144 lines
6.4 KiB
Plaintext
144 lines
6.4 KiB
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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"outputs": [],
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"source": [
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"import pandas as pd"
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],
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"metadata": {
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"collapsed": false,
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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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"outputs": [],
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"source": [
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"power_eva = pd.read_csv('./发电测试结果.csv')"
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],
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"metadata": {
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"collapsed": false,
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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": 4,
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"outputs": [],
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"source": [
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"power_eva.columns = ['real', 'pred']\n",
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"power_eva['error'] = (power_eva.pred - power_eva.real).apply(abs) / power_eva.real"
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],
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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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": 15,
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"outputs": [
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{
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"data": {
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"text/plain": " real pred error\n222 0.517443 0.518051 0.001175\n54 0.701795 0.671254 0.043519\n201 0.539900 0.541033 0.002099\n30 0.532658 0.530621 0.003823\n124 0.410033 0.420981 0.026701\n37 0.390315 0.391309 0.002548\n7 0.571029 0.579793 0.015347\n232 0.580826 0.579876 0.001635\n165 0.352021 0.374194 0.062987\n139 0.584566 0.567410 0.029348",
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"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>real</th>\n <th>pred</th>\n <th>error</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>222</th>\n <td>0.517443</td>\n <td>0.518051</td>\n <td>0.001175</td>\n </tr>\n <tr>\n <th>54</th>\n <td>0.701795</td>\n <td>0.671254</td>\n <td>0.043519</td>\n </tr>\n <tr>\n <th>201</th>\n <td>0.539900</td>\n <td>0.541033</td>\n <td>0.002099</td>\n </tr>\n <tr>\n <th>30</th>\n <td>0.532658</td>\n <td>0.530621</td>\n <td>0.003823</td>\n </tr>\n <tr>\n <th>124</th>\n <td>0.410033</td>\n <td>0.420981</td>\n <td>0.026701</td>\n </tr>\n <tr>\n <th>37</th>\n <td>0.390315</td>\n <td>0.391309</td>\n <td>0.002548</td>\n </tr>\n <tr>\n <th>7</th>\n <td>0.571029</td>\n <td>0.579793</td>\n <td>0.015347</td>\n </tr>\n <tr>\n <th>232</th>\n <td>0.580826</td>\n <td>0.579876</td>\n <td>0.001635</td>\n </tr>\n <tr>\n <th>165</th>\n <td>0.352021</td>\n <td>0.374194</td>\n <td>0.062987</td>\n </tr>\n <tr>\n <th>139</th>\n <td>0.584566</td>\n <td>0.567410</td>\n <td>0.029348</td>\n </tr>\n </tbody>\n</table>\n</div>"
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},
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"execution_count": 15,
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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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"power_eva.sample(10)"
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],
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"metadata": {
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"collapsed": false,
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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": 7,
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"outputs": [],
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"source": [
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"heat_eva = pd.read_csv('./供热测试结果.csv')\n",
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"heat_eva.columns = ['real', 'pred']\n",
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"heat_eva['error'] = (heat_eva.pred - heat_eva.real).apply(abs) / heat_eva.real"
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],
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"metadata": {
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"collapsed": false,
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"outputs": [
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{
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"data": {
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"text/plain": " real pred error\n131 0.071626 0.071494 0.001839\n256 0.076446 0.069821 0.086672\n141 0.067995 0.068865 0.012802\n71 0.071438 0.071276 0.002270\n284 0.072052 0.071835 0.003018\n294 0.075010 0.074507 0.006716\n77 0.052603 0.055783 0.060461\n96 0.062181 0.063483 0.020932\n176 0.077847 0.077317 0.006807\n164 0.082962 0.082844 0.001420",
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"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>real</th>\n <th>pred</th>\n <th>error</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>131</th>\n <td>0.071626</td>\n <td>0.071494</td>\n <td>0.001839</td>\n </tr>\n <tr>\n <th>256</th>\n <td>0.076446</td>\n <td>0.069821</td>\n <td>0.086672</td>\n </tr>\n <tr>\n <th>141</th>\n <td>0.067995</td>\n <td>0.068865</td>\n <td>0.012802</td>\n </tr>\n <tr>\n <th>71</th>\n <td>0.071438</td>\n <td>0.071276</td>\n <td>0.002270</td>\n </tr>\n <tr>\n <th>284</th>\n <td>0.072052</td>\n <td>0.071835</td>\n <td>0.003018</td>\n </tr>\n <tr>\n <th>294</th>\n <td>0.075010</td>\n <td>0.074507</td>\n <td>0.006716</td>\n </tr>\n <tr>\n <th>77</th>\n <td>0.052603</td>\n <td>0.055783</td>\n <td>0.060461</td>\n </tr>\n <tr>\n <th>96</th>\n <td>0.062181</td>\n <td>0.063483</td>\n <td>0.020932</td>\n </tr>\n <tr>\n <th>176</th>\n <td>0.077847</td>\n <td>0.077317</td>\n <td>0.006807</td>\n </tr>\n <tr>\n <th>164</th>\n <td>0.082962</td>\n <td>0.082844</td>\n <td>0.001420</td>\n </tr>\n </tbody>\n</table>\n</div>"
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},
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"execution_count": 9,
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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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"heat_eva.sample(10)"
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],
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"metadata": {
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"collapsed": false,
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"name": "#%%\n"
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"display_name": "Python 3",
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"name": "ipython",
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"version": "2.7.6"
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