Performing calculations on DataFrames of different lengths

I have two different DataFrames that look something like this:

The first DataFrame (name=df) (consisting of the Lat and Lon columns) has just over 1000 rows (values) in it. The second DataFrame (name=new_calc_dist) (consisting of the Calculated_Dist_m column) has over 30000 rows (values) in it. I want to determine the new longitude and latitude coordinates using the Lat, Lon, and Calculated_Dist_m columns. Here is the code I’ve tried:

r_earth = 6371000
new_lat = df['Lat'] + (new_calc_dist['Calculated_Dist_m'] / r_earth) * (180/np.pi)
new_lon = df['Lon'] + (new_calc_dist['Calculated_Dist_m'] / r_earth) * (180/np.pi) / np.cos(df['Lat'] * np.pi/180)

When I run the code, however, it only gives me new calculations for certain index values, and gives me NaNs for the rest. I’m not entirely sure how I should go about writing the code so that new longitude and latitude points are calculated for each of over 30000 row values based on the initial 1000 longitude and latitude points. Any suggestions?

EDIT

Here would be some sample outputs. Note that these are not exact figures, but give the idea.

And so the sample out put would be:

Again, these are just random outputs (I tried getting the exact calculations, but could not get it to work). But overall, this gives an idea of what would be wanted: taking the coordinates from the first dataframe and calculating new coordinates based on each of the calculated distances from the second dataframe.