CODEWITHSUNDEEP
pythonpandasdataframe
G-torChris
G-torChris

Reputation: 23

Comparing values in a pandas Dataframe which are indexed differently

I need help comparing values in a pandas Dataframe which are indexed differently. I've read the Dataframe from a csv containing headers 'Time', 'Predicted', 'Engine'. 'Time' is a timeseries "DD.MM.YYYY hh:mm:ss" in 10 minute steps , 'Predicted' and 'Engine' take values 0 or 1. So it looks like this: 

+--------------------+---------+---------+
|Time                |Predicted|Engine   |
|01.01.2019  00:00:00|        0|        0|
|01.01.2019  00:10:00|        1|        0|
|01.01.2019  00:20:00|        1|        1|
|                 ...|      ...|      ...|

I want to compare the Predicted value at [i] with the Engine value at [i+1]. 

+--------------------+---------+---------+------+
|Time                |Predicted|Engine   |Result|
|01.01.2019  00:00:00|        0|        0|False | <- although prob. not defined ?
|01.01.2019  00:10:00|        1|        0|True  |
|01.01.2019  00:20:00|        1|        1|True  |
|                 ...|      ...|      ...|   ...|

This was my initial code (to clarify what I was aiming for), which resulted in

ValueError: Can only compare identically-labeled Series objects

Code:

res = []
for i in df['Predicted']:
    if df['Predicted'][i:i+1] == df['Engine'][i+1:i+2]:
        res.append(True)
    else:
        res.append(False)
df['Result'] = res

I now get why this isn't working but I can't find a solution to this problem on my own (yet) as I am fairly new to programming.

Upvotes: 2

Views: 50

Answers (1)

Bruno Mello
Bruno Mello

Reputation: 4618

You can use shift, it basically shift your series by some amount and then compare this with the Engine:

df['Result'] = df['Predicted'].shift(1) == df['Engine']

Upvotes: 3

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