[FIXED] How to expand the dataframe based on the column values?

Issue

I have this dataframe:

utc  arc_time_s tec_tecu    elevation_deg   lat_e_deg   lon_e_deg
01.01.2018 01:19    54  3.856   17.35   57.44   25.02
01.01.2018 01:19    53  4.021   17.29   57.47   25.03
01.01.2018 01:19    52  4.029   17.22   57.51   25.05
01.01.2018 01:19    51  4.015   17.15   57.54   25.07
01.01.2018 01:19    50  3.997   17.08   57.57   25.09

What I want is expanding the dataframe based on lat_e_deg column to have all values with decimal scale 2.

I found the method resample but it seems like only can be used for datetime column.

So as an output I want to have like this:

How can I do this?

Solution

import pandas as pd
import numpy as np

# reconstruct part of your DataFrame for testing purposes:
df = pd.DataFrame([[17.35, 57.44], [17.29, 57.47], [17.22, 57.51]], 
                 columns = ['elevation_deg', 'lat_e_deg'])

# create a Series of the desired stepwise values:
lat_e_deg_expanded = pd.Series(np.arange(start = min(df['lat_e_deg']), 
                                         stop = max(df['lat_e_deg']), 
                                         step = 0.01), 
                               name = 'lat_e_deg')

# merge the expanded series with the original DataFrame and sort:
df_expanded = pd.merge(df, lat_e_deg_expanded, 
                       on = 'lat_e_deg', 
                       how = 'outer')
df_expanded.sort_values(by = 'lat_e_deg', inplace = True)

Answered By - Arne