Quick way to get WKT from a GeoDataFrame's geometries in GeoPandas?

Is there a quick way to produce a column/series containing the WKTs of all the geometries in a GeoPandas GeoDataFrame?

I got around this using the apply function (see example below), but I just wanted to check if there is a betterquicker way to do this.

import geopandas as gpd
from shapely.geometry import Point

mydf = gpd.GeoDataFrame({'x':[1,2,3],
                         'y':['a','b','c'],
                         'geometry':[Point([0,0]),Point([1,1]),Point([2,2])]},
                        geometry='geometry')

def get_wkt(geom):
    return geom.wkt

wkt_col = mydf.geometry.apply(get_wkt)

print(type(wkt_col))
print(wkt_col) 

The output I’m looking for (which the code above produces) is copied below:

<class 'pandas.core.series.Series'>
0    POINT (0 0)
1    POINT (1 1)
2    POINT (2 2)
Name: geometry, dtype: object

Edit

Here are some performance measures from the main suggestions:

# My original method:
mydf.geometry.apply(get_wkt)
267 µs ± 9.31 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)


# martinfleis' method:
gpd.array.to_wkt(mydf.geometry.values)
57.9 µs ± 629 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)


# gene's first method:
mydf.apply(lambda row:row['geometry'].wkt, axis=1)
461 µs ± 4.46 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)


# gene's second method
[geom.wkt for geom in mydf.geometry]
61.6 µs ± 1.41 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)

wkt_array = gpd.array.to_wkt(mydf.geometry.values)
mydf['wkt'] = wkt_array

mydf["wkt"]= mydf.apply(lambda row:row['geometry'].wkt, axis=1)
mydf
   x  y            geometry          wkt
0  1  a  POINT (0.00000 0.00000)  POINT (0 0)
1  2  b  POINT (1.00000 1.00000)  POINT (1 1)
2  3  c  POINT (2.00000 2.00000)  POINT (2 2)

wkt = [geom.wkt for geom in mydf.geometry]
mydf['wkt']=wkt