Split an integer cell into two columns based on digit location

I am dealing with thousands of csv GPS files which I want to plot in a GIS software. To do this I need decimal degrees, but the data was collected in degree decimal minutes. I have a function that converts the degree decimal minutes into the format I desire however the layout of the data in each csv file requires manipulation.

Here is an image of the dataframe which requires manipulation:

The format I would like columns 0 and 2 to be in is 57 10.7240 N split across three columns. As there is no whitespace between 57 and 10, and the decimal point is in the correct place I am struggling to find a way to split the integer at the 7 location and insert 57 as a new column in the dataframe. This could be done manually, but I have over 10,000 csv files with 600 rows each is not feasible.

Any help on how to split the integer from 5710.7242 into 57 10.7242 N would be really appreciated as it is the last part of my code to correct before the whole conversion works.

My solution is not very efficient in terms of iteration abuse, but you can try this:

import pandas as pd

data = [[5710.7240,'N',917.3222,'W'],[710.7239,'N',917.3225,'W']]
df = pd.DataFrame(data, columns=['L2','NS','L4','EW'])

split_L1, split_L2 = [],[]
for i, row in df.iterrows():
    split_L1.append(int(int(df['L2'][i])/100))
    split_L2.append(round(df['L2'][i]-split_L1[i]*100, 5))
df.insert(0,'L1',split_L1, True)
df['L2'] = split_L2

split_L3, split_L4 = [],[]
for i, row in df.iterrows():
    split_L3.append(int(int(df['L4'][i])/100))
    split_L4.append(round(df['L4'][i]-split_L3[i]*100, 5))
df.insert(3,'L3',split_L3, True)
df['L4'] = split_L4

df

Input:

Output: