Extract Value From Pandas Dataframe Based On Condition in Another Column

Question

I am trying to develop some code that extracts the power price when a power plant starts up. To give an example refer to the following data frame.

data = {
  'Power_Price':  [10, 11,15, 33, 50, 10, 12, 20, 17],
  'Plant_Ops_1': [0, 0, 10, 10, 10, 0, 0, 10, 10],
  'Plant_Ops_2': [0, 0, 0, 50, 50, 0, 0, 0, 0]
}

df = pd.DataFrame (data, columns = ['Power_Price','Plant_Ops_1','Plant_Ops_2'])

Based on this I aiming to develop some code that would store in a dataframe the power price when the plant ops columns transitions from 0 to a number greater than 0 (ie when the power plant starts). In the case of the data above the output would look something along the lines of:

data_out = {
  'Plant': ['Plant_Ops_1', 'Plant_Ops_1', 'Plant_Ops_2'],
  'Power_price': [15, 20, 33]
}

df_out = pd.DataFrame (data_out, columns = ['Plant','Power_price'])

Hopefully this makes sense. Certainly welcome any advice or guidance you are able to provide.

Answer 1

You can do this:

df = df.melt(id_vars='Power_Price')

df[(df['value'] > df['value'].shift()) & (df['variable'] == df['variable'].shift())]

    Power_Price     variable  value
2            15  Plant_Ops_1     10
7            20  Plant_Ops_1     10
12           33  Plant_Ops_2     50

Answer 2

I hope I've understood your question right:

df = df.melt(id_vars="Power_Price")
x = df["value"].eq(0)
x = df.groupby((x != x.shift()).cumsum()).head(1)
x = x[x["value"] > 0].rename(columns={"variable": "Plant"})[
    ["Plant", "Power_Price"]
]
print(x)

Prints:

          Plant  Power_Price
2   Plant_Ops_1           15
7   Plant_Ops_1           20
12  Plant_Ops_2           33

Answer 3

Use DataFrame.melt with filter rows with shifted per groups equal 0 and also greater like 0 in boolean indexing :

df = df.melt('Power_Price', var_name='Plant')

df = df[df.groupby('Plant')['value'].shift().eq(0) & df['value'].gt(0)].drop('value',axis=1)
print (df)
    Power_Price        Plant
2            15  Plant_Ops_1
7            20  Plant_Ops_1
12           33  Plant_Ops_2

Last if necessary change order of columns:

df = df[["Plant", "Power_Price"]]