A shorter, more compact alternative to loc multiple variables into separate columns in a loop [close-CodePudding

Is there a shorter, more compact alternative to loc multiple variables into separate columns in a loop?

The code that I am using now is looping through each polygon by index, then finding which point (from point file) is located in which polygon by ID. Then find some metrics or any other variable by equation and loc in new columns.

So, I was thinking if there is other way to loc multiple variables in separate columns to avoid great list of polygon.loc[n, 'column name'] = some variable.

for n in polygon.index:
    points_in_poly = points[points['ID'] == n]

    # then equation to find some variables in each row
    maxv = points_in_poly['Height'].max()
    mean = points_in_poly['Height'].mean()
    median = points_in_poly['Height'].median()
    std = points_in_poly['Height'].std()

    half_std = std / 2
    max1 = maxv * std   15

    # then by doing this I'm get each row corresponding value
    polygon.loc[n, 'max'] = maxv
    polygon.loc[n, 'mean'] = mean
    polygon.loc[n, 'median'] = median
    polygon.loc[n, 'std'] = std
    polygon.loc[n, 'half_std'] = half_std
    polygon.loc[n, 'max1'] = max1

CodePudding user response：

You could update your rows by using a dictionary like so:


polygon.loc[n] = {'max': maxv, 'mean': mean, 'median': median, 'std': std, 'half_std': half_std, 'max1': max1}

If there are other rows with values you don't want to overwrite you should add them to the dictionary. Something like this would work:

polygon_data = polygon.loc[n].to_dict()
polygon_data.update({'max': maxv, 'mean': mean, 'median': median, 'std': std, 'half_std': half_std, 'max1': max1})
polygon.loc[n] = polygon_data