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hygull/Resampling.ipynb

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Resampling, pydataset, plotting, pandas.Series.dt (https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.dt.html)
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Resampling.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 247,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 248,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"DatetimeIndex(['2017-12-30', '2017-12-31', '2018-01-01'], dtype='datetime64[ns]', freq='D')"
]
},
"execution_count": 248,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"drange = pd.date_range(\"2017-12-30\", periods=3) # '2017-12-30' is start date, periods > Number of datetime items we want to get\n",
"drange"
]
},
{
"cell_type": "code",
"execution_count": 249,
"metadata": {},
"outputs": [],
"source": [
"# from IPython.core.interactiveshell import InteractiveShell\n",
"# InteractiveShell.ast_node_interactivity = \"all\"\n",
"\n",
"# to_series()\n",
"# drange.head()"
]
},
{
"cell_type": "code",
"execution_count": 250,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 250,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# from pydataset import data\n",
"# https://pypi.org/project/pydataset/\n",
"import os\n",
"os.system(\"pip install pydataset\") # Installing package/library from Jupyter notebook"
]
},
{
"cell_type": "code",
"execution_count": 251,
"metadata": {},
"outputs": [],
"source": [
"from pydataset import data"
]
},
{
"cell_type": "code",
"execution_count": 252,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" vertical-align: middle;\n",
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"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>lat</th>\n",
" <th>long</th>\n",
" <th>depth</th>\n",
" <th>mag</th>\n",
" <th>stations</th>\n",
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" <td>194</td>\n",
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" <td>15</td>\n",
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" <td>181.74</td>\n",
" <td>211</td>\n",
" <td>4.7</td>\n",
" <td>35</td>\n",
" </tr>\n",
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" <th>10</th>\n",
" <td>-17.47</td>\n",
" <td>179.59</td>\n",
" <td>622</td>\n",
" <td>4.3</td>\n",
" <td>19</td>\n",
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" <td>-21.44</td>\n",
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" <td>583</td>\n",
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" <td>13</td>\n",
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" <th>12</th>\n",
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" <td>167.00</td>\n",
" <td>249</td>\n",
" <td>4.6</td>\n",
" <td>16</td>\n",
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" <td>-18.54</td>\n",
" <td>182.11</td>\n",
" <td>554</td>\n",
" <td>4.4</td>\n",
" <td>19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>-21.00</td>\n",
" <td>181.66</td>\n",
" <td>600</td>\n",
" <td>4.4</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>-20.70</td>\n",
" <td>169.92</td>\n",
" <td>139</td>\n",
" <td>6.1</td>\n",
" <td>94</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>-15.94</td>\n",
" <td>184.95</td>\n",
" <td>306</td>\n",
" <td>4.3</td>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>-13.64</td>\n",
" <td>165.96</td>\n",
" <td>50</td>\n",
" <td>6.0</td>\n",
" <td>83</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>-17.83</td>\n",
" <td>181.50</td>\n",
" <td>590</td>\n",
" <td>4.5</td>\n",
" <td>21</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>-23.50</td>\n",
" <td>179.78</td>\n",
" <td>570</td>\n",
" <td>4.4</td>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>-22.63</td>\n",
" <td>180.31</td>\n",
" <td>598</td>\n",
" <td>4.4</td>\n",
" <td>18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>-20.84</td>\n",
" <td>181.16</td>\n",
" <td>576</td>\n",
" <td>4.5</td>\n",
" <td>17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>-10.98</td>\n",
" <td>166.32</td>\n",
" <td>211</td>\n",
" <td>4.2</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>-23.30</td>\n",
" <td>180.16</td>\n",
" <td>512</td>\n",
" <td>4.4</td>\n",
" <td>18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>-30.20</td>\n",
" <td>182.00</td>\n",
" <td>125</td>\n",
" <td>4.7</td>\n",
" <td>22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>-19.66</td>\n",
" <td>180.28</td>\n",
" <td>431</td>\n",
" <td>5.4</td>\n",
" <td>57</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>-17.94</td>\n",
" <td>181.49</td>\n",
" <td>537</td>\n",
" <td>4.0</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>-14.72</td>\n",
" <td>167.51</td>\n",
" <td>155</td>\n",
" <td>4.6</td>\n",
" <td>18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>-16.46</td>\n",
" <td>180.79</td>\n",
" <td>498</td>\n",
" <td>5.2</td>\n",
" <td>79</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>-20.97</td>\n",
" <td>181.47</td>\n",
" <td>582</td>\n",
" <td>4.5</td>\n",
" <td>25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>-19.84</td>\n",
" <td>182.37</td>\n",
" <td>328</td>\n",
" <td>4.4</td>\n",
" <td>17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>-22.58</td>\n",
" <td>179.24</td>\n",
" <td>553</td>\n",
" <td>4.6</td>\n",
" <td>21</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>-16.32</td>\n",
" <td>166.74</td>\n",
" <td>50</td>\n",
" <td>4.7</td>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>-15.55</td>\n",
" <td>185.05</td>\n",
" <td>292</td>\n",
" <td>4.8</td>\n",
" <td>42</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>-23.55</td>\n",
" <td>180.80</td>\n",
" <td>349</td>\n",
" <td>4.0</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>-16.30</td>\n",
" <td>186.00</td>\n",
" <td>48</td>\n",
" <td>4.5</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>-25.82</td>\n",
" <td>179.33</td>\n",
" <td>600</td>\n",
" <td>4.3</td>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>-18.73</td>\n",
" <td>169.23</td>\n",
" <td>206</td>\n",
" <td>4.5</td>\n",
" <td>17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>-17.64</td>\n",
" <td>181.28</td>\n",
" <td>574</td>\n",
" <td>4.6</td>\n",
" <td>17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>-17.66</td>\n",
" <td>181.40</td>\n",
" <td>585</td>\n",
" <td>4.1</td>\n",
" <td>17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>-18.82</td>\n",
" <td>169.33</td>\n",
" <td>230</td>\n",
" <td>4.4</td>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>-37.37</td>\n",
" <td>176.78</td>\n",
" <td>263</td>\n",
" <td>4.7</td>\n",
" <td>34</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>-15.31</td>\n",
" <td>186.10</td>\n",
" <td>96</td>\n",
" <td>4.6</td>\n",
" <td>32</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>-24.97</td>\n",
" <td>179.82</td>\n",
" <td>511</td>\n",
" <td>4.4</td>\n",
" <td>23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>-15.49</td>\n",
" <td>186.04</td>\n",
" <td>94</td>\n",
" <td>4.3</td>\n",
" <td>26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>-19.23</td>\n",
" <td>169.41</td>\n",
" <td>246</td>\n",
" <td>4.6</td>\n",
" <td>27</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>-30.10</td>\n",
" <td>182.30</td>\n",
" <td>56</td>\n",
" <td>4.9</td>\n",
" <td>34</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>-26.40</td>\n",
" <td>181.70</td>\n",
" <td>329</td>\n",
" <td>4.5</td>\n",
" <td>24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>-11.77</td>\n",
" <td>166.32</td>\n",
" <td>70</td>\n",
" <td>4.4</td>\n",
" <td>18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td>-24.12</td>\n",
" <td>180.08</td>\n",
" <td>493</td>\n",
" <td>4.3</td>\n",
" <td>21</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>-18.97</td>\n",
" <td>185.25</td>\n",
" <td>129</td>\n",
" <td>5.1</td>\n",
" <td>73</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td>-18.75</td>\n",
" <td>182.35</td>\n",
" <td>554</td>\n",
" <td>4.2</td>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52</th>\n",
" <td>-19.26</td>\n",
" <td>184.42</td>\n",
" <td>223</td>\n",
" <td>4.0</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53</th>\n",
" <td>-22.75</td>\n",
" <td>173.20</td>\n",
" <td>46</td>\n",
" <td>4.6</td>\n",
" <td>26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54</th>\n",
" <td>-21.37</td>\n",
" <td>180.67</td>\n",
" <td>593</td>\n",
" <td>4.3</td>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55</th>\n",
" <td>-20.10</td>\n",
" <td>182.16</td>\n",
" <td>489</td>\n",
" <td>4.2</td>\n",
" <td>16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>56</th>\n",
" <td>-19.85</td>\n",
" <td>182.13</td>\n",
" <td>562</td>\n",
" <td>4.4</td>\n",
" <td>31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>57</th>\n",
" <td>-22.70</td>\n",
" <td>181.00</td>\n",
" <td>445</td>\n",
" <td>4.5</td>\n",
" <td>17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>58</th>\n",
" <td>-22.06</td>\n",
" <td>180.60</td>\n",
" <td>584</td>\n",
" <td>4.0</td>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59</th>\n",
" <td>-17.80</td>\n",
" <td>181.35</td>\n",
" <td>535</td>\n",
" <td>4.4</td>\n",
" <td>23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>60</th>\n",
" <td>-24.20</td>\n",
" <td>179.20</td>\n",
" <td>530</td>\n",
" <td>4.3</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>61</th>\n",
" <td>-20.69</td>\n",
" <td>181.55</td>\n",
" <td>582</td>\n",
" <td>4.7</td>\n",
" <td>35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>62</th>\n",
" <td>-21.16</td>\n",
" <td>182.40</td>\n",
" <td>260</td>\n",
" <td>4.1</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>63</th>\n",
" <td>-13.82</td>\n",
" <td>172.38</td>\n",
" <td>613</td>\n",
" <td>5.0</td>\n",
" <td>61</td>\n",
" </tr>\n",
" <tr>\n",
" <th>64</th>\n",
" <td>-11.49</td>\n",
" <td>166.22</td>\n",
" <td>84</td>\n",
" <td>4.6</td>\n",
" <td>32</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65</th>\n",
" <td>-20.68</td>\n",
" <td>181.41</td>\n",
" <td>593</td>\n",
" <td>4.9</td>\n",
" <td>40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>66</th>\n",
" <td>-17.10</td>\n",
" <td>184.93</td>\n",
" <td>286</td>\n",
" <td>4.7</td>\n",
" <td>25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>67</th>\n",
" <td>-20.14</td>\n",
" <td>181.60</td>\n",
" <td>587</td>\n",
" <td>4.1</td>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68</th>\n",
" <td>-21.96</td>\n",
" <td>179.62</td>\n",
" <td>627</td>\n",
" <td>5.0</td>\n",
" <td>45</td>\n",
" </tr>\n",
" <tr>\n",
" <th>69</th>\n",
" <td>-20.42</td>\n",
" <td>181.86</td>\n",
" <td>530</td>\n",
" <td>4.5</td>\n",
" <td>27</td>\n",
" </tr>\n",
" <tr>\n",
" <th>70</th>\n",
" <td>-15.46</td>\n",
" <td>187.81</td>\n",
" <td>40</td>\n",
" <td>5.5</td>\n",
" <td>91</td>\n",
" </tr>\n",
" <tr>\n",
" <th>71</th>\n",
" <td>-15.31</td>\n",
" <td>185.80</td>\n",
" <td>152</td>\n",
" <td>4.0</td>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>72</th>\n",
" <td>-19.86</td>\n",
" <td>184.35</td>\n",
" <td>201</td>\n",
" <td>4.5</td>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>73</th>\n",
" <td>-11.55</td>\n",
" <td>166.20</td>\n",
" <td>96</td>\n",
" <td>4.3</td>\n",
" <td>14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>74</th>\n",
" <td>-23.74</td>\n",
" <td>179.99</td>\n",
" <td>506</td>\n",
" <td>5.2</td>\n",
" <td>75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75</th>\n",
" <td>-17.70</td>\n",
" <td>181.23</td>\n",
" <td>546</td>\n",
" <td>4.4</td>\n",
" <td>35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>76</th>\n",
" <td>-23.54</td>\n",
" <td>180.04</td>\n",
" <td>564</td>\n",
" <td>4.3</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>77</th>\n",
" <td>-19.21</td>\n",
" <td>184.70</td>\n",
" <td>197</td>\n",
" <td>4.1</td>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>78</th>\n",
" <td>-12.11</td>\n",
" <td>167.06</td>\n",
" <td>265</td>\n",
" <td>4.5</td>\n",
" <td>23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>79</th>\n",
" <td>-21.81</td>\n",
" <td>181.71</td>\n",
" <td>323</td>\n",
" <td>4.2</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>80</th>\n",
" <td>-28.98</td>\n",
" <td>181.11</td>\n",
" <td>304</td>\n",
" <td>5.3</td>\n",
" <td>60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>81</th>\n",
" <td>-34.02</td>\n",
" <td>180.21</td>\n",
" <td>75</td>\n",
" <td>5.2</td>\n",
" <td>65</td>\n",
" </tr>\n",
" <tr>\n",
" <th>82</th>\n",
" <td>-23.84</td>\n",
" <td>180.99</td>\n",
" <td>367</td>\n",
" <td>4.5</td>\n",
" <td>27</td>\n",
" </tr>\n",
" <tr>\n",
" <th>83</th>\n",
" <td>-19.57</td>\n",
" <td>182.38</td>\n",
" <td>579</td>\n",
" <td>4.6</td>\n",
" <td>38</td>\n",
" </tr>\n",
" <tr>\n",
" <th>84</th>\n",
" <td>-20.12</td>\n",
" <td>183.40</td>\n",
" <td>284</td>\n",
" <td>4.3</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>85</th>\n",
" <td>-17.70</td>\n",
" <td>181.70</td>\n",
" <td>450</td>\n",
" <td>4.0</td>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>916</th>\n",
" <td>-21.52</td>\n",
" <td>169.75</td>\n",
" <td>61</td>\n",
" <td>5.1</td>\n",
" <td>40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>917</th>\n",
" <td>-19.57</td>\n",
" <td>184.47</td>\n",
" <td>202</td>\n",
" <td>4.2</td>\n",
" <td>28</td>\n",
" </tr>\n",
" <tr>\n",
" <th>918</th>\n",
" <td>-23.08</td>\n",
" <td>183.45</td>\n",
" <td>90</td>\n",
" <td>4.7</td>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>919</th>\n",
" <td>-25.06</td>\n",
" <td>182.80</td>\n",
" <td>133</td>\n",
" <td>4.0</td>\n",
" <td>14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>920</th>\n",
" <td>-17.85</td>\n",
" <td>181.44</td>\n",
" <td>589</td>\n",
" <td>5.6</td>\n",
" <td>115</td>\n",
" </tr>\n",
" <tr>\n",
" <th>921</th>\n",
" <td>-15.99</td>\n",
" <td>167.95</td>\n",
" <td>190</td>\n",
" <td>5.3</td>\n",
" <td>81</td>\n",
" </tr>\n",
" <tr>\n",
" <th>922</th>\n",
" <td>-20.56</td>\n",
" <td>184.41</td>\n",
" <td>138</td>\n",
" <td>5.0</td>\n",
" <td>82</td>\n",
" </tr>\n",
" <tr>\n",
" <th>923</th>\n",
" <td>-17.98</td>\n",
" <td>181.61</td>\n",
" <td>598</td>\n",
" <td>4.3</td>\n",
" <td>27</td>\n",
" </tr>\n",
" <tr>\n",
" <th>924</th>\n",
" <td>-18.40</td>\n",
" <td>181.77</td>\n",
" <td>600</td>\n",
" <td>4.1</td>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>925</th>\n",
" <td>-27.64</td>\n",
" <td>182.22</td>\n",
" <td>162</td>\n",
" <td>5.1</td>\n",
" <td>67</td>\n",
" </tr>\n",
" <tr>\n",
" <th>926</th>\n",
" <td>-20.99</td>\n",
" <td>181.02</td>\n",
" <td>626</td>\n",
" <td>4.5</td>\n",
" <td>36</td>\n",
" </tr>\n",
" <tr>\n",
" <th>927</th>\n",
" <td>-14.86</td>\n",
" <td>167.32</td>\n",
" <td>137</td>\n",
" <td>4.9</td>\n",
" <td>22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>928</th>\n",
" <td>-29.33</td>\n",
" <td>182.72</td>\n",
" <td>57</td>\n",
" <td>5.4</td>\n",
" <td>61</td>\n",
" </tr>\n",
" <tr>\n",
" <th>929</th>\n",
" <td>-25.81</td>\n",
" <td>182.54</td>\n",
" <td>201</td>\n",
" <td>4.7</td>\n",
" <td>40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>930</th>\n",
" <td>-14.10</td>\n",
" <td>166.01</td>\n",
" <td>69</td>\n",
" <td>4.8</td>\n",
" <td>29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>931</th>\n",
" <td>-17.63</td>\n",
" <td>185.13</td>\n",
" <td>219</td>\n",
" <td>4.5</td>\n",
" <td>28</td>\n",
" </tr>\n",
" <tr>\n",
" <th>932</th>\n",
" <td>-23.47</td>\n",
" <td>180.21</td>\n",
" <td>553</td>\n",
" <td>4.2</td>\n",
" <td>23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>933</th>\n",
" <td>-23.92</td>\n",
" <td>180.21</td>\n",
" <td>524</td>\n",
" <td>4.6</td>\n",
" <td>50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>934</th>\n",
" <td>-20.88</td>\n",
" <td>185.18</td>\n",
" <td>51</td>\n",
" <td>4.6</td>\n",
" <td>28</td>\n",
" </tr>\n",
" <tr>\n",
" <th>935</th>\n",
" <td>-20.25</td>\n",
" <td>184.75</td>\n",
" <td>107</td>\n",
" <td>5.6</td>\n",
" <td>121</td>\n",
" </tr>\n",
" <tr>\n",
" <th>936</th>\n",
" <td>-19.33</td>\n",
" <td>186.16</td>\n",
" <td>44</td>\n",
" <td>5.4</td>\n",
" <td>110</td>\n",
" </tr>\n",
" <tr>\n",
" <th>937</th>\n",
" <td>-18.14</td>\n",
" <td>181.71</td>\n",
" <td>574</td>\n",
" <td>4.0</td>\n",
" <td>20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>938</th>\n",
" <td>-22.41</td>\n",
" <td>183.99</td>\n",
" <td>128</td>\n",
" <td>5.2</td>\n",
" <td>72</td>\n",
" </tr>\n",
" <tr>\n",
" <th>939</th>\n",
" <td>-20.77</td>\n",
" <td>181.16</td>\n",
" <td>568</td>\n",
" <td>4.2</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>940</th>\n",
" <td>-17.95</td>\n",
" <td>181.73</td>\n",
" <td>583</td>\n",
" <td>4.7</td>\n",
" <td>57</td>\n",
" </tr>\n",
" <tr>\n",
" <th>941</th>\n",
" <td>-20.83</td>\n",
" <td>181.01</td>\n",
" <td>622</td>\n",
" <td>4.3</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>942</th>\n",
" <td>-27.84</td>\n",
" <td>182.10</td>\n",
" <td>193</td>\n",
" <td>4.8</td>\n",
" <td>27</td>\n",
" </tr>\n",
" <tr>\n",
" <th>943</th>\n",
" <td>-19.94</td>\n",
" <td>182.39</td>\n",
" <td>544</td>\n",
" <td>4.6</td>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>944</th>\n",
" <td>-23.60</td>\n",
" <td>183.99</td>\n",
" <td>118</td>\n",
" <td>5.4</td>\n",
" <td>88</td>\n",
" </tr>\n",
" <tr>\n",
" <th>945</th>\n",
" <td>-23.70</td>\n",
" <td>184.13</td>\n",
" <td>51</td>\n",
" <td>4.8</td>\n",
" <td>27</td>\n",
" </tr>\n",
" <tr>\n",
" <th>946</th>\n",
" <td>-30.39</td>\n",
" <td>182.40</td>\n",
" <td>63</td>\n",
" <td>4.6</td>\n",
" <td>22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>947</th>\n",
" <td>-18.98</td>\n",
" <td>182.32</td>\n",
" <td>442</td>\n",
" <td>4.2</td>\n",
" <td>22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>948</th>\n",
" <td>-27.89</td>\n",
" <td>182.92</td>\n",
" <td>87</td>\n",
" <td>5.5</td>\n",
" <td>67</td>\n",
" </tr>\n",
" <tr>\n",
" <th>949</th>\n",
" <td>-23.50</td>\n",
" <td>184.90</td>\n",
" <td>61</td>\n",
" <td>4.7</td>\n",
" <td>16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>950</th>\n",
" <td>-23.73</td>\n",
" <td>184.49</td>\n",
" <td>60</td>\n",
" <td>4.7</td>\n",
" <td>35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>951</th>\n",
" <td>-17.93</td>\n",
" <td>181.62</td>\n",
" <td>561</td>\n",
" <td>4.5</td>\n",
" <td>32</td>\n",
" </tr>\n",
" <tr>\n",
" <th>952</th>\n",
" <td>-35.94</td>\n",
" <td>178.52</td>\n",
" <td>138</td>\n",
" <td>5.5</td>\n",
" <td>78</td>\n",
" </tr>\n",
" <tr>\n",
" <th>953</th>\n",
" <td>-18.68</td>\n",
" <td>184.50</td>\n",
" <td>174</td>\n",
" <td>4.5</td>\n",
" <td>34</td>\n",
" </tr>\n",
" <tr>\n",
" <th>954</th>\n",
" <td>-23.47</td>\n",
" <td>179.95</td>\n",
" <td>543</td>\n",
" <td>4.1</td>\n",
" <td>21</td>\n",
" </tr>\n",
" <tr>\n",
" <th>955</th>\n",
" <td>-23.49</td>\n",
" <td>180.06</td>\n",
" <td>530</td>\n",
" <td>4.0</td>\n",
" <td>23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>956</th>\n",
" <td>-23.85</td>\n",
" <td>180.26</td>\n",
" <td>497</td>\n",
" <td>4.3</td>\n",
" <td>32</td>\n",
" </tr>\n",
" <tr>\n",
" <th>957</th>\n",
" <td>-27.08</td>\n",
" <td>183.44</td>\n",
" <td>63</td>\n",
" <td>4.7</td>\n",
" <td>27</td>\n",
" </tr>\n",
" <tr>\n",
" <th>958</th>\n",
" <td>-20.88</td>\n",
" <td>184.95</td>\n",
" <td>82</td>\n",
" <td>4.9</td>\n",
" <td>50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>959</th>\n",
" <td>-20.97</td>\n",
" <td>181.20</td>\n",
" <td>605</td>\n",
" <td>4.5</td>\n",
" <td>31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>960</th>\n",
" <td>-21.71</td>\n",
" <td>183.58</td>\n",
" <td>234</td>\n",
" <td>4.7</td>\n",
" <td>55</td>\n",
" </tr>\n",
" <tr>\n",
" <th>961</th>\n",
" <td>-23.90</td>\n",
" <td>184.60</td>\n",
" <td>41</td>\n",
" <td>4.5</td>\n",
" <td>22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>962</th>\n",
" <td>-15.78</td>\n",
" <td>167.44</td>\n",
" <td>40</td>\n",
" <td>4.8</td>\n",
" <td>42</td>\n",
" </tr>\n",
" <tr>\n",
" <th>963</th>\n",
" <td>-12.57</td>\n",
" <td>166.72</td>\n",
" <td>137</td>\n",
" <td>4.3</td>\n",
" <td>20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>964</th>\n",
" <td>-19.69</td>\n",
" <td>184.23</td>\n",
" <td>223</td>\n",
" <td>4.1</td>\n",
" <td>23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>965</th>\n",
" <td>-22.04</td>\n",
" <td>183.95</td>\n",
" <td>109</td>\n",
" <td>5.4</td>\n",
" <td>61</td>\n",
" </tr>\n",
" <tr>\n",
" <th>966</th>\n",
" <td>-17.99</td>\n",
" <td>181.59</td>\n",
" <td>595</td>\n",
" <td>4.1</td>\n",
" <td>26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>967</th>\n",
" <td>-23.50</td>\n",
" <td>180.13</td>\n",
" <td>512</td>\n",
" <td>4.8</td>\n",
" <td>40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>968</th>\n",
" <td>-21.40</td>\n",
" <td>180.74</td>\n",
" <td>613</td>\n",
" <td>4.2</td>\n",
" <td>20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>969</th>\n",
" <td>-15.86</td>\n",
" <td>166.98</td>\n",
" <td>60</td>\n",
" <td>4.8</td>\n",
" <td>25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>970</th>\n",
" <td>-23.95</td>\n",
" <td>184.64</td>\n",
" <td>43</td>\n",
" <td>5.4</td>\n",
" <td>45</td>\n",
" </tr>\n",
" <tr>\n",
" <th>971</th>\n",
" <td>-25.79</td>\n",
" <td>182.38</td>\n",
" <td>172</td>\n",
" <td>4.4</td>\n",
" <td>14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>972</th>\n",
" <td>-23.75</td>\n",
" <td>184.50</td>\n",
" <td>54</td>\n",
" <td>5.2</td>\n",
" <td>74</td>\n",
" </tr>\n",
" <tr>\n",
" <th>973</th>\n",
" <td>-24.10</td>\n",
" <td>184.50</td>\n",
" <td>68</td>\n",
" <td>4.7</td>\n",
" <td>23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>974</th>\n",
" <td>-18.56</td>\n",
" <td>169.05</td>\n",
" <td>217</td>\n",
" <td>4.9</td>\n",
" <td>35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>975</th>\n",
" <td>-23.30</td>\n",
" <td>184.68</td>\n",
" <td>102</td>\n",
" <td>4.9</td>\n",
" <td>27</td>\n",
" </tr>\n",
" <tr>\n",
" <th>976</th>\n",
" <td>-17.03</td>\n",
" <td>185.74</td>\n",
" <td>178</td>\n",
" <td>4.2</td>\n",
" <td>32</td>\n",
" </tr>\n",
" <tr>\n",
" <th>977</th>\n",
" <td>-20.77</td>\n",
" <td>183.71</td>\n",
" <td>251</td>\n",
" <td>4.4</td>\n",
" <td>47</td>\n",
" </tr>\n",
" <tr>\n",
" <th>978</th>\n",
" <td>-28.10</td>\n",
" <td>183.50</td>\n",
" <td>42</td>\n",
" <td>4.4</td>\n",
" <td>17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>979</th>\n",
" <td>-18.83</td>\n",
" <td>182.26</td>\n",
" <td>575</td>\n",
" <td>4.3</td>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>980</th>\n",
" <td>-23.00</td>\n",
" <td>170.70</td>\n",
" <td>43</td>\n",
" <td>4.9</td>\n",
" <td>20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>981</th>\n",
" <td>-20.82</td>\n",
" <td>181.67</td>\n",
" <td>577</td>\n",
" <td>5.0</td>\n",
" <td>67</td>\n",
" </tr>\n",
" <tr>\n",
" <th>982</th>\n",
" <td>-22.95</td>\n",
" <td>170.56</td>\n",
" <td>42</td>\n",
" <td>4.7</td>\n",
" <td>21</td>\n",
" </tr>\n",
" <tr>\n",
" <th>983</th>\n",
" <td>-28.22</td>\n",
" <td>183.60</td>\n",
" <td>75</td>\n",
" <td>4.9</td>\n",
" <td>49</td>\n",
" </tr>\n",
" <tr>\n",
" <th>984</th>\n",
" <td>-27.99</td>\n",
" <td>183.50</td>\n",
" <td>71</td>\n",
" <td>4.3</td>\n",
" <td>22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>985</th>\n",
" <td>-15.54</td>\n",
" <td>187.15</td>\n",
" <td>60</td>\n",
" <td>4.5</td>\n",
" <td>17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>986</th>\n",
" <td>-12.37</td>\n",
" <td>166.93</td>\n",
" <td>291</td>\n",
" <td>4.2</td>\n",
" <td>16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>987</th>\n",
" <td>-22.33</td>\n",
" <td>171.66</td>\n",
" <td>125</td>\n",
" <td>5.2</td>\n",
" <td>51</td>\n",
" </tr>\n",
" <tr>\n",
" <th>988</th>\n",
" <td>-22.70</td>\n",
" <td>170.30</td>\n",
" <td>69</td>\n",
" <td>4.8</td>\n",
" <td>27</td>\n",
" </tr>\n",
" <tr>\n",
" <th>989</th>\n",
" <td>-17.86</td>\n",
" <td>181.30</td>\n",
" <td>614</td>\n",
" <td>4.0</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>990</th>\n",
" <td>-16.00</td>\n",
" <td>184.53</td>\n",
" <td>108</td>\n",
" <td>4.7</td>\n",
" <td>33</td>\n",
" </tr>\n",
" <tr>\n",
" <th>991</th>\n",
" <td>-20.73</td>\n",
" <td>181.42</td>\n",
" <td>575</td>\n",
" <td>4.3</td>\n",
" <td>18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>992</th>\n",
" <td>-15.45</td>\n",
" <td>181.42</td>\n",
" <td>409</td>\n",
" <td>4.3</td>\n",
" <td>27</td>\n",
" </tr>\n",
" <tr>\n",
" <th>993</th>\n",
" <td>-20.05</td>\n",
" <td>183.86</td>\n",
" <td>243</td>\n",
" <td>4.9</td>\n",
" <td>65</td>\n",
" </tr>\n",
" <tr>\n",
" <th>994</th>\n",
" <td>-17.95</td>\n",
" <td>181.37</td>\n",
" <td>642</td>\n",
" <td>4.0</td>\n",
" <td>17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>995</th>\n",
" <td>-17.70</td>\n",
" <td>188.10</td>\n",
" <td>45</td>\n",
" <td>4.2</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>996</th>\n",
" <td>-25.93</td>\n",
" <td>179.54</td>\n",
" <td>470</td>\n",
" <td>4.4</td>\n",
" <td>22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>997</th>\n",
" <td>-12.28</td>\n",
" <td>167.06</td>\n",
" <td>248</td>\n",
" <td>4.7</td>\n",
" <td>35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>998</th>\n",
" <td>-20.13</td>\n",
" <td>184.20</td>\n",
" <td>244</td>\n",
" <td>4.5</td>\n",
" <td>34</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999</th>\n",
" <td>-17.40</td>\n",
" <td>187.80</td>\n",
" <td>40</td>\n",
" <td>4.5</td>\n",
" <td>14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1000</th>\n",
" <td>-21.59</td>\n",
" <td>170.56</td>\n",
" <td>165</td>\n",
" <td>6.0</td>\n",
" <td>119</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1000 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
" lat long depth mag stations\n",
"1 -20.42 181.62 562 4.8 41\n",
"2 -20.62 181.03 650 4.2 15\n",
"3 -26.00 184.10 42 5.4 43\n",
"4 -17.97 181.66 626 4.1 19\n",
"5 -20.42 181.96 649 4.0 11\n",
"6 -19.68 184.31 195 4.0 12\n",
"7 -11.70 166.10 82 4.8 43\n",
"8 -28.11 181.93 194 4.4 15\n",
"9 -28.74 181.74 211 4.7 35\n",
"10 -17.47 179.59 622 4.3 19\n",
"11 -21.44 180.69 583 4.4 13\n",
"12 -12.26 167.00 249 4.6 16\n",
"13 -18.54 182.11 554 4.4 19\n",
"14 -21.00 181.66 600 4.4 10\n",
"15 -20.70 169.92 139 6.1 94\n",
"16 -15.94 184.95 306 4.3 11\n",
"17 -13.64 165.96 50 6.0 83\n",
"18 -17.83 181.50 590 4.5 21\n",
"19 -23.50 179.78 570 4.4 13\n",
"20 -22.63 180.31 598 4.4 18\n",
"21 -20.84 181.16 576 4.5 17\n",
"22 -10.98 166.32 211 4.2 12\n",
"23 -23.30 180.16 512 4.4 18\n",
"24 -30.20 182.00 125 4.7 22\n",
"25 -19.66 180.28 431 5.4 57\n",
"26 -17.94 181.49 537 4.0 15\n",
"27 -14.72 167.51 155 4.6 18\n",
"28 -16.46 180.79 498 5.2 79\n",
"29 -20.97 181.47 582 4.5 25\n",
"30 -19.84 182.37 328 4.4 17\n",
"31 -22.58 179.24 553 4.6 21\n",
"32 -16.32 166.74 50 4.7 30\n",
"33 -15.55 185.05 292 4.8 42\n",
"34 -23.55 180.80 349 4.0 10\n",
"35 -16.30 186.00 48 4.5 10\n",
"36 -25.82 179.33 600 4.3 13\n",
"37 -18.73 169.23 206 4.5 17\n",
"38 -17.64 181.28 574 4.6 17\n",
"39 -17.66 181.40 585 4.1 17\n",
"40 -18.82 169.33 230 4.4 11\n",
"41 -37.37 176.78 263 4.7 34\n",
"42 -15.31 186.10 96 4.6 32\n",
"43 -24.97 179.82 511 4.4 23\n",
"44 -15.49 186.04 94 4.3 26\n",
"45 -19.23 169.41 246 4.6 27\n",
"46 -30.10 182.30 56 4.9 34\n",
"47 -26.40 181.70 329 4.5 24\n",
"48 -11.77 166.32 70 4.4 18\n",
"49 -24.12 180.08 493 4.3 21\n",
"50 -18.97 185.25 129 5.1 73\n",
"51 -18.75 182.35 554 4.2 13\n",
"52 -19.26 184.42 223 4.0 15\n",
"53 -22.75 173.20 46 4.6 26\n",
"54 -21.37 180.67 593 4.3 13\n",
"55 -20.10 182.16 489 4.2 16\n",
"56 -19.85 182.13 562 4.4 31\n",
"57 -22.70 181.00 445 4.5 17\n",
"58 -22.06 180.60 584 4.0 11\n",
"59 -17.80 181.35 535 4.4 23\n",
"60 -24.20 179.20 530 4.3 12\n",
"61 -20.69 181.55 582 4.7 35\n",
"62 -21.16 182.40 260 4.1 12\n",
"63 -13.82 172.38 613 5.0 61\n",
"64 -11.49 166.22 84 4.6 32\n",
"65 -20.68 181.41 593 4.9 40\n",
"66 -17.10 184.93 286 4.7 25\n",
"67 -20.14 181.60 587 4.1 13\n",
"68 -21.96 179.62 627 5.0 45\n",
"69 -20.42 181.86 530 4.5 27\n",
"70 -15.46 187.81 40 5.5 91\n",
"71 -15.31 185.80 152 4.0 11\n",
"72 -19.86 184.35 201 4.5 30\n",
"73 -11.55 166.20 96 4.3 14\n",
"74 -23.74 179.99 506 5.2 75\n",
"75 -17.70 181.23 546 4.4 35\n",
"76 -23.54 180.04 564 4.3 15\n",
"77 -19.21 184.70 197 4.1 11\n",
"78 -12.11 167.06 265 4.5 23\n",
"79 -21.81 181.71 323 4.2 15\n",
"80 -28.98 181.11 304 5.3 60\n",
"81 -34.02 180.21 75 5.2 65\n",
"82 -23.84 180.99 367 4.5 27\n",
"83 -19.57 182.38 579 4.6 38\n",
"84 -20.12 183.40 284 4.3 15\n",
"85 -17.70 181.70 450 4.0 11\n",
"... ... ... ... ... ...\n",
"916 -21.52 169.75 61 5.1 40\n",
"917 -19.57 184.47 202 4.2 28\n",
"918 -23.08 183.45 90 4.7 30\n",
"919 -25.06 182.80 133 4.0 14\n",
"920 -17.85 181.44 589 5.6 115\n",
"921 -15.99 167.95 190 5.3 81\n",
"922 -20.56 184.41 138 5.0 82\n",
"923 -17.98 181.61 598 4.3 27\n",
"924 -18.40 181.77 600 4.1 11\n",
"925 -27.64 182.22 162 5.1 67\n",
"926 -20.99 181.02 626 4.5 36\n",
"927 -14.86 167.32 137 4.9 22\n",
"928 -29.33 182.72 57 5.4 61\n",
"929 -25.81 182.54 201 4.7 40\n",
"930 -14.10 166.01 69 4.8 29\n",
"931 -17.63 185.13 219 4.5 28\n",
"932 -23.47 180.21 553 4.2 23\n",
"933 -23.92 180.21 524 4.6 50\n",
"934 -20.88 185.18 51 4.6 28\n",
"935 -20.25 184.75 107 5.6 121\n",
"936 -19.33 186.16 44 5.4 110\n",
"937 -18.14 181.71 574 4.0 20\n",
"938 -22.41 183.99 128 5.2 72\n",
"939 -20.77 181.16 568 4.2 12\n",
"940 -17.95 181.73 583 4.7 57\n",
"941 -20.83 181.01 622 4.3 15\n",
"942 -27.84 182.10 193 4.8 27\n",
"943 -19.94 182.39 544 4.6 30\n",
"944 -23.60 183.99 118 5.4 88\n",
"945 -23.70 184.13 51 4.8 27\n",
"946 -30.39 182.40 63 4.6 22\n",
"947 -18.98 182.32 442 4.2 22\n",
"948 -27.89 182.92 87 5.5 67\n",
"949 -23.50 184.90 61 4.7 16\n",
"950 -23.73 184.49 60 4.7 35\n",
"951 -17.93 181.62 561 4.5 32\n",
"952 -35.94 178.52 138 5.5 78\n",
"953 -18.68 184.50 174 4.5 34\n",
"954 -23.47 179.95 543 4.1 21\n",
"955 -23.49 180.06 530 4.0 23\n",
"956 -23.85 180.26 497 4.3 32\n",
"957 -27.08 183.44 63 4.7 27\n",
"958 -20.88 184.95 82 4.9 50\n",
"959 -20.97 181.20 605 4.5 31\n",
"960 -21.71 183.58 234 4.7 55\n",
"961 -23.90 184.60 41 4.5 22\n",
"962 -15.78 167.44 40 4.8 42\n",
"963 -12.57 166.72 137 4.3 20\n",
"964 -19.69 184.23 223 4.1 23\n",
"965 -22.04 183.95 109 5.4 61\n",
"966 -17.99 181.59 595 4.1 26\n",
"967 -23.50 180.13 512 4.8 40\n",
"968 -21.40 180.74 613 4.2 20\n",
"969 -15.86 166.98 60 4.8 25\n",
"970 -23.95 184.64 43 5.4 45\n",
"971 -25.79 182.38 172 4.4 14\n",
"972 -23.75 184.50 54 5.2 74\n",
"973 -24.10 184.50 68 4.7 23\n",
"974 -18.56 169.05 217 4.9 35\n",
"975 -23.30 184.68 102 4.9 27\n",
"976 -17.03 185.74 178 4.2 32\n",
"977 -20.77 183.71 251 4.4 47\n",
"978 -28.10 183.50 42 4.4 17\n",
"979 -18.83 182.26 575 4.3 11\n",
"980 -23.00 170.70 43 4.9 20\n",
"981 -20.82 181.67 577 5.0 67\n",
"982 -22.95 170.56 42 4.7 21\n",
"983 -28.22 183.60 75 4.9 49\n",
"984 -27.99 183.50 71 4.3 22\n",
"985 -15.54 187.15 60 4.5 17\n",
"986 -12.37 166.93 291 4.2 16\n",
"987 -22.33 171.66 125 5.2 51\n",
"988 -22.70 170.30 69 4.8 27\n",
"989 -17.86 181.30 614 4.0 12\n",
"990 -16.00 184.53 108 4.7 33\n",
"991 -20.73 181.42 575 4.3 18\n",
"992 -15.45 181.42 409 4.3 27\n",
"993 -20.05 183.86 243 4.9 65\n",
"994 -17.95 181.37 642 4.0 17\n",
"995 -17.70 188.10 45 4.2 10\n",
"996 -25.93 179.54 470 4.4 22\n",
"997 -12.28 167.06 248 4.7 35\n",
"998 -20.13 184.20 244 4.5 34\n",
"999 -17.40 187.80 40 4.5 14\n",
"1000 -21.59 170.56 165 6.0 119\n",
"\n",
"[1000 rows x 5 columns]"
]
},
"execution_count": 252,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"quakes = data(\"quakes\") # iris, heats\n",
"quakes"
]
},
{
"cell_type": "code",
"execution_count": 253,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"pandas.core.frame.DataFrame"
]
},
"execution_count": 253,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(quakes)"
]
},
{
"cell_type": "code",
"execution_count": 254,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>lat</th>\n",
" <th>long</th>\n",
" <th>depth</th>\n",
" <th>mag</th>\n",
" <th>stations</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>996</th>\n",
" <td>-25.93</td>\n",
" <td>179.54</td>\n",
" <td>470</td>\n",
" <td>4.4</td>\n",
" <td>22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>997</th>\n",
" <td>-12.28</td>\n",
" <td>167.06</td>\n",
" <td>248</td>\n",
" <td>4.7</td>\n",
" <td>35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>998</th>\n",
" <td>-20.13</td>\n",
" <td>184.20</td>\n",
" <td>244</td>\n",
" <td>4.5</td>\n",
" <td>34</td>\n",
" </tr>\n",
" <tr>\n",
" <th>999</th>\n",
" <td>-17.40</td>\n",
" <td>187.80</td>\n",
" <td>40</td>\n",
" <td>4.5</td>\n",
" <td>14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1000</th>\n",
" <td>-21.59</td>\n",
" <td>170.56</td>\n",
" <td>165</td>\n",
" <td>6.0</td>\n",
" <td>119</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" lat long depth mag stations\n",
"996 -25.93 179.54 470 4.4 22\n",
"997 -12.28 167.06 248 4.7 35\n",
"998 -20.13 184.20 244 4.5 34\n",
"999 -17.40 187.80 40 4.5 14\n",
"1000 -21.59 170.56 165 6.0 119"
]
},
"execution_count": 254,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"quakes.tail()"
]
},
{
"cell_type": "code",
"execution_count": 255,
"metadata": {},
"outputs": [],
"source": [
"# IMP NOTE:-\n",
"# pie does not allow -ve values (so it cannot be used with 'lat' column)"
]
},
{
"cell_type": "code",
"execution_count": 256,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x13d5f5c0>"
]
},
"execution_count": 256,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.plot.html\n",
"\n",
"# Adding one more Y axis on right (FOCUS POINT)\n",
"\n",
"quakes[\"long\"].tail().plot(kind=\"line\")"
]
},
{
"cell_type": "code",
"execution_count": 257,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0xe30b940>"
]
},
"execution_count": 257,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# SAME AS ABOVE\n",
"quakes[\"long\"].tail().plot.pie()"
]
},
{
"cell_type": "code",
"execution_count": 258,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0xe34b898>"
]
},
"execution_count": 258,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.plot.html\n",
"\n",
"quakes['long'].tail().plot(kind=\"bar\")"
]
},
{
"cell_type": "code",
"execution_count": 259,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0xe390518>"
]
},
"execution_count": 259,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Another style of writing above\n",
"quakes.long.tail().plot(kind=\"bar\")"
]
},
{
"cell_type": "code",
"execution_count": 260,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1 41\n",
"2 15\n",
"3 43\n",
"4 19\n",
"5 11\n",
"Name: stations, dtype: int64"
]
},
"execution_count": 260,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"quakes.stations.head()"
]
},
{
"cell_type": "code",
"execution_count": 261,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0xe3a22b0>"
]
},
"execution_count": 261,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"quakes.stations.head().plot(kind=\"bar\")"
]
},
{
"cell_type": "code",
"execution_count": 262,
"metadata": {},
"outputs": [],
"source": [
"# ENDS"
]
},
{
"cell_type": "code",
"execution_count": 263,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 2017-12-30\n",
"1 2017-12-31\n",
"2 2018-01-01\n",
"dtype: datetime64[ns]"
]
},
"execution_count": 263,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dates = pd.Series(drange) \n",
"dates # Series object"
]
},
{
"cell_type": "code",
"execution_count": 264,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 False\n",
"1 True\n",
"2 False\n",
"dtype: bool"
]
},
"execution_count": 264,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dates.dt.is_year_end"
]
},
{
"cell_type": "code",
"execution_count": 265,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 2017\n",
"1 2017\n",
"2 2018\n",
"dtype: int64"
]
},
"execution_count": 265,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dates.dt.year"
]
},
{
"cell_type": "code",
"execution_count": 266,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 30\n",
"1 31\n",
"2 1\n",
"dtype: int64"
]
},
"execution_count": 266,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dates.dt.day # Series"
]
},
{
"cell_type": "code",
"execution_count": 267,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 12\n",
"1 12\n",
"2 1\n",
"dtype: int64"
]
},
"execution_count": 267,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dates.dt.month"
]
},
{
"cell_type": "code",
"execution_count": 268,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 0\n",
"1 0\n",
"2 0\n",
"dtype: int64"
]
},
"execution_count": 268,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dates.dt.hour"
]
},
{
"cell_type": "code",
"execution_count": 269,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 0\n",
"1 0\n",
"2 0\n",
"dtype: int64"
]
},
"execution_count": 269,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dates.dt.minute"
]
},
{
"cell_type": "code",
"execution_count": 270,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 0\n",
"1 0\n",
"2 0\n",
"dtype: int64"
]
},
"execution_count": 270,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dates.dt.second"
]
},
{
"cell_type": "code",
"execution_count": 271,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 0\n",
"1 0\n",
"2 0\n",
"dtype: int64"
]
},
"execution_count": 271,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dates.dt.microsecond"
]
},
{
"cell_type": "code",
"execution_count": 272,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 5\n",
"1 6\n",
"2 0\n",
"dtype: int64"
]
},
"execution_count": 272,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dates.dt.dayofweek"
]
},
{
"cell_type": "code",
"execution_count": 273,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['30-12-2017', '31-12-2017', '01-01-2018'], dtype='object')"
]
},
"execution_count": 273,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"drange.strftime(\"%d-%m-%Y\") # drange is DatetimeIndex object"
]
},
{
"cell_type": "code",
"execution_count": 274,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['2017/12/30', '2017/12/31', '2018/01/01'], dtype='object')"
]
},
"execution_count": 274,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"drange.strftime(\"%Y/%m/%d\")"
]
},
{
"cell_type": "code",
"execution_count": 275,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['30-December-2017', '31-December-2017', '01-January-2018'], dtype='object')"
]
},
"execution_count": 275,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"drange.strftime(\"%d-%B-%Y\") # Complete name of the day => %A"
]
},
{
"cell_type": "code",
"execution_count": 276,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 2017\n",
"1 2017\n",
"2 2018\n",
"dtype: int64"
]
},
"execution_count": 276,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"d = pd.to_datetime(drange.strftime(\"%Y-%m-%d %a\")) # Index => DatetimeIndex\n",
"s = pd.Series(d) # DatetimeIndex => Series\n",
"s.dt.year # Get years from series"
]
},
{
"cell_type": "code",
"execution_count": 277,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['2017-12-30 00:00:00', '2017-12-31 00:00:00', '2018-01-01 00:00:00'], dtype='object')"
]
},
"execution_count": 277,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"drange.strftime(\"%Y-%m-%d %H:%M:%S\")"
]
},
{
"cell_type": "code",
"execution_count": 278,
"metadata": {},
"outputs": [],
"source": [
"#*************************** RESAMPLING (Upsampling, downsampling) *************************"
]
},
{
"cell_type": "code",
"execution_count": 279,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2000-06-09 00:00:00 0\n",
"2000-06-09 00:01:00 1\n",
"2000-06-09 00:02:00 2\n",
"2000-06-09 00:03:00 3\n",
"2000-06-09 00:04:00 4\n",
"2000-06-09 00:05:00 5\n",
"2000-06-09 00:06:00 6\n",
"2000-06-09 00:07:00 7\n",
"2000-06-09 00:08:00 8\n",
"Freq: T, dtype: int64"
]
},
"execution_count": 279,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dri = pd.date_range('20000609', periods=9, freq='T') # Basically for minutes\n",
"series = pd.Series(range(9), index=dri)\n",
"series"
]
},
{
"cell_type": "code",
"execution_count": 280,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"pandas.core.series.Series"
]
},
"execution_count": 280,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(series) # Series is 1 d object, so no columns"
]
},
{
"cell_type": "code",
"execution_count": 281,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2000-06-09 00:00:00 3\n",
"2000-06-09 00:03:00 12\n",
"2000-06-09 00:06:00 21\n",
"Freq: 3T, dtype: int64"
]
},
"execution_count": 281,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Downsample the series into 3 minute bins and sum the values of the timestamps falling into a bin.\n",
"\n",
"t = series.resample('3T').sum() # Getting by years, more examples of resampling data (FOCUS POINT)\n",
"t"
]
},
{
"cell_type": "code",
"execution_count": 282,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"2000-06-09 00:03:00 3\n",
"2000-06-09 00:06:00 12\n",
"2000-06-09 00:09:00 21\n",
"Freq: 3T, dtype: int64"
]
},
"execution_count": 282,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Downsample the series into 3 minute bins as above, but label each bin using the right edge instead of the left. \n",
"\n",
"t2 = series.resample(\"3T\", label=\"right\").sum()\n",
"t2"
]
},
{
"cell_type": "code",
"execution_count": 283,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2000-06-09 00:00:00 0\n",
"2000-06-09 00:03:00 6\n",
"2000-06-09 00:06:00 15\n",
"2000-06-09 00:09:00 15\n",
"Freq: 3T, dtype: int64"
]
},
"execution_count": 283,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Downsample the series into 3 minute bins as above, but close the right side of the bin interval.\n",
"\n",
"t3 = series.resample(\"3T\", label=\"right\", closed=\"right\").sum()\n",
"t3"
]
},
{
"cell_type": "code",
"execution_count": 284,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"2000-06-09 00:00:00 0.0\n",
"2000-06-09 00:00:30 NaN\n",
"2000-06-09 00:01:00 1.0\n",
"2000-06-09 00:01:30 NaN\n",
"2000-06-09 00:02:00 2.0\n",
"Freq: 30S, dtype: float64"
]
},
"execution_count": 284,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Upsample the series into 30 second bins.\n",
"\n",
"series.resample('30S').asfreq()[0:5] #select first 5 rows, it works on Resampler objects"
]
},
{
"cell_type": "code",
"execution_count": 285,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2000-06-09 00:00:00 0\n",
"2000-06-09 00:00:30 0\n",
"2000-06-09 00:01:00 1\n",
"2000-06-09 00:01:30 1\n",
"2000-06-09 00:02:00 2\n",
"Freq: 30S, dtype: int64"
]
},
"execution_count": 285,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Upsample the series into 30 second bins and fill the NaN values using the pad method.\n",
"series.resample(\"30S\").pad()[0:5] "
]
},
{
"cell_type": "code",
"execution_count": 286,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2000-06-09 00:00:00 0\n",
"2000-06-09 00:00:30 0\n",
"2000-06-09 00:01:00 1\n",
"2000-06-09 00:01:30 1\n",
"2000-06-09 00:02:00 2\n",
"Freq: 30S, dtype: int64"
]
},
"execution_count": 286,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# pad == ffill\n",
"series.resample(\"30S\").ffill()[0:5]"
]
},
{
"cell_type": "code",
"execution_count": 287,
"metadata": {},
"outputs": [],
"source": [
"# 2000-06-09 00:00:00 0.0\n",
"# 2000-06-09 00:00:30 NaN\n",
"# 2000-06-09 00:01:00 1.0\n",
"# 2000-06-09 00:01:30 NaN\n",
"# 2000-06-09 00:02:00 2.0\n",
"# Freq: 30S, dtype: float64"
]
},
{
"cell_type": "code",
"execution_count": 288,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2000-06-09 00:00:00 0\n",
"2000-06-09 00:00:30 1\n",
"2000-06-09 00:01:00 1\n",
"2000-06-09 00:01:30 2\n",
"2000-06-09 00:02:00 2\n",
"Freq: 30S, dtype: int64"
]
},
"execution_count": 288,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Upsample the series into 30 second bins and fill the NaN values using the bfill method.\n",
"series.resample('30S').bfill()[0:5]"
]
},
{
"cell_type": "code",
"execution_count": 289,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2000-06-09 00:00:00 0\n",
"2000-06-09 00:00:30 1\n",
"2000-06-09 00:01:00 1\n",
"2000-06-09 00:01:30 2\n",
"2000-06-09 00:02:00 2\n",
"Freq: 30S, dtype: int64"
]
},
"execution_count": 289,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# bfill == backfill\n",
"series.resample(\"30S\").backfill()[0:5]"
]
},
{
"cell_type": "code",
"execution_count": 290,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2000-12-31 36\n",
"Freq: A-DEC, dtype: int64"
]
},
"execution_count": 290,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"series.resample(\"A\").sum() # year start"
]
},
{
"cell_type": "code",
"execution_count": 291,
"metadata": {},
"outputs": [],
"source": [
"# See list of frequency offsets"
]
},
{
"cell_type": "code",
"execution_count": 292,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"DatetimeIndex(['2019-01-01', '2020-01-01', '2021-01-01', '2022-01-01',\n",
" '2023-01-01'],\n",
" dtype='datetime64[ns]', freq='AS-JAN')"
]
},
"execution_count": 292,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dri = pd.date_range(\"20180203\", periods=5, freq=\"AS\")\n",
"dri"
]
},
{
"cell_type": "code",
"execution_count": 293,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2019-01-01 0\n",
"2020-01-01 1\n",
"2021-01-01 2\n",
"2022-01-01 3\n",
"2023-01-01 4\n",
"Freq: AS-JAN, dtype: int64"
]
},
"execution_count": 293,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"series2 = pd.Series(range(5), index=dri)\n",
"series2"
]
},
{
"cell_type": "code",
"execution_count": 294,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2019-01-01 00:00:00 0\n",
"2019-01-01 00:01:00 0\n",
"2019-01-01 00:02:00 0\n",
"2019-01-01 00:03:00 0\n",
"2019-01-01 00:04:00 0\n",
"2019-01-01 00:05:00 0\n",
"2019-01-01 00:06:00 0\n",
"2019-01-01 00:07:00 0\n",
"2019-01-01 00:08:00 0\n",
"2019-01-01 00:09:00 0\n",
"2019-01-01 00:10:00 0\n",
"2019-01-01 00:11:00 0\n",
"2019-01-01 00:12:00 0\n",
"2019-01-01 00:13:00 0\n",
"2019-01-01 00:14:00 0\n",
"2019-01-01 00:15:00 0\n",
"2019-01-01 00:16:00 0\n",
"2019-01-01 00:17:00 0\n",
"2019-01-01 00:18:00 0\n",
"2019-01-01 00:19:00 0\n",
"2019-01-01 00:20:00 0\n",
"2019-01-01 00:21:00 0\n",
"2019-01-01 00:22:00 0\n",
"2019-01-01 00:23:00 0\n",
"2019-01-01 00:24:00 0\n",
"2019-01-01 00:25:00 0\n",
"2019-01-01 00:26:00 0\n",
"2019-01-01 00:27:00 0\n",
"2019-01-01 00:28:00 0\n",
"2019-01-01 00:29:00 0\n",
"2019-01-01 00:30:00 0\n",
"2019-01-01 00:31:00 0\n",
"2019-01-01 00:32:00 0\n",
"2019-01-01 00:33:00 0\n",
"2019-01-01 00:34:00 0\n",
"2019-01-01 00:35:00 0\n",
"2019-01-01 00:36:00 0\n",
"2019-01-01 00:37:00 0\n",
"2019-01-01 00:38:00 0\n",
"2019-01-01 00:39:00 0\n",
"2019-01-01 00:40:00 0\n",
"2019-01-01 00:41:00 0\n",
"2019-01-01 00:42:00 0\n",
"2019-01-01 00:43:00 0\n",
"2019-01-01 00:44:00 0\n",
"2019-01-01 00:45:00 0\n",
"2019-01-01 00:46:00 0\n",
"2019-01-01 00:47:00 0\n",
"2019-01-01 00:48:00 0\n",
"2019-01-01 00:49:00 0\n",
"2019-01-01 00:50:00 0\n",
"2019-01-01 00:51:00 0\n",
"2019-01-01 00:52:00 0\n",
"2019-01-01 00:53:00 0\n",
"2019-01-01 00:54:00 0\n",
"2019-01-01 00:55:00 0\n",
"2019-01-01 00:56:00 0\n",
"2019-01-01 00:57:00 0\n",
"2019-01-01 00:58:00 0\n",
"2019-01-01 00:59:00 0\n",
"2019-01-01 01:00:00 0\n",
"2019-01-01 01:01:00 0\n",
"2019-01-01 01:02:00 0\n",
"2019-01-01 01:03:00 0\n",
"2019-01-01 01:04:00 0\n",
"2019-01-01 01:05:00 0\n",
"2019-01-01 01:06:00 0\n",
"2019-01-01 01:07:00 0\n",
"2019-01-01 01:08:00 0\n",
"2019-01-01 01:09:00 0\n",
"2019-01-01 01:10:00 0\n",
"2019-01-01 01:11:00 0\n",
"2019-01-01 01:12:00 0\n",
"2019-01-01 01:13:00 0\n",
"2019-01-01 01:14:00 0\n",
"2019-01-01 01:15:00 0\n",
"2019-01-01 01:16:00 0\n",
"2019-01-01 01:17:00 0\n",
"2019-01-01 01:18:00 0\n",
"2019-01-01 01:19:00 0\n",
"2019-01-01 01:20:00 0\n",
"2019-01-01 01:21:00 0\n",
"2019-01-01 01:22:00 0\n",
"2019-01-01 01:23:00 0\n",
"2019-01-01 01:24:00 0\n",
" ..\n",
"2022-12-31 22:36:00 0\n",
"2022-12-31 22:37:00 0\n",
"2022-12-31 22:38:00 0\n",
"2022-12-31 22:39:00 0\n",
"2022-12-31 22:40:00 0\n",
"2022-12-31 22:41:00 0\n",
"2022-12-31 22:42:00 0\n",
"2022-12-31 22:43:00 0\n",
"2022-12-31 22:44:00 0\n",
"2022-12-31 22:45:00 0\n",
"2022-12-31 22:46:00 0\n",
"2022-12-31 22:47:00 0\n",
"2022-12-31 22:48:00 0\n",
"2022-12-31 22:49:00 0\n",
"2022-12-31 22:50:00 0\n",
"2022-12-31 22:51:00 0\n",
"2022-12-31 22:52:00 0\n",
"2022-12-31 22:53:00 0\n",
"2022-12-31 22:54:00 0\n",
"2022-12-31 22:55:00 0\n",
"2022-12-31 22:56:00 0\n",
"2022-12-31 22:57:00 0\n",
"2022-12-31 22:58:00 0\n",
"2022-12-31 22:59:00 0\n",
"2022-12-31 23:00:00 0\n",
"2022-12-31 23:01:00 0\n",
"2022-12-31 23:02:00 0\n",
"2022-12-31 23:03:00 0\n",
"2022-12-31 23:04:00 0\n",
"2022-12-31 23:05:00 0\n",
"2022-12-31 23:06:00 0\n",
"2022-12-31 23:07:00 0\n",
"2022-12-31 23:08:00 0\n",
"2022-12-31 23:09:00 0\n",
"2022-12-31 23:10:00 0\n",
"2022-12-31 23:11:00 0\n",
"2022-12-31 23:12:00 0\n",
"2022-12-31 23:13:00 0\n",
"2022-12-31 23:14:00 0\n",
"2022-12-31 23:15:00 0\n",
"2022-12-31 23:16:00 0\n",
"2022-12-31 23:17:00 0\n",
"2022-12-31 23:18:00 0\n",
"2022-12-31 23:19:00 0\n",
"2022-12-31 23:20:00 0\n",
"2022-12-31 23:21:00 0\n",
"2022-12-31 23:22:00 0\n",
"2022-12-31 23:23:00 0\n",
"2022-12-31 23:24:00 0\n",
"2022-12-31 23:25:00 0\n",
"2022-12-31 23:26:00 0\n",
"2022-12-31 23:27:00 0\n",
"2022-12-31 23:28:00 0\n",
"2022-12-31 23:29:00 0\n",
"2022-12-31 23:30:00 0\n",
"2022-12-31 23:31:00 0\n",
"2022-12-31 23:32:00 0\n",
"2022-12-31 23:33:00 0\n",
"2022-12-31 23:34:00 0\n",
"2022-12-31 23:35:00 0\n",
"2022-12-31 23:36:00 0\n",
"2022-12-31 23:37:00 0\n",
"2022-12-31 23:38:00 0\n",
"2022-12-31 23:39:00 0\n",
"2022-12-31 23:40:00 0\n",
"2022-12-31 23:41:00 0\n",
"2022-12-31 23:42:00 0\n",
"2022-12-31 23:43:00 0\n",
"2022-12-31 23:44:00 0\n",
"2022-12-31 23:45:00 0\n",
"2022-12-31 23:46:00 0\n",
"2022-12-31 23:47:00 0\n",
"2022-12-31 23:48:00 0\n",
"2022-12-31 23:49:00 0\n",
"2022-12-31 23:50:00 0\n",
"2022-12-31 23:51:00 0\n",
"2022-12-31 23:52:00 0\n",
"2022-12-31 23:53:00 0\n",
"2022-12-31 23:54:00 0\n",
"2022-12-31 23:55:00 0\n",
"2022-12-31 23:56:00 0\n",
"2022-12-31 23:57:00 0\n",
"2022-12-31 23:58:00 0\n",
"2022-12-31 23:59:00 0\n",
"2023-01-01 00:00:00 4\n",
"Freq: T, Length: 2103841, dtype: int64"
]
},
"execution_count": 294,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r = series2.resample(\"T\").sum() # Upsampling\n",
"r"
]
},
{
"cell_type": "code",
"execution_count": 295,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(2103841,)"
]
},
"execution_count": 295,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r.shape"
]
},
{
"cell_type": "code",
"execution_count": 296,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"pandas.core.series.Series"
]
},
"execution_count": 296,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(r)"
]
},
{
"cell_type": "code",
"execution_count": 297,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2019-12-31 0\n",
"2020-12-31 1\n",
"2021-12-31 2\n",
"2022-12-31 3\n",
"2023-12-31 4\n",
"Freq: A-DEC, dtype: int64"
]
},
"execution_count": 297,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r.resample(\"A\").sum() # Same as series 2"
]
},
{
"cell_type": "code",
"execution_count": 298,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2019-12-31 3\n",
"2020-12-31 4\n",
"2021-12-31 5\n",
"2022-12-31 6\n",
"2023-12-31 7\n",
"Freq: A-DEC, dtype: int64"
]
},
"execution_count": 298,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Use of apply() method\n",
"\n",
"import numpy as np\n",
"\n",
"def f(a):\n",
" return np.sum(a) + 3\n",
"\n",
"\n",
"r.resample(\"A\").sum().apply(f)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"&raquo; Visit https://stackoverflow.com/questions/17001389/pandas-resample-documentation to see the list of frequency offsets"
]
},
{
"cell_type": "code",
"execution_count": 299,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2019-12-31 4\n",
"2020-12-31 5\n",
"2021-12-31 6\n",
"2022-12-31 7\n",
"2023-12-31 8\n",
"Freq: A-DEC, dtype: int64"
]
},
"execution_count": 299,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Same as above using lambda function\n",
"r.resample(\"A\").sum().apply(lambda a: np.sum(a) + 4)"
]
},
{
"cell_type": "code",
"execution_count": 300,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2018-01-01 00:00:00 1\n",
"2018-01-01 01:00:00 2\n",
"2018-01-01 02:00:00 3\n",
"Freq: H, dtype: int64"
]
},
"execution_count": 300,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s = pd.Series([1, 2, 3], index=pd.date_range('20180101', periods=3, freq='h'))\n",
"s"
]
},
{
"cell_type": "code",
"execution_count": 301,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2018-01-01 00:00:00 1\n",
"2018-01-01 00:30:00 2\n",
"2018-01-01 01:00:00 2\n",
"2018-01-01 01:30:00 3\n",
"2018-01-01 02:00:00 3\n",
"Freq: 30T, dtype: int64"
]
},
"execution_count": 301,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s.resample('30min').backfill()"
]
},
{
"cell_type": "code",
"execution_count": 302,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2018-01-01 00:00:00 1\n",
"2018-01-01 00:30:00 1\n",
"2018-01-01 01:00:00 2\n",
"2018-01-01 01:30:00 2\n",
"2018-01-01 02:00:00 3\n",
"Freq: 30T, dtype: int64"
]
},
"execution_count": 302,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s.resample('30min').ffill()"
]
},
{
"cell_type": "code",
"execution_count": 303,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2018-01-01 00:00:00 1\n",
"2018-01-01 00:15:00 0\n",
"2018-01-01 00:30:00 0\n",
"2018-01-01 00:45:00 0\n",
"2018-01-01 01:00:00 2\n",
"2018-01-01 01:15:00 0\n",
"2018-01-01 01:30:00 0\n",
"2018-01-01 01:45:00 0\n",
"2018-01-01 02:00:00 3\n",
"Freq: 15T, dtype: int64"
]
},
"execution_count": 303,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s.resample('15min').sum()"
]
},
{
"cell_type": "code",
"execution_count": 304,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2018-01-01 00:00:00 1.0\n",
"2018-01-01 00:15:00 NaN\n",
"2018-01-01 00:30:00 2.0\n",
"2018-01-01 00:45:00 2.0\n",
"2018-01-01 01:00:00 2.0\n",
"2018-01-01 01:15:00 NaN\n",
"2018-01-01 01:30:00 3.0\n",
"2018-01-01 01:45:00 3.0\n",
"2018-01-01 02:00:00 3.0\n",
"Freq: 15T, dtype: float64"
]
},
"execution_count": 304,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s.resample('15min').backfill(limit=2)"
]
},
{
"cell_type": "code",
"execution_count": 305,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2018-01-01 00:00:00 1\n",
"2018-01-01 00:15:00 2\n",
"2018-01-01 00:30:00 2\n",
"2018-01-01 00:45:00 2\n",
"2018-01-01 01:00:00 2\n",
"2018-01-01 01:15:00 3\n",
"2018-01-01 01:30:00 3\n",
"2018-01-01 01:45:00 3\n",
"2018-01-01 02:00:00 3\n",
"Freq: 15T, dtype: int64"
]
},
"execution_count": 305,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s.resample('15min').backfill(limit=3)"
]
},
{
"cell_type": "code",
"execution_count": 306,
"metadata": {},
"outputs": [],
"source": [
"#########################################################"
]
},
{
"cell_type": "code",
"execution_count": 307,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2018-11-20 10:43:37.317029\n"
]
},
{
"data": {
"text/plain": [
"datetime.datetime"
]
},
"execution_count": 307,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# t2 = series.resample(\"T\")\n",
"# t2\n",
"\n",
"from datetime import datetime\n",
"\n",
"now = datetime.now()\n",
"print(str(now))\n",
"\n",
"type(now)"
]
},
{
"cell_type": "code",
"execution_count": 308,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2018"
]
},
"execution_count": 308,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"now.year"
]
},
{
"cell_type": "code",
"execution_count": 309,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Tuesday'"
]
},
"execution_count": 309,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"now.strftime(\"%A\") # Getting day"
]
},
{
"cell_type": "code",
"execution_count": 310,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<bound method Axes.bar of <matplotlib.axes._subplots.AxesSubplot object at 0x000000000E491C88>>"
]
},
"execution_count": 310,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"series.plot().bar"
]
},
{
"cell_type": "code",
"execution_count": 311,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<bound method Axes.bar of <matplotlib.axes._subplots.AxesSubplot object at 0x000000000E4F1438>>"
]
},
"execution_count": 311,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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dkGeuOp5BHZtEuiyJIwp+kSjh7ryVvpl7Zi8mN6+A357RnV+N6EatGmq3IMGl4BeJAlv25HHHmxl8sDSHgR2b8MiYAfRs0zDSZUmcUvCLRFBxsfP6txt46J2lFBQXc8c5vbl2eGeqq92ChJCCXyRC1m7fz4QZ6XyVtZNhXZozZUx/OjVXUzUJPQW/SJgVFTvT/r2GP8xbTs1q1ZhycX8uO6Gj2i1I2Cj4RcJo+ZZcbpu+iEXZezijdyvuv7A/bRrXiXRZkmAU/CJhkF9YzF8/XsVTn6yiUZ2a/OWK4zh3QFsd5UtEKPhFQmzhht3cNn0RK7bu48JB7bjrvL40q18r0mVJAlPwi4TIgfxC/vj+CqZ9vobWjeow7ZoUTu+lpmoSeRX5svVpwLlAjrv3Cyx7FDgPyAdWA9e6++4ytl0L5AJFQGFFvw9SJNZ9sWo7E2ZksH7nAX5+YhLjR/WioZqqSZSoyEcCXwBGHbZsHtDP3QcAK4CJR9j+NHcfpNCXRLDnYAETUtP52fNfU83g9XEncv+F/RX6ElWOesTv7vPNLPmwZe+XuvoVcElwyxKJPfOWbOWONzPYlnuIG07twu/O6EGdmmqqJtEnGOf4rwP+Wc46B943Mweedfep5e3EzMYB4wCSkpKCUJZIeGzfd4h7Zi9mTvpmerVpyHNXpzCgg5qqSfSqUvCb2e1AIfBKOUOGu/smM2sFzDOzZe4+v6yBgSeFqQApKSlelbpEwsHdmbVwE/e+tZj9h4q45cwe3DiiKzWrq6maRLdKB7+ZjaXkRd+funuZQe3umwK/c8xsJjAEKDP4RWLJpt0HuX1mBh8v38ZxSSVN1bq3VlM1iQ2VCn4zGwWMB0519wPljKkPVHP33MDls4D7Kl2pSBQoLnZe+WY9D7+7jKJi565z+zD2pGQ1VZOYUpG3c74GjABamFk2cDcl7+KpTcnpG4Cv3P1GM2sHPO/uo4HWwMzA+hrAq+7+XkhmIRIGa7bvZ3xqOt+s2clPurXgoYv707FZvUiXJXLMKvKunivKWPy3csZuAkYHLmcBA6tUnUgUKCwq5vl/r+HxeSuoVaMaj4wZwKUpHdRuQWKWPrkrcgRLNu1lfGo6GRv3cFaf1ky+sB+tG6mpmsQ2Bb9IGQ4VFvHkR6t4+pPVNKlXk6euHMzZ/droKF/igoJf5DAL1u1ifGo6q3L2cfHg9tx5Th+aqqmaxBEFv0jA/kOFPPb+cl74Yi3tGtflhWtPYETPVpEuSyToFPwiwGcrtzFxRgbZuw5y9bBO3DaqFw1q689D4pMe2ZLQ9hwo4IF3lvBGWjZdWtTnjRuGMaRzs0iXJRJSCn5JWO9lbuHOWZns3J/Pr0Z05Tc/7a6mapIQFPyScLblljRVeztjM33aNuLv15xAv/aNI12WSNgo+CVhuDszvtvIfXOWcLCgiFtH9mTcKV3UVE0SjoJfEkL2rgNMmpnJ/BXbOL5TUx4eM4BurRpEuiyRiFDwS1wrLnZe/nodD7+7DAfuPb8vV53YiWpqqiYJTMEvcWv1tn1MSE3n27W7OLl7Cx68SE3VREDBL3GooKiY5z7L4okPVlK3ZnUeu3QgYwa3V7sFkQAFv8SVzI17GJ+azuJNexndvw33nN+XVg3VVE2kNAW/xIW8giL+/OFKnp2fRdN6tXjm54MZ1a9tpMsSiUoKfol5aWt3cltqOlnb9nPp8R2445w+NK5XM9JliUQtBb/ErH2HCnn0vWW89NU62jWuy0vXDeGUHi0jXZZI1FPwS0z6dMU2Js3IYNOeg4wdlsytI3tSX03VRCqkQh9ZNLNpZpZjZpmlljUzs3lmtjLwu2k5244NjFlpZmODVbgkpt0H8rnljUWMnfYNdWpW4183DOOe8/sq9EWOQUU/q/4CMOqwZROAD929O/Bh4PqPmFkzSr6cfSgwBLi7vCcIkaN5N2MzZ/xxPrMWbuTm07rx9m9OJiVZnTRFjlWFDpPcfb6ZJR+2+AJgRODyi8AnwPjDxowE5rn7TgAzm0fJE8hrlapWElLO3jzumrWY9xZvoV/7Rrx43Qn0baemaiKVVZX/H7d2980A7r7ZzMr6qqL2wIZS17MDy/6LmY0DxgEkJSVVoSyJF+7OvxZkc/+cJeQVFjN+VC9+eXJnaqipmkiVhPrEaFkflfSyBrr7VGAqQEpKSpljJHFs2HmASTMz+GzldoYkN2PKmP50aammaiLBUJXg32pmbQNH+22BnDLGZPOf00EAHSg5JSRSpqJi56Uv1/Lo3OUYMPmCvlw5VE3VRIKpKsE/GxgLTAn8nlXGmLnAg6Ve0D0LmFiF25Q4tionl/GpGSxYt4tTe7TkwYv7075J3UiXJRJ3KhT8ZvYaJUfuLcwsm5J36kwB3jCzXwDrgUsDY1OAG939enffaWaTgW8Du7rvhxd6RX5QUFTMs5+u5s8frqJe7eo8ftlALhykpmoioWLu0Xc6PSUlxdPS0iJdhoRBRvYebp2+iGVbcjlnQFvuPb8vLRrUjnRZIjHHzBa4e0pFxupTLxIReQVFPPHBSp77LIvm9Wvx7FXHM7Jvm0iXJZIQFPwSdl9n7WDCjAzWbN/PZSkdmXRObxrXVVM1kXBR8EvY5OYV8Mh7y/nHV+vo2Kwur1w/lOHdWkS6LJGEo+CXsPh4WQ63z8xg8948rhvemf8d2YN6tfTwE4kE/eVJSO3cn8/kOUuY+f1GurdqQOpNJzE4Se2aRCJJwS8h4e68nbGZu2ctZs/BAn7z0+78+rSu1K5RPdKliSQ8Bb8E3da9edzxZibzlmxlQIfGvHz9UHq3bRTpskQkQMEvQePuvJG2gfvfXkp+YTGTRvfiuuFqqiYSbRT8EhTrdxxgwox0vli9g6Gdm/HwmAEkt6gf6bJEpAwKfqmSomLn75+v4Q/vr6B6NeOBi/pxxQlJaqomEsUU/FJpK7bmctv0dBZu2M3pvVrxwEX9aNtYTdVEop2CX45ZfmExT3+ymic/XknDOjX50+WDOH9gOzVVE4kRCn45Jos27GZ8ajrLtuRy/sB23H1eH5qrqZpITFHwS4UczC/i8Q9W8PxnWbRqWIfnr07hjD6tI12WiFSCgl+O6svVO5g4I521Ow5wxZAkJo7uRaM6aqomEqsU/FKuvXkFTHl3Ga9+vZ5Ozevx6i+HclJXNVUTiXUKfinTh0u3cvvMTHJy8/jlyZ35/Zk9qVtL7RZE4oGCX35kx75D3PvWEmYv2kTP1g155qrjGdSxSaTLEpEgUvALUNJuYfaiTdz71hJy8wr43Rk9uGlEV2rVULsFkXhT6eA3s57AP0st6gLc5e5PlBozApgFrAksmuHu91X2NiU0Nu85yB0zM/lwWQ4DOzbhkTED6NmmYaTLEpEQqXTwu/tyYBCAmVUHNgIzyxj6mbufW9nbkdApLnZe/3YDD72zlILiYu44pzfXDu9MdbVbEIlrwTrV81NgtbuvC9L+JMTWbt/PhBnpfJW1k2FdmjNlTH86NVdTNZFEEKzgvxx4rZx1w8xsEbAJ+F93X1zWIDMbB4wDSEpKClJZcrjComKmBZqq1apejSkX9+eyEzqq3YJIAjF3r9oOzGpREup93X3rYesaAcXuvs/MRgN/cvfuR9tnSkqKp6WlVaku+W/Ltuxl/PR0FmXv4Yzerbn/wn60aVwn0mWJSBCY2QJ3T6nI2GAc8Z8NfHd46AO4+95Sl98xs6fMrIW7bw/C7UoFHSos4q8fr+apj1fRuG5N/nLFcZw7oK2O8kUSVDCC/wrKOc1jZm2Are7uZjYEqAbsCMJtSgV9v34X41PTWbF1Hxcd1547z+1Ds/q1Il2WiERQlYLfzOoBZwI3lFp2I4C7PwNcAtxkZoXAQeByr+q5JamQA/mF/OH9FUz7fA1tGtVh2jUpnN5LTdVEpIrB7+4HgOaHLXum1OUngSerchty7L5YtZ0JMzJYv/MAPz8xifGjetFQTdVEJECf3I0jew4W8NA7S3n92w0kN6/H6+NO5MQuzY++oYgkFAV/nHh/8RbueDOT7fsOccOpXfjdGT2oU1NN1UTkvyn4Y9z2fYe4Z/Zi5qRvplebhjw/NoUBHdRUTUTKp+CPUe7Omws3cu9bSzhwqIhbzuzBjSO6UrO6mqqJyJEp+GPQpt0HuX1mBh8v38ZxSSVN1bq3VlM1EakYBX8MKS52XvlmPQ+/u4yiYueuc/sw9qRkNVUTkWOi4I8RWdv2MSE1g2/W7uQn3Vrw0MX96disXqTLEpEYpOCPcoVFxTz/7zU8Pm8FtWtU45FLBnDp8R3UbkFEKk3BH8WWbNrLbamLyNy4l5F9WzP5gn60aqSmaiJSNQr+KHSosIgnP1rF05+spkm9mjx15WDO7tdGR/kiEhQK/iizYN1OxqdmsCpnHxcPbs+d5/ShqZqqiUgQKfijxP5DhTw6dzkvfrmWdo3r8sK1JzCiZ6tIlyUicUjBHwU+W7mNiTMyyN51kKuHdeK2Ub1oUFt3jYiEhtIlgvYcKOD+t5fwrwXZdGlRnzduGMaQzs0iXZaIxDkFf4S8l7mFO2dlsnN/Pr8a0ZXf/LS7mqqJSFgo+MMsJzePe2Yv5p2MLfRp24i/X3MC/do3jnRZIpJAFPxh4u6kfreRyXOWcLCgiFtH9mTcKV3UVE1Ewk7BHwbZuw4waWYm81ds4/hOTXl4zAC6tWoQ6bJEJEFVOfjNbC2QCxQBhe6ecth6A/4EjAYOANe4+3dVvd1YUFzs/OOrdTz83jIA7j2/L1ed2IlqaqomIhEUrCP+09x9eznrzga6B36GAk8Hfse11dv2MX56OmnrdnFKj5Y8eFE/OjRVUzURibxwnOq5AHjJ3R34ysyamFlbd98chtsOu4KiYqbOz+JPH66kbs3qPHbpQMYMbq92CyISNYIR/A68b2YOPOvuUw9b3x7YUOp6dmDZj4LfzMYB4wCSkpKCUFb4ZW7cw/jUdBZv2svo/m245/y+tGqopmoiEl2CEfzD3X2TmbUC5pnZMnefX2p9WYe6/l8LSp4wpgKkpKT81/polldQxJ8/XMmz87NoWq8Wz/x8MKP6tY10WSIiZapy8Lv7psDvHDObCQwBSgd/NtCx1PUOwKaq3m60+HbtTsZPTydr+34uPb4Dd5zTh8b1aka6LBGRclUp+M2sPlDN3XMDl88C7jts2GzgZjN7nZIXdffEw/n9fYcKeeS9Zbz05TraN6nLS9cN4ZQeLSNdlojIUVX1iL81MDPwwmUN4FV3f8/MbgRw92eAdyh5K+cqSt7OeW0VbzPiPl2xjUkzMti05yDXnJTMrSN7Ul9N1UQkRlQprdw9CxhYxvJnSl124NdVuZ1osftAPvfNWcKM7zbStWV9pt84jOM7qamaiMQWHaZW0DsZm7lrVia7DxRw82nduPn0bmqqJiIxScF/FDl787hzViZzF2+lX/tGvHjdEPq2U1M1EYldCv5yuDv/WpDN/XOWkFdYzPhRvfjlyZ2poaZqIhLjFPxl2LDzABNnZPDvVdsZktyMKWP606WlmqqJSHxQ8JdSVOy89OVaHnlvOdUMJl/QlyuHqqmaiMQXBX/AqpxcbpueznfrdzOiZ0seuKg/7ZvUjXRZIiJBl/DBX1BUzLOfrubPH66iXu3qPH7ZQC4cpKZqIhK/Ejr4M7L3cOv0RSzbkss5A9py7/l9adGgdqTLEhEJqYQM/ryCIh7/YAXPzc+iRYPaPHvV8Yzs2ybSZYmIhEXCBf/XWTuYMCODNdv3c1lKRyad05vGddVUTUQSR8IEf25eAQ+/t4yXv1pPx2Z1eeX6oQzv1iLSZYmIhF1CBP/Hy3K4fWYGm/fm8YufdOaWs3pQr1ZCTF1E5L/Edfrt3J/P5DlLmPn9Rrq3akDqTScxOKlppMsSEYmouAx+d2dO+mbumb2YPQcL+M1Pu/Pr07pSu4aaqomIxF3wb92bx+0zM/lg6VYGdGjMy9cPpXfbRpEuS0QkasRN8Ls7//x2Aw+8s5T8wmImje7FdcPVVE1E5HBxE/x7Dxby6Nzl9GnbiIfHDCC5Rf1IlyQiEpXiJvgb16vJjF+dRMem9dRUTUTkCOIm+AE6NddRvojI0VT6BLiZdTSzj81sqZktNrP/KWPMCDPbY2YLAz82PVMXAAAHLklEQVR3Va1cERGpqqoc8RcCt7j7d2bWEFhgZvPcfclh4z5z93OrcDsiIhJElT7id/fN7v5d4HIusBRoH6zCREQkNILyXkczSwaOA74uY/UwM1tkZu+aWd8j7GOcmaWZWdq2bduCUZaIiJShysFvZg2AVOC37r73sNXfAZ3cfSDwF+DN8vbj7lPdPcXdU1q2bFnVskREpBxVCn4zq0lJ6L/i7jMOX+/ue919X+DyO0BNM1NLTBGRCKrKu3oM+Buw1N3/WM6YNoFxmNmQwO3tqOxtiohI1Zm7V25Ds58AnwEZQHFg8SQgCcDdnzGzm4GbKHkH0EHg9+7+RQX2vQ1YV6nCSm5/fSW3jTaNgT2RLiII4mUeoLlEo3iZB1Qtvzq5e4XOk1c6+KOVmW2r6OSjnZlNdfdxka6jquJlHqC5RKN4mQeEL7/isYPZ7kgXEERvRbqAIImXeYDmEo3iZR4QpvyKxyP+NHdPiXQdIiLHKlz5FY9H/FMjXYCISCWFJb/i7ohfRESOLB6P+EVE5AgU/GFiZqPMbLmZrTKzCYFlnc3sazNbaWb/NLNa5Ww7MbDdcjMbWWr5NDPLMbPMcM0jcLtBnUtFOr3GyDzqmNk3gRYli83s3nDMIxRzKbWuupl9b2ZzwjGPwG2G4m9lrZllBLoEp4VrLlHL3aP2BxgFLAdWARMCyzpT0hNoJfBPoFY5204MbLccGHmkfYZhHtWB1UAXoBawCOgDvAFcHhjzDHBTGdv2CYyvHZj7aqB6YN0pwGAgM4z3SdDnArQFBgfGNARWAH1icB4GNAiMqRl4nJ4Yi/dJqfW/B14F5sTq4yuwbi3QIlx/J6VqCkWGNQGmA8soaY457JjrCvc/RCQfAOXtMwxzGQbMPewOnQhsB2qUNebwsaWuzy19RwPJhDf4QzaXUstnAWfG8jyAepT0qhoaq/cJ0AH4EDid8AV/qOayljAHf3l5Q9WfxF4Erg9crgU0OdbaovlUzxBglbtnuXs+8DpwASUPwumBMS8CF5ax7QXA6+5+yN3XUPKsOeQI+wy19sCGUtezA8t2u3vhYcsws/PN7L6jbBspIZ3LUTq9BlNI5hE4NbIQyAHmuXuo53Gkeqp6nzwB3MZ/PpkfDqGaiwPvm9kCMwvXh72CnmFm1oiS/+n/DcDd8939mN/7H83BH4oHQKRCtKwvAa5exjIHcPfZ7v7Dt5WVtW0k34oVsrnYkTu9BltI5uHuRe4+iJKj5SFm1i8YxR5F0OdiZucCOe6+IEg1VlSoHl/D3X0wcDbwazM7pcqVHl0oMqwLsA34e+C1l+fN7Ji/czaagz8UD4BIhWg20LHU9Q6U9ONoYmY1Si3bVMFtyxoXLiGZix2l02sIhPQ+CRyFfULJOd5QC8VchgPnm9laSo5UTzezl4Ncd1lCcr+4+w+/c4CZlByNh1ooMqwGJa/rPe3uxwH7gQnHWlg0B38oHgCRCtFvge6BdybUAi4HZgMfA5cExoyl5Nz24WYDl5tZbTPrDHQHvglDzeUJ+lzMjt7pNQRCMY+WZtYEwMzqAmdQ8gJcqAV9Lu4+0d07uHtyYH8fufvPQz0RQnO/1LeSr4clcHR8FhCOd8KFKsOyS51CnE7JE8GxCeeLHcf4wkgNIIuSFzZ+eGGkL/AvfvzCyK/K2LYvP35hJIuSZ9oy9xmm+Yym5N0qq4HbA8u6UBLiqwLzqh1Yfj5wX6ltbw9stxw4u9Ty14DNQEHgAfGLWJwL8BNKjmbSgYWBn9ExOI8BwPeBeWQCd4Xx7yXoj69S60cQphd3Q3S/dAn8rS8CFv+wzzDMI+gZFlj3GdAzcPke4NFjri1cd2Y0PADK26d+9KMf/YTiJ0QZNghICxxgvAk0Pda61LJBRCTBRPM5fhERCQEFv4hIglHwi4gkmKgM/nKaNJ1uZt+ZWaaZvVjq7VCHb/uJmemLWEREyhF1wW9m1YG/UvIJuz7AFWbWh5KPNl/u7v0o+SL2sZGrUkQkdkVd8FN2f4sxwCF3XxEYMy+w7IjMbF+py5eY2QuByy+Y2Z/N7AszyzKzS8rdiYhInInG4C+rR0UboGapUziX8ONPtVVGW0o+OHQuMKWK+xIRiRnRGPxl9agopuSj24+b2TdALlBYxrhj8aa7F7v7EqB1FfclIhIzynyBNMLK7FHh7l8CJwOY2VlAj8DluZQEd5q7X3/Yvkp/Oq3OYesOlbpc1pONiEhcisbg/78mTcBGSo70f2Zmrdw9x8xqA+OBBwDcfWT5u2KrmfWm5CPPF1HyPwURkYQWdcHv7oVmdjMl355THZjm7ovN7NFAj/BqlLQk/aicXdTgP0fzE4A5lLxmkAk0CG31IiLRL6569QT+N7AK6OfueyJdj4hINIrGF3crJfCOn4XAUwp9EZHyxdURv4iIHF3cHPGLiEjFKPhFRBKMgl9EJMEo+EVEEoyCX0Qkwfx/beDGzIfbwBoAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"t.plot().bar"
]
},
{
"cell_type": "code",
"execution_count": 312,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Sepal.Length</th>\n",
" <th>Sepal.Width</th>\n",
" <th>Petal.Length</th>\n",
" <th>Petal.Width</th>\n",
" <th>Species</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>5.1</td>\n",
" <td>3.5</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4.9</td>\n",
" <td>3.0</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4.7</td>\n",
" <td>3.2</td>\n",
" <td>1.3</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4.6</td>\n",
" <td>3.1</td>\n",
" <td>1.5</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>5.0</td>\n",
" <td>3.6</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>5.4</td>\n",
" <td>3.9</td>\n",
" <td>1.7</td>\n",
" <td>0.4</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>4.6</td>\n",
" <td>3.4</td>\n",
" <td>1.4</td>\n",
" <td>0.3</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>5.0</td>\n",
" <td>3.4</td>\n",
" <td>1.5</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>4.4</td>\n",
" <td>2.9</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>4.9</td>\n",
" <td>3.1</td>\n",
" <td>1.5</td>\n",
" <td>0.1</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>5.4</td>\n",
" <td>3.7</td>\n",
" <td>1.5</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>4.8</td>\n",
" <td>3.4</td>\n",
" <td>1.6</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>4.8</td>\n",
" <td>3.0</td>\n",
" <td>1.4</td>\n",
" <td>0.1</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>4.3</td>\n",
" <td>3.0</td>\n",
" <td>1.1</td>\n",
" <td>0.1</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>5.8</td>\n",
" <td>4.0</td>\n",
" <td>1.2</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>5.7</td>\n",
" <td>4.4</td>\n",
" <td>1.5</td>\n",
" <td>0.4</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>5.4</td>\n",
" <td>3.9</td>\n",
" <td>1.3</td>\n",
" <td>0.4</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>5.1</td>\n",
" <td>3.5</td>\n",
" <td>1.4</td>\n",
" <td>0.3</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>5.7</td>\n",
" <td>3.8</td>\n",
" <td>1.7</td>\n",
" <td>0.3</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>5.1</td>\n",
" <td>3.8</td>\n",
" <td>1.5</td>\n",
" <td>0.3</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>5.4</td>\n",
" <td>3.4</td>\n",
" <td>1.7</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>5.1</td>\n",
" <td>3.7</td>\n",
" <td>1.5</td>\n",
" <td>0.4</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>4.6</td>\n",
" <td>3.6</td>\n",
" <td>1.0</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>5.1</td>\n",
" <td>3.3</td>\n",
" <td>1.7</td>\n",
" <td>0.5</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>4.8</td>\n",
" <td>3.4</td>\n",
" <td>1.9</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>5.0</td>\n",
" <td>3.0</td>\n",
" <td>1.6</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>5.0</td>\n",
" <td>3.4</td>\n",
" <td>1.6</td>\n",
" <td>0.4</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>5.2</td>\n",
" <td>3.5</td>\n",
" <td>1.5</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>5.2</td>\n",
" <td>3.4</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>4.7</td>\n",
" <td>3.2</td>\n",
" <td>1.6</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>4.8</td>\n",
" <td>3.1</td>\n",
" <td>1.6</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>5.4</td>\n",
" <td>3.4</td>\n",
" <td>1.5</td>\n",
" <td>0.4</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>5.2</td>\n",
" <td>4.1</td>\n",
" <td>1.5</td>\n",
" <td>0.1</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>5.5</td>\n",
" <td>4.2</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>4.9</td>\n",
" <td>3.1</td>\n",
" <td>1.5</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>5.0</td>\n",
" <td>3.2</td>\n",
" <td>1.2</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>5.5</td>\n",
" <td>3.5</td>\n",
" <td>1.3</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>4.9</td>\n",
" <td>3.6</td>\n",
" <td>1.4</td>\n",
" <td>0.1</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>4.4</td>\n",
" <td>3.0</td>\n",
" <td>1.3</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>5.1</td>\n",
" <td>3.4</td>\n",
" <td>1.5</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>5.0</td>\n",
" <td>3.5</td>\n",
" <td>1.3</td>\n",
" <td>0.3</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>4.5</td>\n",
" <td>2.3</td>\n",
" <td>1.3</td>\n",
" <td>0.3</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>4.4</td>\n",
" <td>3.2</td>\n",
" <td>1.3</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>5.0</td>\n",
" <td>3.5</td>\n",
" <td>1.6</td>\n",
" <td>0.6</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>5.1</td>\n",
" <td>3.8</td>\n",
" <td>1.9</td>\n",
" <td>0.4</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>4.8</td>\n",
" <td>3.0</td>\n",
" <td>1.4</td>\n",
" <td>0.3</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>5.1</td>\n",
" <td>3.8</td>\n",
" <td>1.6</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>4.6</td>\n",
" <td>3.2</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td>5.3</td>\n",
" <td>3.7</td>\n",
" <td>1.5</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>5.0</td>\n",
" <td>3.3</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td>7.0</td>\n",
" <td>3.2</td>\n",
" <td>4.7</td>\n",
" <td>1.4</td>\n",
" <td>versicolor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52</th>\n",
" <td>6.4</td>\n",
" <td>3.2</td>\n",
" <td>4.5</td>\n",
" <td>1.5</td>\n",
" <td>versicolor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53</th>\n",
" <td>6.9</td>\n",
" <td>3.1</td>\n",
" <td>4.9</td>\n",
" <td>1.5</td>\n",
" <td>versicolor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54</th>\n",
" <td>5.5</td>\n",
" <td>2.3</td>\n",
" <td>4.0</td>\n",
" <td>1.3</td>\n",
" <td>versicolor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55</th>\n",
" <td>6.5</td>\n",
" <td>2.8</td>\n",
" <td>4.6</td>\n",
" <td>1.5</td>\n",
" <td>versicolor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>56</th>\n",
" <td>5.7</td>\n",
" <td>2.8</td>\n",
" <td>4.5</td>\n",
" <td>1.3</td>\n",
" <td>versicolor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>57</th>\n",
" <td>6.3</td>\n",
" <td>3.3</td>\n",
" <td>4.7</td>\n",
" <td>1.6</td>\n",
" <td>versicolor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>58</th>\n",
" <td>4.9</td>\n",
" <td>2.4</td>\n",
" <td>3.3</td>\n",
" <td>1.0</td>\n",
" <td>versicolor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59</th>\n",
" <td>6.6</td>\n",
" <td>2.9</td>\n",
" <td>4.6</td>\n",
" <td>1.3</td>\n",
" <td>versicolor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>60</th>\n",
" <td>5.2</td>\n",
" <td>2.7</td>\n",
" <td>3.9</td>\n",
" <td>1.4</td>\n",
" <td>versicolor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>61</th>\n",
" <td>5.0</td>\n",
" <td>2.0</td>\n",
" <td>3.5</td>\n",
" <td>1.0</td>\n",
" <td>versicolor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>62</th>\n",
" <td>5.9</td>\n",
" <td>3.0</td>\n",
" <td>4.2</td>\n",
" <td>1.5</td>\n",
" <td>versicolor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>63</th>\n",
" <td>6.0</td>\n",
" <td>2.2</td>\n",
" <td>4.0</td>\n",
" <td>1.0</td>\n",
" <td>versicolor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>64</th>\n",
" <td>6.1</td>\n",
" <td>2.9</td>\n",
" <td>4.7</td>\n",
" <td>1.4</td>\n",
" <td>versicolor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65</th>\n",
" <td>5.6</td>\n",
" <td>2.9</td>\n",
" <td>3.6</td>\n",
" <td>1.3</td>\n",
" <td>versicolor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>66</th>\n",
" <td>6.7</td>\n",
" <td>3.1</td>\n",
" <td>4.4</td>\n",
" <td>1.4</td>\n",
" <td>versicolor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>67</th>\n",
" <td>5.6</td>\n",
" <td>3.0</td>\n",
" <td>4.5</td>\n",
" <td>1.5</td>\n",
" <td>versicolor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68</th>\n",
" <td>5.8</td>\n",
" <td>2.7</td>\n",
" <td>4.1</td>\n",
" <td>1.0</td>\n",
" <td>versicolor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>69</th>\n",
" <td>6.2</td>\n",
" <td>2.2</td>\n",
" <td>4.5</td>\n",
" <td>1.5</td>\n",
" <td>versicolor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>70</th>\n",
" <td>5.6</td>\n",
" <td>2.5</td>\n",
" <td>3.9</td>\n",
" <td>1.1</td>\n",
" <td>versicolor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>71</th>\n",
" <td>5.9</td>\n",
" <td>3.2</td>\n",
" <td>4.8</td>\n",
" <td>1.8</td>\n",
" <td>versicolor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>72</th>\n",
" <td>6.1</td>\n",
" <td>2.8</td>\n",
" <td>4.0</td>\n",
" <td>1.3</td>\n",
" <td>versicolor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>73</th>\n",
" <td>6.3</td>\n",
" <td>2.5</td>\n",
" <td>4.9</td>\n",
" <td>1.5</td>\n",
" <td>versicolor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>74</th>\n",
" <td>6.1</td>\n",
" <td>2.8</td>\n",
" <td>4.7</td>\n",
" <td>1.2</td>\n",
" <td>versicolor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75</th>\n",
" <td>6.4</td>\n",
" <td>2.9</td>\n",
" <td>4.3</td>\n",
" <td>1.3</td>\n",
" <td>versicolor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>76</th>\n",
" <td>6.6</td>\n",
" <td>3.0</td>\n",
" <td>4.4</td>\n",
" <td>1.4</td>\n",
" <td>versicolor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>77</th>\n",
" <td>6.8</td>\n",
" <td>2.8</td>\n",
" <td>4.8</td>\n",
" <td>1.4</td>\n",
" <td>versicolor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>78</th>\n",
" <td>6.7</td>\n",
" <td>3.0</td>\n",
" <td>5.0</td>\n",
" <td>1.7</td>\n",
" <td>versicolor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>79</th>\n",
" <td>6.0</td>\n",
" <td>2.9</td>\n",
" <td>4.5</td>\n",
" <td>1.5</td>\n",
" <td>versicolor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>80</th>\n",
" <td>5.7</td>\n",
" <td>2.6</td>\n",
" <td>3.5</td>\n",
" <td>1.0</td>\n",
" <td>versicolor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>81</th>\n",
" <td>5.5</td>\n",
" <td>2.4</td>\n",
" <td>3.8</td>\n",
" <td>1.1</td>\n",
" <td>versicolor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>82</th>\n",
" <td>5.5</td>\n",
" <td>2.4</td>\n",
" <td>3.7</td>\n",
" <td>1.0</td>\n",
" <td>versicolor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>83</th>\n",
" <td>5.8</td>\n",
" <td>2.7</td>\n",
" <td>3.9</td>\n",
" <td>1.2</td>\n",
" <td>versicolor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>84</th>\n",
" <td>6.0</td>\n",
" <td>2.7</td>\n",
" <td>5.1</td>\n",
" <td>1.6</td>\n",
" <td>versicolor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>85</th>\n",
" <td>5.4</td>\n",
" <td>3.0</td>\n",
" <td>4.5</td>\n",
" <td>1.5</td>\n",
" <td>versicolor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>86</th>\n",
" <td>6.0</td>\n",
" <td>3.4</td>\n",
" <td>4.5</td>\n",
" <td>1.6</td>\n",
" <td>versicolor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87</th>\n",
" <td>6.7</td>\n",
" <td>3.1</td>\n",
" <td>4.7</td>\n",
" <td>1.5</td>\n",
" <td>versicolor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>88</th>\n",
" <td>6.3</td>\n",
" <td>2.3</td>\n",
" <td>4.4</td>\n",
" <td>1.3</td>\n",
" <td>versicolor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>89</th>\n",
" <td>5.6</td>\n",
" <td>3.0</td>\n",
" <td>4.1</td>\n",
" <td>1.3</td>\n",
" <td>versicolor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>90</th>\n",
" <td>5.5</td>\n",
" <td>2.5</td>\n",
" <td>4.0</td>\n",
" <td>1.3</td>\n",
" <td>versicolor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>91</th>\n",
" <td>5.5</td>\n",
" <td>2.6</td>\n",
" <td>4.4</td>\n",
" <td>1.2</td>\n",
" <td>versicolor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>92</th>\n",
" <td>6.1</td>\n",
" <td>3.0</td>\n",
" <td>4.6</td>\n",
" <td>1.4</td>\n",
" <td>versicolor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>93</th>\n",
" <td>5.8</td>\n",
" <td>2.6</td>\n",
" <td>4.0</td>\n",
" <td>1.2</td>\n",
" <td>versicolor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>94</th>\n",
" <td>5.0</td>\n",
" <td>2.3</td>\n",
" <td>3.3</td>\n",
" <td>1.0</td>\n",
" <td>versicolor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>95</th>\n",
" <td>5.6</td>\n",
" <td>2.7</td>\n",
" <td>4.2</td>\n",
" <td>1.3</td>\n",
" <td>versicolor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>96</th>\n",
" <td>5.7</td>\n",
" <td>3.0</td>\n",
" <td>4.2</td>\n",
" <td>1.2</td>\n",
" <td>versicolor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>97</th>\n",
" <td>5.7</td>\n",
" <td>2.9</td>\n",
" <td>4.2</td>\n",
" <td>1.3</td>\n",
" <td>versicolor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>98</th>\n",
" <td>6.2</td>\n",
" <td>2.9</td>\n",
" <td>4.3</td>\n",
" <td>1.3</td>\n",
" <td>versicolor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99</th>\n",
" <td>5.1</td>\n",
" <td>2.5</td>\n",
" <td>3.0</td>\n",
" <td>1.1</td>\n",
" <td>versicolor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>100</th>\n",
" <td>5.7</td>\n",
" <td>2.8</td>\n",
" <td>4.1</td>\n",
" <td>1.3</td>\n",
" <td>versicolor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>101</th>\n",
" <td>6.3</td>\n",
" <td>3.3</td>\n",
" <td>6.0</td>\n",
" <td>2.5</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>102</th>\n",
" <td>5.8</td>\n",
" <td>2.7</td>\n",
" <td>5.1</td>\n",
" <td>1.9</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>103</th>\n",
" <td>7.1</td>\n",
" <td>3.0</td>\n",
" <td>5.9</td>\n",
" <td>2.1</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>104</th>\n",
" <td>6.3</td>\n",
" <td>2.9</td>\n",
" <td>5.6</td>\n",
" <td>1.8</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105</th>\n",
" <td>6.5</td>\n",
" <td>3.0</td>\n",
" <td>5.8</td>\n",
" <td>2.2</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>106</th>\n",
" <td>7.6</td>\n",
" <td>3.0</td>\n",
" <td>6.6</td>\n",
" <td>2.1</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>107</th>\n",
" <td>4.9</td>\n",
" <td>2.5</td>\n",
" <td>4.5</td>\n",
" <td>1.7</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>108</th>\n",
" <td>7.3</td>\n",
" <td>2.9</td>\n",
" <td>6.3</td>\n",
" <td>1.8</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>109</th>\n",
" <td>6.7</td>\n",
" <td>2.5</td>\n",
" <td>5.8</td>\n",
" <td>1.8</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>110</th>\n",
" <td>7.2</td>\n",
" <td>3.6</td>\n",
" <td>6.1</td>\n",
" <td>2.5</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>111</th>\n",
" <td>6.5</td>\n",
" <td>3.2</td>\n",
" <td>5.1</td>\n",
" <td>2.0</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>112</th>\n",
" <td>6.4</td>\n",
" <td>2.7</td>\n",
" <td>5.3</td>\n",
" <td>1.9</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>113</th>\n",
" <td>6.8</td>\n",
" <td>3.0</td>\n",
" <td>5.5</td>\n",
" <td>2.1</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>114</th>\n",
" <td>5.7</td>\n",
" <td>2.5</td>\n",
" <td>5.0</td>\n",
" <td>2.0</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>115</th>\n",
" <td>5.8</td>\n",
" <td>2.8</td>\n",
" <td>5.1</td>\n",
" <td>2.4</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>116</th>\n",
" <td>6.4</td>\n",
" <td>3.2</td>\n",
" <td>5.3</td>\n",
" <td>2.3</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>117</th>\n",
" <td>6.5</td>\n",
" <td>3.0</td>\n",
" <td>5.5</td>\n",
" <td>1.8</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118</th>\n",
" <td>7.7</td>\n",
" <td>3.8</td>\n",
" <td>6.7</td>\n",
" <td>2.2</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>119</th>\n",
" <td>7.7</td>\n",
" <td>2.6</td>\n",
" <td>6.9</td>\n",
" <td>2.3</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>120</th>\n",
" <td>6.0</td>\n",
" <td>2.2</td>\n",
" <td>5.0</td>\n",
" <td>1.5</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>121</th>\n",
" <td>6.9</td>\n",
" <td>3.2</td>\n",
" <td>5.7</td>\n",
" <td>2.3</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>122</th>\n",
" <td>5.6</td>\n",
" <td>2.8</td>\n",
" <td>4.9</td>\n",
" <td>2.0</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>123</th>\n",
" <td>7.7</td>\n",
" <td>2.8</td>\n",
" <td>6.7</td>\n",
" <td>2.0</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>124</th>\n",
" <td>6.3</td>\n",
" <td>2.7</td>\n",
" <td>4.9</td>\n",
" <td>1.8</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>125</th>\n",
" <td>6.7</td>\n",
" <td>3.3</td>\n",
" <td>5.7</td>\n",
" <td>2.1</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>126</th>\n",
" <td>7.2</td>\n",
" <td>3.2</td>\n",
" <td>6.0</td>\n",
" <td>1.8</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>127</th>\n",
" <td>6.2</td>\n",
" <td>2.8</td>\n",
" <td>4.8</td>\n",
" <td>1.8</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>128</th>\n",
" <td>6.1</td>\n",
" <td>3.0</td>\n",
" <td>4.9</td>\n",
" <td>1.8</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>129</th>\n",
" <td>6.4</td>\n",
" <td>2.8</td>\n",
" <td>5.6</td>\n",
" <td>2.1</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>130</th>\n",
" <td>7.2</td>\n",
" <td>3.0</td>\n",
" <td>5.8</td>\n",
" <td>1.6</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>131</th>\n",
" <td>7.4</td>\n",
" <td>2.8</td>\n",
" <td>6.1</td>\n",
" <td>1.9</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>132</th>\n",
" <td>7.9</td>\n",
" <td>3.8</td>\n",
" <td>6.4</td>\n",
" <td>2.0</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>133</th>\n",
" <td>6.4</td>\n",
" <td>2.8</td>\n",
" <td>5.6</td>\n",
" <td>2.2</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>134</th>\n",
" <td>6.3</td>\n",
" <td>2.8</td>\n",
" <td>5.1</td>\n",
" <td>1.5</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>135</th>\n",
" <td>6.1</td>\n",
" <td>2.6</td>\n",
" <td>5.6</td>\n",
" <td>1.4</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>136</th>\n",
" <td>7.7</td>\n",
" <td>3.0</td>\n",
" <td>6.1</td>\n",
" <td>2.3</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>137</th>\n",
" <td>6.3</td>\n",
" <td>3.4</td>\n",
" <td>5.6</td>\n",
" <td>2.4</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>138</th>\n",
" <td>6.4</td>\n",
" <td>3.1</td>\n",
" <td>5.5</td>\n",
" <td>1.8</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>139</th>\n",
" <td>6.0</td>\n",
" <td>3.0</td>\n",
" <td>4.8</td>\n",
" <td>1.8</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>140</th>\n",
" <td>6.9</td>\n",
" <td>3.1</td>\n",
" <td>5.4</td>\n",
" <td>2.1</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>141</th>\n",
" <td>6.7</td>\n",
" <td>3.1</td>\n",
" <td>5.6</td>\n",
" <td>2.4</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>142</th>\n",
" <td>6.9</td>\n",
" <td>3.1</td>\n",
" <td>5.1</td>\n",
" <td>2.3</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>143</th>\n",
" <td>5.8</td>\n",
" <td>2.7</td>\n",
" <td>5.1</td>\n",
" <td>1.9</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>144</th>\n",
" <td>6.8</td>\n",
" <td>3.2</td>\n",
" <td>5.9</td>\n",
" <td>2.3</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>145</th>\n",
" <td>6.7</td>\n",
" <td>3.3</td>\n",
" <td>5.7</td>\n",
" <td>2.5</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>146</th>\n",
" <td>6.7</td>\n",
" <td>3.0</td>\n",
" <td>5.2</td>\n",
" <td>2.3</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>147</th>\n",
" <td>6.3</td>\n",
" <td>2.5</td>\n",
" <td>5.0</td>\n",
" <td>1.9</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>148</th>\n",
" <td>6.5</td>\n",
" <td>3.0</td>\n",
" <td>5.2</td>\n",
" <td>2.0</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>149</th>\n",
" <td>6.2</td>\n",
" <td>3.4</td>\n",
" <td>5.4</td>\n",
" <td>2.3</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>150</th>\n",
" <td>5.9</td>\n",
" <td>3.0</td>\n",
" <td>5.1</td>\n",
" <td>1.8</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Sepal.Length Sepal.Width Petal.Length Petal.Width Species\n",
"1 5.1 3.5 1.4 0.2 setosa\n",
"2 4.9 3.0 1.4 0.2 setosa\n",
"3 4.7 3.2 1.3 0.2 setosa\n",
"4 4.6 3.1 1.5 0.2 setosa\n",
"5 5.0 3.6 1.4 0.2 setosa\n",
"6 5.4 3.9 1.7 0.4 setosa\n",
"7 4.6 3.4 1.4 0.3 setosa\n",
"8 5.0 3.4 1.5 0.2 setosa\n",
"9 4.4 2.9 1.4 0.2 setosa\n",
"10 4.9 3.1 1.5 0.1 setosa\n",
"11 5.4 3.7 1.5 0.2 setosa\n",
"12 4.8 3.4 1.6 0.2 setosa\n",
"13 4.8 3.0 1.4 0.1 setosa\n",
"14 4.3 3.0 1.1 0.1 setosa\n",
"15 5.8 4.0 1.2 0.2 setosa\n",
"16 5.7 4.4 1.5 0.4 setosa\n",
"17 5.4 3.9 1.3 0.4 setosa\n",
"18 5.1 3.5 1.4 0.3 setosa\n",
"19 5.7 3.8 1.7 0.3 setosa\n",
"20 5.1 3.8 1.5 0.3 setosa\n",
"21 5.4 3.4 1.7 0.2 setosa\n",
"22 5.1 3.7 1.5 0.4 setosa\n",
"23 4.6 3.6 1.0 0.2 setosa\n",
"24 5.1 3.3 1.7 0.5 setosa\n",
"25 4.8 3.4 1.9 0.2 setosa\n",
"26 5.0 3.0 1.6 0.2 setosa\n",
"27 5.0 3.4 1.6 0.4 setosa\n",
"28 5.2 3.5 1.5 0.2 setosa\n",
"29 5.2 3.4 1.4 0.2 setosa\n",
"30 4.7 3.2 1.6 0.2 setosa\n",
"31 4.8 3.1 1.6 0.2 setosa\n",
"32 5.4 3.4 1.5 0.4 setosa\n",
"33 5.2 4.1 1.5 0.1 setosa\n",
"34 5.5 4.2 1.4 0.2 setosa\n",
"35 4.9 3.1 1.5 0.2 setosa\n",
"36 5.0 3.2 1.2 0.2 setosa\n",
"37 5.5 3.5 1.3 0.2 setosa\n",
"38 4.9 3.6 1.4 0.1 setosa\n",
"39 4.4 3.0 1.3 0.2 setosa\n",
"40 5.1 3.4 1.5 0.2 setosa\n",
"41 5.0 3.5 1.3 0.3 setosa\n",
"42 4.5 2.3 1.3 0.3 setosa\n",
"43 4.4 3.2 1.3 0.2 setosa\n",
"44 5.0 3.5 1.6 0.6 setosa\n",
"45 5.1 3.8 1.9 0.4 setosa\n",
"46 4.8 3.0 1.4 0.3 setosa\n",
"47 5.1 3.8 1.6 0.2 setosa\n",
"48 4.6 3.2 1.4 0.2 setosa\n",
"49 5.3 3.7 1.5 0.2 setosa\n",
"50 5.0 3.3 1.4 0.2 setosa\n",
"51 7.0 3.2 4.7 1.4 versicolor\n",
"52 6.4 3.2 4.5 1.5 versicolor\n",
"53 6.9 3.1 4.9 1.5 versicolor\n",
"54 5.5 2.3 4.0 1.3 versicolor\n",
"55 6.5 2.8 4.6 1.5 versicolor\n",
"56 5.7 2.8 4.5 1.3 versicolor\n",
"57 6.3 3.3 4.7 1.6 versicolor\n",
"58 4.9 2.4 3.3 1.0 versicolor\n",
"59 6.6 2.9 4.6 1.3 versicolor\n",
"60 5.2 2.7 3.9 1.4 versicolor\n",
"61 5.0 2.0 3.5 1.0 versicolor\n",
"62 5.9 3.0 4.2 1.5 versicolor\n",
"63 6.0 2.2 4.0 1.0 versicolor\n",
"64 6.1 2.9 4.7 1.4 versicolor\n",
"65 5.6 2.9 3.6 1.3 versicolor\n",
"66 6.7 3.1 4.4 1.4 versicolor\n",
"67 5.6 3.0 4.5 1.5 versicolor\n",
"68 5.8 2.7 4.1 1.0 versicolor\n",
"69 6.2 2.2 4.5 1.5 versicolor\n",
"70 5.6 2.5 3.9 1.1 versicolor\n",
"71 5.9 3.2 4.8 1.8 versicolor\n",
"72 6.1 2.8 4.0 1.3 versicolor\n",
"73 6.3 2.5 4.9 1.5 versicolor\n",
"74 6.1 2.8 4.7 1.2 versicolor\n",
"75 6.4 2.9 4.3 1.3 versicolor\n",
"76 6.6 3.0 4.4 1.4 versicolor\n",
"77 6.8 2.8 4.8 1.4 versicolor\n",
"78 6.7 3.0 5.0 1.7 versicolor\n",
"79 6.0 2.9 4.5 1.5 versicolor\n",
"80 5.7 2.6 3.5 1.0 versicolor\n",
"81 5.5 2.4 3.8 1.1 versicolor\n",
"82 5.5 2.4 3.7 1.0 versicolor\n",
"83 5.8 2.7 3.9 1.2 versicolor\n",
"84 6.0 2.7 5.1 1.6 versicolor\n",
"85 5.4 3.0 4.5 1.5 versicolor\n",
"86 6.0 3.4 4.5 1.6 versicolor\n",
"87 6.7 3.1 4.7 1.5 versicolor\n",
"88 6.3 2.3 4.4 1.3 versicolor\n",
"89 5.6 3.0 4.1 1.3 versicolor\n",
"90 5.5 2.5 4.0 1.3 versicolor\n",
"91 5.5 2.6 4.4 1.2 versicolor\n",
"92 6.1 3.0 4.6 1.4 versicolor\n",
"93 5.8 2.6 4.0 1.2 versicolor\n",
"94 5.0 2.3 3.3 1.0 versicolor\n",
"95 5.6 2.7 4.2 1.3 versicolor\n",
"96 5.7 3.0 4.2 1.2 versicolor\n",
"97 5.7 2.9 4.2 1.3 versicolor\n",
"98 6.2 2.9 4.3 1.3 versicolor\n",
"99 5.1 2.5 3.0 1.1 versicolor\n",
"100 5.7 2.8 4.1 1.3 versicolor\n",
"101 6.3 3.3 6.0 2.5 virginica\n",
"102 5.8 2.7 5.1 1.9 virginica\n",
"103 7.1 3.0 5.9 2.1 virginica\n",
"104 6.3 2.9 5.6 1.8 virginica\n",
"105 6.5 3.0 5.8 2.2 virginica\n",
"106 7.6 3.0 6.6 2.1 virginica\n",
"107 4.9 2.5 4.5 1.7 virginica\n",
"108 7.3 2.9 6.3 1.8 virginica\n",
"109 6.7 2.5 5.8 1.8 virginica\n",
"110 7.2 3.6 6.1 2.5 virginica\n",
"111 6.5 3.2 5.1 2.0 virginica\n",
"112 6.4 2.7 5.3 1.9 virginica\n",
"113 6.8 3.0 5.5 2.1 virginica\n",
"114 5.7 2.5 5.0 2.0 virginica\n",
"115 5.8 2.8 5.1 2.4 virginica\n",
"116 6.4 3.2 5.3 2.3 virginica\n",
"117 6.5 3.0 5.5 1.8 virginica\n",
"118 7.7 3.8 6.7 2.2 virginica\n",
"119 7.7 2.6 6.9 2.3 virginica\n",
"120 6.0 2.2 5.0 1.5 virginica\n",
"121 6.9 3.2 5.7 2.3 virginica\n",
"122 5.6 2.8 4.9 2.0 virginica\n",
"123 7.7 2.8 6.7 2.0 virginica\n",
"124 6.3 2.7 4.9 1.8 virginica\n",
"125 6.7 3.3 5.7 2.1 virginica\n",
"126 7.2 3.2 6.0 1.8 virginica\n",
"127 6.2 2.8 4.8 1.8 virginica\n",
"128 6.1 3.0 4.9 1.8 virginica\n",
"129 6.4 2.8 5.6 2.1 virginica\n",
"130 7.2 3.0 5.8 1.6 virginica\n",
"131 7.4 2.8 6.1 1.9 virginica\n",
"132 7.9 3.8 6.4 2.0 virginica\n",
"133 6.4 2.8 5.6 2.2 virginica\n",
"134 6.3 2.8 5.1 1.5 virginica\n",
"135 6.1 2.6 5.6 1.4 virginica\n",
"136 7.7 3.0 6.1 2.3 virginica\n",
"137 6.3 3.4 5.6 2.4 virginica\n",
"138 6.4 3.1 5.5 1.8 virginica\n",
"139 6.0 3.0 4.8 1.8 virginica\n",
"140 6.9 3.1 5.4 2.1 virginica\n",
"141 6.7 3.1 5.6 2.4 virginica\n",
"142 6.9 3.1 5.1 2.3 virginica\n",
"143 5.8 2.7 5.1 1.9 virginica\n",
"144 6.8 3.2 5.9 2.3 virginica\n",
"145 6.7 3.3 5.7 2.5 virginica\n",
"146 6.7 3.0 5.2 2.3 virginica\n",
"147 6.3 2.5 5.0 1.9 virginica\n",
"148 6.5 3.0 5.2 2.0 virginica\n",
"149 6.2 3.4 5.4 2.3 virginica\n",
"150 5.9 3.0 5.1 1.8 virginica"
]
},
"execution_count": 312,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"iris = data(\"iris\")\n",
"iris"
]
},
{
"cell_type": "code",
"execution_count": 313,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0xed706a0>"
]
},
"execution_count": 313,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"iris[\"Sepal.Length\"].head().plot.bar() # Plotting first 5 of Sepal.Length"
]
},
{
"cell_type": "code",
"execution_count": 314,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>odd</th>\n",
" <th>even</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2018-01-01</th>\n",
" <td>13</td>\n",
" <td>34</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-01-02</th>\n",
" <td>33</td>\n",
" <td>66</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-01-03</th>\n",
" <td>45</td>\n",
" <td>54</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-01-04</th>\n",
" <td>67</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-01-05</th>\n",
" <td>31</td>\n",
" <td>12</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" odd even\n",
"2018-01-01 13 34\n",
"2018-01-02 33 66\n",
"2018-01-03 45 54\n",
"2018-01-04 67 12\n",
"2018-01-05 31 12"
]
},
"execution_count": 314,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_df = pd.DataFrame({'odd': [13, 33, 45, 67, 31], 'even': [34, 66, 54, 12, 12]}, index=pd.date_range('20180101', periods=5))\n",
"new_df"
]
},
{
"cell_type": "code",
"execution_count": 315,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>odd</th>\n",
" <th>even</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2018-01-01</th>\n",
" <td>46</td>\n",
" <td>100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-01-03</th>\n",
" <td>112</td>\n",
" <td>66</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-01-05</th>\n",
" <td>31</td>\n",
" <td>12</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" odd even\n",
"2018-01-01 46 100\n",
"2018-01-03 112 66\n",
"2018-01-05 31 12"
]
},
"execution_count": 315,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_df.resample(\"2D\").sum() # T"
]
},
{
"cell_type": "code",
"execution_count": 316,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0xedd2ef0>"
]
},
"execution_count": 316,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"new_df.odd.plot.bar()"
]
},
{
"cell_type": "code",
"execution_count": 317,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0xed70630>"
]
},
"execution_count": 317,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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m0yVbuevc02il9UGjQlJ8LDf3zeTzlduZs2aH13FEjqvCQnfOrXLOdfb/Ot0593v/9u3OuYHOuWz/bcT/Tt+5/wi/eX8xnZrV5qdaHzSqXH1OC+qnJGhKl5CmT4qehIc+XMLug0d5bFgn4mL10kWT5IQ4RvTJZGbBNuau3el1HJFyqZUqaeryIiZ8vZFb+7WmXXotr+OIB67t3pJ6KQmM0ZQuIUqFXgl7Dx3llxMWkt2wJiMHaH3QaJWcEMdNvTOZsaKYr9dpSpfQo0KvhD9OWs7mPb71QRPjtGhFNLu2e0vqJMfrWLqEJBV6Bb5avYO/fbmW63u04swWUXn9MTlGzUTflD51eTEL1u/yOo7Id6jQT+DQ0VLuH59P83o1uOe807yOIyHiuu4tqV1DU7qEHhX6CTw1uYDV2/bz6KWdSE6I8zqOhIjUpHh+2qsVucuKWLRxt9dxRL6lQj+OhRt289LMVVyR05yeWVofVL7rJz0zqJUUpzNeJKSo0MtxtLSM+8bnUz8lgQcu1Pqg8n21kuK5oVcrPl2ylcWbNKVLaFChl+Ov01eydPMeHh7agdo1tD6olO/6Hq1ITYzjmdxCr6OIACr07yks2svTuYVc2CmdH5ze2Os4EsJqJ8dzfc8MJi3ewtLNe7yOI6JCP1ZpmeO+cfkkJ8by4MWnex1HwsANvVpRMzGOZ6boWLp4T4V+jDe+WMO8dbv4zcXtSUvV+qBSsTrJCfykRwYfL9zC8i17vY4jUU6F7rd+xwH+OGk5/dqkMbRLU6/jSBj5aa9WpCTEakoXz6nQ8S1a8cC7C4kx+P0lHbU+qJyUuikJXNcjg48WbqZgq6Z08Y4KHfjX3A3MLNjG6PPb0lTrg0oV3NQ7kxrxsTwzRWe8iHeivtCL9hzi4Q+X0DWjHtec09LrOBKm6qUkcG23lnyQv4nCon1ex5EoFfWF/n/vL+ZQSRmPDuuo9UHllNzUJ5OkuFiem6opXbwR1YX+8cLNTFq8hTsHnUZmWk2v40iYa1AzkR93a8H78zeyqlhTulS/qC30XQeO8H/vL6JD01rc1Fvrg0pg3NQnk/jYGJ6butLrKBKForbQH/pwKbsOHOWPwzprfVAJmIapSVxzTkvem7+Rtdv3ex1HokxUNtn0FcWMn7eBm/u2pn0TrQ8qgXVz30ziYoxndcaLVLOoK/R9h0t4YMJCWqelcPtArQ8qgdewVhJXdW3BhK83sn7HAa/jSBSJukL/06RlbNp9kD9qfVAJopv7tibWTGe8SLWKqkKfs2YHb3y5luHdMzirZT2v40gEa1w7iSu7Nmfc3A2a0qXaRE2hf7M+aNM6Nbj3vDZex5EocEu/1sSY8fw0nfEi1SNqCv3p3AJWFe/nD5d2JCVR64NK8KXXrsHlOc0YN3c9G3cd9DqORIGoKPRFG3fz1xmruPysZvTOTvM6jkSRW/v73nj/yzQdS5fgq3Shm1msmX1tZh/677cys9lmVmBmb5tZQvBiVt3R0jLuG5dPvZQEfnVhe6/jSJRpWqcGl53VnHfmbGDzbk3pElwnM6GPApYec/8x4EnnXDawE/hpIIMFyoszVrFk8x4eGtKB2slaH1Sq3639WlPmHC/oWLoEWaUK3cyaARcCL/vvGzAAGOd/yFhgaDACnorCon2MyS3ggo6NGdxB64OKN5rXS2bYmc14a856tu455HUciWCVndCfAu4Dyvz36wO7nHMl/vsbgHKX+TGzEWaWZ2Z5xcXFpxT2ZJSVOUaPz6dGfCwP/lDrg4q3RvbPorTM8RdN6RJEFRa6mV0EFDnn5h67uZyHuvKe75x70TmX45zLSUurvjck//blWvLW7uT/LmpPw9SkatuvSHla1E/m0jOa8tZX6yjSlC5BUpkJvSfwQzNbA/wT36GWp4A6ZvbN+X/NgE1BSVgFG3Ye4LFJy+hzWhqXnqn1QSU0jOyfRUmZ468zVnkdRSJUhYXunPuFc66Zcy4DuBKY4py7BpgKXOZ/2HDg/aClPAm+9UEXAfDIJR20PqiEjIwGKQzp0oQ3Z6+leO9hr+NIBDqV89DvB+4ys0J8x9RfCUykUzN+3kZmrCjm/sFtaVY32es4It9xW/8sjpSU8dJMTekSeCdV6M65ac65i/xfr3LOdXXOZTnnLnfOeT5yFO09xEMfLiGnZV2u7ab1QSX0ZKbV5Iedm/C3L9aybZ/nf2QkwkTUJ0UfnLiYg0dLeeyyTlofVELWbQOyOVRSqildAi5iCn3Sos18vHALowZm01rrg0oIy2pYk4s7+ab0HfuPeB1HIkhEFPruA0f59fuLOb1JLUb0yfQ6jkiFbh+QxcGjpbysKV0CKCIK/eGPlrBj/xEeG9aJeK0PKmEgu1EqF3RMZ+zna9ipKV0CJOzbb2ZBMf+au4Gf9cmkQ9PaXscRqbQ7BmSz/0gpr3y22usoEiHCutD3Hy5h9PiFZKalcMfAbK/jiJyUNo1TuaBjY17/fA27Dxz1Oo5EgLAu9D/9Z7lvfdBhnUiK1/qgEn5uH5DNvsMlvDJLU7qcurAt9LlrdzD2izVc160lORlaH1TCU7v0Wpx3eiNem7Wa3Qc1pcupCctCP3S0lPvG5dOkdg3uHdzW6zgip+SOgdnsPVTC67PWeB1FwlxYFvqzUwpZWbyfRy7tSE2tDyph7vQmtRnUrhGvfLaKPYc0pUvVhV2hL960mxemr2TYmc3oe5rWB5XIMGpgNnsOlTBWU7qcgrAq9JLSMu4fn0+d5AR+fVE7r+OIBEzHZrUZ2LYhL3+2mr2a0qWKwqrQX5q5mkUb9/DQkNOpkxySa1KLVNkdA7PZffAob3yx1usoEqbCptBXFe/jyckrGHx6Y87vmO51HJGA69y8Dv3apPHyzFXsP1xS8RNE/kdYFLpvfdCFJMXF8LshWh9UIteogdnsPKApXaomLAr9zdlr+WrNDn59UXsa1tL6oBK5zmhRlz6npfHSzFUcOKIpXU5OWBT65yu30zu7AZed1czrKCJBN2pgFjv2H+HvX2pKl5MTFidxP3/Nmew/Uqr1QSUqnNWyHr2yGvDijFVc2y2DGgm6rIVUTlhM6GamDxBJVBk1KJtt+47w5mxN6VJ5YVHoItHm7Ix6dM+sz19nrOLQ0VKv40iYUKGLhKhRg7Ip3nuYf8xe53UUCRMqdJEQ1S2zPue0qscL01dqSpdKUaGLhLBRg7Ip2nuYt+es9zqKhAEVukgI655Zn7Mz6vKXaSs5XKIpXU5MhS4SwsyMUQNPY8ueQ7yjKV0qoEIXCXE9s+pzVsu6PK8pXSqgQhcJcWbGHQOz2bz7EOPmbvA6joSwCgvdzJLM7CszW2Bmi83st/7trcxstpkVmNnbZqbr2YoESZ/sBnRpXofnp67kSEmZ13EkRFVmQj8MDHDOdQa6AIPNrBvwGPCkcy4b2An8NHgxRaKbmTFqUDYbdx1kwjxN6VK+Cgvd+ezz3433/3LAAGCcf/tYYGhQEooIAP1OS6NTs9o8O7WQo6Wa0uX7KnUM3cxizWw+UAR8CqwEdjnnvrm+5wagaXAiigh8c8ZLNht2HuTdeRu9jiMhqFKF7pwrdc51AZoBXYHyFvR05T3XzEaYWZ6Z5RUXF1c9qYgwoG1DOjb1TeklmtLlf5zUWS7OuV3ANKAbUMfMvrkEYjNg03Ge86JzLsc5l5OWlnYqWUWi3jdnvKzbcYD35pf7R06iWGXOckkzszr+r2sAg4ClwFTgMv/DhgPvByukiPzXoHYNaZ9ei2enFGhKl++ozISeDkw1s3xgDvCpc+5D4H7gLjMrBOoDrwQvpoh845spfc32A0xcoCld/qvCVSOcc/nAGeVsX4XveLqIVLMftG9E28apPDulkCFdmhIbo9W8RJ8UFQlLMTG+KX3Vtv18mK8pXXxU6CJhavDpjWnTKJWncwsoLSv3JDOJMip0kTAVE2PcPjCLlcX7+XjhZq/jSAhQoYuEsfM7pJPVsCbPTCmgTFN61FOhi4Sx2Bjj9gFZrNi6j38v2uJ1HPGYCl0kzF3UqQmt01I0pYsKXSTc+ab0bJZt2csnSzSlRzMVukgEuKhTOq0apDAmt1BTehRToYtEgLjYGG7rn8XSzXuYvHSr13HEIyp0kQgxpEsTWtZPZkxuAc5pSo9GKnSRCBEXG8PI/lks3rSH3KVFXscRD6jQRSLIJWc0pXm9GprSo5QKXSSCxPuPpS/cuJtpy7WgTLRRoYtEmEvOaEbTOjV4SlN61FGhi0SYhDjfsfQF63cxfYWm9GiiQheJQJed5ZvSdSw9uqjQRSJQQlwMt/RrzdfrdvFZ4Tav40g1UaGLRKjLc5qRXjuJMZM1pUcLFbpIhEqMi+WWfq3JW7uTL1Zu9zqOVAMVukgE+1FOcxrVSuSp3AKvo0g1UKGLRLCk+Fhu7tuar1bv0JQeBVToIhHuqq4tSEtNZEzuCq+jSJCp0EUi3DdT+perdjB7lab0SKZCF4kCV3dtQYOaiTw9RcfSI5kKXSQK1EiI5Wd9MplVuJ28NTu8jiNBokIXiRLXdGtB/ZQExuiMl4ilQheJEskJcYzok8nMgm3MW7fT6zgSBCp0kSjy424tqZeSwJjJmtIjUYWFbmbNzWyqmS01s8VmNsq/vZ6ZfWpmBf7busGPKyKnIiUxjht7t2L6imLmr9/ldRwJsMpM6CXA3c65dkA3YKSZtQdGA7nOuWwg139fRELcdd0zqJMcz9M6lh5xKix059xm59w8/9d7gaVAU2AIMNb/sLHA0GCFFJHAqZkYx429WjFlWRH5GzSlR5KTOoZuZhnAGcBsoJFzbjP4Sh9oeJznjDCzPDPLKy7WxfZFQsHwHhnUrhHP07mFXkeRAKp0oZtZTWA88HPn3J7KPs8596JzLsc5l5OWllaVjCISYKlJ8fy0VysmL93Koo27vY4jAVKpQjezeHxl/qZzboJ/81YzS/d/Px0oCk5EEQmG4T0ySE2K07H0CFKZs1wMeAVY6px74phvTQSG+78eDrwf+HgiEiy1a8RzQ89WfLJkK0s2Vfof3RLCKjOh9wSuBQaY2Xz/rwuAR4FzzawAONd/X0TCyA09W5GaGMczusZLRIir6AHOuc8AO863BwY2johUp9rJ8VzfM4OnpxSybMse2jau5XUkOQX6pKhIlLuhVytqJsbxjM54CXsqdJEoVyc5geE9WvLxos2s2LrX6zhyClToIsKNvTKpER/LM1M0pYczFbqIUDclgeu6Z/Bh/iYKizSlhysVuogAcFPvViTFxfKspvSwpUIXEQDq10zkuu4tmbhgE6uK93kdR6pAhS4i37qxdyYJcTGa0sOUCl1EvpWWmsiPz2nJe/M3snrbfq/jyElSoYvId4zom0l8bAzPTdWUHm5U6CLyHQ1Tk7jmnJa8+/VG1m7XlB5OVOgi8j0/65tJbIxpSg8zKnQR+Z5GtZK4umsLJszbyPodB7yOI5WkQheRct3ctzUxZjw/TVN6uFChi0i5GtdO4oqzm/OvvA1s2KkpPRyo0EXkuG7p1xoz+Mu0lV5HkUpQoYvIcTWpU4Mf5TTnnbz1bNp10Os4UgEVuoic0C39WgOa0sOBCl1ETqhZ3WQuO6sZb89Zz5bdh7yOIyegQheRCt3aL4sy53hhuqb0UKZCF5EKNa+XzLAzm/GPr9axdY+m9FClQheRShnZP4vSMk3poUyFLiKV0qJ+Mpec0ZR/zF5H0V5N6aEozusAIhI+buufxYR5G3hh2iruG9zG6zhhJSE2hpgYC+o+VOgiUmkZDVIY2qUpr85azauzVnsdJ6xMvqsvWQ1rBnUfKnQROSkPXNiOtumplJZ5nSS81E9JCPo+VOgiclIa1ExkRJ/WXseQcuhNURGRCKFCFxGJEBUWupm9amZFZrbomG31zOxTMyvw39YNbkwREalIZSb014HB/7NtNJDrnMsGcv33RUTEQxUWunNuBrDjfzYPAcb6vx4LDA1wLhEROUlVPYbeyDm3GcB/2/B4DzSzEWaWZ2Z5xcXFVdydiIhUJOhvijrnXnTO5TjnctLS0oK9OxGRqFXVQt9qZukA/tuiwEUSEZGqqOoHiyYCw4FH/bfvV+ZJc+fO3WZma6u4zxbAuio+NxrVBnZ7HSKM6PU6OXq9Ts7LM1lGAAAGAElEQVSp9lfLyjzInHMnfoDZW0A/oAGwFfgN8B7wzjEhL3fO/e8bpwFlZsXOOR2zqSQze9E5N8LrHOFCr9fJ0et1cqqrvyqc0J1zVx3nWwMDnKUiu6p5f+HuA68DhBm9XidHr9fJqZb+qnBCDxVmluecy/E6h4jIyaqu/gqnj/6/6HUAEZEqqpb+CpsJXURETiycJnQ5DjMbbGbLzazQzEb7t73p37bIfz2eeK9zhpLjvGavmNkCM8s3s3FmFtzVCMJIea/XMd97xsz2eZVN/iskC/04f9hu8993ZtbA64yhwsxigeeA84H2wFVm1h54E2gLdARqADd6FjLEnOA1u9M519k51wnf2Vu3eRgzZJzg9cLMcoA6HsYLScfpsNfNbLWZzff/6hLo/YZcoZ/gN88sYBBQ1fPYI1VXoNA5t8o5dwT4JzDEOfex8wO+App5mjK0HO812wNgZobvL0Edj/Qp9/Xy/1n9E3Cfp+lCzIn+AgTudc518f+aH+h9h1yhc/w/bF8759Z4Gy0kNQXWH3N/g38bAP5DLdcCk6o5Vyg77mtmZq8BW/D96+aZ6o8Wko73et0GTPzmuk7yrXI7rDp2HIqFfsKCku8pbxnxYyfL54EZzrmZ1ZQnHBz3NXPOXQ80AZYCV1RnqBBW3uuVCFyO/tIrz4k67Pf+92ieNLPEQO84FAu9ooKS79oAND/mfjNgE4CZ/QZIA+7yIFcoO+5rBuCcKwXeBoZVc65QVd7rtQbIAgrNbA2QbGaF1R8tJB2vw36B719+ZwP1gPsDveNQLPQT/mGT75kDZJtZKzNLAK4EJprZjcB5wFXOOa3P/l3He82y4Ntj6BcDyzzMGErKe73ec841ds5lOOcygAPOuSxPU4aOcjvMObfZ/7bWYeA1fIdmAqqqF+cKpm9/8wAb8f3mudrbSKHLOVdiZrcB/wFigVedc4vNbAG+N5C/8PUTE5xzv/Mwasgo7zXDd4hlppnVwjdhLQBu8S5l6Dje7zGPY4WycjvMzNKdc5v9A8NQYNGJfkhVhOQHi8zsAuAp/vub5/dmdge+d9Mb47tc78fOOZ2KJyIh5zgdNgXfIVAD5gM3O+cCev5+SBa6iIicvFA8hi4iIlWgQhcRiRAqdBGRCBFyha6L/IiIVE3IFbqIiFRNSBa6mdU0s1wzm2dmC81siH97hpktNbOXzGyxmX1iZjW8zisiEgpC7rRF/yGXOkCyc26P/1K5XwLZ+Fa+LgRynHPzzewdfBcH+rt3iUVEQkMoflIUfCfeP2JmfYAyfBe2aeT/3upjLjs5F8io/ngiIqEnVAv9GnyfqDrLOXfUf/GfJP/3Dh/zuFJ8160WEYl6IXkMHagNFPnLvD++Qy0iInICITWhm1kcvgn8TeADM8vDd80DXfVORKQCIfWmqJl1Bl5yzgX8spIiIpEuZA65mNnNwFvAr7zOIiISjkJqQhcRkarzbEI3s+ZmNtX/QaHFZjbKv72emX1qZgX+27r+7W3N7AszO2xm9/zPz7rT/zMWmdlbZpZU3j5FRCKZl4dcSoC7nXPtgG7ASDNrD4wGcp1z2UCu/z7ADuAO4PFjf4iZNfVvz3HOdcB3Qfkrq+c/QUQkdHhW6P719eb5v96LbwmwpsAQYKz/YWPxLdWEc67IOTcHOFrOj4sDavjPkklGa5CKSBQKiTdFzSwDOAOYDTRyzm0GX+kDDU/0XOfcRnxT+zpgM7DbOfdJMPOKiIQizwvdzGoC44GfO+f2VOH5dfFN9a2AJkCKmf04sClFREKfp4VuZvH4yvxN59wE/+atZpbu/346vgWhT2QQvuu7FDvnjgITgB7ByiwiEqq8PMvFgFeApc65J4751kRguP/r4cD7FfyodUA3M0v2/8yB+I7Hi4hEFc/OQzezXsBMYCG+KyoCPIDvOPo7QAt8ZX25c26HmTUG8oBa/sfvA9r7L7H7W+AKfGfOfA3c6Jw79iJeIiIRTx8sEhGJEJ6/KSoiIoGhQhcRiRAqdBGRCKFCFxGJECp0EZEIoUIXEYkQKnQRkQihQhcRiRD/DzurOsh7ENU2AAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"new_df.even.plot()"
]
},
{
"cell_type": "code",
"execution_count": 318,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0xee9e550>"
]
},
"execution_count": 318,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"new_df.plot()"
]
},
{
"cell_type": "code",
"execution_count": 319,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([<matplotlib.axes._subplots.AxesSubplot object at 0x000000000EED09B0>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x00000000120A6FD0>],\n",
" dtype=object)"
]
},
"execution_count": 319,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"new_df.plot(subplots=True)"
]
},
{
"cell_type": "code",
"execution_count": 320,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([<matplotlib.axes._subplots.AxesSubplot object at 0x0000000013BB2A20>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x0000000013DEC668>],\n",
" dtype=object)"
]
},
"execution_count": 320,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"new_df.plot(subplots=True, grid=True)"
]
},
{
"cell_type": "code",
"execution_count": 321,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([<matplotlib.axes._subplots.AxesSubplot object at 0x000000001424F5F8>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x00000000142CE1D0>],\n",
" dtype=object)"
]
},
"execution_count": 321,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"new_df.plot(subplots=True, legend=False)"
]
},
{
"cell_type": "code",
"execution_count": 322,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([<matplotlib.axes._subplots.AxesSubplot object at 0x000000000ED7ED30>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x000000001436BFD0>],\n",
" dtype=object)"
]
},
"execution_count": 322,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"new_df.plot(subplots=True, logx=True, logy=True)"
]
},
{
"cell_type": "code",
"execution_count": 323,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([<matplotlib.axes._subplots.AxesSubplot object at 0x00000000143A6908>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x0000000014518940>],\n",
" dtype=object)"
]
},
"execution_count": 323,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"new_df.plot(subplots=True, loglog=True, grid=True) # logx=True, logy=True"
]
},
{
"cell_type": "code",
"execution_count": 324,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1764a630>"
]
},
"execution_count": 324,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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J+E5mvgO4jNb/y24a38ll5rz/AVYCT05a/zGwrHq8DPhxu2ucxbFuB36jG8cI/ArwKPBrtD6o01O1vxv453bXdwbjGqT1i+Jq4AEguml81RieA5a8qa0r3qPAecB/Up0z7bbxzeSnE2YEU7kwM18BqJYXtLmeWRERK4HLgZ100RirwyZ7gP3Ag8BPgEOZebTqMgIsb1d9s+CLwEbg2P0yF9Nd4wNI4LsRsTsiNlRt3fIefTtwAPhadXjvzog4h+4Z37Q6NQi6TkQsBLYBn87Mn7e7ntmUmW9k5hCtv5yvBC6dqtvcVjU7IuJaYH9m7p7cPEXXjhzfJKsz8wrgt2kdvnxvuwuaRT3AFcBXM/Ny4Bd082GgKXRqELwaEcsAquX+NtdzRiKil1YIfCMzv101d9UYATLzEPAwrXMhiyLi2B3yBoGX21XXGVoNrImI54Bv0To89EW6Z3wAZObL1XI/cB+tQO+W9+gIMJKZO6v1e2kFQ7eMb1qdGgT3A+uqx+toHVfvSBERwF3Avsy8Y9JTXTHGiFgaEYuqx/3AB2idiHsIuL7q1rHjy8zPZuZgZq4EPgZ8LzNvpEvGBxAR50TEucceA78JPEmXvEcz86fAixFxSdV0DfAUXTK+mZj3HyiLiH8A3kfrmwBfBf4c+EfgHmAF8AJwQ2b+V7tqPBMR8R7gX4An+OUx5s/ROk/Q8WOMiFXAVuAsWn943JOZfxERb6f1F/T5wGPAxzNzvH2VnrmIeB/wp5l5bTeNrxrLfdVqD/DNzPx8RCymC96jABExBNwJnA08C3yS6v1KF4xvOvM+CCRJ9erUQ0OSpFliEEhS4QwCSSqcQSBJhTMIJKlwBoEkFc4gkKTCGQTSm0TEx6t7KOyJiL+JiJsiYvOk5z8REV8+Qd+zqvaxiPh8dR+GH0TEhe0ajzQdg0CaJCIuBT5K60vWhoA3gDHgw5O6fRS4+wR9b6z6nAP8IFv3Yfg+8MdzNATplPVM30UqyjXAu4Aftr4Gin5aXzb2bHVDnWeAS4B/A246QV+A12ndmwBgN617TEjzkkEgHS+ArZn52eMaI9YDHwGeBu7LzKy+MPD/9a1M5C+/v+UN/LemecxDQ9LxdgDXR8QF8H/35X0r8G3gOuD3gbun6St1FINAmiQznwL+jNbduPbSuqPasswcpfXVxG/NzEdO1rc9lUunz28flaTCOSOQpMIZBJJUOINAkgpnEEhS4QwCSSqcQSBJhTMIJKlwBoEkFe5/AXy++tDSzmyeAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# SCATTER PLOT\n",
"# \n",
"# x, y, c: color(s), s: size(s)\n",
"new_df.plot.scatter('even', 'odd') # scatter requires and x and y column"
]
},
{
"cell_type": "code",
"execution_count": 325,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x176b34e0>"
]
},
"execution_count": 325,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"new_df.plot('even', 'odd', c='red', kind=\"scatter\")"
]
},
{
"cell_type": "code",
"execution_count": 326,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x176dbe48>"
]
},
"execution_count": 326,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"new_df.plot('even', 'odd', c=['red', 'blue', 'green', 'orange', 'black'], kind=\"scatter\", )"
]
},
{
"cell_type": "code",
"execution_count": 327,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x176a89b0>"
]
},
"execution_count": 327,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"new_df.plot('even', 'odd', c=['red', 'blue'], kind=\"scatter\") # Alternately"
]
},
{
"cell_type": "code",
"execution_count": 328,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1779d3c8>"
]
},
"execution_count": 328,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 216x216 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"new_df.plot('even', 'odd', c=[\"blue\", \"red\"], kind=\"scatter\", s=[12, 22, 300, 40, 200], figsize=(3, 3)) # s => size"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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