Question

我是python的新手，想在Python中使用 sqlalchemy 从我的MySQL数据库中读取数据。如何将数据读入熊猫并使用pandas pivot？数据库结构如下所示：

Date_String Experiment  Experiment_Type RESET_FREQUENCY MEASURE_LENGTH  Value   Date_Integer
28-Sep-16   A   FORWARD_Detector    1 Minute    1   0.99994 20160928
28-Sep-16   A   FORWARD_Detector    1 Minute    7   0.99959 20160928
28-Sep-16   A   FORWARD_Detector    1 Minute    14  0.99917 20160928
28-Sep-16   A   FORWARD_Detector    1 Minute    21  0.99876 20160928
28-Sep-16   A   FORWARD_Detector    1 Minute    30  0.99823 20160928
28-Sep-16   A   FORWARD_Detector    1 Minute    60  0.99647 20160928
28-Sep-16   A   FORWARD_Detector    1 Minute    90  0.99469 20160928
28-Sep-16   A   FORWARD_Detector    1 Minute    120 0.99288 20160928
29-Sep-16   A   FORWARD_Detector    1 Minute    1   0.99994 20160929
29-Sep-16   A   FORWARD_Detector    1 Minute    7   0.99959 20160929
29-Sep-16   A   FORWARD_Detector    1 Minute    14  0.99918 20160929
29-Sep-16   A   FORWARD_Detector    1 Minute    21  0.99877 20160929
29-Sep-16   A   FORWARD_Detector    1 Minute    30  0.99824 20160929
29-Sep-16   A   FORWARD_Detector    1 Minute    60  0.99646 20160929
29-Sep-16   A   FORWARD_Detector    1 Minute    90  0.99472 20160929
29-Sep-16   A   FORWARD_Detector    1 Minute    120 0.99287 20160929
30-Sep-16   A   FORWARD_Detector    1 Minute    1   0.99994 20160930
30-Sep-16   A   FORWARD_Detector    1 Minute    7   0.99959 20160930
30-Sep-16   A   FORWARD_Detector    1 Minute    14  0.99918 20160930
30-Sep-16   A   FORWARD_Detector    1 Minute    21  0.99877 20160930
30-Sep-16   A   FORWARD_Detector    1 Minute    30  0.99824 20160930
30-Sep-16   A   FORWARD_Detector    1 Minute    60  0.99647 20160930
30-Sep-16   A   FORWARD_Detector    1 Minute    90  0.99469 20160930
30-Sep-16   A   FORWARD_Detector    1 Minute    120 0.99286 20160930
...

代码如下所示：

import sqlalchemy as sqlal
import matplotlib.pyplot as plt
import pandas as pd
mysql_engine = sqlal.create_engine('mysql+mysqlconnector://xxx@localhost/rates data',poolclass=sqlal.pool.NullPool)
mysql_engine.echo = False
mysql_engine.connect()
metadata = sqlal.MetaData()
'''
experiment_data = sqlal.Table('experiment_data', metadata,
                                             sqlal.Column('Date_String', sqlal.Date(), nullable=True), 
                                             sqlal.Column('Experiment', sqlal.String(3), nullable=True),
                                             sqlal.Column('Experiment_Type', sqlal.String(8), nullable=True),
                                             sqlal.Column('RESET_FREQUENCY', sqlal.String(3), nullable=True),
                                             sqlal.Column('MEASURE_LENGHT', sqlal.Integer(), nullable=True),
                                             sqlal.Column('Value', sqlal.Float(), nullable=True),
                                             sqlal.Column('Date_Integer', sqlal.Integer(), nullable=True)
                                             )
'''
#print(mysql_engine.table_names())
Data_exp = sqlal.Table('experiment_data', metadata, autoload=True, autoload_with=mysql_engine)
stmt = sqlal.select([Data_exp])
results = mysql_engine.execute(stmt).fetchall()
data_dataframe = pd.DataFrame(results)
mysql_engine.dispose()

# Print the Dataframe
print(data_dataframe)



data_test= pd.pivot_table(Data_IR,index=["Date_String","MEASURE_LENGTH"],values=["Value"])

#optional way to get a pivot table
#data_test= pd.pivot_table(Data_IR,index=["Date_String"],columns=["MEASURE_LENGTH"],values=["Value"])

如何使用生成的数据透视表来绘制我的结果，如下图所示？

Answer 1

只需在pivot_table对象上使用pandas.DataFrame.plot，指定行图表即可。另外，在pivot_table的列中指定Date_String，并为{em> index 保留MEASURE_LENGTH：

下面包含使用pd.read_table()重建已发布数据的数据重建，但是因为您从MySQL中获取表格，所以可以忽略。另请查看可以读取sqlAlchemy对象的pandas.read_sql。

Data_IR = pandas.read_sql(stmt, con=mysql_engine)

转载数据（数据略微调整为不是所有三个日期的结果完全相同）

from io import StringIO
import pandas as pd

txt="""
Date_String Experiment  Experiment_Type RESET_FREQUENCY MEASURE_LENGTH  Value   Date_Integer
28-Sep-16   A   FORWARD_Detector    "1 Minute"    1   0.99974 20160928
28-Sep-16   A   FORWARD_Detector    "1 Minute"    7   0.99939 20160928
28-Sep-16   A   FORWARD_Detector    "1 Minute"    14  0.99897 20160928
28-Sep-16   A   FORWARD_Detector    "1 Minute"    21  0.99856 20160928
28-Sep-16   A   FORWARD_Detector    "1 Minute"    30  0.99803 20160928
28-Sep-16   A   FORWARD_Detector    "1 Minute"    60  0.99627 20160928
28-Sep-16   A   FORWARD_Detector    "1 Minute"    90  0.99449 20160928
28-Sep-16   A   FORWARD_Detector    "1 Minute"    120 0.99268 20160928
29-Sep-16   A   FORWARD_Detector    "1 Minute"    1   0.99994 20160929
29-Sep-16   A   FORWARD_Detector    "1 Minute"    7   0.99959 20160929
29-Sep-16   A   FORWARD_Detector    "1 Minute"    14  0.99918 20160929
29-Sep-16   A   FORWARD_Detector    "1 Minute"    21  0.99877 20160929
29-Sep-16   A   FORWARD_Detector    "1 Minute"    30  0.99824 20160929
29-Sep-16   A   FORWARD_Detector    "1 Minute"    60  0.99646 20160929
29-Sep-16   A   FORWARD_Detector    "1 Minute"    90  0.99472 20160929
29-Sep-16   A   FORWARD_Detector    "1 Minute"    120 0.99287 20160929
30-Sep-16   A   FORWARD_Detector    "1 Minute"    1   0.99954 20160930
30-Sep-16   A   FORWARD_Detector    "1 Minute"    7   0.99919 20160930
30-Sep-16   A   FORWARD_Detector    "1 Minute"    14  0.99878 20160930
30-Sep-16   A   FORWARD_Detector    "1 Minute"    21  0.99837 20160930
30-Sep-16   A   FORWARD_Detector    "1 Minute"    30  0.99784 20160930
30-Sep-16   A   FORWARD_Detector    "1 Minute"    60  0.99607 20160930
30-Sep-16   A   FORWARD_Detector    "1 Minute"    90  0.99429 20160930
30-Sep-16   A   FORWARD_Detector    "1 Minute"   120 0.99246 20160930
"""

Data_IR = pd.read_table(StringIO(txt), sep="\\s+")

剧情

import matplotlib.pyplot as plt

data_test= pd.pivot_table(Data_IR,index=["MEASURE_LENGTH"], columns=["Date_String"], values="Value")
data_test.plot(kind='line')

来自mysql数据库python的Group Pivot Pandas数据

1 个答案: