python - 如何在 python 中绘制程序的持续时间

Question

我试图绘制一些在夜间运行的程序的持续时间，我将程序持续时间数据导出到 CSV 文件中，以便稍后进行分析。 (像这样)

这是我的代码和 CSV 示例:

CSV:

 na,programName,totaal,na,startDate,endDate,Date
 ?,"to/check.apl",54006,?,2017-02-27T20:04:07.233,2017-02- 27T20:05:01.239,2017-02-27T00:00:00.000
 ?,"to/ibx.apl",143887,?,2017-02-27T20:07:55.627,2017-02-27T20:10:19.514,2017-02-27T00:00:00.000
 ?,"to/checker.apl",2039600,?,2017-02-27T20:14:37.662,2017-02-27T20:48:37.262,2017-02-27T00:00:00.000

python 代码:

 import matplotlib
 from pandas import *
 import pandas as pd
 import numpy  as np
 import matplotlib.pyplot as plt

 matplotlib.style.use('ggplot')

 data = "miFile.csv"
 df = pd.DataFrame.from_csv(data)
 df = df.set_index('totaal')

 newDf = df[['programName','startDate','endDate']]

到目前为止，我得到了日期时间错误，所以我尝试通过这样做来解决这个问题(也没有运气来绘制):

 newDf['startDate'] = pd.to_datetime(newDf['startDate'])
 newDf['endDate'] = pd.to_datetime(newDf['endDate'])

 #pd.to_datetime(pd.Series(["2017-02-27T20:04:07.233"]) format= "%d, %m, %y, %H: %M: %S")

 newDf.plot('programName','startDate','endDate')

 plt.show()

Answer 1

我想你需要read_csv用于创建 df，然后获取列的差异和 convert timedelta到 分钟 plot :

temp=u"""na,programName,totaal,na,startDate,endDate,Date
?,"to/check.apl",54006,?,2017-02-27T20:04:07.233,2017-02-27T20:05:01.239,2017-02-27T00:00:00.000
?,"to/ibx.apl",143887,?,2017-02-27T20:07:55.627,2017-02-27T20:10:19.514,2017-02-27T00:00:00.000
?,"to/checker.apl",2039600,?,2017-02-27T20:14:37.662,2017-02-27T20:48:37.262,2017-02-27T00:00:00.000"""
#after testing replace 'StringIO(temp)' to 'filename.csv'
df = pd.read_csv(StringIO(temp), index_col=[2], parse_dates=[4,5,6])

print (df.dtypes)
na                     object
programName            object
na.1                   object
startDate      datetime64[ns]
endDate        datetime64[ns]
Date           datetime64[ns]
dtype: object

df['duration'] = (df['endDate'] - df['startDate']).astype('timedelta64[m]')
newDf = df[['programName','duration']]
print (newDf)
            programName  duration
totaal                           
54006      to/check.apl       0.0
143887       to/ibx.apl       2.0
2039600  to/checker.apl      33.0

newDf.plot()

plt.show()