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" <tr>\n",
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" <td>923</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>TailBot</td>\n",
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" <td>538</td>\n",
" <td>10472</td>\n",
" <td>M2_01 Wolf 2020</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>Pajabol</td>\n",
" <td>PL</td>\n",
" <td>820</td>\n",
" <td>10471</td>\n",
" <td>M2_01 Wolf 2020</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>Adzikov</td>\n",
" <td>PL</td>\n",
" <td>1105</td>\n",
" <td>10442</td>\n",
" <td>M2_01 Wolf 2020</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>14790</td>\n",
" <td>MagCCG</td>\n",
" <td>RU</td>\n",
" <td>4</td>\n",
" <td>389</td>\n",
" <td>M4_02 Love 2022</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>14791</td>\n",
" <td>可乐加冰.</td>\n",
" <td>CN</td>\n",
" <td>4</td>\n",
" <td>386</td>\n",
" <td>M4_02 Love 2022</td>\n",
" </tr>\n",
" <tr>\n",
" <th>109</th>\n",
" <td>14792</td>\n",
" <td>RomanTrooper</td>\n",
" <td>RU</td>\n",
" <td>4</td>\n",
" <td>386</td>\n",
" <td>M4_02 Love 2022</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>14793</td>\n",
" <td>Naveless</td>\n",
" <td>DE</td>\n",
" <td>4</td>\n",
" <td>384</td>\n",
" <td>M4_02 Love 2022</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55</th>\n",
" <td>14794</td>\n",
" <td>Pargali</td>\n",
" <td>PL</td>\n",
" <td>1</td>\n",
" <td>96</td>\n",
" <td>M4_02 Love 2022</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>182799 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" rank name country matches mmr season\n",
"0 1 kolemoen DE 431 10484 M2_01 Wolf 2020\n",
"1 2 kams134 PL 923 10477 M2_01 Wolf 2020\n",
"2 3 TailBot PL 538 10472 M2_01 Wolf 2020\n",
"3 4 Pajabol PL 820 10471 M2_01 Wolf 2020\n",
"4 5 Adzikov PL 1105 10442 M2_01 Wolf 2020\n",
".. ... ... ... ... ... ...\n",
"15 14790 MagCCG RU 4 389 M4_02 Love 2022\n",
"41 14791 可乐加冰. CN 4 386 M4_02 Love 2022\n",
"109 14792 RomanTrooper RU 4 386 M4_02 Love 2022\n",
"20 14793 Naveless DE 4 384 M4_02 Love 2022\n",
"55 14794 Pargali PL 1 96 M4_02 Love 2022\n",
"\n",
"[182799 rows x 6 columns]"
]
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" var nbb_formatted_code = \"%load_ext nb_black\\nimport pandas as pd\\nimport numpy as np\\nfrom os import listdir, path\\n\\noutput_path = \\\"./output\\\"\\n\\nplayers_df = (\\n pd.concat(\\n pd.read_excel(path.join(output_path, f))\\n for f in listdir(\\\"./output\\\")\\n if f.startswith(\\\"season_\\\")\\n )\\n .drop(columns=[\\\"Unnamed: 0\\\"])\\n .dropna()\\n).sort_values([\\\"season\\\", \\\"rank\\\"])\\nplayers_df\";\n",
" var nbb_cells = Jupyter.notebook.get_cells();\n",
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"\n",
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"\n",
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" pd.concat(\n",
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"cell_type": "code",
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" <tr>\n",
" <th>1137</th>\n",
" <td>1138</td>\n",
" <td>sepro</td>\n",
" <td>BE</td>\n",
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" <td>9746</td>\n",
" <td>M2_06 Magic 2020</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1166</th>\n",
" <td>1167</td>\n",
" <td>sepro</td>\n",
" <td>BE</td>\n",
" <td>260</td>\n",
" <td>9820</td>\n",
" <td>M2_07 Griffin 2020</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1254</th>\n",
" <td>1255</td>\n",
" <td>sepro</td>\n",
" <td>BE</td>\n",
" <td>214</td>\n",
" <td>9779</td>\n",
" <td>M2_08 Draconid 2020</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2237</th>\n",
" <td>12816</td>\n",
" <td>sepro</td>\n",
" <td>BE</td>\n",
" <td>97</td>\n",
" <td>3360</td>\n",
" <td>M2_09 Dryad 2020</td>\n",
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" <th>1056</th>\n",
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" <td>9846</td>\n",
" <td>M2_10 Cat 2020</td>\n",
" </tr>\n",
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" <th>1638</th>\n",
" <td>1639</td>\n",
" <td>sepro</td>\n",
" <td>BE</td>\n",
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" <td>9785</td>\n",
" <td>M2_11 Mahakam 2020</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2515</th>\n",
" <td>12856</td>\n",
" <td>sepro</td>\n",
" <td>BE</td>\n",
" <td>80</td>\n",
" <td>7407</td>\n",
" <td>M2_12 Wild Hunt 2020</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1466</th>\n",
" <td>1467</td>\n",
" <td>sepro</td>\n",
" <td>BE</td>\n",
" <td>175</td>\n",
" <td>9724</td>\n",
" <td>M3_01 Wolf 2021</td>\n",
" </tr>\n",
" <tr>\n",
" <th>679</th>\n",
" <td>680</td>\n",
" <td>sepro</td>\n",
" <td>BE</td>\n",
" <td>406</td>\n",
" <td>9903</td>\n",
" <td>M3_02 Love 2021</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1784</th>\n",
" <td>9353</td>\n",
" <td>sepro</td>\n",
" <td>BE</td>\n",
" <td>63</td>\n",
" <td>5384</td>\n",
" <td>M3_03 Bear 2021</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2553</th>\n",
" <td>14501</td>\n",
" <td>sepro</td>\n",
" <td>BE</td>\n",
" <td>40</td>\n",
" <td>2836</td>\n",
" <td>M3_04 Elf 2021</td>\n",
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"</div>"
],
"text/plain": [
" rank name country matches mmr season\n",
"2084 3259 sepro BE 142 9617 M2_05 Viper 2020\n",
"1137 1138 sepro BE 254 9746 M2_06 Magic 2020\n",
"1166 1167 sepro BE 260 9820 M2_07 Griffin 2020\n",
"1254 1255 sepro BE 214 9779 M2_08 Draconid 2020\n",
"2237 12816 sepro BE 97 3360 M2_09 Dryad 2020\n",
"1056 1057 sepro BE 351 9846 M2_10 Cat 2020\n",
"1638 1639 sepro BE 154 9785 M2_11 Mahakam 2020\n",
"2515 12856 sepro BE 80 7407 M2_12 Wild Hunt 2020\n",
"1466 1467 sepro BE 175 9724 M3_01 Wolf 2021\n",
"679 680 sepro BE 406 9903 M3_02 Love 2021\n",
"1784 9353 sepro BE 63 5384 M3_03 Bear 2021\n",
"2553 14501 sepro BE 40 2836 M3_04 Elf 2021"
]
},
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" var nbb_formatted_code = \"def sdi(data) -> float:\\n \\\"\\\"\\\"\\n Given a list of counts, calculate the Shannon Diversity Index\\n\\n :param data: list of integers\\n :return: Shannon Diversity Index\\n \\\"\\\"\\\"\\n\\n def p(n, N):\\n return n / N * np.log(n / N)\\n\\n x = np.array([d for d in data if d != 0])\\n\\n N = np.sum(x.astype(\\\"float64\\\"))\\n\\n if N == 0:\\n raise ZeroDivisionError(\\n \\\"Cannot calculate Shannon Diversity Index when the sum of all observations is zero\\\"\\n )\\n\\n return 0 - np.sum(np.array([p(n, N) for n in x]))\\n\\n\\ndef dominance(data) -> float:\\n N = np.sum(np.array(data).astype(\\\"float64\\\"))\\n\\n if N > 0:\\n proportions = data / N\\n\\n return np.sum(np.square(proportions))\\n else:\\n return np.nan\\n\\n\\ndef simpson(data) -> float:\\n d = dominance(data)\\n\\n if np.isnan(d):\\n return np.nan\\n else:\\n return 1 - d\";\n",
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" :param data: list of integers\n",
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" <tr>\n",
" <th>...</th>\n",
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" <tr>\n",
" <th>2116</th>\n",
" <td>M4_02 Love 2022</td>\n",
" <td>US</td>\n",
" <td>101</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2117</th>\n",
" <td>M4_02 Love 2022</td>\n",
" <td>UZ</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2118</th>\n",
" <td>M4_02 Love 2022</td>\n",
" <td>VN</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2119</th>\n",
" <td>M4_02 Love 2022</td>\n",
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" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2120</th>\n",
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" </tr>\n",
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"</table>\n",
"<p>2121 rows × 3 columns</p>\n",
"</div>"
],
"text/plain": [
" season country count\n",
"0 M2_01 Wolf 2020 AE 1\n",
"1 M2_01 Wolf 2020 AM 1\n",
"2 M2_01 Wolf 2020 AR 4\n",
"3 M2_01 Wolf 2020 AT 11\n",
"4 M2_01 Wolf 2020 AU 20\n",
"... ... ... ...\n",
"2116 M4_02 Love 2022 US 101\n",
"2117 M4_02 Love 2022 UZ 3\n",
"2118 M4_02 Love 2022 VN 6\n",
"2119 M4_02 Love 2022 ZA 7\n",
"2120 M4_02 Love 2022 ZW 1\n",
"\n",
"[2121 rows x 3 columns]"
]
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" var nbb_unformatted_code = \"season_country_df = (\\n players_df[players_df[\\\"rank\\\"] <= 2000]\\n .groupby([\\\"season\\\", \\\"country\\\"])\\n .agg(count=pd.NamedAgg(\\\"name\\\", \\\"count\\\"))\\n .reset_index()\\n)\\nseason_country_df\";\n",
" var nbb_formatted_code = \"season_country_df = (\\n players_df[players_df[\\\"rank\\\"] <= 2000]\\n .groupby([\\\"season\\\", \\\"country\\\"])\\n .agg(count=pd.NamedAgg(\\\"name\\\", \\\"count\\\"))\\n .reset_index()\\n)\\nseason_country_df\";\n",
" var nbb_cells = Jupyter.notebook.get_cells();\n",
" for (var i = 0; i < nbb_cells.length; ++i) {\n",
" if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
" if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
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"text/plain": [
"<IPython.core.display.Javascript object>"
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"metadata": {},
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"source": [
"season_country_df = (\n",
" players_df[players_df[\"rank\"] <= 2000]\n",
" .groupby([\"season\", \"country\"])\n",
" .agg(count=pd.NamedAgg(\"name\", \"count\"))\n",
" .reset_index()\n",
")\n",
"season_country_df"
]
},
{
"cell_type": "code",
"execution_count": 5,
"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>season</th>\n",
" <th>richness</th>\n",
" <th>shannon_diversity</th>\n",
" <th>dominance</th>\n",
" <th>simpson</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>M2_01 Wolf 2020</td>\n",
" <td>79</td>\n",
" <td>2.911451</td>\n",
" <td>0.109716</td>\n",
" <td>0.890284</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>M2_02 Love 2020</td>\n",
" <td>73</td>\n",
" <td>2.784907</td>\n",
" <td>0.124585</td>\n",
" <td>0.875415</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>M2_03 Bear 2020</td>\n",
" <td>74</td>\n",
" <td>2.770397</td>\n",
" <td>0.123829</td>\n",
" <td>0.876171</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>M2_04 Elf 2020</td>\n",
" <td>78</td>\n",
" <td>2.921609</td>\n",
" <td>0.105208</td>\n",
" <td>0.894792</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>M2_05 Viper 2020</td>\n",
" <td>82</td>\n",
" <td>2.975021</td>\n",
" <td>0.106955</td>\n",
" <td>0.893045</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>M2_06 Magic 2020</td>\n",
" <td>79</td>\n",
" <td>3.028216</td>\n",
" <td>0.100404</td>\n",
" <td>0.899596</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>M2_07 Griffin 2020</td>\n",
" <td>80</td>\n",
" <td>3.085621</td>\n",
" <td>0.092351</td>\n",
" <td>0.907649</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>M2_08 Draconid 2020</td>\n",
" <td>83</td>\n",
" <td>3.043551</td>\n",
" <td>0.097257</td>\n",
" <td>0.902743</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>M2_09 Dryad 2020</td>\n",
" <td>83</td>\n",
" <td>3.071002</td>\n",
" <td>0.093658</td>\n",
" <td>0.906342</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>M2_10 Cat 2020</td>\n",
" <td>82</td>\n",
" <td>3.028427</td>\n",
" <td>0.101374</td>\n",
" <td>0.898626</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>M2_11 Mahakam 2020</td>\n",
" <td>83</td>\n",
" <td>2.981676</td>\n",
" <td>0.110916</td>\n",
" <td>0.889084</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>M2_12 Wild Hunt 2020</td>\n",
" <td>76</td>\n",
" <td>3.040627</td>\n",
" <td>0.099488</td>\n",
" <td>0.900512</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>M3_01 Wolf 2021</td>\n",
" <td>86</td>\n",
" <td>3.088299</td>\n",
" <td>0.098081</td>\n",
" <td>0.901919</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>M3_02 Love 2021</td>\n",
" <td>84</td>\n",
" <td>3.045043</td>\n",
" <td>0.101261</td>\n",
" <td>0.898739</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>M3_03 Bear 2021</td>\n",
" <td>86</td>\n",
" <td>3.086869</td>\n",
" <td>0.090533</td>\n",
" <td>0.909467</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>M3_04 Elf 2021</td>\n",
" <td>83</td>\n",
" <td>3.124564</td>\n",
" <td>0.091177</td>\n",
" <td>0.908823</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>M3_05 Viper 2021</td>\n",
" <td>82</td>\n",
" <td>3.147269</td>\n",
" <td>0.086137</td>\n",
" <td>0.913863</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>M3_06 Magic 2021</td>\n",
" <td>82</td>\n",
" <td>3.162725</td>\n",
" <td>0.084816</td>\n",
" <td>0.915184</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>M3_07 Griffin 2021</td>\n",
" <td>84</td>\n",
" <td>3.083937</td>\n",
" <td>0.098090</td>\n",
" <td>0.901910</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>M3_08 Draconid 2021</td>\n",
" <td>82</td>\n",
" <td>3.023176</td>\n",
" <td>0.103806</td>\n",
" <td>0.896194</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>M3_09 Dryad 2021</td>\n",
" <td>86</td>\n",
" <td>3.009823</td>\n",
" <td>0.108551</td>\n",
" <td>0.891449</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>M3_10 Cat 2021</td>\n",
" <td>89</td>\n",
" <td>3.132842</td>\n",
" <td>0.091885</td>\n",
" <td>0.908115</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>M3_11 Mahakam 2021</td>\n",
" <td>82</td>\n",
" <td>2.989644</td>\n",
" <td>0.110278</td>\n",
" <td>0.889722</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>M3_12 Wild Hunt 2021</td>\n",
" <td>80</td>\n",
" <td>3.101439</td>\n",
" <td>0.092863</td>\n",
" <td>0.907137</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>M4_01 Wolf 2022</td>\n",
" <td>84</td>\n",
" <td>3.012610</td>\n",
" <td>0.112893</td>\n",
" <td>0.887107</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>M4_02 Love 2022</td>\n",
" <td>79</td>\n",
" <td>3.030534</td>\n",
" <td>0.104149</td>\n",
" <td>0.895851</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" season richness shannon_diversity dominance simpson\n",
"0 M2_01 Wolf 2020 79 2.911451 0.109716 0.890284\n",
"1 M2_02 Love 2020 73 2.784907 0.124585 0.875415\n",
"2 M2_03 Bear 2020 74 2.770397 0.123829 0.876171\n",
"3 M2_04 Elf 2020 78 2.921609 0.105208 0.894792\n",
"4 M2_05 Viper 2020 82 2.975021 0.106955 0.893045\n",
"5 M2_06 Magic 2020 79 3.028216 0.100404 0.899596\n",
"6 M2_07 Griffin 2020 80 3.085621 0.092351 0.907649\n",
"7 M2_08 Draconid 2020 83 3.043551 0.097257 0.902743\n",
"8 M2_09 Dryad 2020 83 3.071002 0.093658 0.906342\n",
"9 M2_10 Cat 2020 82 3.028427 0.101374 0.898626\n",
"10 M2_11 Mahakam 2020 83 2.981676 0.110916 0.889084\n",
"11 M2_12 Wild Hunt 2020 76 3.040627 0.099488 0.900512\n",
"12 M3_01 Wolf 2021 86 3.088299 0.098081 0.901919\n",
"13 M3_02 Love 2021 84 3.045043 0.101261 0.898739\n",
"14 M3_03 Bear 2021 86 3.086869 0.090533 0.909467\n",
"15 M3_04 Elf 2021 83 3.124564 0.091177 0.908823\n",
"16 M3_05 Viper 2021 82 3.147269 0.086137 0.913863\n",
"17 M3_06 Magic 2021 82 3.162725 0.084816 0.915184\n",
"18 M3_07 Griffin 2021 84 3.083937 0.098090 0.901910\n",
"19 M3_08 Draconid 2021 82 3.023176 0.103806 0.896194\n",
"20 M3_09 Dryad 2021 86 3.009823 0.108551 0.891449\n",
"21 M3_10 Cat 2021 89 3.132842 0.091885 0.908115\n",
"22 M3_11 Mahakam 2021 82 2.989644 0.110278 0.889722\n",
"23 M3_12 Wild Hunt 2021 80 3.101439 0.092863 0.907137\n",
"24 M4_01 Wolf 2022 84 3.012610 0.112893 0.887107\n",
"25 M4_02 Love 2022 79 3.030534 0.104149 0.895851"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"\n",
" setTimeout(function() {\n",
" var nbb_cell_id = 5;\n",
" var nbb_unformatted_code = \"season_diversity_df = (\\n season_country_df.groupby([\\\"season\\\"])\\n .agg(\\n richness=pd.NamedAgg(\\\"count\\\", \\\"count\\\"),\\n shannon_diversity=pd.NamedAgg(\\\"count\\\", sdi),\\n dominance=pd.NamedAgg(\\\"count\\\", dominance),\\n simpson=pd.NamedAgg(\\\"count\\\", simpson),\\n )\\n .reset_index()\\n)\\nseason_diversity_df.set_index(\\\"season\\\").to_excel(\\n \\\"./output/seasonal_diversity_of_nations.xlsx\\\"\\n)\\nseason_diversity_df\";\n",
" var nbb_formatted_code = \"season_diversity_df = (\\n season_country_df.groupby([\\\"season\\\"])\\n .agg(\\n richness=pd.NamedAgg(\\\"count\\\", \\\"count\\\"),\\n shannon_diversity=pd.NamedAgg(\\\"count\\\", sdi),\\n dominance=pd.NamedAgg(\\\"count\\\", dominance),\\n simpson=pd.NamedAgg(\\\"count\\\", simpson),\\n )\\n .reset_index()\\n)\\nseason_diversity_df.set_index(\\\"season\\\").to_excel(\\n \\\"./output/seasonal_diversity_of_nations.xlsx\\\"\\n)\\nseason_diversity_df\";\n",
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"season_diversity_df = (\n",
" season_country_df.groupby([\"season\"])\n",
" .agg(\n",
" richness=pd.NamedAgg(\"count\", \"count\"),\n",
" shannon_diversity=pd.NamedAgg(\"count\", sdi),\n",
" dominance=pd.NamedAgg(\"count\", dominance),\n",
" simpson=pd.NamedAgg(\"count\", simpson),\n",
" )\n",
" .reset_index()\n",
")\n",
"season_diversity_df.set_index(\"season\").to_excel(\n",
" \"./output/seasonal_diversity_of_nations.xlsx\"\n",
")\n",
"season_diversity_df"
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},
{
"cell_type": "code",
"execution_count": 6,
"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>season</th>\n",
" <th>players_found</th>\n",
" <th>players_total</th>\n",
" <th>players_captured</th>\n",
" <th>extra_players_captured</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>M2_01 Wolf 2020</td>\n",
" <td>2905</td>\n",
" <td>2997</td>\n",
" <td>96.930264</td>\n",
" <td>32.846715</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>M2_02 Love 2020</td>\n",
" <td>3984</td>\n",
" <td>4883</td>\n",
" <td>81.589187</td>\n",
" <td>55.561048</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>M2_03 Bear 2020</td>\n",
" <td>4772</td>\n",
" <td>6632</td>\n",
" <td>71.954162</td>\n",
" <td>50.689290</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>M2_04 Elf 2020</td>\n",
" <td>6070</td>\n",
" <td>10209</td>\n",
" <td>59.457342</td>\n",
" <td>43.679412</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>M2_05 Viper 2020</td>\n",
" <td>5977</td>\n",
" <td>10079</td>\n",
" <td>59.301518</td>\n",
" <td>43.177725</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>M2_06 Magic 2020</td>\n",
" <td>6104</td>\n",
" <td>9919</td>\n",
" <td>61.538462</td>\n",
" <td>45.955518</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>M2_07 Griffin 2020</td>\n",
" <td>7372</td>\n",
" <td>14791</td>\n",
" <td>49.841120</td>\n",
" <td>37.817450</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>M2_08 Draconid 2020</td>\n",
" <td>7037</td>\n",
" <td>13800</td>\n",
" <td>50.992754</td>\n",
" <td>38.180987</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>M2_09 Dryad 2020</td>\n",
" <td>7056</td>\n",
" <td>14554</td>\n",
" <td>48.481517</td>\n",
" <td>35.881649</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>M2_10 Cat 2020</td>\n",
" <td>6987</td>\n",
" <td>16011</td>\n",
" <td>43.638748</td>\n",
" <td>31.381644</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>M2_11 Mahakam 2020</td>\n",
" <td>6644</td>\n",
" <td>16752</td>\n",
" <td>39.660936</td>\n",
" <td>27.238699</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>M2_12 Wild Hunt 2020</td>\n",
" <td>7806</td>\n",
" <td>22464</td>\n",
" <td>34.748932</td>\n",
" <td>25.229545</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>M3_01 Wolf 2021</td>\n",
" <td>6197</td>\n",
" <td>13428</td>\n",
" <td>46.149836</td>\n",
" <td>31.576457</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>M3_02 Love 2021</td>\n",
" <td>7119</td>\n",
" <td>16016</td>\n",
" <td>44.449301</td>\n",
" <td>32.373062</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>M3_03 Bear 2021</td>\n",
" <td>7012</td>\n",
" <td>13636</td>\n",
" <td>51.422705</td>\n",
" <td>38.530067</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>M3_04 Elf 2021</td>\n",
" <td>10107</td>\n",
" <td>16049</td>\n",
" <td>62.975886</td>\n",
" <td>54.947305</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>M3_05 Viper 2021</td>\n",
" <td>7771</td>\n",
" <td>17623</td>\n",
" <td>44.095784</td>\n",
" <td>33.265596</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>M3_06 Magic 2021</td>\n",
" <td>10899</td>\n",
" <td>17442</td>\n",
" <td>62.487100</td>\n",
" <td>55.129612</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>M3_07 Griffin 2021</td>\n",
" <td>7771</td>\n",
" <td>16772</td>\n",
" <td>46.333174</td>\n",
" <td>35.300460</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>M3_08 Draconid 2021</td>\n",
" <td>8333</td>\n",
" <td>17523</td>\n",
" <td>47.554642</td>\n",
" <td>37.325240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>M3_09 Dryad 2021</td>\n",
" <td>7561</td>\n",
" <td>17486</td>\n",
" <td>43.240307</td>\n",
" <td>32.141392</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>M3_10 Cat 2021</td>\n",
" <td>8023</td>\n",
" <td>14048</td>\n",
" <td>57.111333</td>\n",
" <td>46.147658</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>M3_11 Mahakam 2021</td>\n",
" <td>9147</td>\n",
" <td>19902</td>\n",
" <td>45.960205</td>\n",
" <td>36.891210</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>M3_12 Wild Hunt 2021</td>\n",
" <td>9566</td>\n",
" <td>21651</td>\n",
" <td>44.182717</td>\n",
" <td>35.687297</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>M4_01 Wolf 2022</td>\n",
" <td>7607</td>\n",
" <td>15520</td>\n",
" <td>49.014175</td>\n",
" <td>37.496051</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>M4_02 Love 2022</td>\n",
" <td>2972</td>\n",
" <td>14794</td>\n",
" <td>20.089225</td>\n",
" <td>0.938495</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" season players_found players_total players_captured \\\n",
"0 M2_01 Wolf 2020 2905 2997 96.930264 \n",
"1 M2_02 Love 2020 3984 4883 81.589187 \n",
"2 M2_03 Bear 2020 4772 6632 71.954162 \n",
"3 M2_04 Elf 2020 6070 10209 59.457342 \n",
"4 M2_05 Viper 2020 5977 10079 59.301518 \n",
"5 M2_06 Magic 2020 6104 9919 61.538462 \n",
"6 M2_07 Griffin 2020 7372 14791 49.841120 \n",
"7 M2_08 Draconid 2020 7037 13800 50.992754 \n",
"8 M2_09 Dryad 2020 7056 14554 48.481517 \n",
"9 M2_10 Cat 2020 6987 16011 43.638748 \n",
"10 M2_11 Mahakam 2020 6644 16752 39.660936 \n",
"11 M2_12 Wild Hunt 2020 7806 22464 34.748932 \n",
"12 M3_01 Wolf 2021 6197 13428 46.149836 \n",
"13 M3_02 Love 2021 7119 16016 44.449301 \n",
"14 M3_03 Bear 2021 7012 13636 51.422705 \n",
"15 M3_04 Elf 2021 10107 16049 62.975886 \n",
"16 M3_05 Viper 2021 7771 17623 44.095784 \n",
"17 M3_06 Magic 2021 10899 17442 62.487100 \n",
"18 M3_07 Griffin 2021 7771 16772 46.333174 \n",
"19 M3_08 Draconid 2021 8333 17523 47.554642 \n",
"20 M3_09 Dryad 2021 7561 17486 43.240307 \n",
"21 M3_10 Cat 2021 8023 14048 57.111333 \n",
"22 M3_11 Mahakam 2021 9147 19902 45.960205 \n",
"23 M3_12 Wild Hunt 2021 9566 21651 44.182717 \n",
"24 M4_01 Wolf 2022 7607 15520 49.014175 \n",
"25 M4_02 Love 2022 2972 14794 20.089225 \n",
"\n",
" extra_players_captured \n",
"0 32.846715 \n",
"1 55.561048 \n",
"2 50.689290 \n",
"3 43.679412 \n",
"4 43.177725 \n",
"5 45.955518 \n",
"6 37.817450 \n",
"7 38.180987 \n",
"8 35.881649 \n",
"9 31.381644 \n",
"10 27.238699 \n",
"11 25.229545 \n",
"12 31.576457 \n",
"13 32.373062 \n",
"14 38.530067 \n",
"15 54.947305 \n",
"16 33.265596 \n",
"17 55.129612 \n",
"18 35.300460 \n",
"19 37.325240 \n",
"20 32.141392 \n",
"21 46.147658 \n",
"22 36.891210 \n",
"23 35.687297 \n",
"24 37.496051 \n",
"25 0.938495 "
]
},
"execution_count": 6,
"metadata": {},
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{
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" var nbb_unformatted_code = \"summary_df = (\\n players_df.groupby([\\\"season\\\"])\\n .agg(\\n players_found=pd.NamedAgg(\\\"name\\\", \\\"count\\\"),\\n players_total=pd.NamedAgg(\\\"rank\\\", \\\"max\\\"),\\n )\\n .reset_index()\\n)\\n\\nsummary_df[\\\"players_captured\\\"] = (\\n summary_df[\\\"players_found\\\"] * 100 / summary_df[\\\"players_total\\\"]\\n)\\nsummary_df[\\\"extra_players_captured\\\"] = (\\n (summary_df[\\\"players_found\\\"] - 2860) * 100 / (summary_df[\\\"players_total\\\"] - 2860)\\n)\\nsummary_df.set_index(\\\"season\\\").to_excel(\\\"./output/seasonal_player_counts.xlsx\\\")\\nsummary_df\";\n",
" var nbb_formatted_code = \"summary_df = (\\n players_df.groupby([\\\"season\\\"])\\n .agg(\\n players_found=pd.NamedAgg(\\\"name\\\", \\\"count\\\"),\\n players_total=pd.NamedAgg(\\\"rank\\\", \\\"max\\\"),\\n )\\n .reset_index()\\n)\\n\\nsummary_df[\\\"players_captured\\\"] = (\\n summary_df[\\\"players_found\\\"] * 100 / summary_df[\\\"players_total\\\"]\\n)\\nsummary_df[\\\"extra_players_captured\\\"] = (\\n (summary_df[\\\"players_found\\\"] - 2860) * 100 / (summary_df[\\\"players_total\\\"] - 2860)\\n)\\nsummary_df.set_index(\\\"season\\\").to_excel(\\\"./output/seasonal_player_counts.xlsx\\\")\\nsummary_df\";\n",
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" for (var i = 0; i < nbb_cells.length; ++i) {\n",
" if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
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"source": [
"summary_df = (\n",
" players_df.groupby([\"season\"])\n",
" .agg(\n",
" players_found=pd.NamedAgg(\"name\", \"count\"),\n",
" players_total=pd.NamedAgg(\"rank\", \"max\"),\n",
" )\n",
" .reset_index()\n",
")\n",
"\n",
"summary_df[\"players_captured\"] = (\n",
" summary_df[\"players_found\"] * 100 / summary_df[\"players_total\"]\n",
")\n",
"summary_df[\"extra_players_captured\"] = (\n",
" (summary_df[\"players_found\"] - 2860) * 100 / (summary_df[\"players_total\"] - 2860)\n",
")\n",
"summary_df.set_index(\"season\").to_excel(\"./output/seasonal_player_counts.xlsx\")\n",
"summary_df"
]
},
{
"cell_type": "code",
"execution_count": 7,
"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>season</th>\n",
" <th>players_found</th>\n",
" <th>players_total</th>\n",
" <th>players_captured</th>\n",
" <th>extra_players_captured</th>\n",
" <th>richness</th>\n",
" <th>shannon_diversity</th>\n",
" <th>dominance</th>\n",
" <th>simpson</th>\n",
" <th>top500_cutoff</th>\n",
" <th>top200_cutoff</th>\n",
" <th>top64_cutoff</th>\n",
" <th>low_estimate</th>\n",
" <th>high_estimate</th>\n",
" <th>mean_estimate</th>\n",
" <th>std_err</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>M2_01 Wolf 2020</td>\n",
" <td>2905</td>\n",
" <td>2997</td>\n",
" <td>96.930264</td>\n",
" <td>32.846715</td>\n",
" <td>79</td>\n",
" <td>2.911451</td>\n",
" <td>0.109716</td>\n",
" <td>0.890284</td>\n",
" <td>9749</td>\n",
" <td>9872</td>\n",
" <td>10061</td>\n",
" <td>2900.000000</td>\n",
" <td>3600.0</td>\n",
" <td>3117.636364</td>\n",
" <td>124.944153</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>M2_02 Love 2020</td>\n",
" <td>3984</td>\n",
" <td>4883</td>\n",
" <td>81.589187</td>\n",
" <td>55.561048</td>\n",
" <td>73</td>\n",
" <td>2.784907</td>\n",
" <td>0.124585</td>\n",
" <td>0.875415</td>\n",
" <td>9832</td>\n",
" <td>9952</td>\n",
" <td>10117</td>\n",
" <td>4566.666667</td>\n",
" <td>7100.0</td>\n",
" <td>5620.242424</td>\n",
" <td>441.315936</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>M2_03 Bear 2020</td>\n",
" <td>4772</td>\n",
" <td>6632</td>\n",
" <td>71.954162</td>\n",
" <td>50.689290</td>\n",
" <td>74</td>\n",
" <td>2.770397</td>\n",
" <td>0.123829</td>\n",
" <td>0.876171</td>\n",
" <td>9867</td>\n",
" <td>9995</td>\n",
" <td>10204</td>\n",
" <td>6036.363636</td>\n",
" <td>10300.0</td>\n",
" <td>7329.272727</td>\n",
" <td>760.229978</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>M2_04 Elf 2020</td>\n",
" <td>6070</td>\n",
" <td>10209</td>\n",
" <td>59.457342</td>\n",
" <td>43.679412</td>\n",
" <td>78</td>\n",
" <td>2.921609</td>\n",
" <td>0.105208</td>\n",
" <td>0.894792</td>\n",
" <td>9952</td>\n",
" <td>10087</td>\n",
" <td>10293</td>\n",
" <td>9927.272727</td>\n",
" <td>18000.0</td>\n",
" <td>12319.454545</td>\n",
" <td>1494.370140</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>M2_05 Viper 2020</td>\n",
" <td>5977</td>\n",
" <td>10079</td>\n",
" <td>59.301518</td>\n",
" <td>43.177725</td>\n",
" <td>82</td>\n",
" <td>2.975021</td>\n",
" <td>0.106955</td>\n",
" <td>0.893045</td>\n",
" <td>9910</td>\n",
" <td>10028</td>\n",
" <td>10255</td>\n",
" <td>7766.666667</td>\n",
" <td>11400.0</td>\n",
" <td>9372.060606</td>\n",
" <td>727.332197</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>M2_06 Magic 2020</td>\n",
" <td>6104</td>\n",
" <td>9919</td>\n",
" <td>61.538462</td>\n",
" <td>45.955518</td>\n",
" <td>79</td>\n",
" <td>3.028216</td>\n",
" <td>0.100404</td>\n",
" <td>0.899596</td>\n",
" <td>9896</td>\n",
" <td>10002</td>\n",
" <td>10191</td>\n",
" <td>6800.000000</td>\n",
" <td>9800.0</td>\n",
" <td>8320.181818</td>\n",
" <td>618.201230</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>M2_07 Griffin 2020</td>\n",
" <td>7372</td>\n",
" <td>14791</td>\n",
" <td>49.841120</td>\n",
" <td>37.817450</td>\n",
" <td>80</td>\n",
" <td>3.085621</td>\n",
" <td>0.092351</td>\n",
" <td>0.907649</td>\n",
" <td>9978</td>\n",
" <td>10100</td>\n",
" <td>10289</td>\n",
" <td>12836.363636</td>\n",
" <td>19900.0</td>\n",
" <td>14683.272727</td>\n",
" <td>1331.618216</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>M2_08 Draconid 2020</td>\n",
" <td>7037</td>\n",
" <td>13800</td>\n",
" <td>50.992754</td>\n",
" <td>38.180987</td>\n",
" <td>83</td>\n",
" <td>3.043551</td>\n",
" <td>0.097257</td>\n",
" <td>0.902743</td>\n",
" <td>9946</td>\n",
" <td>10061</td>\n",
" <td>10246</td>\n",
" <td>9566.666667</td>\n",
" <td>13300.0</td>\n",
" <td>11186.242424</td>\n",
" <td>696.853229</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>M2_09 Dryad 2020</td>\n",
" <td>7056</td>\n",
" <td>14554</td>\n",
" <td>48.481517</td>\n",
" <td>35.881649</td>\n",
" <td>83</td>\n",
" <td>3.071002</td>\n",
" <td>0.093658</td>\n",
" <td>0.906342</td>\n",
" <td>9946</td>\n",
" <td>10046</td>\n",
" <td>10183</td>\n",
" <td>9733.333333</td>\n",
" <td>12580.0</td>\n",
" <td>11218.666667</td>\n",
" <td>458.757501</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>M2_10 Cat 2020</td>\n",
" <td>6987</td>\n",
" <td>16011</td>\n",
" <td>43.638748</td>\n",
" <td>31.381644</td>\n",
" <td>82</td>\n",
" <td>3.028427</td>\n",
" <td>0.101374</td>\n",
" <td>0.898626</td>\n",
" <td>9977</td>\n",
" <td>10067</td>\n",
" <td>10176</td>\n",
" <td>12800.000000</td>\n",
" <td>14620.0</td>\n",
" <td>13774.181818</td>\n",
" <td>369.275995</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>M2_11 Mahakam 2020</td>\n",
" <td>6644</td>\n",
" <td>16752</td>\n",
" <td>39.660936</td>\n",
" <td>27.238699</td>\n",
" <td>83</td>\n",
" <td>2.981676</td>\n",
" <td>0.110916</td>\n",
" <td>0.889084</td>\n",
" <td>10000</td>\n",
" <td>10090</td>\n",
" <td>10216</td>\n",
" <td>12995.454545</td>\n",
" <td>18900.0</td>\n",
" <td>16041.757576</td>\n",
" <td>976.287680</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>M2_12 Wild Hunt 2020</td>\n",
" <td>7806</td>\n",
" <td>22464</td>\n",
" <td>34.748932</td>\n",
" <td>25.229545</td>\n",
" <td>76</td>\n",
" <td>3.040627</td>\n",
" <td>0.099488</td>\n",
" <td>0.900512</td>\n",
" <td>10070</td>\n",
" <td>10172</td>\n",
" <td>10313</td>\n",
" <td>12995.454545</td>\n",
" <td>35900.0</td>\n",
" <td>23051.757576</td>\n",
" <td>3678.743988</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>M3_01 Wolf 2021</td>\n",
" <td>6197</td>\n",
" <td>13428</td>\n",
" <td>46.149836</td>\n",
" <td>31.576457</td>\n",
" <td>86</td>\n",
" <td>3.088299</td>\n",
" <td>0.098081</td>\n",
" <td>0.901919</td>\n",
" <td>9916</td>\n",
" <td>10044</td>\n",
" <td>10295</td>\n",
" <td>7968.181818</td>\n",
" <td>13700.0</td>\n",
" <td>9929.636364</td>\n",
" <td>1046.105394</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>M3_02 Love 2021</td>\n",
" <td>7119</td>\n",
" <td>16016</td>\n",
" <td>44.449301</td>\n",
" <td>32.373062</td>\n",
" <td>84</td>\n",
" <td>3.045043</td>\n",
" <td>0.101261</td>\n",
" <td>0.898739</td>\n",
" <td>9975</td>\n",
" <td>10097</td>\n",
" <td>10325</td>\n",
" <td>11645.454545</td>\n",
" <td>19500.0</td>\n",
" <td>13977.757576</td>\n",
" <td>1430.087791</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>M3_03 Bear 2021</td>\n",
" <td>7012</td>\n",
" <td>13636</td>\n",
" <td>51.422705</td>\n",
" <td>38.530067</td>\n",
" <td>86</td>\n",
" <td>3.086869</td>\n",
" <td>0.090533</td>\n",
" <td>0.909467</td>\n",
" <td>9914</td>\n",
" <td>10026</td>\n",
" <td>10230</td>\n",
" <td>7466.666667</td>\n",
" <td>10900.0</td>\n",
" <td>9195.333333</td>\n",
" <td>715.137594</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>M3_04 Elf 2021</td>\n",
" <td>10107</td>\n",
" <td>16049</td>\n",
" <td>62.975886</td>\n",
" <td>54.947305</td>\n",
" <td>83</td>\n",
" <td>3.124564</td>\n",
" <td>0.091177</td>\n",
" <td>0.908823</td>\n",
" <td>9992</td>\n",
" <td>10102</td>\n",
" <td>10323</td>\n",
" <td>11586.363636</td>\n",
" <td>20300.0</td>\n",
" <td>14814.606061</td>\n",
" <td>1527.809485</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>M3_05 Viper 2021</td>\n",
" <td>7771</td>\n",
" <td>17623</td>\n",
" <td>44.095784</td>\n",
" <td>33.265596</td>\n",
" <td>82</td>\n",
" <td>3.147269</td>\n",
" <td>0.086137</td>\n",
" <td>0.913863</td>\n",
" <td>9998</td>\n",
" <td>10106</td>\n",
" <td>10300</td>\n",
" <td>12990.909091</td>\n",
" <td>20900.0</td>\n",
" <td>16159.515152</td>\n",
" <td>1322.547676</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>M3_06 Magic 2021</td>\n",
" <td>10899</td>\n",
" <td>17442</td>\n",
" <td>62.487100</td>\n",
" <td>55.129612</td>\n",
" <td>82</td>\n",
" <td>3.162725</td>\n",
" <td>0.084816</td>\n",
" <td>0.915184</td>\n",
" <td>9974</td>\n",
" <td>10082</td>\n",
" <td>10278</td>\n",
" <td>11263.636364</td>\n",
" <td>17200.0</td>\n",
" <td>13526.060606</td>\n",
" <td>1034.558786</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>M3_07 Griffin 2021</td>\n",
" <td>7771</td>\n",
" <td>16772</td>\n",
" <td>46.333174</td>\n",
" <td>35.300460</td>\n",
" <td>84</td>\n",
" <td>3.083937</td>\n",
" <td>0.098090</td>\n",
" <td>0.901910</td>\n",
" <td>9958</td>\n",
" <td>10067</td>\n",
" <td>10287</td>\n",
" <td>10140.909091</td>\n",
" <td>15800.0</td>\n",
" <td>12241.515152</td>\n",
" <td>1040.267969</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>M3_08 Draconid 2021</td>\n",
" <td>8333</td>\n",
" <td>17523</td>\n",
" <td>47.554642</td>\n",
" <td>37.325240</td>\n",
" <td>82</td>\n",
" <td>3.023176</td>\n",
" <td>0.103806</td>\n",
" <td>0.896194</td>\n",
" <td>9954</td>\n",
" <td>10079</td>\n",
" <td>10281</td>\n",
" <td>11200.000000</td>\n",
" <td>16900.0</td>\n",
" <td>12854.545455</td>\n",
" <td>1051.176279</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>M3_09 Dryad 2021</td>\n",
" <td>7561</td>\n",
" <td>17486</td>\n",
" <td>43.240307</td>\n",
" <td>32.141392</td>\n",
" <td>86</td>\n",
" <td>3.009823</td>\n",
" <td>0.108551</td>\n",
" <td>0.891449</td>\n",
" <td>9968</td>\n",
" <td>10078</td>\n",
" <td>10250</td>\n",
" <td>11868.181818</td>\n",
" <td>15900.0</td>\n",
" <td>13442.969697</td>\n",
" <td>746.264455</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>M3_10 Cat 2021</td>\n",
" <td>8023</td>\n",
" <td>14048</td>\n",
" <td>57.111333</td>\n",
" <td>46.147658</td>\n",
" <td>89</td>\n",
" <td>3.132842</td>\n",
" <td>0.091885</td>\n",
" <td>0.908115</td>\n",
" <td>9879</td>\n",
" <td>9974</td>\n",
" <td>10083</td>\n",
" <td>5100.000000</td>\n",
" <td>7770.0</td>\n",
" <td>6559.939394</td>\n",
" <td>575.186402</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>M3_11 Mahakam 2021</td>\n",
" <td>9147</td>\n",
" <td>19902</td>\n",
" <td>45.960205</td>\n",
" <td>36.891210</td>\n",
" <td>82</td>\n",
" <td>2.989644</td>\n",
" <td>0.110278</td>\n",
" <td>0.889722</td>\n",
" <td>10012</td>\n",
" <td>10122</td>\n",
" <td>10252</td>\n",
" <td>12981.818182</td>\n",
" <td>23300.0</td>\n",
" <td>17920.363636</td>\n",
" <td>1647.708090</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>M3_12 Wild Hunt 2021</td>\n",
" <td>9566</td>\n",
" <td>21651</td>\n",
" <td>44.182717</td>\n",
" <td>35.687297</td>\n",
" <td>80</td>\n",
" <td>3.101439</td>\n",
" <td>0.092863</td>\n",
" <td>0.907137</td>\n",
" <td>10032</td>\n",
" <td>10140</td>\n",
" <td>10271</td>\n",
" <td>12981.818182</td>\n",
" <td>27500.0</td>\n",
" <td>19567.696970</td>\n",
" <td>2331.781053</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>M4_01 Wolf 2022</td>\n",
" <td>7607</td>\n",
" <td>15520</td>\n",
" <td>49.014175</td>\n",
" <td>37.496051</td>\n",
" <td>84</td>\n",
" <td>3.012610</td>\n",
" <td>0.112893</td>\n",
" <td>0.887107</td>\n",
" <td>9941</td>\n",
" <td>10093</td>\n",
" <td>10340</td>\n",
" <td>8690.909091</td>\n",
" <td>18900.0</td>\n",
" <td>12063.515152</td>\n",
" <td>1789.893240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>M4_02 Love 2022</td>\n",
" <td>2972</td>\n",
" <td>14794</td>\n",
" <td>20.089225</td>\n",
" <td>0.938495</td>\n",
" <td>79</td>\n",
" <td>3.030534</td>\n",
" <td>0.104149</td>\n",
" <td>0.895851</td>\n",
" <td>9932</td>\n",
" <td>10068</td>\n",
" <td>10312</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" season players_found players_total players_captured \\\n",
"0 M2_01 Wolf 2020 2905 2997 96.930264 \n",
"1 M2_02 Love 2020 3984 4883 81.589187 \n",
"2 M2_03 Bear 2020 4772 6632 71.954162 \n",
"3 M2_04 Elf 2020 6070 10209 59.457342 \n",
"4 M2_05 Viper 2020 5977 10079 59.301518 \n",
"5 M2_06 Magic 2020 6104 9919 61.538462 \n",
"6 M2_07 Griffin 2020 7372 14791 49.841120 \n",
"7 M2_08 Draconid 2020 7037 13800 50.992754 \n",
"8 M2_09 Dryad 2020 7056 14554 48.481517 \n",
"9 M2_10 Cat 2020 6987 16011 43.638748 \n",
"10 M2_11 Mahakam 2020 6644 16752 39.660936 \n",
"11 M2_12 Wild Hunt 2020 7806 22464 34.748932 \n",
"12 M3_01 Wolf 2021 6197 13428 46.149836 \n",
"13 M3_02 Love 2021 7119 16016 44.449301 \n",
"14 M3_03 Bear 2021 7012 13636 51.422705 \n",
"15 M3_04 Elf 2021 10107 16049 62.975886 \n",
"16 M3_05 Viper 2021 7771 17623 44.095784 \n",
"17 M3_06 Magic 2021 10899 17442 62.487100 \n",
"18 M3_07 Griffin 2021 7771 16772 46.333174 \n",
"19 M3_08 Draconid 2021 8333 17523 47.554642 \n",
"20 M3_09 Dryad 2021 7561 17486 43.240307 \n",
"21 M3_10 Cat 2021 8023 14048 57.111333 \n",
"22 M3_11 Mahakam 2021 9147 19902 45.960205 \n",
"23 M3_12 Wild Hunt 2021 9566 21651 44.182717 \n",
"24 M4_01 Wolf 2022 7607 15520 49.014175 \n",
"25 M4_02 Love 2022 2972 14794 20.089225 \n",
"\n",
" extra_players_captured richness shannon_diversity dominance simpson \\\n",
"0 32.846715 79 2.911451 0.109716 0.890284 \n",
"1 55.561048 73 2.784907 0.124585 0.875415 \n",
"2 50.689290 74 2.770397 0.123829 0.876171 \n",
"3 43.679412 78 2.921609 0.105208 0.894792 \n",
"4 43.177725 82 2.975021 0.106955 0.893045 \n",
"5 45.955518 79 3.028216 0.100404 0.899596 \n",
"6 37.817450 80 3.085621 0.092351 0.907649 \n",
"7 38.180987 83 3.043551 0.097257 0.902743 \n",
"8 35.881649 83 3.071002 0.093658 0.906342 \n",
"9 31.381644 82 3.028427 0.101374 0.898626 \n",
"10 27.238699 83 2.981676 0.110916 0.889084 \n",
"11 25.229545 76 3.040627 0.099488 0.900512 \n",
"12 31.576457 86 3.088299 0.098081 0.901919 \n",
"13 32.373062 84 3.045043 0.101261 0.898739 \n",
"14 38.530067 86 3.086869 0.090533 0.909467 \n",
"15 54.947305 83 3.124564 0.091177 0.908823 \n",
"16 33.265596 82 3.147269 0.086137 0.913863 \n",
"17 55.129612 82 3.162725 0.084816 0.915184 \n",
"18 35.300460 84 3.083937 0.098090 0.901910 \n",
"19 37.325240 82 3.023176 0.103806 0.896194 \n",
"20 32.141392 86 3.009823 0.108551 0.891449 \n",
"21 46.147658 89 3.132842 0.091885 0.908115 \n",
"22 36.891210 82 2.989644 0.110278 0.889722 \n",
"23 35.687297 80 3.101439 0.092863 0.907137 \n",
"24 37.496051 84 3.012610 0.112893 0.887107 \n",
"25 0.938495 79 3.030534 0.104149 0.895851 \n",
"\n",
" top500_cutoff top200_cutoff top64_cutoff low_estimate high_estimate \\\n",
"0 9749 9872 10061 2900.000000 3600.0 \n",
"1 9832 9952 10117 4566.666667 7100.0 \n",
"2 9867 9995 10204 6036.363636 10300.0 \n",
"3 9952 10087 10293 9927.272727 18000.0 \n",
"4 9910 10028 10255 7766.666667 11400.0 \n",
"5 9896 10002 10191 6800.000000 9800.0 \n",
"6 9978 10100 10289 12836.363636 19900.0 \n",
"7 9946 10061 10246 9566.666667 13300.0 \n",
"8 9946 10046 10183 9733.333333 12580.0 \n",
"9 9977 10067 10176 12800.000000 14620.0 \n",
"10 10000 10090 10216 12995.454545 18900.0 \n",
"11 10070 10172 10313 12995.454545 35900.0 \n",
"12 9916 10044 10295 7968.181818 13700.0 \n",
"13 9975 10097 10325 11645.454545 19500.0 \n",
"14 9914 10026 10230 7466.666667 10900.0 \n",
"15 9992 10102 10323 11586.363636 20300.0 \n",
"16 9998 10106 10300 12990.909091 20900.0 \n",
"17 9974 10082 10278 11263.636364 17200.0 \n",
"18 9958 10067 10287 10140.909091 15800.0 \n",
"19 9954 10079 10281 11200.000000 16900.0 \n",
"20 9968 10078 10250 11868.181818 15900.0 \n",
"21 9879 9974 10083 5100.000000 7770.0 \n",
"22 10012 10122 10252 12981.818182 23300.0 \n",
"23 10032 10140 10271 12981.818182 27500.0 \n",
"24 9941 10093 10340 8690.909091 18900.0 \n",
"25 9932 10068 10312 NaN NaN \n",
"\n",
" mean_estimate std_err \n",
"0 3117.636364 124.944153 \n",
"1 5620.242424 441.315936 \n",
"2 7329.272727 760.229978 \n",
"3 12319.454545 1494.370140 \n",
"4 9372.060606 727.332197 \n",
"5 8320.181818 618.201230 \n",
"6 14683.272727 1331.618216 \n",
"7 11186.242424 696.853229 \n",
"8 11218.666667 458.757501 \n",
"9 13774.181818 369.275995 \n",
"10 16041.757576 976.287680 \n",
"11 23051.757576 3678.743988 \n",
"12 9929.636364 1046.105394 \n",
"13 13977.757576 1430.087791 \n",
"14 9195.333333 715.137594 \n",
"15 14814.606061 1527.809485 \n",
"16 16159.515152 1322.547676 \n",
"17 13526.060606 1034.558786 \n",
"18 12241.515152 1040.267969 \n",
"19 12854.545455 1051.176279 \n",
"20 13442.969697 746.264455 \n",
"21 6559.939394 575.186402 \n",
"22 17920.363636 1647.708090 \n",
"23 19567.696970 2331.781053 \n",
"24 12063.515152 1789.893240 \n",
"25 NaN NaN "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"application/javascript": [
"\n",
" setTimeout(function() {\n",
" var nbb_cell_id = 7;\n",
" var nbb_unformatted_code = \"season_df = pd.read_excel(\\\"./output/seasonal_stats.xlsx\\\").drop(\\n columns=[\\\"Unnamed: 0\\\", \\\"min_mmr\\\", \\\"max_mmr\\\", \\\"num_matches\\\"]\\n)\\nestimates_df = pd.read_excel(\\\"./output/player_estimates.xlsx\\\").drop(\\n columns=[\\\"Unnamed: 0\\\"]\\n)\\nmerged_df = pd.merge(summary_df, season_diversity_df, how=\\\"left\\\", on=\\\"season\\\")\\nmerged_df = pd.merge(merged_df, season_df, how=\\\"left\\\", on=\\\"season\\\")\\nmerged_df = pd.merge(merged_df, estimates_df, how=\\\"left\\\", on=\\\"season\\\")\\nmerged_df\";\n",
" var nbb_formatted_code = \"season_df = pd.read_excel(\\\"./output/seasonal_stats.xlsx\\\").drop(\\n columns=[\\\"Unnamed: 0\\\", \\\"min_mmr\\\", \\\"max_mmr\\\", \\\"num_matches\\\"]\\n)\\nestimates_df = pd.read_excel(\\\"./output/player_estimates.xlsx\\\").drop(\\n columns=[\\\"Unnamed: 0\\\"]\\n)\\nmerged_df = pd.merge(summary_df, season_diversity_df, how=\\\"left\\\", on=\\\"season\\\")\\nmerged_df = pd.merge(merged_df, season_df, how=\\\"left\\\", on=\\\"season\\\")\\nmerged_df = pd.merge(merged_df, estimates_df, how=\\\"left\\\", on=\\\"season\\\")\\nmerged_df\";\n",
" var nbb_cells = Jupyter.notebook.get_cells();\n",
" for (var i = 0; i < nbb_cells.length; ++i) {\n",
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"text/plain": [
"<IPython.core.display.Javascript object>"
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"metadata": {},
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"source": [
"season_df = pd.read_excel(\"./output/seasonal_stats.xlsx\").drop(\n",
" columns=[\"Unnamed: 0\", \"min_mmr\", \"max_mmr\", \"num_matches\"]\n",
")\n",
"estimates_df = pd.read_excel(\"./output/player_estimates.xlsx\").drop(\n",
" columns=[\"Unnamed: 0\"]\n",
")\n",
"merged_df = pd.merge(summary_df, season_diversity_df, how=\"left\", on=\"season\")\n",
"merged_df = pd.merge(merged_df, season_df, how=\"left\", on=\"season\")\n",
"merged_df = pd.merge(merged_df, estimates_df, how=\"left\", on=\"season\")\n",
"merged_df"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"application/javascript": [
"\n",
" setTimeout(function() {\n",
" var nbb_cell_id = 8;\n",
" var nbb_unformatted_code = \"merged_df[\\\"series\\\"] = merged_df.season.apply(lambda x: x.split(\\\"_\\\")[0])\";\n",
" var nbb_formatted_code = \"merged_df[\\\"series\\\"] = merged_df.season.apply(lambda x: x.split(\\\"_\\\")[0])\";\n",
" var nbb_cells = Jupyter.notebook.get_cells();\n",
" for (var i = 0; i < nbb_cells.length; ++i) {\n",
" if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
" if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
" nbb_cells[i].set_text(nbb_formatted_code);\n",
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" "
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"source": [
"merged_df[\"series\"] = merged_df.season.apply(lambda x: x.split(\"_\")[0])"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"application/javascript": [
"\n",
" setTimeout(function() {\n",
" var nbb_cell_id = 9;\n",
" var nbb_unformatted_code = \"import seaborn as sns\\nimport matplotlib.pyplot as plt\\n\\n\\nsns.regplot(\\n data=merged_df, x=\\\"players_total\\\", y=\\\"mean_estimate\\\", scatter=False, color=\\\".25\\\"\\n)\\nsns.scatterplot(data=merged_df, x=\\\"players_total\\\", y=\\\"mean_estimate\\\", hue=\\\"series\\\")\\nplt.title(\\\"Actual Players vs Estimated\\\")\\nplt.xlabel(\\\"Number of Players\\\")\\nplt.ylabel(\\\"Estimated Players\\\")\\nplt.xlim(2500, 23000)\\nplt.show()\";\n",
" var nbb_formatted_code = \"import seaborn as sns\\nimport matplotlib.pyplot as plt\\n\\n\\nsns.regplot(\\n data=merged_df, x=\\\"players_total\\\", y=\\\"mean_estimate\\\", scatter=False, color=\\\".25\\\"\\n)\\nsns.scatterplot(data=merged_df, x=\\\"players_total\\\", y=\\\"mean_estimate\\\", hue=\\\"series\\\")\\nplt.title(\\\"Actual Players vs Estimated\\\")\\nplt.xlabel(\\\"Number of Players\\\")\\nplt.ylabel(\\\"Estimated Players\\\")\\nplt.xlim(2500, 23000)\\nplt.show()\";\n",
" var nbb_cells = Jupyter.notebook.get_cells();\n",
" for (var i = 0; i < nbb_cells.length; ++i) {\n",
" if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
" if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
" nbb_cells[i].set_text(nbb_formatted_code);\n",
" }\n",
" break;\n",
" }\n",
" }\n",
" }, 500);\n",
" "
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"\n",
"\n",
"sns.regplot(\n",
" data=merged_df, x=\"players_total\", y=\"mean_estimate\", scatter=False, color=\".25\"\n",
")\n",
"sns.scatterplot(data=merged_df, x=\"players_total\", y=\"mean_estimate\", hue=\"series\")\n",
"plt.title(\"Actual Players vs Estimated\")\n",
"plt.xlabel(\"Number of Players\")\n",
"plt.ylabel(\"Estimated Players\")\n",
"plt.xlim(2500, 23000)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"application/javascript": [
"\n",
" setTimeout(function() {\n",
" var nbb_cell_id = 10;\n",
" var nbb_unformatted_code = \"import seaborn as sns\\nimport matplotlib.pyplot as plt\\n\\nsns.regplot(\\n data=merged_df, x=\\\"players_total\\\", y=\\\"shannon_diversity\\\", scatter=False, color=\\\".25\\\"\\n)\\nsns.scatterplot(data=merged_df, x=\\\"players_total\\\", y=\\\"shannon_diversity\\\", hue=\\\"series\\\")\\nplt.title(\\\"Actual Players vs Diversity\\\")\\nplt.xlabel(\\\"Number of Players\\\")\\nplt.ylabel(\\\"Shannon Diversity\\\")\\nplt.xlim(2500, 23000)\\nplt.show()\";\n",
" var nbb_formatted_code = \"import seaborn as sns\\nimport matplotlib.pyplot as plt\\n\\nsns.regplot(\\n data=merged_df, x=\\\"players_total\\\", y=\\\"shannon_diversity\\\", scatter=False, color=\\\".25\\\"\\n)\\nsns.scatterplot(data=merged_df, x=\\\"players_total\\\", y=\\\"shannon_diversity\\\", hue=\\\"series\\\")\\nplt.title(\\\"Actual Players vs Diversity\\\")\\nplt.xlabel(\\\"Number of Players\\\")\\nplt.ylabel(\\\"Shannon Diversity\\\")\\nplt.xlim(2500, 23000)\\nplt.show()\";\n",
" var nbb_cells = Jupyter.notebook.get_cells();\n",
" for (var i = 0; i < nbb_cells.length; ++i) {\n",
" if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
" if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
" nbb_cells[i].set_text(nbb_formatted_code);\n",
" }\n",
" break;\n",
" }\n",
" }\n",
" }, 500);\n",
" "
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"\n",
"sns.regplot(\n",
" data=merged_df, x=\"players_total\", y=\"shannon_diversity\", scatter=False, color=\".25\"\n",
")\n",
"sns.scatterplot(data=merged_df, x=\"players_total\", y=\"shannon_diversity\", hue=\"series\")\n",
"plt.title(\"Actual Players vs Diversity\")\n",
"plt.xlabel(\"Number of Players\")\n",
"plt.ylabel(\"Shannon Diversity\")\n",
"plt.xlim(2500, 23000)\n",
"plt.show()"
]
}
],
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