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Currently I have a DataFrame as shown below:
Device TimeSec Current
1 0.1 0.02
1 0.25 0.05
1 0.32 0.07
1 0.45 0.12
1 1.32 0.34
1 2.37 2.24
2 0.22 0.56
2 0.34 0.79
2 1.87 2.76
2 3.21 3.11
3 0.16 1.87
3 1.12 2.33
3 2.45 3.21
3 3.45 5.11
......
I would like to do the numerical integration of Current with TimeSec (∫Idt) for different Devices and collect the data into a new DataFrame as below:
Device IntegratedCurrent
1 x
2 y
3 z
The problem is that the time interval is not even and the number of data for each device is not even as well.
Many thanks!
489
The best way to convert one or more columns of a DataFrame to numeric values is to use pandas.to_numeric() . This function will try to change non-numeric objects (such as strings) into integers or floating-point numbers as appropriate.
Numba can be used in 2 ways with pandas: Specify the engine="numba" keyword in select pandas methods. Define your own Python function decorated with @jit and pass the underlying NumPy array of Series or DataFrame (using to_numpy() ) into the function.
Pandas series is a One-dimensional ndarray with axis labels. The labels need not be unique but must be a hashable type. The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index.
In order to sort the data frame in pandas, function sort_values() is used. Pandas sort_values() can sort the data frame in Ascending or Descending order.
Use some numerical integration function, e.g., scipy.integrate.trapz:
from scipy import integrate
df.groupby(df.Device).apply(lambda g: integrate.trapz(g.Current, x=g.TimeSec))
Note that this function, using the trapezoid integration rule, allows to specify the x values.
120
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