forked from EEBD_AI/wgz_forecast
232 lines
119 KiB
Plaintext
232 lines
119 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": 63,
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"metadata": {
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"ExecuteTime": {
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"start_time": "2025-02-09T20:52:57.522951Z",
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"end_time": "2025-02-09T20:52:57.531694Z"
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}
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},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\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": 64,
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"metadata": {
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"ExecuteTime": {
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"start_time": "2025-02-09T20:52:57.526116Z",
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"end_time": "2025-02-09T20:52:57.559094Z"
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}
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},
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"outputs": [
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{
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"data": {
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"text/plain": " MSN YYYYMM Value Column_Order \\\n0 CLEIEUS 197301 72.076 1 \n1 CLEIEUS 197302 64.442 1 \n2 CLEIEUS 197303 64.084 1 \n3 CLEIEUS 197304 60.842 1 \n4 CLEIEUS 197305 61.798 1 \n\n Description \\\n0 Coal Electric Power Sector CO2 Emissions \n1 Coal Electric Power Sector CO2 Emissions \n2 Coal Electric Power Sector CO2 Emissions \n3 Coal Electric Power Sector CO2 Emissions \n4 Coal Electric Power Sector CO2 Emissions \n\n Unit \n0 Million Metric Tons of Carbon Dioxide \n1 Million Metric Tons of Carbon Dioxide \n2 Million Metric Tons of Carbon Dioxide \n3 Million Metric Tons of Carbon Dioxide \n4 Million Metric Tons of Carbon Dioxide ",
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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>MSN</th>\n <th>YYYYMM</th>\n <th>Value</th>\n <th>Column_Order</th>\n <th>Description</th>\n <th>Unit</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>CLEIEUS</td>\n <td>197301</td>\n <td>72.076</td>\n <td>1</td>\n <td>Coal Electric Power Sector CO2 Emissions</td>\n <td>Million Metric Tons of Carbon Dioxide</td>\n </tr>\n <tr>\n <th>1</th>\n <td>CLEIEUS</td>\n <td>197302</td>\n <td>64.442</td>\n <td>1</td>\n <td>Coal Electric Power Sector CO2 Emissions</td>\n <td>Million Metric Tons of Carbon Dioxide</td>\n </tr>\n <tr>\n <th>2</th>\n <td>CLEIEUS</td>\n <td>197303</td>\n <td>64.084</td>\n <td>1</td>\n <td>Coal Electric Power Sector CO2 Emissions</td>\n <td>Million Metric Tons of Carbon Dioxide</td>\n </tr>\n <tr>\n <th>3</th>\n <td>CLEIEUS</td>\n <td>197304</td>\n <td>60.842</td>\n <td>1</td>\n <td>Coal Electric Power Sector CO2 Emissions</td>\n <td>Million Metric Tons of Carbon Dioxide</td>\n </tr>\n <tr>\n <th>4</th>\n <td>CLEIEUS</td>\n <td>197305</td>\n <td>61.798</td>\n <td>1</td>\n <td>Coal Electric Power Sector CO2 Emissions</td>\n <td>Million Metric Tons of Carbon Dioxide</td>\n </tr>\n </tbody>\n</table>\n</div>"
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},
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"execution_count": 64,
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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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"data = pd.read_csv('./data/carbon_original.csv')\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": 65,
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"metadata": {
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"ExecuteTime": {
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"start_time": "2025-02-09T20:52:57.543394Z",
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"end_time": "2025-02-09T20:52:57.559094Z"
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}
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},
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"outputs": [],
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"source": [
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"use_data = data[data['MSN']=='CLEIEUS'].iloc[:,[2]]"
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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": 66,
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"outputs": [
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{
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"data": {
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"text/plain": " Value\n0 72.076\n1 64.442\n2 64.084\n3 60.842\n4 61.798\n.. ...\n561 72.84\n562 71.41\n563 82.51\n564 115.772\n565 135.958\n\n[566 rows x 1 columns]",
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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>Value</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>72.076</td>\n </tr>\n <tr>\n <th>1</th>\n <td>64.442</td>\n </tr>\n <tr>\n <th>2</th>\n <td>64.084</td>\n </tr>\n <tr>\n <th>3</th>\n <td>60.842</td>\n </tr>\n <tr>\n <th>4</th>\n <td>61.798</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n </tr>\n <tr>\n <th>561</th>\n <td>72.84</td>\n </tr>\n <tr>\n <th>562</th>\n <td>71.41</td>\n </tr>\n <tr>\n <th>563</th>\n <td>82.51</td>\n </tr>\n <tr>\n <th>564</th>\n <td>115.772</td>\n </tr>\n <tr>\n <th>565</th>\n <td>135.958</td>\n </tr>\n </tbody>\n</table>\n<p>566 rows × 1 columns</p>\n</div>"
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},
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"execution_count": 66,
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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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"use_data"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"start_time": "2025-02-09T20:52:57.546122Z",
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"end_time": "2025-02-09T20:52:57.559094Z"
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}
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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": 66,
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"outputs": [],
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"source": [],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"start_time": "2025-02-09T20:52:57.553791Z",
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"end_time": "2025-02-09T20:52:57.559094Z"
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}
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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": 67,
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"metadata": {
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"ExecuteTime": {
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"start_time": "2025-02-09T20:52:57.556321Z",
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"end_time": "2025-02-09T20:52:57.560093Z"
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}
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"C:\\Users\\liuhao\\AppData\\Local\\Temp\\ipykernel_17444\\217526566.py:2: FutureWarning: 'H' is deprecated and will be removed in a future version, please use 'h' instead.\n",
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" dates = pd.date_range(start='2022-01-01', periods=566, freq='H')\n"
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]
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}
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],
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"source": [
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"# 生成日期范围（484天）\n",
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"dates = pd.date_range(start='2022-01-01', periods=566, freq='H')\n",
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"dates=pd.DataFrame(dates, columns=['date'])"
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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": 68,
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"outputs": [],
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"source": [
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"date_data=pd.concat([dates,use_data],axis=1)"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"start_time": "2025-02-09T20:52:57.561164Z",
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"end_time": "2025-02-09T20:52:57.565207Z"
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}
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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": 69,
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"outputs": [],
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"source": [
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"# 将 'Value' 列转换为 float 类型\n",
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"date_data['Value'] = date_data['Value'].astype(float)"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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|
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"start_time": "2025-02-09T20:52:57.565207Z",
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"end_time": "2025-02-09T20:52:57.569128Z"
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}
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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": 70,
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"metadata": {
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"ExecuteTime": {
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"start_time": "2025-02-09T20:52:57.569508Z",
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"end_time": "2025-02-09T20:52:57.675345Z"
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}
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},
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"outputs": [
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{
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"data": {
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"text/plain": "<Figure size 1000x600 with 1 Axes>",
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"image/png": "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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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"source": [
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"# 绘制第二列的图像\n",
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"plt.figure(figsize=(10, 6))\n",
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"plt.plot(date_data.index, date_data['Value'], label='Value')\n",
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"plt.xlabel('Date')\n",
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"plt.ylabel('Value')\n",
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"plt.title('Time Series Plot of Second Column')\n",
|
|||
|
|
"plt.legend()\n",
|
|||
|
|
"plt.grid(True)\n",
|
|||
|
|
"plt.show()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 71,
|
|||
|
|
"metadata": {
|
|||
|
|
"ExecuteTime": {
|
|||
|
|
"start_time": "2025-02-09T20:52:57.666305Z",
|
|||
|
|
"end_time": "2025-02-09T20:52:57.675345Z"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"date_data.to_csv('data/carbon_data_hourly.csv', index=False, encoding='utf-8-sig')"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 71,
|
|||
|
|
"metadata": {
|
|||
|
|
"ExecuteTime": {
|
|||
|
|
"start_time": "2025-02-09T20:52:57.670401Z",
|
|||
|
|
"end_time": "2025-02-09T20:52:57.675898Z"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": []
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"kernelspec": {
|
|||
|
|
"display_name": "py39",
|
|||
|
|
"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": "3.9.21"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"nbformat": 4,
|
|||
|
|
"nbformat_minor": 2
|
|||
|
|
}
|