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python - 将值与以前的值进行比较并打印它是否相同或不同

问题描述

我需要确定输出“开”或“关”是否与前一个单元格相同，如果相同则打印“相同周期”或不同打印“不同周期”。我在 jupyter python 中，我当前的代码是：

import pandas as pd
import numpy as np 

header_list = ['Time']

df = pd.read_csv('S8-1.csv' , skiprows=6 , names = header_list)#splits the data into proper columns

df[['Date/Time','Pressure']] = df.Time.str.split(",,", expand=True)#deletes orginal messy column
df.pop('Time')
#convert Pressure from object to numeric

df['Pressure'] = pd.to_numeric(df['Pressure'], errors = 'coerce')#converts to a time

df['Date/Time'] = pd.to_datetime(df['Date/Time'], format = '%m/%d/%y %H:%M:%S.%f' , errors = 'coerce')

df['Moving Average'] = df['Pressure'].rolling(window=5).mean()

df['Rolling Average Center']= df['Pressure'].rolling(window=5, center=True).mean()

df['Machine On/Off'] = ['1' if x >= 115 else '0' for x in df['Rolling Average Center'] ]

df['Moving Avg of Binary'] = df['Machine On/Off'].rolling(window=3).mean()

df['On/Off'] = ['On' if x > 0 else 'Off' for x in df ['Moving Avg of Binary']]

df['Cycle change'] = ['Same Cycle' if x == last else 'New Cycle' for x != last in df['On/Off']]

最后一行给我带来了麻烦。我的数据看起来像这样：Excel 数据

标签： pythonpandasjupyter

解决方案

欢迎来到 Stackoverflow，让我们假设您的数据框如下：

import pandas as pd 
your_dataframe = pd.DataFrame({'your_on/of_column':['ON', 'OFF', 'ON', 'ON', 'OFF', 'ON', 'ON', 'OFF', 'ON']})
your_dataframe['other_column1'] = ['foo' for item in range(len(your_dataframe))]
your_dataframe['other_column2'] = ['bar' for item in range(len(your_dataframe))]
######
  your_on/of_column other_column1 other_column2
0                ON           foo           bar
1               OFF           foo           bar
2                ON           foo           bar
3                ON           foo           bar
4               OFF           foo           bar
5                ON           foo           bar
6                ON           foo           bar
7               OFF           foo           bar
8                ON           foo           bar

有一个著名的函数用于确定命名的连续值之间的差异diff，您可以在此处阅读！唯一需要注意的是它只能对数字/逻辑值进行操作所以将列转换your_on/of_column为布尔值并继续！

your_dataframe['Cycle'] = your_dataframe['your_on/of_column'].apply(lambda item:item=='ON').diff().apply(lambda item:"Different Cycle" if item else "Same Cycle")

输出：

  your_on/of_column other_column1 other_column2            Cycle
0                ON           foo           bar  Different Cycle
1               OFF           foo           bar  Different Cycle
2                ON           foo           bar  Different Cycle
3                ON           foo           bar       Same Cycle
4               OFF           foo           bar  Different Cycle
5                ON           foo           bar  Different Cycle
6                ON           foo           bar       Same Cycle
7               OFF           foo           bar  Different Cycle
8                ON           foo           bar  Different Cycle

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