669 lines
1.9 MiB
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669 lines
1.9 MiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"from pandas import DataFrame, Series\n",
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"from numpy import nan\n",
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"import pathlib\n",
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"import matplotlib.pyplot as plt\n",
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"plt.rcParams[\"figure.figsize\"] = (30,5)\n",
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"import seaborn as sns\n",
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"sns.set()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>TEST</th>\n",
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" <th>MAP</th>\n",
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" <th>50000</th>\n",
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" <th>100000</th>\n",
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" <th>150000</th>\n",
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" <th>200000</th>\n",
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" <th>250000</th>\n",
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" <th>300000</th>\n",
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" <th>350000</th>\n",
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" <th>400000</th>\n",
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" <th>...</th>\n",
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" <th>9000000</th>\n",
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" <th>10000000</th>\n",
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" <th>15000000</th>\n",
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" <th>20000000</th>\n",
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" <th>25000000</th>\n",
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" <th>30000000</th>\n",
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" <th>35000000</th>\n",
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" <th>40000000</th>\n",
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" <th>45000000</th>\n",
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" <th>50000000</th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Unnamed: 0</th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>int_delete</td>\n",
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" <td>absl::flat_hash_map</td>\n",
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" <td>9</td>\n",
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" <td>14</td>\n",
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" <td>11</td>\n",
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" <td>14</td>\n",
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" <td>11</td>\n",
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" <td>12</td>\n",
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" <td>13</td>\n",
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" <td>15</td>\n",
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" <td>...</td>\n",
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" <td>21</td>\n",
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" <td>21</td>\n",
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" <td>24</td>\n",
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" <td>28</td>\n",
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" <td>25</td>\n",
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" <td>25</td>\n",
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" <td>26</td>\n",
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" <td>25</td>\n",
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" <td>26</td>\n",
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" <td>26</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>int_delete</td>\n",
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" <td>absl::flat_hash_map</td>\n",
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" <td>9</td>\n",
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" <td>14</td>\n",
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" <td>11</td>\n",
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" <td>14</td>\n",
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" <td>11</td>\n",
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" <td>12</td>\n",
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" <td>13</td>\n",
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" <td>16</td>\n",
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" <td>...</td>\n",
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" <td>20</td>\n",
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" <td>23</td>\n",
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" <td>24</td>\n",
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" <td>23</td>\n",
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" <td>27</td>\n",
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" <td>26</td>\n",
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" <td>26</td>\n",
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" <td>26</td>\n",
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" <td>26</td>\n",
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" <td>26</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>int_delete</td>\n",
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" <td>absl::flat_hash_map</td>\n",
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|
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" <td>9</td>\n",
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|
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" <td>14</td>\n",
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|||
|
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" <td>11</td>\n",
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|
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" <td>14</td>\n",
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|
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" <td>11</td>\n",
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" <td>12</td>\n",
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" <td>13</td>\n",
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" <td>15</td>\n",
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" <td>...</td>\n",
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" <td>20</td>\n",
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" <td>21</td>\n",
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" <td>24</td>\n",
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" <td>24</td>\n",
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" <td>25</td>\n",
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" <td>26</td>\n",
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" <td>26</td>\n",
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" <td>30</td>\n",
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" <td>26</td>\n",
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" <td>27</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>int_delete</td>\n",
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" <td>absl::flat_hash_map</td>\n",
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" <td>9</td>\n",
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" <td>14</td>\n",
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|
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" <td>11</td>\n",
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" <td>14</td>\n",
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" <td>11</td>\n",
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" <td>12</td>\n",
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" <td>13</td>\n",
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" <td>15</td>\n",
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" <td>...</td>\n",
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" <td>21</td>\n",
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" <td>21</td>\n",
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" <td>24</td>\n",
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" <td>24</td>\n",
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" <td>25</td>\n",
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" <td>25</td>\n",
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" <td>26</td>\n",
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" <td>29</td>\n",
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" <td>26</td>\n",
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" <td>26</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>int_delete</td>\n",
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|
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" <td>absl::flat_hash_map</td>\n",
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" <td>9</td>\n",
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|||
|
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" <td>14</td>\n",
|
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|
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" <td>11</td>\n",
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" <td>14</td>\n",
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" <td>11</td>\n",
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" <td>12</td>\n",
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" <td>13</td>\n",
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" <td>15</td>\n",
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" <td>...</td>\n",
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" <td>20</td>\n",
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" <td>20</td>\n",
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" <td>24</td>\n",
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" <td>24</td>\n",
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" <td>25</td>\n",
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" <td>26</td>\n",
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" <td>26</td>\n",
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" <td>26</td>\n",
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" <td>26</td>\n",
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" <td>27</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"<p>5 rows × 33 columns</p>\n",
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"</div>"
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],
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"text/plain": [
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" TEST MAP 50000 100000 150000 200000 \\\n",
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"Unnamed: 0 \n",
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"0 int_delete absl::flat_hash_map 9 14 11 14 \n",
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"1 int_delete absl::flat_hash_map 9 14 11 14 \n",
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"2 int_delete absl::flat_hash_map 9 14 11 14 \n",
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"3 int_delete absl::flat_hash_map 9 14 11 14 \n",
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"4 int_delete absl::flat_hash_map 9 14 11 14 \n",
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"\n",
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" 250000 300000 350000 400000 ... 9000000 10000000 15000000 \\\n",
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"Unnamed: 0 ... \n",
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"0 11 12 13 15 ... 21 21 24 \n",
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"1 11 12 13 16 ... 20 23 24 \n",
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"2 11 12 13 15 ... 20 21 24 \n",
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"3 11 12 13 15 ... 21 21 24 \n",
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"4 11 12 13 15 ... 20 20 24 \n",
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"\n",
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" 20000000 25000000 30000000 35000000 40000000 45000000 \\\n",
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"Unnamed: 0 \n",
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"0 28 25 25 26 25 26 \n",
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"1 23 27 26 26 26 26 \n",
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"2 24 25 26 26 30 26 \n",
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"3 24 25 25 26 29 26 \n",
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"4 24 25 26 26 26 26 \n",
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"\n",
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" 50000000 \n",
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"Unnamed: 0 \n",
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"0 26 \n",
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"1 26 \n",
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"2 27 \n",
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"3 26 \n",
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"4 27 \n",
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"\n",
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"[5 rows x 33 columns]"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"cols = [\n",
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"\"TEST\",\"MAP\",\n",
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"50000, 100000, 150000, 200000, 250000, 300000, 350000, 400000, 500000,\n",
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"600000, 700000, 800000, 900000, 1000000,\n",
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"2000000, 3000000, 4000000, 5000000, 6000000, 7000000, 8000000, 9000000, 10000000,\n",
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"15000000, 20000000, 25000000, 30000000, 35000000, 40000000, 45000000, 50000000\n",
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"]\n",
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"if not pathlib.Path(\"./sorted.csv\").is_file():\n",
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" data = pd.read_csv(\"results.csv\", quotechar=\"'\", header=None)\n",
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" data.columns= cols\n",
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|
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" data = data.sort_values(by=['TEST', 'MAP'], ignore_index=True)\n",
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" data.to_csv(\"sorted.csv\")\n",
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"\n",
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"else:\n",
|
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|
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" data = pd.read_csv(\"sorted.csv\")\n",
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|
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" data.set_index(\"Unnamed: 0\", inplace=True)\n",
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"data.head()"
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]
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},
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{
|
|||
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"cell_type": "code",
|
|||
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
|
|||
|
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"def remove_with_iqr(data):\n",
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|
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" stats = data.describe()\n",
|
|||
|
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" IQR = stats[\"75%\"] - stats[\"25%\"]\n",
|
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|
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" min_cutoff = stats[\"25%\"] - 1.5 * IQR\n",
|
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|
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" max_cutoff = stats[\"75%\"] + 1.5 * IQR\n",
|
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|
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" lower_max = data <= max_cutoff\n",
|
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|
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" higher_min = data >= min_cutoff\n",
|
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|
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" data = data * lower_max * higher_min\n",
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|
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" data = data.replace(0, nan)\n",
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|
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" return data\n",
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|||
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" \n",
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|
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"def remove_with_zscore(data, cutoff=3.5):\n",
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|||
|
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" stats = data.describe()\n",
|
|||
|
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" if not stats[\"std\"]:\n",
|
|||
|
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" return data\n",
|
|||
|
|
" z = abs((data - stats[\"mean\"]) / stats[\"std\"])\n",
|
|||
|
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" cut = z <= cutoff\n",
|
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|
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" data = data * cut\n",
|
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|
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" data = data.replace(0, nan)\n",
|
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|
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" return data \n",
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"\n",
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|
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"def remove_with_modified_z_score(data, treshold=3.5):\n",
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" stats = data.describe()\n",
|
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|
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" median_absolute_deviation = abs(data - data.median()).median()\n",
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|
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" if not median_absolute_deviation:\n",
|
|||
|
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" return data\n",
|
|||
|
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" modified_z_scores = abs(0.6745 * (data - data.median()) / median_absolute_deviation)\n",
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|
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" cutoff = modified_z_scores <= treshold\n",
|
|||
|
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" data = data * cutoff\n",
|
|||
|
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" data = data.replace(0, nan)\n",
|
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|
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" return data\n",
|
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"\n",
|
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|
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"def max_val(hmap, test):\n",
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|
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" return groups_mean.loc[test, hmap].max()\n",
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"\n",
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|
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"def sort_maps(test):\n",
|
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" maps = list(groups_mean.loc[test].index)\n",
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|||
|
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" new = [(max_val(i, test), i) for i in maps]\n",
|
|||
|
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" new.sort()\n",
|
|||
|
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" new = [i[1] for i in new]\n",
|
|||
|
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" return new\n",
|
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"\n",
|
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|
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"def plot_test(test, include_error=True):\n",
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|
|
" sizes = [50000, 100000, 150000, 200000, 250000, 300000, 350000, 400000, 500000,\n",
|
|||
|
|
" 600000, 700000, 800000, 900000, 1000000,\n",
|
|||
|
|
" 2000000, 3000000, 4000000, 5000000, 6000000, 7000000, 8000000, 9000000, 10000000,\n",
|
|||
|
|
" 15000000, 20000000, 25000000, 30000000, 35000000, 40000000, 45000000, 50000000]\n",
|
|||
|
|
" maps = sort_maps(test)\n",
|
|||
|
|
" count = 16\n",
|
|||
|
|
" repeats = [0, 5, 11]\n",
|
|||
|
|
" while count > -1:\n",
|
|||
|
|
" if not count and count not in repeats:\n",
|
|||
|
|
" break\n",
|
|||
|
|
" mp = maps[count]\n",
|
|||
|
|
" else:\n",
|
|||
|
|
" sns.lineplot(x=sizes, y=groups_mean.loc[test, mp], label=mp, order=2)\n",
|
|||
|
|
" if count in repeats:\n",
|
|||
|
|
" plt.xscale(\"log\")\n",
|
|||
|
|
" plt.ylabel(\"{} time (ns)\".format(test))\n",
|
|||
|
|
" plt.legend()\n",
|
|||
|
|
" plt.title(test)\n",
|
|||
|
|
" plt.show()\n",
|
|||
|
|
" repeats.pop(repeats.index(count))\n",
|
|||
|
|
" else:\n",
|
|||
|
|
" count -=1"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 4,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"iqr_data = data.copy(True)\n",
|
|||
|
|
"# modz_data = data.copy(True)\n",
|
|||
|
|
"# z_data = data.copy(True)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 5,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"for col in range(2,33):\n",
|
|||
|
|
" for row in range(8 * 17):\n",
|
|||
|
|
" start = row * 30\n",
|
|||
|
|
" end = (row + 1 ) * 30\n",
|
|||
|
|
" temp = data.iloc[start : end,col]\n",
|
|||
|
|
" iqr_data.iloc[start : end,col] = remove_with_iqr(temp)\n",
|
|||
|
|
"# modz_data.iloc[start : end,col] = remove_with_modified_z_score(temp)\n",
|
|||
|
|
"# z_data.iloc[start : end,col] = remove_with_zscore(temp)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 6,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 2160x360 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 2160x360 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 2160x360 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"tester = iqr_data # use removed outliers data\n",
|
|||
|
|
"groups_modz = tester.groupby([\"TEST\", \"MAP\"])\n",
|
|||
|
|
"groups_mean = groups_modz.mean()\n",
|
|||
|
|
"groups_std = groups_modz.std()\n",
|
|||
|
|
"groups_std.columns = [int(i) for i in groups_std.columns]\n",
|
|||
|
|
"groups_mean.columns = groups_std.columns\n",
|
|||
|
|
"plot_test(\"int_insert\")"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 7,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 2160x360 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 2160x360 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 2160x360 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"tester = data # use original data\n",
|
|||
|
|
"groups_modz = tester.groupby([\"TEST\", \"MAP\"])\n",
|
|||
|
|
"groups_mean = groups_modz.mean()\n",
|
|||
|
|
"groups_std = groups_modz.std()\n",
|
|||
|
|
"groups_std.columns = [int(i) for i in groups_std.columns]\n",
|
|||
|
|
"groups_mean.columns = groups_std.columns\n",
|
|||
|
|
"plot_test(\"int_insert\")"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 8,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
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|
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"text/plain": [
|
|||
|
|
"<Figure size 2160x360 with 1 Axes>"
|
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|
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]
|
|||
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|
},
|
|||
|
|
"metadata": {},
|
|||
|
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"output_type": "display_data"
|
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},
|
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{
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"data": {
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"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 2160x360 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
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"data": {
|
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|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 2160x360 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"tests = data[\"TEST\"].unique()\n",
|
|||
|
|
"plot_test(tests[1])\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 9,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 2160x360 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 2160x360 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 2160x360 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"plot_test(tests[2])"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 10,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
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|||
|
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"text/plain": [
|
|||
|
|
"<Figure size 2160x360 with 1 Axes>"
|
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]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
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},
|
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{
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"data": {
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"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 2160x360 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
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|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 2160x360 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"plot_test(tests[3])"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 11,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 2160x360 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
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|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 2160x360 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 2160x360 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"plot_test(tests[4])"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": []
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"kernelspec": {
|
|||
|
|
"display_name": "Python 3",
|
|||
|
|
"language": "python",
|
|||
|
|
"name": "python3"
|
|||
|
|
},
|
|||
|
|
"language_info": {
|
|||
|
|
"codemirror_mode": {
|
|||
|
|
"name": "ipython",
|
|||
|
|
"version": 3
|
|||
|
|
},
|
|||
|
|
"file_extension": ".py",
|
|||
|
|
"mimetype": "text/x-python",
|
|||
|
|
"name": "python",
|
|||
|
|
"nbconvert_exporter": "python",
|
|||
|
|
"pygments_lexer": "ipython3",
|
|||
|
|
"version": "3.8.2"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"nbformat": 4,
|
|||
|
|
"nbformat_minor": 4
|
|||
|
|
}
|