Average for similar looking data in a column using Pandas

First get the indexes of every row where there's a jump. Use Pandas' DataFrame.diff() to get the difference between the value in each row and the previous row, then check to see if it's greater than 0.15 with >. Use that to filter the dataframe index, and save the resulting indices (in the case of your sample data, three) in a variable.

indices = df.index[df['Curr(mA)'].diff() > 0.15]

The next steps depend on if there are more columns in the source dataframe that you want in the output, or if it's really just curr(mA) and index. In the latter case, you can use np.split() to cut the dataframe into a list of dataframes based on the indexes you just pulled. Then you can go ahead and average them in a list comphrension.

[df['Curr(mA)'].mean() for df in np.split(df, indices)]

> [1.3490729374999997, 1.5455638666666667, 1.7498627333333332, 1.9608515]

To get it to match your desired output above (same thing but as one-column dataframe rather than list) convert the list to pd.Series and reset_index().

pd.Series(
    [df['Curr(mA)'].mean() for df in np.split(df, indices)]
).reset_index(drop=True)

index   0
0   0   1.349073
1   1   1.545564
2   2   1.749863
3   3   1.960851