pandas.dataframe doesn't have a built-in reshape method, but you can use .values to access the underlying numpy array object and call reshape on it:

start = 0
for i in range(0, len(df.index)):
    if (i + 1)%10 == 0:
        result = df.iloc[start:i+1].values.reshape(2,5)
        start = i + 1
        print result

#[[ 52.1  32.2  44.6  99.1  12.3]
# [ 43.2  79.4  45.5  56.3  15.4]]
#[[ 35.7  23.7  66.7  33.8  12.9]
# [ 34.8  21.6  43.7  44.2  55.8]]

Solution was linked on reshaped method on documentation page.

Insted of Y.reshape(-1,1) you need to use:

Y.values.reshape(-1,1)

I just tested your code* with pandas=0.24.1 and geopandas=0.4.0, and it ran without error.

For what it's worth, the geopandas documentation offers example code for Creating a GeoDataFrame from a DataFrame with coordinates, and it is not necessary to use iterrows on the DataFrame to build the geometries point by point, nor is it necessary to add the geometry column to the DataFrame first. Arguably, this should be avoided, so that you can distinguish between DataFrame that don't have geometries and GeoDataFrames that do.

The example code from the documentation. The code is slightly modified to follow your conventions:

import pandas as pd
import geopandas as gpd

city_dict = {'City': ['Buenos Aires', 'Brasilia', 'Santiago', 'Bogota', 'Caracas'],
     'Country': ['Argentina', 'Brazil', 'Chile', 'Colombia', 'Venezuela'],
     'Latitude': [-34.58, -15.78, -33.45, 4.60, 10.48],
     'Longitude': [-58.66, -47.91, -70.66, -74.08, -66.86]}
df = pd.DataFrame(city_dict)
crs_dict = {'init': 'epsg:4326'}

gdf = gpd.GeoDataFrame(df, crs=crs_dict, 
    geometry=gpd.points_from_xy(df.Longitude, df.Latitude)
    )

If you are using geopandas<0.5.0, points_from_xy won't be available to you, in which case you can use a list compehension with the shapely Point constructor:

from shapely.geometry import Point

gdf = gpd.GeoDataFrame(df, crs=crs_dict, 
    geometry=[Point(x, y) for x, y in zip(df.Longitude, df.Latitude)]
    )

* I didn't test your exact code because you didn't supply city data. I used the example dict given above from the geopandas documentation.