Pandas interpolate NaNs based on different column

interpolation, pandas, python

Solution

I have found a solution to the issue above. The trick is to use groupby and define a function that interpolates over the dataframe that is created by groupby and passed to apply(). In my case, this is the function

def interp(x, wind_speed):
    g = interpolate.interp1d(np.array(x["wind_speed"]), np.array(x["Velocity"]))
    return g(wind_speed)

and this is my groupby

group = df.groupby("point").apply(interp, wind_speed)

The function interp has to be called with a parameter that represents the point where to perform the interpolation.

I wonder whether there is a better way to do it.

Problem

I have the following DataFrame (extract) ``` data = pd.DataFrame([[0., -10.88948939, 74.22099994, 1.5, "NW", 0], [0.819377018, -10.88948939, 74.22099994, 1.5, "NW", 1], [8.47965933, -10.88948939, 74.22099994, 1.5, "NW", 10], [15.38036833, -10.88948939, 74.22099994, 1.5, "NW", 20]], columns=["Velocity", "X", "Y", "Z", "wind_direction", "wind_speed"]) Velocity X Y Z wind_direction wind_speed 0 -10.88 74.22 1.5 NW 0 0.82 -10.89 74.22 1.5 NW 1 8.48 -10.89 74.22 1.5 NW 10 15.38 -10.89 74.22 1.5 NW 20 ``` It represents the results of a CFD simulation for a specific coordinate (X, Y, Z) and two boundary conditions (wind_direction and wind_speed). I would like to estimate Velocity for the same point (X, Y, Z), same wind_direction, but intermediate wind_speed, say 4.6. I have this additional row in my dataframe ``` NaN -10.89 74.22 1.5 NW 4.6 ``` Now I would like to interpolate to fill the NaN based on the wind_speed. For the example above I would expect to get 6.643773541 The number comes from the linear interpolation: 0.82 + (4.6 - 1)/(10 - 1) * (8.48 - 0.82) Any idea? Thanks UPDATE I have found a solution to the issue above. The trick is to use groupby and define a function that interpolates over the dataframe that is created by groupby and passed to apply(). In my case, this is the function ``` def interp(x, wind_speed): g = interpolate.interp1d(np.array(x["wind_speed"]), np.array(x["Velocity"])) return g(wind_speed) ``` and this is my groupby ``` group = df.groupby("point").apply(interp, wind_speed) ``` The function interp has to be called with a parameter that represents the point where to perform the interpolation. I wonder whether there is a better way to do it.

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