</td><td>97.45</td><td>84.86</td><td>84.67</td><td>83.65</td><td>81.06</td><td>76.73</td><td>71.01</td>
</tr><tr align="left">
<td>All India Average</td><td>49.19</td><td>49.2</td><td>49.97</td><td>50.96</td><td>53.88</td><td>56.36</td><td>60.98</td><td>66</td><td>66.65</td><td>66</td><td>65.9</td><td>64.94</td><td>62.44</td><td>60.49</td><td>57.65</td><td>56.18</td><td>55.79</td><td>54.72</td><td>52.56</td><td>50.96</td><td>51.3</td><td>51.34</td><td>50.95</td><td>50.46</td><td>49.58</td><td>48.21</td><td>48.79</td><td>48.7</td><td>48.59</td><td>47.41</td><td>46.36</td><td>46.08</td><td>46.12</td><td>46.2</td><td>45.64</td><td>45.48</td><td>46.97</td><td>48.33</td><td>49.39</td><td>51.31</td><td>56.54</td><td>59.4</td><td>60</td><td>61.09</td><td>63.78</td><td>68.05</td><td>69.56</td><td>69.01</td><td>67.42</td><td>66.05</td><td>65.48</td><td>68.26</td><td>73.58</td><td>79.26</td><td>93.07</td><td>99.79</td><td>100.28</td><td>112.11</td><td>123.41</td><td>123.7</td><td>114.84</td><td>100.76</td><td>89.43</td><td>88.8</td><td>86.79</td><td>85.14</td><td>81.95</td><td> </td>
</tr>
</tbody></table>""")
data["Tur"] = table_to_series("""<table cellspacing="0" align="Center" rules="all" border="1" style="border-collapse:collapse;">
<tbody><tr>
<th scope="col">Zone</th><th scope="col">Jan 2012</th><th scope="col">Feb 2012</th><th scope="col">Mar 2012</th><th scope="col">Apr 2012</th><th scope="col">May 2012</th><th scope="col">Jun 2012</th><th scope="col">Jul 2012</th><th scope="col">Aug 2012</th><th scope="col">Sep 2012</th><th scope="col">Oct 2012</th><th scope="col">Nov 2012</th><th scope="col">Dec 2012</th><th scope="col">Jan 2013</th><th scope="col">Feb 2013</th><th scope="col">Mar 2013</th><th scope="col">Apr 2013</th><th scope="col">May 2013</th><th scope="col">Jun 2013</th><th scope="col">Jul 2013</th><th scope="col">Aug 2013</th><th scope="col">Sep 2013</th><th scope="col">Oct 2013</th><th scope="col">Nov 2013</th><th scope="col">Dec 2013</th><th scope="col">Jan 2014</th><th scope="col">Feb 2014</th><th scope="col">Mar 2014</th><th scope="col">Apr 2014</th><th scope="col">May 2014</th><th scope="col">Jun 2014</th><th scope="col">Jul 2014</th><th scope="col">Aug 2014</th><th scope="col">Sep 2014</th><th scope="col">Oct 2014</th><th scope="col">Nov 2014</th><th scope="col">Dec 2014</th><th scope="col">Jan 2015</th><th scope="col">Feb 2015</th><th scope="col">Mar 2015</th><th scope="col">Apr 2015</th><th scope="col">May 2015</th><th scope="col">Jun 2015</th><th scope="col">Jul 2015</th><th scope="col">Aug 2015</th><th scope="col">Sep 2015</th><th scope="col">Oct 2015</th><th scope="col">Nov 2015</th><th scope="col">Dec 2015</th><th scope="col">Jan 2016</th><th scope="col">Feb 2016</th><th scope="col">Mar 2016</th><th scope="col">Apr 2016</th><th scope="col">May 2016</th><th scope="col">Jun 2016</th><th scope="col">Jul 2016</th><th scope="col">Aug 2016</th><th scope="col">Sep 2016</th><th scope="col">Oct 2016</th><th scope="col">Nov 2016</th><th scope="col">Dec 2016</th><th scope="col">Jan 2017</th><th scope="col">Feb 2017</th><th scope="col">Mar 2017</th><th scope="col">Apr 2017</th><th scope="col">May 2017</th><th scope="col">Jun 2017</th><th scope="col">Jul 2017</th><th scope="col">Average</th>
</tr><tr align="left">
<td>NORTH ZONE</td><td>63.21</td><td>62.58</td><td>62.48</td><td>62.1</td><td>63.37</td><td>63.97</td><td>65.09</td><td>69.75</td><td>71.55</td><td>70.59</td><td>70.86</td><td>70.46</td><td>69.89</td><td>68.25</td><td>67.47</td><td>68.16</td><td>69.14</td><td>69.37</td><td>69.5</td><td>68.74</td><td>69.4</td><td>70.1</td><td>71.36</td><td>71.58</td><td>70.84</td><td>71.41</td><td>72.19</td><td>71.74</td><td>72.15</td><td>72.23</td><td>72.35</td><td>72.64</td><td>74.9</td><td>75.04</td><td>75.53</td><td>76.53</td><td>77.77</td><td>78.66</td><td>80.38</td><td>83.95</td><td>89.27</td><td>92.08</td><td>94.82</td><td>99.64</td><td>112.12</td><td>134.8</td><td>155.8</td><td>153.31</td><td>150.76</td><td>145.12</td><td>141.21</td><td>144.74</td><td>146.13</td><td>144.41</td><td>143.44</td><td>137.22</td><td>123.45</td><td>123.31</td><td>121.73</td><td>118.15</td><td>109.36</td><td>102.54</td><td>94.73</td><td>92.81</td><td>90.59</td><td>86.19</td><td>82.04</td><td>99.17</td>
</tr><tr align="left">
<td>All India Average</td><td>61.36</td><td>61.15</td><td>60.82</td><td>60.59</td><td>61.74</td><td>63.08</td><td>65.63</td><td>69.99</td><td>71.15</td><td>70.09</td><td>69.45</td><td>69.02</td><td>67.98</td><td>67.17</td><td>67.07</td><td>68.28</td><td>68.89</td><td>69.04</td><td>68.78</td><td>68.4</td><td>69.07</td><td>69.53</td><td>70.18</td><td>70.41</td><td>70.02</td><td>69.95</td><td>70.14</td><td>70.25</td><td>70.41</td><td>69.93</td><td>70.35</td><td>71.68</td><td>73.93</td><td>74.11</td><td>75.1</td><td>75.65</td><td>77.13</td><td>79</td><td>81.75</td><td>85.23</td><td>91.89</td><td>95.33</td><td>98.42</td><td>105.13</td><td>119.95</td><td>143.78</td><td>152.29</td><td>150.08</td><td>145.74</td><td>140.14</td><td>135.27</td><td>138.22</td><td>141.19</td><td>140.1</td><td>139.33</td><td>132.27</td><td>121.27</td><td>121.57</td><td>118.82</td><td>113.03</td><td>102.96</td><td>95.95</td><td>89.55</td><td>88.13</td><td>85.35</td><td>82.5</td><td>79.02</td><td> </td>
</tr>
</tbody></table>""")
data["Urad"] = table_to_series("""<table cellspacing="0" align="Center" rules="all" border="1" style="border-collapse:collapse;">
<tbody><tr>
<th scope="col">Zone</th><th scope="col">Jan 2012</th><th scope="col">Feb 2012</th><th scope="col">Mar 2012</th><th scope="col">Apr 2012</th><th scope="col">May 2012</th><th scope="col">Jun 2012</th><th scope="col">Jul 2012</th><th scope="col">Aug 2012</th><th scope="col">Sep 2012</th><th scope="col">Oct 2012</th><th scope="col">Nov 2012</th><th scope="col">Dec 2012</th><th scope="col">Jan 2013</th><th scope="col">Feb 2013</th><th scope="col">Mar 2013</th><th scope="col">Apr 2013</th><th scope="col">May 2013</th><th scope="col">Jun 2013</th><th scope="col">Jul 2013</th><th scope="col">Aug 2013</th><th scope="col">Sep 2013</th><th scope="col">Oct 2013</th><th scope="col">Nov 2013</th><th scope="col">Dec 2013</th><th scope="col">Jan 2014</th><th scope="col">Feb 2014</th><th scope="col">Mar 2014</th><th scope="col">Apr 2014</th><th scope="col">May 2014</th><th scope="col">Jun 2014</th><th scope="col">Jul 2014</th><th scope="col">Aug 2014</th><th scope="col">Sep 2014</th><th scope="col">Oct 2014</th><th scope="col">Nov 2014</th><th scope="col">Dec 2014</th><th scope="col">Jan 2015</th><th scope="col">Feb 2015</th><th scope="col">Mar 2015</th><th scope="col">Apr 2015</th><th scope="col">May 2015</th><th scope="col">Jun 2015</th><th scope="col">Jul 2015</th><th scope="col">Aug 2015</th><th scope="col">Sep 2015</th><th scope="col">Oct 2015</th><th scope="col">Nov 2015</th><th scope="col">Dec 2015</th><th scope="col">Jan 2016</th><th scope="col">Feb 2016</th><th scope="col">Mar 2016</th><th scope="col">Apr 2016</th><th scope="col">May 2016</th><th scope="col">Jun 2016</th><th scope="col">Jul 2016</th><th scope="col">Aug 2016</th><th scope="col">Sep 2016</th><th scope="col">Oct 2016</th><th scope="col">Nov 2016</th><th scope="col">Dec 2016</th><th scope="col">Jan 2017</th><th scope="col">Feb 2017</th><th scope="col">Mar 2017</th><th scope="col">Apr 2017</th><th scope="col">May 2017</th><th scope="col">Jun 2017</th><th scope="col">Jul 2017</th><th scope="col">Average</th>
</tr><tr align="left">
<td>NORTH ZONE</td><td>59.88</td><td>59.35</td><td>59.05</td><td>59.47</td><td>59.81</td><td>60.59</td><td>61.82</td><td>63.89</td><td>64.2</td><td>63.78</td><td>63.24</td><td>62.97</td><td>61.86</td><td>61.33</td><td>59.84</td><td>59.55</td><td>60.84</td><td>61.44</td><td>61.81</td><td>61.49</td><td>62.5</td><td>62.93</td><td>66.24</td><td>65.23</td><td>65.49</td><td>66.07</td><td>67</td><td>67.06</td><td>67.79</td><td>68.8</td><td>69.86</td><td>72.55</td><td>74.58</td><td>74.42</td><td>73.45</td><td>74.65</td><td>75.71</td><td>76.7</td><td>76.78</td><td>79.63</td><td>86.18</td><td>90.39</td><td>92.89</td><td>95.23</td><td>100.51</td><td>118.97</td><td>136.1</td><td>133.16</td><td>127.86</td><td>126.02</td><td>125.4</td><td>132.2</td><td>143.9</td><td>143.11</td><td>141.39</td><td>134.69</td><td>124.24</td><td>121.51</td><td>115.65</td><td>110.67</td><td>105.04</td><td>101.47</td><td>96.64</td><td>96.59</td><td>95.05</td><td>91.18</td><td>86.88</td><td>94.97</td>
</tr><tr align="left">
<td>All India Average</td><td>60.29</td><td>59.33</td><td>58.59</td><td>58.42</td><td>58.1</td><td>58.36</td><td>60.07</td><td>62.44</td><td>62.8</td><td>62.35</td><td>61.3</td><td>60.93</td><td>59.76</td><td>59.13</td><td>58.15</td><td>58.07</td><td>58.43</td><td>58.47</td><td>58.33</td><td>58.73</td><td>59.81</td><td>60.75</td><td>63</td><td>63.41</td><td>64.35</td><td>65.13</td><td>65.87</td><td>67.26</td><td>69.07</td><td>71.2</td><td>72.75</td><td>76.01</td><td>78.61</td><td>76.32</td><td>75.71</td><td>76.27</td><td>77.97</td><td>79.15</td><td>79.62</td><td>82.99</td><td>91.33</td><td>97.34</td><td>98.86</td><td>100.88</td><td>107.54</td><td>129.42</td><td>142.15</td><td>142.64</td><td>139.56</td><td>137.17</td><td>135.29</td><td>141.35</td><td>152.19</td><td>152.73</td><td>151.67</td><td>143.84</td><td>131.5</td><td>126.13</td><td>119.57</td><td>113.77</td><td>107.86</td><td>103.44</td><td>98.84</td><td>99.33</td><td>97.29</td><td>95.02</td><td>90.5</td><td> </td>
</tr>
</tbody></table>""")
data["Moong"] = table_to_series("""<table cellspacing="0" align="Center" rules="all" border="1" style="border-collapse:collapse;">
<tbody><tr>
<th scope="col">Zone</th><th scope="col">Jan 2012</th><th scope="col">Feb 2012</th><th scope="col">Mar 2012</th><th scope="col">Apr 2012</th><th scope="col">May 2012</th><th scope="col">Jun 2012</th><th scope="col">Jul 2012</th><th scope="col">Aug 2012</th><th scope="col">Sep 2012</th><th scope="col">Oct 2012</th><th scope="col">Nov 2012</th><th scope="col">Dec 2012</th><th scope="col">Jan 2013</th><th scope="col">Feb 2013</th><th scope="col">Mar 2013</th><th scope="col">Apr 2013</th><th scope="col">May 2013</th><th scope="col">Jun 2013</th><th scope="col">Jul 2013</th><th scope="col">Aug 2013</th><th scope="col">Sep 2013</th><th scope="col">Oct 2013</th><th scope="col">Nov 2013</th><th scope="col">Dec 2013</th><th scope="col">Jan 2014</th><th scope="col">Feb 2014</th><th scope="col">Mar 2014</th><th scope="col">Apr 2014</th><th scope="col">May 2014</th><th scope="col">Jun 2014</th><th scope="col">Jul 2014</th><th scope="col">Aug 2014</th><th scope="col">Sep 2014</th><th scope="col">Oct 2014</th><th scope="col">Nov 2014</th><th scope="col">Dec 2014</th><th scope="col">Jan 2015</th><th scope="col">Feb 2015</th><th scope="col">Mar 2015</th><th scope="col">Apr 2015</th><th scope="col">May 2015</th><th scope="col">Jun 2015</th><th scope="col">Jul 2015</th><th scope="col">Aug 2015</th><th scope="col">Sep 2015</th><th scope="col">Oct 2015</th><th scope="col">Nov 2015</th><th scope="col">Dec 2015</th><th scope="col">Jan 2016</th><th scope="col">Feb 2016</th><th scope="col">Mar 2016</th><th scope="col">Apr 2016</th><th scope="col">May 2016</th><th scope="col">Jun 2016</th><th scope="col">Jul 2016</th><th scope="col">Aug 2016</th><th scope="col">Sep 2016</th><th scope="col">Oct 2016</th><th scope="col">Nov 2016</th><th scope="col">Dec 2016</th><th scope="col">Jan 2017</th><th scope="col">Feb 2017</th><th scope="col">Mar 2017</th><th scope="col">Apr 2017</th><th scope="col">May 2017</th><th scope="col">Jun 2017</th><th scope="col">Jul 2017</th><th scope="col">Average</th>
</tr><tr align="left">
<td>NORTH ZONE</td><td>63.8</td><td>64.51</td><td>64.4</td><td>64.52</td><td>64.51</td><td>65.33</td><td>66.68</td><td>71.18</td><td>72.39</td><td>72.73</td><td>73.56</td><td>74.67</td><td>74.55</td><td>74.24</td><td>74.25</td><td>74.92</td><td>77.24</td><td>79.06</td><td>78.02</td><td>76.47</td><td>75.84</td><td>76</td><td>77.71</td><td>77.66</td><td>78.95</td><td>82.18</td><td>86.65</td><td>90.13</td><td>91.24</td><td>87.97</td><td>85.98</td><td>87.81</td><td>88.05</td><td>87.3</td><td>90.56</td><td>93.89</td><td>95.79</td><td>96.98</td><td>97.06</td><td>98.72</td><td>100.74</td><td>99.54</td><td>97.37</td><td>96.25</td><td>97.28</td><td>104.6</td><td>107.78</td><td>106.07</td><td>104.38</td><td>101.79</td><td>100.55</td><td>101.64</td><td>101.96</td><td>99.35</td><td>96.35</td><td>93.76</td><td>88.55</td><td>87.56</td><td>85.58</td><td>85.99</td><td>83.06</td><td>81.64</td><td>79.81</td><td>81.39</td><td>80.89</td><td>78.31</td><td>76.42</td><td>87.28</td>
</tr><tr align="left">
<td>All India Average</td><td>63.11</td><td>63.04</td><td>62.21</td><td>62.29</td><td>62.13</td><td>62.27</td><td>64.21</td><td>69.22</td><td>70.47</td><td>70.26</td><td>71.5</td><td>73.02</td><td>72.55</td><td>72.65</td><td>72.7</td><td>73.12</td><td>73.91</td><td>74.75</td><td>74.26</td><td>73.71</td><td>73.19</td><td>74.08</td><td>75.71</td><td>76.64</td><td>78.78</td><td>82.24</td><td>85.13</td><td>88.31</td><td>89.08</td><td>87.08</td><td>86.69</td><td>88.29</td><td>89.09</td><td>89.25</td><td>93.64</td><td>96.02</td><td>98.14</td><td>99.32</td><td>99.21</td><td>100.41</td><td>102.29</td><td>101.29</td><td>99.11</td><td>98.36</td><td>99.71</td><td>107.2</td><td>108.78</td><td>107.31</td><td>105.31</td><td>102.56</td><td>100.96</td><td>100.88</td><td>100.72</td><td>98.41</td><td>96.86</td><td>92.93</td><td>87.75</td><td>86.58</td><td>84.18</td><td>82.69</td><td>80.64</td><td>79.24</td><td>78.75</td><td>80.89</td><td>80.59</td><td>78.9</td><td>76.83</td><td> </td>
</tr>
</tbody></table>""")
data["Masoor"] = table_to_series("""<table cellspacing="0" align="Center" rules="all" border="1" style="border-collapse:collapse;">
<tbody><tr>
<th scope="col">Zone</th><th scope="col">Jan 2012</th><th scope="col">Feb 2012</th><th scope="col">Mar 2012</th><th scope="col">Apr 2012</th><th scope="col">May 2012</th><th scope="col">Jun 2012</th><th scope="col">Jul 2012</th><th scope="col">Aug 2012</th><th scope="col">Sep 2012</th><th scope="col">Oct 2012</th><th scope="col">Nov 2012</th><th scope="col">Dec 2012</th><th scope="col">Jan 2013</th><th scope="col">Feb 2013</th><th scope="col">Mar 2013</th><th scope="col">Apr 2013</th><th scope="col">May 2013</th><th scope="col">Jun 2013</th><th scope="col">Jul 2013</th><th scope="col">Aug 2013</th><th scope="col">Sep 2013</th><th scope="col">Oct 2013</th><th scope="col">Nov 2013</th><th scope="col">Dec 2013</th><th scope="col">Jan 2014</th><th scope="col">Feb 2014</th><th scope="col">Mar 2014</th><th scope="col">Apr 2014</th><th scope="col">May 2014</th><th scope="col">Jun 2014</th><th scope="col">Jul 2014</th><th scope="col">Aug 2014</th><th scope="col">Sep 2014</th><th scope="col">Oct 2014</th><th scope="col">Nov 2014</th><th scope="col">Dec 2014</th><th scope="col">Jan 2015</th><th scope="col">Feb 2015</th><th scope="col">Mar 2015</th><th scope="col">Apr 2015</th><th scope="col">May 2015</th><th scope="col">Jun 2015</th><th scope="col">Jul 2015</th><th scope="col">Aug 2015</th><th scope="col">Sep 2015</th><th scope="col">Oct 2015</th><th scope="col">Nov 2015</th><th scope="col">Dec 2015</th><th scope="col">Jan 2016</th><th scope="col">Feb 2016</th><th scope="col">Mar 2016</th><th scope="col">Apr 2016</th><th scope="col">May 2016</th><th scope="col">Jun 2016</th><th scope="col">Jul 2016</th><th scope="col">Aug 2016</th><th scope="col">Sep 2016</th><th scope="col">Oct 2016</th><th scope="col">Nov 2016</th><th scope="col">Dec 2016</th><th scope="col">Jan 2017</th><th scope="col">Feb 2017</th><th scope="col">Mar 2017</th><th scope="col">Apr 2017</th><th scope="col">May 2017</th><th scope="col">Jun 2017</th><th scope="col">Jul 2017</th><th scope="col">Average</th>
</tr><tr align="left">
<td>NORTH ZONE</td><td>46.53</td><td>46.95</td><td>47.11</td><td>47.39</td><td>49.81</td><td>51.89</td><td>53.34</td><td>56.5</td><td>57.11</td><td>56.99</td><td>56.56</td><td>56.08</td><td>56.65</td><td>56.72</td><td>56.05</td><td>56.83</td><td>59.21</td><td>61.12</td><td>61.1</td><td>61.07</td><td>62.48</td><td>62.26</td><td>63.08</td><td>63.4</td><td>62.57</td><td>62.51</td><td>66.05</td><td>66.89</td><td>69.02</td><td>68.93</td><td>69.54</td><td>70.76</td><td>71.97</td><td>71.87</td><td>73.13</td><td>74.3</td><td>76.64</td><td>77.24</td><td>76.92</td><td>78.3</td><td>81.97</td><td>84.95</td><td>87.28</td><td>88.85</td><td>91.26</td><td>94.19</td><td>95.69</td><td>92.36</td><td>87.78</td><td>83.15</td><td>81.75</td><td>85.59</td><td>86.47</td><td>86.37</td><td>87.13</td><td>87.64</td><td>85.99</td><td>86.89</td><td>84.56</td><td>83.58</td><td>82.65</td><td>81.28</td><td>78.43</td><td>77.79</td><td>76.31</td><td>74</td><td>70.91</td><td>75.96</td>
</tr><tr align="left">
<td>All India Average</td><td>45.6</td><td>46.09</td><td>45.96</td><td>46.63</td><td>49.03</td><td>50.75</td><td>52.69</td><td>54.96</td><td>55.2</td><td>55.09</td><td>54.78</td><td>53.8</td><td>53.71</td><td>54.02</td><td>53.77</td><td>54.74</td><td>55.72</td><td>57.43</td><td>58</td><td>58.3</td><td>58.54</td><td>58.38</td><td>59.29</td><td>58.45</td><td>59.07</td><td>59.07</td><td>60.33</td><td>62.57</td><td>64.79</td><td>65.45</td><td>66.31</td><td>67.81</td><td>69.22</td><td>69.81</td><td>71.05</td><td>71.8</td><td>74</td><td>74.32</td><td>73.49</td><td>74.18</td><td>77.84</td><td>80.78</td><td>82.32</td><td>84.75</td><td>88.48</td><td>90.27</td><td>89.74</td><td>87.6</td><td>83.61</td><td>80.33</td><td>79.12</td><td>81.1</td><td>82.84</td><td>83.8</td><td>85.57</td><td>85.39</td><td>84.16</td><td>83.49</td><td>82.01</td><td>80.27</td><td>78.69</td><td>77.11</td><td>74.8</td><td>74.89</td><td>73.61</td><td>71.72</td><td>68.95</td><td> </td>
</tr>
</tbody></table>""")
data["Groundnut Oil"] = table_to_series("""<table cellspacing="0" align="Center" rules="all" border="1" style="border-collapse:collapse;">
<tbody><tr>
<th scope="col">Zone</th><th scope="col">Jan 2012</th><th scope="col">Feb 2012</th><th scope="col">Mar 2012</th><th scope="col">Apr 2012</th><th scope="col">May 2012</th><th scope="col">Jun 2012</th><th scope="col">Jul 2012</th><th scope="col">Aug 2012</th><th scope="col">Sep 2012</th><th scope="col">Oct 2012</th><th scope="col">Nov 2012</th><th scope="col">Dec 2012</th><th scope="col">Jan 2013</th><th scope="col">Feb 2013</th><th scope="col">Mar 2013</th><th scope="col">Apr 2013</th><th scope="col">May 2013</th><th scope="col">Jun 2013</th><th scope="col">Jul 2013</th><th scope="col">Aug 2013</th><th scope="col">Sep 2013</th><th scope="col">Oct 2013</th><th scope="col">Nov 2013</th><th scope="col">Dec 2013</th><th scope="col">Jan 2014</th><th scope="col">Feb 2014</th><th scope="col">Mar 2014</th><th scope="col">Apr 2014</th><th scope="col">May 2014</th><th scope="col">Jun 2014</th><th scope="col">Jul 2014</th><th scope="col">Aug 2014</th><th scope="col">Sep 2014</th><th scope="col">Oct 2014</th><th scope="col">Nov 2014</th><th scope="col">Dec 2014</th><th scope="col">Jan 2015</th><th scope="col">Feb 2015</th><th scope="col">Mar 2015</th><th scope="col">Apr 2015</th><th scope="col">May 2015</th><th scope="col">Jun 2015</th><th scope="col">Jul 2015</th><th scope="col">Aug 2015</th><th scope="col">Sep 2015</th><th scope="col">Oct 2015</th><th scope="col">Nov 2015</th><th scope="col">Dec 2015</th><th scope="col">Jan 2016</th><th scope="col">Feb 2016</th><th scope="col">Mar 2016</th><th scope="col">Apr 2016</th><th scope="col">May 2016</th><th scope="col">Jun 2016</th><th scope="col">Jul 2016</th><th scope="col">Aug 2016</th><th scope="col">Sep 2016</th><th scope="col">Oct 2016</th><th scope="col">Nov 2016</th><th scope="col">Dec 2016</th><th scope="col">Jan 2017</th><th scope="col">Feb 2017</th><th scope="col">Mar 2017</th><th scope="col">Apr 2017</th><th scope="col">May 2017</th><th scope="col">Jun 2017</th><th scope="col">Jul 2017</th><th scope="col">Average</th>
</tr><tr align="left">
<td>NORTH ZONE</td><td>115.26</td><td>116.06</td><td>117.12</td><td>119.74</td><td>121.36</td><td>121.14</td><td>122.64</td><td>128.23</td><td>130.29</td><td>132.23</td><td>131.59</td><td>132.83</td><td>136.09</td><td>137.28</td><td>137.75</td><td>136.26</td><td>134.52</td><td>134.8</td><td>134.54</td><td>136.08</td><td>137.87</td><td>137.02</td><td>136.43</td><td>135.16</td><td>133.14</td><td>133.6</td><td>137.87</td><td>138.94</td><td>139.3</td><td>134.85</td><td>137.69</td><td>137.3</td><td>137.05</td><td>134.31</td><td>134.57</td><td>132.24</td><td>132.51</td><td>132.13</td><td>132.46</td><td>132.73</td><td>132.83</td><td>131.27</td><td>130.09</td><td>129.74</td><td>128.55</td><td>130.32</td><td>131.44</td><td>130.54</td><td>130.24</td><td>132.32</td><td>133.12</td><td>132.14</td><td>134.01</td><td>134.94</td><td>136.19</td><td>137</td><td>136.81</td><td>138.7</td><td>138.58</td><td>140.94</td><td>141.47</td><td>140.29</td><td>140.78</td><td>139.46</td><td>138.51</td><td>137.28</td><td>137.02</td><td>133.93</td>
</tr><tr align="left">
<td>All India Average</td><td>110.03</td><td>112.06</td><td>115.81</td><td>120.18</td><td>121.98</td><td>123.45</td><td>125.67</td><td>129.18</td><td>129.54</td><td>130.26</td><td>131.63</td><td>133.6</td><td>134.08</td><td>134.74</td><td>134.74</td><td>133.61</td><td>132.44</td><td>131.04</td><td>130.38</td><td>128.96</td><td>128.55</td><td>126.09</td><td>125.3</td><td>124.65</td><td>122.32</td><td>120.82</td><td>122.24</td><td>121.53</td><td>122.39</td><td>118.76</td><td>121.93</td><td>120.95</td><td>119.61</td><td>119.05</td><td>119.13</td><td>118.24</td><td>119.14</td><td>119.95</td><td>119.1</td><td>119.63</td><td>120.88</td><td>120.54</td><td>120.62</td><td>121.48</td><td>123.57</td><td>124.1</td><td>123.66</td><td>123.79</td><td>123.62</td><td>123.2</td><td>122.8</td><td>125.03</td><td>129.3</td><td>132.23</td><td>134.52</td><td>135.05</td><td>135.47</td><td>135.8</td><td>135.55</td><td>135.39</td><td>135.42</td><td>133.84</td><td>133.46</td><td>133.37</td><td>132.76</td><td>131.69</td><td>131.09</td><td> </td>
</tr>
</tbody></table>""")
data["Mustard Oil"] = table_to_series("""<table cellspacing="0" align="Center" rules="all" border="1" style="border-collapse:collapse;">
<tbody><tr>
<th scope="col">Zone</th><th scope="col">Jan 2012</th><th scope="col">Feb 2012</th><th scope="col">Mar 2012</th><th scope="col">Apr 2012</th><th scope="col">May 2012</th><th scope="col">Jun 2012</th><th scope="col">Jul 2012</th><th scope="col">Aug 2012</th><th scope="col">Sep 2012</th><th scope="col">Oct 2012</th><th scope="col">Nov 2012</th><th scope="col">Dec 2012</th><th scope="col">Jan 2013</th><th scope="col">Feb 2013</th><th scope="col">Mar 2013</th><th scope="col">Apr 2013</th><th scope="col">May 2013</th><th scope="col">Jun 2013</th><th scope="col">Jul 2013</th><th scope="col">Aug 2013</th><th scope="col">Sep 2013</th><th scope="col">Oct 2013</th><th scope="col">Nov 2013</th><th scope="col">Dec 2013</th><th scope="col">Jan 2014</th><th scope="col">Feb 2014</th><th scope="col">Mar 2014</th><th scope="col">Apr 2014</th><th scope="col">May 2014</th><th scope="col">Jun 2014</th><th scope="col">Jul 2014</th><th scope="col">Aug 2014</th><th scope="col">Sep 2014</th><th scope="col">Oct 2014</th><th scope="col">Nov 2014</th><th scope="col">Dec 2014</th><th scope="col">Jan 2015</th><th scope="col">Feb 2015</th><th scope="col">Mar 2015</th><th scope="col">Apr 2015</th><th scope="col">May 2015</th><th scope="col">Jun 2015</th><th scope="col">Jul 2015</th><th scope="col">Aug 2015</th><th scope="col">Sep 2015</th><th scope="col">Oct 2015</th><th scope="col">Nov 2015</th><th scope="col">Dec 2015</th><th scope="col">Jan 2016</th><th scope="col">Feb 2016</th><th scope="col">Mar 2016</th><th scope="col">Apr 2016</th><th scope="col">May 2016</th><th scope="col">Jun 2016</th><th scope="col">Jul 2016</th><th scope="col">Aug 2016</th><th scope="col">Sep 2016</th><th scope="col">Oct 2016</th><th scope="col">Nov 2016</th><th scope="col">Dec 2016</th><th scope="col">Jan 2017</th><th scope="col">Feb 2017</th><th scope="col">Mar 2017</th><th scope="col">Apr 2017</th><th scope="col">May 2017</th><th scope="col">Jun 2017</th><th scope="col">Jul 2017</th><th scope="col">Average</th>
</tr><tr align="left">
<td>NORTH ZONE</td><td>92.87</td><td>95.65</td><td>96.9</td><td>99.02</td><td>100.31</td><td>99.33</td><td>98.97</td><td>103.38</td><td>105.62</td><td>106.78</td><td>106.43</td><td>106.59</td><td>106.25</td><td>106.99</td><td>106.82</td><td>104.79</td><td>102.44</td><td>100.6</td><td>99.97</td><td>97.32</td><td>97.44</td><td>97.09</td><td>98.68</td><td>97.91</td><td>96.69</td><td>96.08</td><td>95.63</td><td>96.04</td><td>95.54</td><td>94.03</td><td>94.72</td><td>95.09</td><td>95.22</td><td>94.94</td><td>95.38</td><td>95.82</td><td>97.61</td><td>97.06</td><td>97.13</td><td>97.73</td><td>98.02</td><td>99.93</td><td>100.1</td><td>102.06</td><td>102.95</td><td>107.21</td><td>118.6</td><td>115.05</td><td>113.06</td><td>111.89</td><td>108.16</td><td>106.66</td><td>106.32</td><td>106</td><td>106.48</td><td>109.04</td><td>108.33</td><td>108.95</td><td>110.48</td><td>111.23</td><td>110.2</td><td>110.16</td><td>108.67</td><td>105.54</td><td>105.22</td><td>104.17</td><td>102.58</td><td>103.94</td>
</tr><tr align="left">
<td>All India Average</td><td>91.08</td><td>92.55</td><td>94.22</td><td>96.72</td><td>97.94</td><td>97.97</td><td>99.74</td><td>103.01</td><td>104.63</td><td>104.73</td><td>105</td><td>104.98</td><td>104.66</td><td>105.54</td><td>104.38</td><td>103.22</td><td>100.73</td><td>99.93</td><td>98.76</td><td>97.23</td><td>96.64</td><td>96.22</td><td>98.54</td><td>98.77</td><td>98.51</td><td>98.06</td><td>97.91</td><td>98.14</td><td>97.63</td><td>96.84</td><td>97.17</td><td>97.02</td><td>97.42</td><td>97.71</td><td>97.62</td><td>97.74</td><td>99.41</td><td>101.32</td><td>99.64</td><td>99.75</td><td>100.22</td><td>101.53</td><td>102.77</td><td>103.23</td><td>105.24</td><td>107.17</td><td>113.32</td><td>112.57</td><td>111.91</td><td>110.87</td><td>107.76</td><td>106.34</td><td>108.36</td><td>108.42</td><td>108.37</td><td>109.4</td><td>110.05</td><td>109.53</td><td>110.59</td><td>111.58</td><td>110.58</td><td>110.06</td><td>109.65</td><td>107.62</td><td>106.9</td><td>105.88</td><td>105.58</td><td> </td>
</tr>
</tbody></table>""")
data["Vanaspati"] = table_to_series("""<table cellspacing="0" align="Center" rules="all" border="1" style="border-collapse:collapse;">
<tbody><tr>
<th scope="col">Zone</th><th scope="col">Jan 2012</th><th scope="col">Feb 2012</th><th scope="col">Mar 2012</th><th scope="col">Apr 2012</th><th scope="col">May 2012</th><th scope="col">Jun 2012</th><th scope="col">Jul 2012</th><th scope="col">Aug 2012</th><th scope="col">Sep 2012</th><th scope="col">Oct 2012</th><th scope="col">Nov 2012</th><th scope="col">Dec 2012</th><th scope="col">Jan 2013</th><th scope="col">Feb 2013</th><th scope="col">Mar 2013</th><th scope="col">Apr 2013</th><th scope="col">May 2013</th><th scope="col">Jun 2013</th><th scope="col">Jul 2013</th><th scope="col">Aug 2013</th><th scope="col">Sep 2013</th><th scope="col">Oct 2013</th><th scope="col">Nov 2013</th><th scope="col">Dec 2013</th><th scope="col">Jan 2014</th><th scope="col">Feb 2014</th><th scope="col">Mar 2014</th><th scope="col">Apr 2014</th><th scope="col">May 2014</th><th scope="col">Jun 2014</th><th scope="col">Jul 2014</th><th scope="col">Aug 2014</th><th scope="col">Sep 2014</th><th scope="col">Oct 2014</th><th scope="col">Nov 2014</th><th scope="col">Dec 2014</th><th scope="col">Jan 2015</th><th scope="col">Feb 2015</th><th scope="col">Mar 2015</th><th scope="col">Apr 2015</th><th scope="col">May 2015</th><th scope="col">Jun 2015</th><th scope="col">Jul 2015</th><th scope="col">Aug 2015</th><th scope="col">Sep 2015</th><th scope="col">Oct 2015</th><th scope="col">Nov 2015</th><th scope="col">Dec 2015</th><th scope="col">Jan 2016</th><th scope="col">Feb 2016</th><th scope="col">Mar 2016</th><th scope="col">Apr 2016</th><th scope="col">May 2016</th><th scope="col">Jun 2016</th><th scope="col">Jul 2016</th><th scope="col">Aug 2016</th><th scope="col">Sep 2016</th><th scope="col">Oct 2016</th><th scope="col">Nov 2016</th><th scope="col">Dec 2016</th><th scope="col">Jan 2017</th><th scope="col">Feb 2017</th><th scope="col">Mar 2017</th><th scope="col">Apr 2017</th><th scope="col">May 2017</th><th scope="col">Jun 2017</th><th scope="col">Jul 2017</th><th scope="col">Average</th>
</tr><tr align="left">
<td>NORTH ZONE</td><td>71.65</td><td>72.75</td><td>73.08</td><td>74.63</td><td>76.13</td><td>76.6</td><td>76.77</td><td>77.89</td><td>78.06</td><td>75.35</td><td>73.81</td><td>72.55</td><td>72.4</td><td>72.13</td><td>71.09</td><td>71.03</td><td>71.19</td><td>70.72</td><td>71.28</td><td>71.42</td><td>73.55</td><td>74.61</td><td>76.29</td><td>76.24</td><td>75.2</td><td>74.46</td><td>77.27</td><td>78.21</td><td>78.37</td><td>77.64</td><td>78.53</td><td>78.2</td><td>78.09</td><td>77.49</td><td>77.37</td><td>76.5</td><td>76.83</td><td>76.56</td><td>77.01</td><td>77.27</td><td>77.18</td><td>76.31</td><td>75.86</td><td>75.93</td><td>75.54</td><td>75.55</td><td>75.74</td><td>74.78</td><td>73.85</td><td>72.76</td><td>72.43</td><td>73.6</td><td>73.87</td><td>74.54</td><td>74.86</td><td>76.21</td><td>77.22</td><td>77.76</td><td>77.96</td><td>78.53</td><td>78.7</td><td>79.32</td><td>79.42</td><td>78.98</td><td>79.25</td><td>79.96</td><td>80.21</td><td>76.12</td>
</tr><tr align="left">
<td>All India Average</td><td>71.23</td><td>71.67</td><td>72.26</td><td>74.16</td><td>75.22</td><td>75.98</td><td>77.35</td><td>77.91</td><td>78.32</td><td>75.24</td><td>73.89</td><td>72.51</td><td>71.83</td><td>71.67</td><td>70.56</td><td>70.81</td><td>70.67</td><td>70.9</td><td>71.05</td><td>72.01</td><td>73.81</td><td>74.46</td><td>75.43</td><td>75.01</td><td>74.6</td><td>74.72</td><td>76.09</td><td>76.62</td><td>76.69</td><td>76.32</td><td>77.22</td><td>77.53</td><td>76.78</td><td>76.5</td><td>76.12</td><td>75.75</td><td>76.18</td><td>76.85</td><td>76.36</td><td>75.95</td><td>75.61</td><td>74.84</td><td>74.66</td><td>74.15</td><td>73.67</td><td>73.4</td><td>72.71</td><td>71.95</td><td>71.51</td><td>71.31</td><td>71.22</td><td>72.23</td><td>73.67</td><td>74.27</td><td>74.56</td><td>75.01</td><td>76.23</td><td>76.58</td><td>76.68</td><td>77.6</td><td>77.57</td><td>77.79</td><td>77.72</td><td>77.35</td><td>77.26</td><td>77.27</td><td>77.23</td><td> </td>
</tr>
</tbody></table>""")
data["Soya Oil"] = table_to_series("""<table cellspacing="0" align="Center" rules="all" border="1" style="border-collapse:collapse;">
<tbody><tr>
<th scope="col">Zone</th><th scope="col">Jan 2012</th><th scope="col">Feb 2012</th><th scope="col">Mar 2012</th><th scope="col">Apr 2012</th><th scope="col">May 2012</th><th scope="col">Jun 2012</th><th scope="col">Jul 2012</th><th scope="col">Aug 2012</th><th scope="col">Sep 2012</th><th scope="col">Oct 2012</th><th scope="col">Nov 2012</th><th scope="col">Dec 2012</th><th scope="col">Jan 2013</th><th scope="col">Feb 2013</th><th scope="col">Mar 2013</th><th scope="col">Apr 2013</th><th scope="col">May 2013</th><th scope="col">Jun 2013</th><th scope="col">Jul 2013</th><th scope="col">Aug 2013</th><th scope="col">Sep 2013</th><th scope="col">Oct 2013</th><th scope="col">Nov 2013</th><th scope="col">Dec 2013</th><th scope="col">Jan 2014</th><th scope="col">Feb 2014</th><th scope="col">Mar 2014</th><th scope="col">Apr 2014</th><th scope="col">May 2014</th><th scope="col">Jun 2014</th><th scope="col">Jul 2014</th><th scope="col">Aug 2014</th><th scope="col">Sep 2014</th><th scope="col">Oct 2014</th><th scope="col">Nov 2014</th><th scope="col">Dec 2014</th><th scope="col">Jan 2015</th><th scope="col">Feb 2015</th><th scope="col">Mar 2015</th><th scope="col">Apr 2015</th><th scope="col">May 2015</th><th scope="col">Jun 2015</th><th scope="col">Jul 2015</th><th scope="col">Aug 2015</th><th scope="col">Sep 2015</th><th scope="col">Oct 2015</th><th scope="col">Nov 2015</th><th scope="col">Dec 2015</th><th scope="col">Jan 2016</th><th scope="col">Feb 2016</th><th scope="col">Mar 2016</th><th scope="col">Apr 2016</th><th scope="col">May 2016</th><th scope="col">Jun 2016</th><th scope="col">Jul 2016</th><th scope="col">Aug 2016</th><th scope="col">Sep 2016</th><th scope="col">Oct 2016</th><th scope="col">Nov 2016</th><th scope="col">Dec 2016</th><th scope="col">Jan 2017</th><th scope="col">Feb 2017</th><th scope="col">Mar 2017</th><th scope="col">Apr 2017</th><th scope="col">May 2017</th><th scope="col">Jun 2017</th><th scope="col">Jul 2017</th><th scope="col">Average</th>
</tr><tr align="left">
<td>NORTH ZONE</td><td>84.87</td><td>87.25</td><td>85.97</td><td>88.1</td><td>88.81</td><td>88.73</td><td>88.61</td><td>89.13</td><td>90.75</td><td>90.01</td><td>89.75</td><td>89.61</td><td>89.46</td><td>89.35</td><td>89.56</td><td>88.69</td><td>88.66</td><td>88.28</td><td>87.6</td><td>85.13</td><td>86.56</td><td>86.72</td><td>86.83</td><td>86.33</td><td>86.4</td><td>85.38</td><td>85.78</td><td>88.81</td><td>87.26</td><td>87.76</td><td>87.93</td><td>87.48</td><td>88.11</td><td>89.08</td><td>88.8</td><td>88.19</td><td>89.01</td><td>89.23</td><td>88.73</td><td>88.83</td><td>88.2</td><td>87.55</td><td>85.71</td><td>86.19</td><td>86.47</td><td>87.39</td><td>86.82</td><td>85.37</td><td>85.09</td><td>84.03</td><td>83.65</td><td>83.85</td><td>84.53</td><td>84.41</td><td>83.86</td><td>84.39</td><td>84.87</td><td>84.73</td><td>85.79</td><td>87.37</td><td>88.85</td><td>89.11</td><td>87.83</td><td>86.37</td><td>85.96</td><td>85.58</td><td>85.36</td><td>86.59</td>
</tr><tr align="left">
<td>All India Average</td><td>81.25</td><td>81.64</td><td>82.16</td><td>83.87</td><td>84.34</td><td>84.27</td><td>85.44</td><td>86.45</td><td>86.87</td><td>86.35</td><td>85.53</td><td>85.46</td><td>85.94</td><td>86.31</td><td>86.08</td><td>85.59</td><td>85.37</td><td>84.81</td><td>84.79</td><td>83.75</td><td>84.58</td><td>84.3</td><td>85.2</td><td>85.34</td><td>84.81</td><td>84.14</td><td>84.76</td><td>85</td><td>84.16</td><td>83.88</td><td>84.49</td><td>84.26</td><td>84.26</td><td>84.02</td><td>83.9</td><td>83.36</td><td>84.3</td><td>84.73</td><td>84.91</td><td>84.94</td><td>84.24</td><td>83.4</td><td>82.77</td><td>82.57</td><td>82.48</td><td>82.6</td><td>81.25</td><td>81.77</td><td>81.46</td><td>81.23</td><td>80.66</td><td>81.57</td><td>82.28</td><td>82.27</td><td>82.39</td><td>82.82</td><td>82.9</td><td>82.88</td><td>83.91</td><td>85.35</td><td>86.3</td><td>86.45</td><td>86.17</td><td>85.27</td><td>84.77</td><td>84.11</td><td>83.85</td><td> </td>
</tr>
</tbody></table>""")
data["Sunflower Oil"] = table_to_series("""<table cellspacing="0" align="Center" rules="all" border="1" style="border-collapse:collapse;">
<tbody><tr>
<th scope="col">Zone</th><th scope="col">Jan 2012</th><th scope="col">Feb 2012</th><th scope="col">Mar 2012</th><th scope="col">Apr 2012</th><th scope="col">May 2012</th><th scope="col">Jun 2012</th><th scope="col">Jul 2012</th><th scope="col">Aug 2012</th><th scope="col">Sep 2012</th><th scope="col">Oct 2012</th><th scope="col">Nov 2012</th><th scope="col">Dec 2012</th><th scope="col">Jan 2013</th><th scope="col">Feb 2013</th><th scope="col">Mar 2013</th><th scope="col">Apr 2013</th><th scope="col">May 2013</th><th scope="col">Jun 2013</th><th scope="col">Jul 2013</th><th scope="col">Aug 2013</th><th scope="col">Sep 2013</th><th scope="col">Oct 2013</th><th scope="col">Nov 2013</th><th scope="col">Dec 2013</th><th scope="col">Jan 2014</th><th scope="col">Feb 2014</th><th scope="col">Mar 2014</th><th scope="col">Apr 2014</th><th scope="col">May 2014</th><th scope="col">Jun 2014</th><th scope="col">Jul 2014</th><th scope="col">Aug 2014</th><th scope="col">Sep 2014</th><th scope="col">Oct 2014</th><th scope="col">Nov 2014</th><th scope="col">Dec 2014</th><th scope="col">Jan 2015</th><th scope="col">Feb 2015</th><th scope="col">Mar 2015</th><th scope="col">Apr 2015</th><th scope="col">May 2015</th><th scope="col">Jun 2015</th><th scope="col">Jul 2015</th><th scope="col">Aug 2015</th><th scope="col">Sep 2015</th><th scope="col">Oct 2015</th><th scope="col">Nov 2015</th><th scope="col">Dec 2015</th><th scope="col">Jan 2016</th><th scope="col">Feb 2016</th><th scope="col">Mar 2016</th><th scope="col">Apr 2016</th><th scope="col">May 2016</th><th scope="col">Jun 2016</th><th scope="col">Jul 2016</th><th scope="col">Aug 2016</th><th scope="col">Sep 2016</th><th scope="col">Oct 2016</th><th scope="col">Nov 2016</th><th scope="col">Dec 2016</th><th scope="col">Jan 2017</th><th scope="col">Feb 2017</th><th scope="col">Mar 2017</th><th scope="col">Apr 2017</th><th scope="col">May 2017</th><th scope="col">Jun 2017</th><th scope="col">Jul 2017</th><th scope="col">Average</th>
</tr><tr align="left">
<td>NORTH ZONE</td><td>94.09</td><td>96.53</td><td>95.09</td><td>98.88</td><td>100.29</td><td>99.51</td><td>99.83</td><td>101.62</td><td>100.84</td><td>99.97</td><td>98.12</td><td>99.56</td><td>102.06</td><td>102.64</td><td>102.48</td><td>102.96</td><td>103.67</td><td>103.47</td><td>103.53</td><td>101.62</td><td>102.76</td><td>102.22</td><td>102.24</td><td>100.09</td><td>100.12</td><td>100.95</td><td>105.6</td><td>105.15</td><td>104.3</td><td>104.23</td><td>106.38</td><td>104.93</td><td>104.25</td><td>105.28</td><td>104.35</td><td>105.53</td><td>106.79</td><td>107.09</td><td>107.68</td><td>107.29</td><td>107.11</td><td>105.4</td><td>101.61</td><td>102.47</td><td>102.38</td><td>102.88</td><td>103.71</td><td>103.32</td><td>103.9</td><td>102.54</td><td>102.65</td><td>102.65</td><td>102.4</td><td>103.48</td><td>102.46</td><td>103.12</td><td>102.44</td><td>101.62</td><td>102.81</td><td>103.52</td><td>103.45</td><td>103.39</td><td>103.33</td><td>102.39</td><td>102.38</td><td>101.93</td><td>101.93</td><td>102.89</td>
</tr><tr align="left">
<td>All India Average</td><td>90.85</td><td>90.99</td><td>90.76</td><td>92.1</td><td>92.65</td><td>92.34</td><td>92.32</td><td>93.66</td><td>94.74</td><td>94.38</td><td>94.98</td><td>95.17</td><td>96.5</td><td>97.96</td><td>97.52</td><td>98.22</td><td>98.03</td><td>97.84</td><td>98.03</td><td>98.49</td><td>99.47</td><td>98.61</td><td>98.81</td><td>97.66</td><td>96.9</td><td>95.85</td><td>96.62</td><td>96.05</td><td>96.06</td><td>94.79</td><td>95.93</td><td>95.16</td><td>94.13</td><td>93.93</td><td>94.06</td><td>94.32</td><td>94.99</td><td>94.77</td><td>93.91</td><td>93.56</td><td>93.54</td><td>92.59</td><td>92.65</td><td>92.74</td><td>94.01</td><td>95.02</td><td>95.24</td><td>95.72</td><td>96.11</td><td>95.9</td><td>95.5</td><td>95.5</td><td>95.07</td><td>96.28</td><td>95.67</td><td>95.43</td><td>94.28</td><td>93.84</td><td>93.86</td><td>94.38</td><td>94.46</td><td>94.06</td><td>93.73</td><td>93.21</td><td>92.76</td><td>92.36</td><td>92.25</td><td> </td>
</tr>
</tbody></table>""")
data["Palm Oil"] = table_to_series("""<table cellspacing="0" align="Center" rules="all" border="1" style="border-collapse:collapse;">
<tbody><tr>
<th scope="col">Zone</th><th scope="col">Jan 2012</th><th scope="col">Feb 2012</th><th scope="col">Mar 2012</th><th scope="col">Apr 2012</th><th scope="col">May 2012</th><th scope="col">Jun 2012</th><th scope="col">Jul 2012</th><th scope="col">Aug 2012</th><th scope="col">Sep 2012</th><th scope="col">Oct 2012</th><th scope="col">Nov 2012</th><th scope="col">Dec 2012</th><th scope="col">Jan 2013</th><th scope="col">Feb 2013</th><th scope="col">Mar 2013</th><th scope="col">Apr 2013</th><th scope="col">May 2013</th><th scope="col">Jun 2013</th><th scope="col">Jul 2013</th><th scope="col">Aug 2013</th><th scope="col">Sep 2013</th><th scope="col">Oct 2013</th><th scope="col">Nov 2013</th><th scope="col">Dec 2013</th><th scope="col">Jan 2014</th><th scope="col">Feb 2014</th><th scope="col">Mar 2014</th><th scope="col">Apr 2014</th><th scope="col">May 2014</th><th scope="col">Jun 2014</th><th scope="col">Jul 2014</th><th scope="col">Aug 2014</th><th scope="col">Sep 2014</th><th scope="col">Oct 2014</th><th scope="col">Nov 2014</th><th scope="col">Dec 2014</th><th scope="col">Jan 2015</th><th scope="col">Feb 2015</th><th scope="col">Mar 2015</th><th scope="col">Apr 2015</th><th scope="col">May 2015</th><th scope="col">Jun 2015</th><th scope="col">Jul 2015</th><th scope="col">Aug 2015</th><th scope="col">Sep 2015</th><th scope="col">Oct 2015</th><th scope="col">Nov 2015</th><th scope="col">Dec 2015</th><th scope="col">Jan 2016</th><th scope="col">Feb 2016</th><th scope="col">Mar 2016</th><th scope="col">Apr 2016</th><th scope="col">May 2016</th><th scope="col">Jun 2016</th><th scope="col">Jul 2016</th><th scope="col">Aug 2016</th><th scope="col">Sep 2016</th><th scope="col">Oct 2016</th><th scope="col">Nov 2016</th><th scope="col">Dec 2016</th><th scope="col">Jan 2017</th><th scope="col">Feb 2017</th><th scope="col">Mar 2017</th><th scope="col">Apr 2017</th><th scope="col">May 2017</th><th scope="col">Jun 2017</th><th scope="col">Jul 2017</th><th scope="col">Average</th>
</tr><tr align="left">
<td>NORTH ZONE</td><td>84.23</td><td>84.64</td><td>86.39</td><td>88.82</td><td>88.72</td><td>89.71</td><td>87.65</td><td>87.95</td><td>90.13</td><td>88.21</td><td>89.48</td><td>87.11</td><td>87</td><td>86.55</td><td>88.28</td><td>88</td><td>86.98</td><td>85.86</td><td>87.61</td><td>88.93</td><td>87.27</td><td>88.64</td><td>85.07</td><td>85.98</td><td>86.8</td><td>84.29</td><td>71.08</td><td>74.35</td><td>69.89</td><td>71.65</td><td>70.73</td><td>71</td><td>71.28</td><td>77.4</td><td>76.24</td><td>75.95</td><td>80.37</td><td>72.15</td><td>70.02</td><td>70.43</td><td>72.34</td><td>74.18</td><td>70.17</td><td>70.72</td><td>71.8</td><td>71.06</td><td>69.18</td><td>70.15</td><td>69.07</td><td>68.54</td><td>68.77</td><td>68.28</td><td>69.41</td><td>69.7</td><td>69.5</td><td>70.47</td><td>72.12</td><td>73.06</td><td>73.86</td><td>73.52</td><td>73.38</td><td>74.32</td><td>74.42</td><td>72.73</td><td>73.8</td><td>73.68</td><td>73.51</td><td>74.23</td>
</tr><tr align="left">
<td>All India Average</td><td>68.84</td><td>68.3</td><td>69.7</td><td>73.27</td><td>72.93</td><td>71.95</td><td>73.21</td><td>73.83</td><td>74.09</td><td>68.91</td><td>66.72</td><td>66</td><td>66.52</td><td>66.69</td><td>66.87</td><td>66.51</td><td>65.72</td><td>66.36</td><td>67.29</td><td>68.18</td><td>71.18</td><td>70.29</td><td>71.66</td><td>71.17</td><td>70.8</td><td>70.72</td><td>71.98</td><td>71.97</td><td>71.38</td><td>70.56</td><td>70.53</td><td>69.34</td><td>67.49</td><td>67.79</td><td>67.15</td><td>66.25</td><td>68.08</td><td>67.14</td><td>67.5</td><td>66.55</td><td>66.85</td><td>66.86</td><td>66.21</td><td>65.16</td><td>64.42</td><td>64.11</td><td>62.54</td><td>62.56</td><td>62.57</td><td>64.11</td><td>64.77</td><td>67.4</td><td>69.94</td><td>69.67</td><td>68.71</td><td>69.79</td><td>71.33</td><td>70.66</td><td>70.15</td><td>70.73</td><td>71.27</td><td>71.41</td><td>70.76</td><td>69.63</td><td>69.63</td><td>69.3</td><td>68.69</td><td> </td>
</tr>
</tbody></table>""")
data["Potato"] = table_to_series("""<table cellspacing="0" align="Center" rules="all" border="1" style="border-collapse:collapse;">
<tbody><tr>
<th scope="col">Zone</th><th scope="col">Jan 2012</th><th scope="col">Feb 2012</th><th scope="col">Mar 2012</th><th scope="col">Apr 2012</th><th scope="col">May 2012</th><th scope="col">Jun 2012</th><th scope="col">Jul 2012</th><th scope="col">Aug 2012</th><th scope="col">Sep 2012</th><th scope="col">Oct 2012</th><th scope="col">Nov 2012</th><th scope="col">Dec 2012</th><th scope="col">Jan 2013</th><th scope="col">Feb 2013</th><th scope="col">Mar 2013</th><th scope="col">Apr 2013</th><th scope="col">May 2013</th><th scope="col">Jun 2013</th><th scope="col">Jul 2013</th><th scope="col">Aug 2013</th><th scope="col">Sep 2013</th><th scope="col">Oct 2013</th><th scope="col">Nov 2013</th><th scope="col">Dec 2013</th><th scope="col">Jan 2014</th><th scope="col">Feb 2014</th><th scope="col">Mar 2014</th><th scope="col">Apr 2014</th><th scope="col">May 2014</th><th scope="col">Jun 2014</th><th scope="col">Jul 2014</th><th scope="col">Aug 2014</th><th scope="col">Sep 2014</th><th scope="col">Oct 2014</th><th scope="col">Nov 2014</th><th scope="col">Dec 2014</th><th scope="col">Jan 2015</th><th scope="col">Feb 2015</th><th scope="col">Mar 2015</th><th scope="col">Apr 2015</th><th scope="col">May 2015</th><th scope="col">Jun 2015</th><th scope="col">Jul 2015</th><th scope="col">Aug 2015</th><th scope="col">Sep 2015</th><th scope="col">Oct 2015</th><th scope="col">Nov 2015</th><th scope="col">Dec 2015</th><th scope="col">Jan 2016</th><th scope="col">Feb 2016</th><th scope="col">Mar 2016</th><th scope="col">Apr 2016</th><th scope="col">May 2016</th><th scope="col">Jun 2016</th><th scope="col">Jul 2016</th><th scope="col">Aug 2016</th><th scope="col">Sep 2016</th><th scope="col">Oct 2016</th><th scope="col">Nov 2016</th><th scope="col">Dec 2016</th><th scope="col">Jan 2017</th><th scope="col">Feb 2017</th><th scope="col">Mar 2017</th><th scope="col">Apr 2017</th><th scope="col">May 2017</th><th scope="col">Jun 2017</th><th scope="col">Jul 2017</th><th scope="col">Average</th>
</tr><tr align="left">
<td>NORTH ZONE</td><td>7.11</td><td>7.12</td><td>8.83</td><td>12.1</td><td>14.07</td><td>14.9</td><td>17.16</td><td>18.74</td><td>18.14</td><td>17.55</td><td>17.04</td><td>14.21</td><td>11.02</td><td>10.42</td><td>10.34</td><td>11.69</td><td>14.22</td><td>15.51</td><td>18.51</td><td>18.77</td><td>18.65</td><td>19.5</td><td>24.21</td><td>18.63</td><td>14.83</td><td>13.2</td><td>14.84</td><td>16.39</td><td>19.36</td><td>20.4</td><td>24.64</td><td>26.66</td><td>27.75</td><td>29.39</td><td>29.47</td><td>19.85</td><td>14.4</td><td>12.95</td><td>12.24</td><td>11.21</td><td>10.81</td><td>11.75</td><td>13.23</td><td>13.55</td><td>13.66</td><td>15.6</td><td>17.1</td><td>13.78</td><td>10.95</td><td>10.39</td><td>11.2</td><td>12.97</td><td>15.9</td><td>18.67</td><td>20.81</td><td>21.61</td><td>20.91</td><td>20.68</td><td>19.11</td><td>13.82</td><td>10.7</td><td>10.33</td><td>10.15</td><td>10.01</td><td>10.18</td><td>11.36</td><td>13.32</td><td>15.32</td>
</tr><tr align="left">
<td>All India Average</td><td>9.14</td><td>9.49</td><td>10.29</td><td>12.95</td><td>14.82</td><td>16.02</td><td>17.82</td><td>18.8</td><td>18.58</td><td>18.27</td><td>18.19</td><td>16.58</td><td>14.79</td><td>14.26</td><td>13.59</td><td>14.2</td><td>15.76</td><td>16.81</td><td>17.84</td><td>17.95</td><td>17.49</td><td>18.96</td><td>25.08</td><td>21.47</td><td>18.12</td><td>15.27</td><td>15.98</td><td>18.08</td><td>20.38</td><td>21.58</td><td>24.19</td><td>26.88</td><td>28.47</td><td>29.29</td><td>29.81</td><td>24.16</td><td>18.98</td><td>17.53</td><td>15.72</td><td>14.25</td><td>13.79</td><td>15.06</td><td>15.78</td><td>15.75</td><td>15.84</td><td>16.83</td><td>17.68</td><td>16.73</td><td>15.19</td><td>14.92</td><td>15.32</td><td>16.55</td><td>19.01</td><td>21.24</td><td>22.63</td><td>22.65</td><td>22.1</td><td>21.38</td><td>20.53</td><td>17.1</td><td>14.66</td><td>13.97</td><td>13.54</td><td>13.64</td><td>13.9</td><td>14.53</td><td>15.32</td><td> </td>
</tr>
</tbody></table>""")
data["Onion"] = table_to_series("""<table cellspacing="0" align="Center" rules="all" border="1" style="border-collapse:collapse;">
<tbody><tr>
<th scope="col">Zone</th><th scope="col">Jan 2012</th><th scope="col">Feb 2012</th><th scope="col">Mar 2012</th><th scope="col">Apr 2012</th><th scope="col">May 2012</th><th scope="col">Jun 2012</th><th scope="col">Jul 2012</th><th scope="col">Aug 2012</th><th scope="col">Sep 2012</th><th scope="col">Oct 2012</th><th scope="col">Nov 2012</th><th scope="col">Dec 2012</th><th scope="col">Jan 2013</th><th scope="col">Feb 2013</th><th scope="col">Mar 2013</th><th scope="col">Apr 2013</th><th scope="col">May 2013</th><th scope="col">Jun 2013</th><th scope="col">Jul 2013</th><th scope="col">Aug 2013</th><th scope="col">Sep 2013</th><th scope="col">Oct 2013</th><th scope="col">Nov 2013</th><th scope="col">Dec 2013</th><th scope="col">Jan 2014</th><th scope="col">Feb 2014</th><th scope="col">Mar 2014</th><th scope="col">Apr 2014</th><th scope="col">May 2014</th><th scope="col">Jun 2014</th><th scope="col">Jul 2014</th><th scope="col">Aug 2014</th><th scope="col">Sep 2014</th><th scope="col">Oct 2014</th><th scope="col">Nov 2014</th><th scope="col">Dec 2014</th><th scope="col">Jan 2015</th><th scope="col">Feb 2015</th><th scope="col">Mar 2015</th><th scope="col">Apr 2015</th><th scope="col">May 2015</th><th scope="col">Jun 2015</th><th scope="col">Jul 2015</th><th scope="col">Aug 2015</th><th scope="col">Sep 2015</th><th scope="col">Oct 2015</th><th scope="col">Nov 2015</th><th scope="col">Dec 2015</th><th scope="col">Jan 2016</th><th scope="col">Feb 2016</th><th scope="col">Mar 2016</th><th scope="col">Apr 2016</th><th scope="col">May 2016</th><th scope="col">Jun 2016</th><th scope="col">Jul 2016</th><th scope="col">Aug 2016</th><th scope="col">Sep 2016</th><th scope="col">Oct 2016</th><th scope="col">Nov 2016</th><th scope="col">Dec 2016</th><th scope="col">Jan 2017</th><th scope="col">Feb 2017</th><th scope="col">Mar 2017</th><th scope="col">Apr 2017</th><th scope="col">May 2017</th><th scope="col">Jun 2017</th><th scope="col">Jul 2017</th><th scope="col">Average</th>
</tr><tr align="left">
<td>NORTH ZONE</td><td>12.28</td><td>11.52</td><td>11.62</td><td>12.12</td><td>11.35</td><td>11.61</td><td>12.96</td><td>13.53</td><td>13.5</td><td>14</td><td>17</td><td>18.45</td><td>19.97</td><td>24.95</td><td>20.9</td><td>20.51</td><td>18.68</td><td>19.01</td><td>27.18</td><td>48.47</td><td>59.37</td><td>62.1</td><td>56.51</td><td>31.24</td><td>21.25</td><td>19.58</td><td>18.51</td><td>19.17</td><td>19.22</td><td>19.39</td><td>28.17</td><td>29.03</td><td>27.55</td><td>26.81</td><td>26.81</td><td>26.11</td><td>25.7</td><td>24.83</td><td>25.72</td><td>23.64</td><td>22.47</td><td>23.79</td><td>27</td><td>46.45</td><td>58.73</td><td>50.85</td><td>39.74</td><td>26.13</td><td>21.95</td><td>19.8</td><td>18.13</td><td>16.95</td><td>15.78</td><td>15.51</td><td>16.43</td><td>17.21</td><td>15.85</td><td>15.68</td><td>17.04</td><td>16.58</td><td>15.08</td><td>14.83</td><td>14.76</td><td>14.55</td><td>14.69</td><td>14.94</td><td>15.45</td><td>21.65</td>
</tr><tr align="left">
<td>All India Average</td><td>12.7</td><td>11.78</td><td>11.42</td><td>11.76</td><td>11.82</td><td>12.24</td><td>13.73</td><td>14.46</td><td>14.8</td><td>15.36</td><td>19.37</td><td>20.69</td><td>21.13</td><td>25.55</td><td>21.62</td><td>20.2</td><td>19.1</td><td>21.98</td><td>28.91</td><td>44.88</td><td>55.03</td><td>57.21</td><td>53.94</td><td>33.22</td><td>22.45</td><td>18.46</td><td>17.22</td><td>17.43</td><td>18.69</td><td>20.82</td><td>28.22</td><td>28.45</td><td>26.86</td><td>25.69</td><td>25.63</td><td>25.33</td><td>24.76</td><td>24.94</td><td>24.14</td><td>22.01</td><td>22.07</td><td>24.85</td><td>28.61</td><td>44.87</td><td>54.14</td><td>45.04</td><td>36.74</td><td>28.19</td><td>22.66</td><td>19.62</td><td>17.16</td><td>16.4</td><td>15.68</td><td>15.77</td><td>16.64</td><td>16.6</td><td>15.65</td><td>15.27</td><td>15.97</td><td>15.53</td><td>14.84</td><td>14.61</td><td>14.52</td><td>14.36</td><td>14.07</td><td>14.56</td><td>14.99</td><td> </td>
</tr>
</tbody></table>""")
data["Tomato"] = table_to_series("""<table cellspacing="0" align="Center" rules="all" border="1" style="border-collapse:collapse;">
<tbody><tr>
<th scope="col">Zone</th><th scope="col">Jan 2012</th><th scope="col">Feb 2012</th><th scope="col">Mar 2012</th><th scope="col">Apr 2012</th><th scope="col">May 2012</th><th scope="col">Jun 2012</th><th scope="col">Jul 2012</th><th scope="col">Aug 2012</th><th scope="col">Sep 2012</th><th scope="col">Oct 2012</th><th scope="col">Nov 2012</th><th scope="col">Dec 2012</th><th scope="col">Jan 2013</th><th scope="col">Feb 2013</th><th scope="col">Mar 2013</th><th scope="col">Apr 2013</th><th scope="col">May 2013</th><th scope="col">Jun 2013</th><th scope="col">Jul 2013</th><th scope="col">Aug 2013</th><th scope="col">Sep 2013</th><th scope="col">Oct 2013</th><th scope="col">Nov 2013</th><th scope="col">Dec 2013</th><th scope="col">Jan 2014</th><th scope="col">Feb 2014</th><th scope="col">Mar 2014</th><th scope="col">Apr 2014</th><th scope="col">May 2014</th><th scope="col">Jun 2014</th><th scope="col">Jul 2014</th><th scope="col">Aug 2014</th><th scope="col">Sep 2014</th><th scope="col">Oct 2014</th><th scope="col">Nov 2014</th><th scope="col">Dec 2014</th><th scope="col">Jan 2015</th><th scope="col">Feb 2015</th><th scope="col">Mar 2015</th><th scope="col">Apr 2015</th><th scope="col">May 2015</th><th scope="col">Jun 2015</th><th scope="col">Jul 2015</th><th scope="col">Aug 2015</th><th scope="col">Sep 2015</th><th scope="col">Oct 2015</th><th scope="col">Nov 2015</th><th scope="col">Dec 2015</th><th scope="col">Jan 2016</th><th scope="col">Feb 2016</th><th scope="col">Mar 2016</th><th scope="col">Apr 2016</th><th scope="col">May 2016</th><th scope="col">Jun 2016</th><th scope="col">Jul 2016</th><th scope="col">Aug 2016</th><th scope="col">Sep 2016</th><th scope="col">Oct 2016</th><th scope="col">Nov 2016</th><th scope="col">Dec 2016</th><th scope="col">Jan 2017</th><th scope="col">Feb 2017</th><th scope="col">Mar 2017</th><th scope="col">Apr 2017</th><th scope="col">May 2017</th><th scope="col">Jun 2017</th><th scope="col">Jul 2017</th><th scope="col">Average</th>
</tr><tr align="left">
<td>NORTH ZONE</td><td>12.39</td><td>12.18</td><td>18.85</td><td>24.67</td><td>18.98</td><td>16.38</td><td>29.55</td><td>31.48</td><td>25.22</td><td>20.89</td><td>18.61</td><td>16.74</td><td>15.32</td><td>15.27</td><td>17.13</td><td>17.9</td><td>17.88</td><td>20.86</td><td>44.5</td><td>42.06</td><td>35.73</td><td>35.35</td><td>51.21</td><td>38.24</td><td>24.12</td><td>18.84</td><td>18.87</td><td>19.93</td><td>17.32</td><td>14.9</td><td>29.33</td><td>52.51</td><td>45.91</td><td>36.43</td><td>24.32</td><td>23.9</td><td>27.78</td><td>26.93</td><td>27.81</td><td>28</td><td>31.55</td><td>27.75</td><td>30.47</td><td>28.29</td><td>30.52</td><td>33.92</td><td>44.54</td><td>34.42</td><td>29.16</td><td>25.93</td><td>21.95</td><td>20.99</td><td>19.9</td><td>32.05</td><td>44.56</td><td>35.08</td><td>29.1</td><td>29.98</td><td>25.65</td><td>20.49</td><td>16.82</td><td>16.48</td><td>18.01</td><td>19.73</td><td>16.94</td><td>21.02</td><td>57.22</td><td>27.1</td>
</tr><tr align="left">
<td>All India Average</td><td>12.79</td><td>12.92</td><td>17.2</td><td>20.71</td><td>19.84</td><td>19.83</td><td>27.99</td><td>26.9</td><td>23.14</td><td>20.61</td><td>19.92</td><td>17.8</td><td>15.99</td><td>15.9</td><td>15.5</td><td>15.9</td><td>21.68</td><td>30.42</td><td>40.53</td><td>32.93</td><td>29.75</td><td>32.53</td><td>43.61</td><td>30.85</td><td>20.25</td><td>15.68</td><td>15.83</td><td>17.39</td><td>18.42</td><td>18.04</td><td>35.3</td><td>50.17</td><td>36.87</td><td>30.19</td><td>24</td><td>23.03</td><td>23.04</td><td>20.86</td><td>20.42</td><td>20.36</td><td>25.62</td><td>25.63</td><td>28.89</td><td>25.35</td><td>25.29</td><td>28.47</td><td>38.79</td><td>30.57</td><td>27.99</td><td>19.92</td><td>16.83</td><td>18.84</td><td>26.73</td><td>40.17</td><td>40.67</td><td>29.61</td><td>24.55</td><td>25.37</td><td>21.45</td><td>16.8</td><td>14.67</td><td>15.62</td><td>16.66</td><td>17.35</td><td>16.86</td><td>21.44</td><td>56.97</td><td> </td>
</tr>
</tbody></table>""")
data["Sugar"] = table_to_series("""<table cellspacing="0" align="Center" rules="all" border="1" style="border-collapse:collapse;">
<tbody><tr>
<th scope="col">Zone</th><th scope="col">Jan 2012</th><th scope="col">Feb 2012</th><th scope="col">Mar 2012</th><th scope="col">Apr 2012</th><th scope="col">May 2012</th><th scope="col">Jun 2012</th><th scope="col">Jul 2012</th><th scope="col">Aug 2012</th><th scope="col">Sep 2012</th><th scope="col">Oct 2012</th><th scope="col">Nov 2012</th><th scope="col">Dec 2012</th><th scope="col">Jan 2013</th><th scope="col">Feb 2013</th><th scope="col">Mar 2013</th><th scope="col">Apr 2013</th><th scope="col">May 2013</th><th scope="col">Jun 2013</th><th scope="col">Jul 2013</th><th scope="col">Aug 2013</th><th scope="col">Sep 2013</th><th scope="col">Oct 2013</th><th scope="col">Nov 2013</th><th scope="col">Dec 2013</th><th scope="col">Jan 2014</th><th scope="col">Feb 2014</th><th scope="col">Mar 2014</th><th scope="col">Apr 2014</th><th scope="col">May 2014</th><th scope="col">Jun 2014</th><th scope="col">Jul 2014</th><th scope="col">Aug 2014</th><th scope="col">Sep 2014</th><th scope="col">Oct 2014</th><th scope="col">Nov 2014</th><th scope="col">Dec 2014</th><th scope="col">Jan 2015</th><th scope="col">Feb 2015</th><th scope="col">Mar 2015</th><th scope="col">Apr 2015</th><th scope="col">May 2015</th><th scope="col">Jun 2015</th><th scope="col">Jul 2015</th><th scope="col">Aug 2015</th><th scope="col">Sep 2015</th><th scope="col">Oct 2015</th><th scope="col">Nov 2015</th><th scope="col">Dec 2015</th><th scope="col">Jan 2016</th><th scope="col">Feb 2016</th><th scope="col">Mar 2016</th><th scope="col">Apr 2016</th><th scope="col">May 2016</th><th scope="col">Jun 2016</th><th scope="col">Jul 2016</th><th scope="col">Aug 2016</th><th scope="col">Sep 2016</th><th scope="col">Oct 2016</th><th scope="col">Nov 2016</th><th scope="col">Dec 2016</th><th scope="col">Jan 2017</th><th scope="col">Feb 2017</th><th scope="col">Mar 2017</th><th scope="col">Apr 2017</th><th scope="col">May 2017</th><th scope="col">Jun 2017</th><th scope="col">Jul 2017</th><th scope="col">Average</th>
</tr><tr align="left">
<td>NORTH ZONE</td><td>34.77</td><td>34.39</td><td>33.74</td><td>33.45</td><td>34.14</td><td>34.43</td><td>35.47</td><td>39.14</td><td>39.82</td><td>40.42</td><td>40.32</td><td>39.8</td><td>38.74</td><td>38.08</td><td>37.44</td><td>36.9</td><td>36.94</td><td>36.96</td><td>36.59</td><td>36.37</td><td>36.45</td><td>36.15</td><td>36.19</td><td>35.9</td><td>35.46</td><td>34.99</td><td>35.34</td><td>36.17</td><td>36.35</td><td>36.65</td><td>37.41</td><td>37.33</td><td>36.84</td><td>36.5</td><td>36.35</td><td>35.87</td><td>35.09</td><td>34.71</td><td>34.09</td><td>33.88</td><td>33.37</td><td>32.51</td><td>31.43</td><td>31.4</td><td>31.26</td><td>31.82</td><td>32.49</td><td>32.73</td><td>33.63</td><td>34.5</td><td>35.21</td><td>37.25</td><td>38.97</td><td>39.22</td><td>39.69</td><td>40.84</td><td>40.92</td><td>41.42</td><td>41.74</td><td>41.43</td><td>41.77</td><td>42.19</td><td>42.64</td><td>42.38</td><td>42.72</td><td>42.75</td><td>42.84</td><td>37.69</td>
</tr><tr align="left">
<td>All India Average</td><td>33.47</td><td>33.23</td><td>32.84</td><td>32.84</td><td>33.3</td><td>33.52</td><td>35.07</td><td>38.89</td><td>39.41</td><td>39.75</td><td>39.71</td><td>39.07</td><td>38.18</td><td>37.52</td><td>37.1</td><td>36.64</td><td>36.49</td><td>36.39</td><td>36.22</td><td>36.11</td><td>36.06</td><td>35.7</td><td>35.42</td><td>34.91</td><td>34.76</td><td>34.28</td><td>34.62</td><td>35.79</td><td>36.2</td><td>36.12</td><td>36.47</td><td>36.37</td><td>36.14</td><td>35.87</td><td>35.63</td><td>34.8</td><td>33.96</td><td>33.55</td><td>32.85</td><td>31.85</td><td>31.39</td><td>30.51</td><td>29.52</td><td>29.35</td><td>29.84</td><td>30.55</td><td>31.11</td><td>31.57</td><td>33.15</td><td>33.92</td><td>34.64</td><td>37.4</td><td>39.23</td><td>39.5</td><td>39.78</td><td>40.59</td><td>40.51</td><td>40.63</td><td>40.76</td><td>40.68</td><td>41.14</td><td>41.83</td><td>42.38</td><td>42.43</td><td>42.57</td><td>42.52</td><td>42.75</td><td> </td>
</tr>
</tbody></table>""")
data["Gur"] = table_to_series("""<table cellspacing="0" align="Center" rules="all" border="1" style="border-collapse:collapse;">
<tbody><tr>
<th scope="col">Zone</th><th scope="col">Jan 2012</th><th scope="col">Feb 2012</th><th scope="col">Mar 2012</th><th scope="col">Apr 2012</th><th scope="col">May 2012</th><th scope="col">Jun 2012</th><th scope="col">Jul 2012</th><th scope="col">Aug 2012</th><th scope="col">Sep 2012</th><th scope="col">Oct 2012</th><th scope="col">Nov 2012</th><th scope="col">Dec 2012</th><th scope="col">Jan 2013</th><th scope="col">Feb 2013</th><th scope="col">Mar 2013</th><th scope="col">Apr 2013</th><th scope="col">May 2013</th><th scope="col">Jun 2013</th><th scope="col">Jul 2013</th><th scope="col">Aug 2013</th><th scope="col">Sep 2013</th><th scope="col">Oct 2013</th><th scope="col">Nov 2013</th><th scope="col">Dec 2013</th><th scope="col">Jan 2014</th><th scope="col">Feb 2014</th><th scope="col">Mar 2014</th><th scope="col">Apr 2014</th><th scope="col">May 2014</th><th scope="col">Jun 2014</th><th scope="col">Jul 2014</th><th scope="col">Aug 2014</th><th scope="col">Sep 2014</th><th scope="col">Oct 2014</th><th scope="col">Nov 2014</th><th scope="col">Dec 2014</th><th scope="col">Jan 2015</th><th scope="col">Feb 2015</th><th scope="col">Mar 2015</th><th scope="col">Apr 2015</th><th scope="col">May 2015</th><th scope="col">Jun 2015</th><th scope="col">Jul 2015</th><th scope="col">Aug 2015</th><th scope="col">Sep 2015</th><th scope="col">Oct 2015</th><th scope="col">Nov 2015</th><th scope="col">Dec 2015</th><th scope="col">Jan 2016</th><th scope="col">Feb 2016</th><th scope="col">Mar 2016</th><th scope="col">Apr 2016</th><th scope="col">May 2016</th><th scope="col">Jun 2016</th><th scope="col">Jul 2016</th><th scope="col">Aug 2016</th><th scope="col">Sep 2016</th><th scope="col">Oct 2016</th><th scope="col">Nov 2016</th><th scope="col">Dec 2016</th><th scope="col">Jan 2017</th><th scope="col">Feb 2017</th><th scope="col">Mar 2017</th><th scope="col">Apr 2017</th><th scope="col">May 2017</th><th scope="col">Jun 2017</th><th scope="col">Jul 2017</th><th scope="col">Average</th>
</tr><tr align="left">
<td>NORTH ZONE</td><td>31.95</td><td>32.17</td><td>32.01</td><td>31.84</td><td>33.03</td><td>34.07</td><td>36.07</td><td>38.32</td><td>38.39</td><td>38.49</td><td>37.1</td><td>36.07</td><td>35.4</td><td>35.63</td><td>35.59</td><td>35.97</td><td>36.77</td><td>37.47</td><td>38.03</td><td>39.03</td><td>39.5</td><td>39.44</td><td>39.32</td><td>36.71</td><td>35.58</td><td>35.29</td><td>35.91</td><td>36.52</td><td>37.68</td><td>38.7</td><td>39.39</td><td>39.77</td><td>40.71</td><td>40.27</td><td>38.8</td><td>37.03</td><td>37.41</td><td>37.26</td><td>36.88</td><td>36.68</td><td>37.28</td><td>37.83</td><td>37.9</td><td>38.05</td><td>37.99</td><td>37.99</td><td>37.61</td><td>36.69</td><td>36.6</td><td>36.4</td><td>36.05</td><td>36.87</td><td>37.71</td><td>38.2</td><td>39.43</td><td>40.64</td><td>41.43</td><td>41.54</td><td>40.67</td><td>39.59</td><td>39</td><td>38.75</td><td>39.02</td><td>38.89</td><td>40.53</td><td>41.2</td><td>41.86</td><td>38.11</td>
</tr><tr align="left">
<td>All India Average</td><td>34.88</td><td>34.53</td><td>34.36</td><td>34.53</td><td>35.27</td><td>35.94</td><td>37.54</td><td>38.92</td><td>39.44</td><td>39.63</td><td>39.48</td><td>38.77</td><td>38.04</td><td>38.13</td><td>37.89</td><td>38.69</td><td>39.72</td><td>40.72</td><td>41.18</td><td>41.62</td><td>41.93</td><td>42.09</td><td>42.08</td><td>40.35</td><td>40.23</td><td>39.58</td><td>39.11</td><td>39.2</td><td>39.74</td><td>40.62</td><td>40.97</td><td>41.27</td><td>42.03</td><td>42.76</td><td>42.01</td><td>40.37</td><td>39.98</td><td>40.66</td><td>40.18</td><td>39.64</td><td>40.52</td><td>40.84</td><td>40.8</td><td>40.11</td><td>40.23</td><td>40.5</td><td>40.61</td><td>40.12</td><td>39.65</td><td>39.49</td><td>39.25</td><td>40.08</td><td>40.98</td><td>41.72</td><td>42.43</td><td>43.62</td><td>44.41</td><td>44.2</td><td>43.49</td><td>43.14</td><td>42.8</td><td>42.88</td><td>43.17</td><td>43.38</td><td>44.24</td><td>44.81</td><td>45.25</td><td> </td>
</tr>
</tbody></table>""")
data["Milk"] = table_to_series("""<table cellspacing="0" align="Center" rules="all" border="1" style="border-collapse:collapse;">
<tbody><tr>
<th scope="col">Zone</th><th scope="col">Jan 2012</th><th scope="col">Feb 2012</th><th scope="col">Mar 2012</th><th scope="col">Apr 2012</th><th scope="col">May 2012</th><th scope="col">Jun 2012</th><th scope="col">Jul 2012</th><th scope="col">Aug 2012</th><th scope="col">Sep 2012</th><th scope="col">Oct 2012</th><th scope="col">Nov 2012</th><th scope="col">Dec 2012</th><th scope="col">Jan 2013</th><th scope="col">Feb 2013</th><th scope="col">Mar 2013</th><th scope="col">Apr 2013</th><th scope="col">May 2013</th><th scope="col">Jun 2013</th><th scope="col">Jul 2013</th><th scope="col">Aug 2013</th><th scope="col">Sep 2013</th><th scope="col">Oct 2013</th><th scope="col">Nov 2013</th><th scope="col">Dec 2013</th><th scope="col">Jan 2014</th><th scope="col">Feb 2014</th><th scope="col">Mar 2014</th><th scope="col">Apr 2014</th><th scope="col">May 2014</th><th scope="col">Jun 2014</th><th scope="col">Jul 2014</th><th scope="col">Aug 2014</th><th scope="col">Sep 2014</th><th scope="col">Oct 2014</th><th scope="col">Nov 2014</th><th scope="col">Dec 2014</th><th scope="col">Jan 2015</th><th scope="col">Feb 2015</th><th scope="col">Mar 2015</th><th scope="col">Apr 2015</th><th scope="col">May 2015</th><th scope="col">Jun 2015</th><th scope="col">Jul 2015</th><th scope="col">Aug 2015</th><th scope="col">Sep 2015</th><th scope="col">Oct 2015</th><th scope="col">Nov 2015</th><th scope="col">Dec 2015</th><th scope="col">Jan 2016</th><th scope="col">Feb 2016</th><th scope="col">Mar 2016</th><th scope="col">Apr 2016</th><th scope="col">May 2016</th><th scope="col">Jun 2016</th><th scope="col">Jul 2016</th><th scope="col">Aug 2016</th><th scope="col">Sep 2016</th><th scope="col">Oct 2016</th><th scope="col">Nov 2016</th><th scope="col">Dec 2016</th><th scope="col">Jan 2017</th><th scope="col">Feb 2017</th><th scope="col">Mar 2017</th><th scope="col">Apr 2017</th><th scope="col">May 2017</th><th scope="col">Jun 2017</th><th scope="col">Jul 2017</th><th scope="col">Average</th>
</tr><tr align="left">
<td>NORTH ZONE</td><td>31.9</td><td>32.15</td><td>32.1</td><td>32.63</td><td>33.09</td><td>33.22</td><td>33.6</td><td>33.82</td><td>33.87</td><td>33.84</td><td>34.05</td><td>34.22</td><td>34.44</td><td>34.73</td><td>35.01</td><td>35.05</td><td>35.37</td><td>35.74</td><td>35.73</td><td>35.53</td><td>35.62</td><td>35.95</td><td>36.59</td><td>36.85</td><td>37</td><td>37.49</td><td>37.54</td><td>38.26</td><td>38.63</td><td>39.77</td><td>40.03</td><td>40.33</td><td>40.18</td><td>40.97</td><td>40.53</td><td>40.6</td><td>40.82</td><td>40.81</td><td>40.49</td><td>40.15</td><td>40.48</td><td>40.89</td><td>41.09</td><td>40.9</td><td>40.96</td><td>41.13</td><td>40.51</td><td>41.07</td><td>40.96</td><td>41.26</td><td>41.06</td><td>40.68</td><td>41.3</td><td>41.96</td><td>42.35</td><td>41.27</td><td>41.19</td><td>41.53</td><td>41.69</td><td>41.54</td><td>42.13</td><td>41.72</td><td>41.95</td><td>42.55</td><td>42.77</td><td>42.95</td><td>43.05</td><td>39.55</td>
</tr><tr align="left">
<td>All India Average</td><td>30.07</td><td>30.39</td><td>30.4</td><td>30.74</td><td>31.24</td><td>31.32</td><td>31.53</td><td>31.74</td><td>31.95</td><td>32.41</td><td>32.66</td><td>32.46</td><td>32.67</td><td>33.49</td><td>33.3</td><td>33.32</td><td>33.64</td><td>33.7</td><td>33.8</td><td>33.78</td><td>34.18</td><td>34.24</td><td>34.7</td><td>34.89</td><td>35.28</td><td>35.22</td><td>35.61</td><td>36.33</td><td>36.45</td><td>36.72</td><td>37.22</td><td>37.69</td><td>37.39</td><td>37.6</td><td>38.22</td><td>38.33</td><td>38.78</td><td>38.75</td><td>38.77</td><td>38.62</td><td>38.97</td><td>39.22</td><td>39.45</td><td>39.38</td><td>39.54</td><td>39.4</td><td>38.86</td><td>39.37</td><td>39.58</td><td>39.69</td><td>39.53</td><td>39.47</td><td>39.75</td><td>40.26</td><td>40.37</td><td>40.12</td><td>40.04</td><td>40.1</td><td>40.16</td><td>40.2</td><td>40.56</td><td>40.45</td><td>40.92</td><td>41.24</td><td>41.56</td><td>41.58</td><td>41.8</td><td> </td>
</tr>
</tbody></table>""")
data["Tea (Loose)"] = table_to_series("""<table cellspacing="0" align="Center" rules="all" border="1" style="border-collapse:collapse;">
<tbody><tr>
<th scope="col">Zone</th><th scope="col">Jan 2012</th><th scope="col">Feb 2012</th><th scope="col">Mar 2012</th><th scope="col">Apr 2012</th><th scope="col">May 2012</th><th scope="col">Jun 2012</th><th scope="col">Jul 2012</th><th scope="col">Aug 2012</th><th scope="col">Sep 2012</th><th scope="col">Oct 2012</th><th scope="col">Nov 2012</th><th scope="col">Dec 2012</th><th scope="col">Jan 2013</th><th scope="col">Feb 2013</th><th scope="col">Mar 2013</th><th scope="col">Apr 2013</th><th scope="col">May 2013</th><th scope="col">Jun 2013</th><th scope="col">Jul 2013</th><th scope="col">Aug 2013</th><th scope="col">Sep 2013</th><th scope="col">Oct 2013</th><th scope="col">Nov 2013</th><th scope="col">Dec 2013</th><th scope="col">Jan 2014</th><th scope="col">Feb 2014</th><th scope="col">Mar 2014</th><th scope="col">Apr 2014</th><th scope="col">May 2014</th><th scope="col">Jun 2014</th><th scope="col">Jul 2014</th><th scope="col">Aug 2014</th><th scope="col">Sep 2014</th><th scope="col">Oct 2014</th><th scope="col">Nov 2014</th><th scope="col">Dec 2014</th><th scope="col">Jan 2015</th><th scope="col">Feb 2015</th><th scope="col">Mar 2015</th><th scope="col">Apr 2015</th><th scope="col">May 2015</th><th scope="col">Jun 2015</th><th scope="col">Jul 2015</th><th scope="col">Aug 2015</th><th scope="col">Sep 2015</th><th scope="col">Oct 2015</th><th scope="col">Nov 2015</th><th scope="col">Dec 2015</th><th scope="col">Jan 2016</th><th scope="col">Feb 2016</th><th scope="col">Mar 2016</th><th scope="col">Apr 2016</th><th scope="col">May 2016</th><th scope="col">Jun 2016</th><th scope="col">Jul 2016</th><th scope="col">Aug 2016</th><th scope="col">Sep 2016</th><th scope="col">Oct 2016</th><th scope="col">Nov 2016</th><th scope="col">Dec 2016</th><th scope="col">Jan 2017</th><th scope="col">Feb 2017</th><th scope="col">Mar 2017</th><th scope="col">Apr 2017</th><th scope="col">May 2017</th><th scope="col">Jun 2017</th><th scope="col">Jul 2017</th><th scope="col">Average</th>
</tr><tr align="left">
<td>NORTH ZONE</td><td>193.67</td><td>188.67</td><td>184.55</td><td>185.31</td><td>187.02</td><td>188.8</td><td>194.16</td><td>198.33</td><td>199.88</td><td>203.67</td><td>206.19</td><td>209.71</td><td>214.89</td><td>207.29</td><td>203.61</td><td>208.24</td><td>214.75</td><td>216.89</td><td>215.99</td><td>212.88</td><td>218.07</td><td>216.72</td><td>218.13</td><td>220.33</td><td>223.53</td><td>219.49</td><td>219.58</td><td>227.02</td><td>216.21</td><td>216.61</td><td>210.86</td><td>211.33</td><td>207.67</td><td>208.06</td><td>209.4</td><td>206.27</td><td>204.64</td><td>208.79</td><td>205.38</td><td>203.15</td><td>203.88</td><td>205.05</td><td>202.47</td><td>203.21</td><td>203.68</td><td>204.48</td><td>204.81</td><td>205.53</td><td>205.87</td><td>204.4</td><td>197.47</td><td>198.41</td><td>197.77</td><td>197.29</td><td>196.86</td><td>199.72</td><td>200.47</td><td>203.56</td><td>202.31</td><td>201.84</td><td>202.58</td><td>202.68</td><td>203.02</td><td>203.45</td><td>203.68</td><td>203.96</td><td>206.19</td><td>204.37</td>
</tr><tr align="left">
<td>All India Average</td><td>183.88</td><td>183.75</td><td>181.48</td><td>183.58</td><td>185.53</td><td>186.05</td><td>188.28</td><td>192.72</td><td>195.13</td><td>200.92</td><td>197.93</td><td>200.59</td><td>201.93</td><td>198.97</td><td>195</td><td>197.03</td><td>200.37</td><td>200.94</td><td>202.86</td><td>203.34</td><td>205.12</td><td>204.85</td><td>203.47</td><td>203.06</td><td>204.05</td><td>203.02</td><td>201</td><td>207.29</td><td>203.93</td><td>205.71</td><td>204.24</td><td>205.09</td><td>212.26</td><td>211.4</td><td>205.09</td><td>204.68</td><td>205.02</td><td>205.25</td><td>205.23</td><td>205.46</td><td>207.19</td><td>206.75</td><td>207.26</td><td>206.29</td><td>205.61</td><td>206.19</td><td>204.85</td><td>204.83</td><td>204.46</td><td>202.81</td><td>197.72</td><td>199.15</td><td>199.02</td><td>198.97</td><td>197.64</td><td>198.71</td><td>197.01</td><td>197.77</td><td>197.38</td><td>198.96</td><td>200.55</td><td>200.9</td><td>200.24</td><td>200.88</td><td>201.43</td><td>202.68</td><td>204.14</td><td> </td>
</tr>
</tbody></table>""")
data["Salt (Iodised)"] = table_to_series("""<table cellspacing="0" align="Center" rules="all" border="1" style="border-collapse:collapse;">
<tbody><tr>
<th scope="col">Zone</th><th scope="col">Jan 2012</th><th scope="col">Feb 2012</th><th scope="col">Mar 2012</th><th scope="col">Apr 2012</th><th scope="col">May 2012</th><th scope="col">Jun 2012</th><th scope="col">Jul 2012</th><th scope="col">Aug 2012</th><th scope="col">Sep 2012</th><th scope="col">Oct 2012</th><th scope="col">Nov 2012</th><th scope="col">Dec 2012</th><th scope="col">Jan 2013</th><th scope="col">Feb 2013</th><th scope="col">Mar 2013</th><th scope="col">Apr 2013</th><th scope="col">May 2013</th><th scope="col">Jun 2013</th><th scope="col">Jul 2013</th><th scope="col">Aug 2013</th><th scope="col">Sep 2013</th><th scope="col">Oct 2013</th><th scope="col">Nov 2013</th><th scope="col">Dec 2013</th><th scope="col">Jan 2014</th><th scope="col">Feb 2014</th><th scope="col">Mar 2014</th><th scope="col">Apr 2014</th><th scope="col">May 2014</th><th scope="col">Jun 2014</th><th scope="col">Jul 2014</th><th scope="col">Aug 2014</th><th scope="col">Sep 2014</th><th scope="col">Oct 2014</th><th scope="col">Nov 2014</th><th scope="col">Dec 2014</th><th scope="col">Jan 2015</th><th scope="col">Feb 2015</th><th scope="col">Mar 2015</th><th scope="col">Apr 2015</th><th scope="col">May 2015</th><th scope="col">Jun 2015</th><th scope="col">Jul 2015</th><th scope="col">Aug 2015</th><th scope="col">Sep 2015</th><th scope="col">Oct 2015</th><th scope="col">Nov 2015</th><th scope="col">Dec 2015</th><th scope="col">Jan 2016</th><th scope="col">Feb 2016</th><th scope="col">Mar 2016</th><th scope="col">Apr 2016</th><th scope="col">May 2016</th><th scope="col">Jun 2016</th><th scope="col">Jul 2016</th><th scope="col">Aug 2016</th><th scope="col">Sep 2016</th><th scope="col">Oct 2016</th><th scope="col">Nov 2016</th><th scope="col">Dec 2016</th><th scope="col">Jan 2017</th><th scope="col">Feb 2017</th><th scope="col">Mar 2017</th><th scope="col">Apr 2017</th><th scope="col">May 2017</th><th scope="col">Jun 2017</th><th scope="col">Jul 2017</th><th scope="col">Average</th>
</tr><tr align="left">
<td>NORTH ZONE</td><td>13.53</td><td>13.53</td><td>13.51</td><td>13.56</td><td>13.83</td><td>14.07</td><td>14.2</td><td>14.47</td><td>14.75</td><td>14.76</td><td>14.92</td><td>15.04</td><td>15.07</td><td>15.26</td><td>15.27</td><td>15.29</td><td>15.34</td><td>15.37</td><td>15.48</td><td>15.53</td><td>15.56</td><td>15.49</td><td>15.57</td><td>15.74</td><td>15.92</td><td>15.97</td><td>16</td><td>16.03</td><td>16.03</td><td>16.07</td><td>16.3</td><td>16.38</td><td>16.33</td><td>16.41</td><td>16.55</td><td>16.62</td><td>16.63</td><td>16.6</td><td>16.64</td><td>16.65</td><td>16.64</td><td>16.73</td><td>16.84</td><td>16.85</td><td>16.85</td><td>16.96</td><td>17.13</td><td>17.14</td><td>17.04</td><td>16.93</td><td>16.96</td><td>17.18</td><td>17.06</td><td>17.23</td><td>17.23</td><td>17.37</td><td>17.42</td><td>17.41</td><td>17.44</td><td>17.5</td><td>17.56</td><td>17.63</td><td>17.56</td><td>17.53</td><td>17.69</td><td>17.69</td><td>17.65</td><td>16.52</td>
</tr><tr align="left">
<td>All India Average</td><td>11.99</td><td>12.06</td><td>12.1</td><td>12.15</td><td>12.33</td><td>12.56</td><td>12.73</td><td>12.94</td><td>13.13</td><td>13.24</td><td>13.21</td><td>13.22</td><td>13.21</td><td>13.53</td><td>13.44</td><td>13.51</td><td>13.56</td><td>13.68</td><td>13.75</td><td>13.71</td><td>13.79</td><td>13.91</td><td>13.82</td><td>13.86</td><td>14.05</td><td>14.02</td><td>14.1</td><td>14.32</td><td>14.44</td><td>14.49</td><td>14.61</td><td>14.75</td><td>14.81</td><td>14.86</td><td>14.91</td><td>14.83</td><td>14.88</td><td>14.72</td><td>14.72</td><td>14.85</td><td>14.75</td><td>14.63</td><td>14.76</td><td>14.85</td><td>14.9</td><td>14.9</td><td>14.91</td><td>15.03</td><td>15.19</td><td>15.01</td><td>15.04</td><td>15.23</td><td>15</td><td>15.08</td><td>14.74</td><td>14.8</td><td>14.77</td><td>14.89</td><td>15.06</td><td>15.08</td><td>15.12</td><td>15.15</td><td>14.98</td><td>14.99</td><td>15.12</td><td>15.15</td><td>15.12</td><td> </td>
</tr>
</tbody></table>""")
The final DataFrame ready to be analyzed:
data
data.to_pickle("retail_price_data.pkl")