如何使用 Python 根据输入 'id' 从 table 中检索记录？
How to retreive records from the table based on input 'id' using Python?
我有 table1 就像 ,
project_id | metric | design_target | median_property | design_project
------------+----------------------------------------+---------------+-----------------+----------------
300 | ENERGY STAR score (1-100) | Not Available | 50 | Not Available
300 | Source EUI (kBtu/ft\u00b2) | 35.4 | 141.4 | Not Available
300 | Site EUI (kBtu/ft\u00b2) | 15.8 | 63.1 | Not Available
300 | Source Energy Use (kBtu) | 3,536.0 | 14,144.1 | Not Available
300 | Site Energy Use (kBtu) | 1,578.7 | 6,314.9 | Not Available
300 | Energy Cost ($) | 34.61 | 138.44 | Not Available
300 | Total GHG Emissions (Metric Tons CO2e) | 0.2 | 0.6 | 0.0
400 | ENERGY STAR score (1-100) | Not Available | 50 | Not Available
400 | Source EUI (kBtu/ft\u00b2) | 35.4 | 141.4 | Not Available
400 | Site EUI (kBtu/ft\u00b2) | 15.8 | 63.1 | Not Available
400 | Source Energy Use (kBtu) | 3,536.0 | 14,144.1 | Not Available
400 | Site Energy Use (kBtu) | 1,578.7 | 6,314.9 | Not Available
400 | Energy Cost ($) | 34.61 | 138.44 | Not Available
400 | Total GHG Emissions (Metric Tons CO2e) | 0.2 | 0.6 | 0.0
我有 table2 就像，
project_id | energy_type | uses | target | title
------------+-------------------------------------------------------------------------------------------------------------------------+------+----------------------------------+------------------------------
300 | {"Energy Not Entered","Assumed Mix Based on State & Property Type:","","Electric - Grid (56.9%)","Natural Gas (43.1%)"} | | Target % Better than Median: 75 | About this Property's Design
400 | {"Energy Not Entered","Assumed Mix Based on State & Property Type:","","Electric - Grid (56.9%)","Natural Gas (43.1%)"} | | Target % Better than Median: 75 | About this Property's Design
我有 table3 就像，
project_id | your_design_score
------------+-------------------
300 | N/A
400 | N/A
我加入 table 基于 project_id 使用 python,
import psycopg2
con = psycopg2.connect(dbname="db",
user="postgres", host="localhost",
password="")
d1 = "select t1.project_id,t1.Metric, t1.Design_Target, t1.Median_Property, t1.Design_Project, t2.Energy_Type, t2.Uses, t2.Target, t2.Title, t3.your_design_score from metric_comparison t1, property_design t2, design_score t3 where t1.project_id = t2.project_id and t2.project_id = t3.project_id"
cursor.execute(d1)
d2 = cursor.fetchall()
print d2
con.commit()
我得到了这样的输出，
[('300', 'ENERGY STAR score (1-100)', 'Not Available', '50', 'Not Available', '{"Energy Not Entered","Assumed Mix Based on State & Property Type:","","Electric - Grid (56.9%)","Natural Gas (43.1%)"}', None, ' Target % Better than Median: 75', "About this Property's Design", 'N/A'), ('300', 'Source EUI (kBtu/ft\u00b2)', '35.4', '141.4', 'Not Available', '{"Energy Not Entered","Assumed Mix Based on State & Property Type:","","Electric - Grid (56.9%)","Natural Gas (43.1%)"}', None, ' Target % Better than Median: 75', "About this Property's Design", 'N/A'), ('300', 'Site EUI (kBtu/ft\u00b2)', '15.8', '63.1', 'Not Available', '{"Energy Not Entered","Assumed Mix Based on State & Property Type:","","Electric - Grid (56.9%)","Natural Gas (43.1%)"}', None, ' Target % Better than Median: 75', "About this Property's Design", 'N/A'), ('300', 'Source Energy Use (kBtu)', '3,536.0', '14,144.1', 'Not Available', '{"Energy Not Entered","Assumed Mix Based on State & Property Type:","","Electric - Grid (56.9%)","Natural Gas (43.1%)"}', None, ' Target % Better than Median: 75', "About this Property's Design", 'N/A'), ('300', 'Site Energy Use (kBtu)', '1,578.7', '6,314.9', 'Not Available', '{"Energy Not Entered","Assumed Mix Based on State & Property Type:","","Electric - Grid (56.9%)","Natural Gas (43.1%)"}', None, ' Target % Better than Median: 75', "About this Property's Design", 'N/A'), ('300', 'Energy Cost ($)', '34.61', '138.44', 'Not Available', '{"Energy Not Entered","Assumed Mix Based on State & Property Type:","","Electric - Grid (56.9%)","Natural Gas (43.1%)"}', None, ' Target % Better than Median: 75', "About this Property's Design", 'N/A'), ('300', 'Total GHG Emissions (Metric Tons CO2e)', '0.2', '0.6', '0.0', '{"Energy Not Entered","Assumed Mix Based on State & Property Type:","","Electric - Grid (56.9%)","Natural Gas (43.1%)"}', None, ' Target % Better than Median: 75', "About this Property's Design", 'N/A'), ('400', 'ENERGY STAR score (1-100)', 'Not Available', '50', 'Not Available', '{"Energy Not Entered","Assumed Mix Based on State & Property Type:","","Electric - Grid (56.9%)","Natural Gas (43.1%)"}', None, ' Target % Better than Median: 75', "About this Property's Design", 'N/A'), ('400', 'Source EUI (kBtu/ft\u00b2)', '35.4', '141.4', 'Not Available', '{"Energy Not Entered","Assumed Mix Based on State & Property Type:","","Electric - Grid (56.9%)","Natural Gas (43.1%)"}', None, ' Target % Better than Median: 75', "About this Property's Design", 'N/A'), ('400', 'Site EUI (kBtu/ft\u00b2)', '15.8', '63.1', 'Not Available', '{"Energy Not Entered","Assumed Mix Based on State & Property Type:","","Electric - Grid (56.9%)","Natural Gas (43.1%)"}', None, ' Target % Better than Median: 75', "About this Property's Design", 'N/A'), ('400', 'Source Energy Use (kBtu)', '3,536.0', '14,144.1', 'Not Available', '{"Energy Not Entered","Assumed Mix Based on State & Property Type:","","Electric - Grid (56.9%)","Natural Gas (43.1%)"}', None, ' Target % Better than Median: 75', "About this Property's Design", 'N/A'), ('400', 'Site Energy Use (kBtu)', '1,578.7', '6,314.9', 'Not Available', '{"Energy Not Entered","Assumed Mix Based on State & Property Type:","","Electric - Grid (56.9%)","Natural Gas (43.1%)"}', None, ' Target % Better than Median: 75', "About this Property's Design", 'N/A'), ('400', 'Energy Cost ($)', '34.61', '138.44', 'Not Available', '{"Energy Not Entered","Assumed Mix Based on State & Property Type:","","Electric - Grid (56.9%)","Natural Gas (43.1%)"}', None, ' Target % Better than Median: 75', "About this Property's Design", 'N/A'), ('400', 'Total GHG Emissions (Metric Tons CO2e)', '0.2', '0.6', '0.0', '{"Energy Not Entered","Assumed Mix Based on State & Property Type:","","Electric - Grid (56.9%)","Natural Gas (43.1%)"}', None, ' Target % Better than Median: 75', "About this Property's Design", 'N/A')]
现在我正在尝试根据 project_id 输入显示记录，
PROJECT_ID = '300' #在 python 代码中声明
基于 PROJECT_ID 的输入，我必须显示上面输出的记录。
我必须显示 project_id 与 PROJECT_ID 匹配的记录。
在SQL端过滤，在execute方法中传递参数：
d1 = """
select
project_id,
t1.Metric,
t1.Design_Target,
t1.Median_Property,
t1.Design_Project,
t2.Energy_Type,
t2.Uses, t2.Target,
t2.Title,
t3.your_design_score
from
metric_comparison t1
inner join
property_design t2 using (project_id)
inner join
design_score t3 using (project_id)
where project_id = %s
"""
project_id = '300'
cursor.execute(d1, [project_id])
我有 table1 就像 ,
project_id | metric | design_target | median_property | design_project
------------+----------------------------------------+---------------+-----------------+----------------
300 | ENERGY STAR score (1-100) | Not Available | 50 | Not Available
300 | Source EUI (kBtu/ft\u00b2) | 35.4 | 141.4 | Not Available
300 | Site EUI (kBtu/ft\u00b2) | 15.8 | 63.1 | Not Available
300 | Source Energy Use (kBtu) | 3,536.0 | 14,144.1 | Not Available
300 | Site Energy Use (kBtu) | 1,578.7 | 6,314.9 | Not Available
300 | Energy Cost ($) | 34.61 | 138.44 | Not Available
300 | Total GHG Emissions (Metric Tons CO2e) | 0.2 | 0.6 | 0.0
400 | ENERGY STAR score (1-100) | Not Available | 50 | Not Available
400 | Source EUI (kBtu/ft\u00b2) | 35.4 | 141.4 | Not Available
400 | Site EUI (kBtu/ft\u00b2) | 15.8 | 63.1 | Not Available
400 | Source Energy Use (kBtu) | 3,536.0 | 14,144.1 | Not Available
400 | Site Energy Use (kBtu) | 1,578.7 | 6,314.9 | Not Available
400 | Energy Cost ($) | 34.61 | 138.44 | Not Available
400 | Total GHG Emissions (Metric Tons CO2e) | 0.2 | 0.6 | 0.0
我有 table2 就像，
project_id | energy_type | uses | target | title
------------+-------------------------------------------------------------------------------------------------------------------------+------+----------------------------------+------------------------------
300 | {"Energy Not Entered","Assumed Mix Based on State & Property Type:","","Electric - Grid (56.9%)","Natural Gas (43.1%)"} | | Target % Better than Median: 75 | About this Property's Design
400 | {"Energy Not Entered","Assumed Mix Based on State & Property Type:","","Electric - Grid (56.9%)","Natural Gas (43.1%)"} | | Target % Better than Median: 75 | About this Property's Design
我有 table3 就像，
project_id | your_design_score
------------+-------------------
300 | N/A
400 | N/A
我加入 table 基于 project_id 使用 python,
import psycopg2
con = psycopg2.connect(dbname="db",
user="postgres", host="localhost",
password="")
d1 = "select t1.project_id,t1.Metric, t1.Design_Target, t1.Median_Property, t1.Design_Project, t2.Energy_Type, t2.Uses, t2.Target, t2.Title, t3.your_design_score from metric_comparison t1, property_design t2, design_score t3 where t1.project_id = t2.project_id and t2.project_id = t3.project_id"
cursor.execute(d1)
d2 = cursor.fetchall()
print d2
con.commit()
我得到了这样的输出，
[('300', 'ENERGY STAR score (1-100)', 'Not Available', '50', 'Not Available', '{"Energy Not Entered","Assumed Mix Based on State & Property Type:","","Electric - Grid (56.9%)","Natural Gas (43.1%)"}', None, ' Target % Better than Median: 75', "About this Property's Design", 'N/A'), ('300', 'Source EUI (kBtu/ft\u00b2)', '35.4', '141.4', 'Not Available', '{"Energy Not Entered","Assumed Mix Based on State & Property Type:","","Electric - Grid (56.9%)","Natural Gas (43.1%)"}', None, ' Target % Better than Median: 75', "About this Property's Design", 'N/A'), ('300', 'Site EUI (kBtu/ft\u00b2)', '15.8', '63.1', 'Not Available', '{"Energy Not Entered","Assumed Mix Based on State & Property Type:","","Electric - Grid (56.9%)","Natural Gas (43.1%)"}', None, ' Target % Better than Median: 75', "About this Property's Design", 'N/A'), ('300', 'Source Energy Use (kBtu)', '3,536.0', '14,144.1', 'Not Available', '{"Energy Not Entered","Assumed Mix Based on State & Property Type:","","Electric - Grid (56.9%)","Natural Gas (43.1%)"}', None, ' Target % Better than Median: 75', "About this Property's Design", 'N/A'), ('300', 'Site Energy Use (kBtu)', '1,578.7', '6,314.9', 'Not Available', '{"Energy Not Entered","Assumed Mix Based on State & Property Type:","","Electric - Grid (56.9%)","Natural Gas (43.1%)"}', None, ' Target % Better than Median: 75', "About this Property's Design", 'N/A'), ('300', 'Energy Cost ($)', '34.61', '138.44', 'Not Available', '{"Energy Not Entered","Assumed Mix Based on State & Property Type:","","Electric - Grid (56.9%)","Natural Gas (43.1%)"}', None, ' Target % Better than Median: 75', "About this Property's Design", 'N/A'), ('300', 'Total GHG Emissions (Metric Tons CO2e)', '0.2', '0.6', '0.0', '{"Energy Not Entered","Assumed Mix Based on State & Property Type:","","Electric - Grid (56.9%)","Natural Gas (43.1%)"}', None, ' Target % Better than Median: 75', "About this Property's Design", 'N/A'), ('400', 'ENERGY STAR score (1-100)', 'Not Available', '50', 'Not Available', '{"Energy Not Entered","Assumed Mix Based on State & Property Type:","","Electric - Grid (56.9%)","Natural Gas (43.1%)"}', None, ' Target % Better than Median: 75', "About this Property's Design", 'N/A'), ('400', 'Source EUI (kBtu/ft\u00b2)', '35.4', '141.4', 'Not Available', '{"Energy Not Entered","Assumed Mix Based on State & Property Type:","","Electric - Grid (56.9%)","Natural Gas (43.1%)"}', None, ' Target % Better than Median: 75', "About this Property's Design", 'N/A'), ('400', 'Site EUI (kBtu/ft\u00b2)', '15.8', '63.1', 'Not Available', '{"Energy Not Entered","Assumed Mix Based on State & Property Type:","","Electric - Grid (56.9%)","Natural Gas (43.1%)"}', None, ' Target % Better than Median: 75', "About this Property's Design", 'N/A'), ('400', 'Source Energy Use (kBtu)', '3,536.0', '14,144.1', 'Not Available', '{"Energy Not Entered","Assumed Mix Based on State & Property Type:","","Electric - Grid (56.9%)","Natural Gas (43.1%)"}', None, ' Target % Better than Median: 75', "About this Property's Design", 'N/A'), ('400', 'Site Energy Use (kBtu)', '1,578.7', '6,314.9', 'Not Available', '{"Energy Not Entered","Assumed Mix Based on State & Property Type:","","Electric - Grid (56.9%)","Natural Gas (43.1%)"}', None, ' Target % Better than Median: 75', "About this Property's Design", 'N/A'), ('400', 'Energy Cost ($)', '34.61', '138.44', 'Not Available', '{"Energy Not Entered","Assumed Mix Based on State & Property Type:","","Electric - Grid (56.9%)","Natural Gas (43.1%)"}', None, ' Target % Better than Median: 75', "About this Property's Design", 'N/A'), ('400', 'Total GHG Emissions (Metric Tons CO2e)', '0.2', '0.6', '0.0', '{"Energy Not Entered","Assumed Mix Based on State & Property Type:","","Electric - Grid (56.9%)","Natural Gas (43.1%)"}', None, ' Target % Better than Median: 75', "About this Property's Design", 'N/A')]
现在我正在尝试根据 project_id 输入显示记录，
PROJECT_ID = '300' #在 python 代码中声明 基于 PROJECT_ID 的输入，我必须显示上面输出的记录。
我必须显示 project_id 与 PROJECT_ID 匹配的记录。
在SQL端过滤，在execute方法中传递参数：
d1 = """
select
project_id,
t1.Metric,
t1.Design_Target,
t1.Median_Property,
t1.Design_Project,
t2.Energy_Type,
t2.Uses, t2.Target,
t2.Title,
t3.your_design_score
from
metric_comparison t1
inner join
property_design t2 using (project_id)
inner join
design_score t3 using (project_id)
where project_id = %s
"""
project_id = '300'
cursor.execute(d1, [project_id])