1. dr.as_integer(): Convert to Integer

2. dr.as_double(): Convert to Double (float64)

3. dr.as_numeric(): Convert to Numeric

4. Use Pandas conversion methods or functions

In [5]:

import datar.all as dr
from datar import f
import pandas as pd

from loguru import logger

from pipda import register_verb
dr.filter = register_verb(func=dr.filter_)
dr.slice = register_verb(func=dr.slice_)

##-----------------##

s_float = pd.Series([1.0, 2.5, 3.7, 4.2, 5.9])
s_str = pd.Series(['1', '2', '3', '4.6', '5.7'])
s_mixed = pd.Series(['1', 2, '3', 4, 'five'])

import datar.all as dr from datar import f import pandas as pd from loguru import logger from pipda import register_verb dr.filter = register_verb(func=dr.filter_) dr.slice = register_verb(func=dr.slice_) ##-----------------## s_float = pd.Series([1.0, 2.5, 3.7, 4.2, 5.9]) s_str = pd.Series(['1', '2', '3', '4.6', '5.7']) s_mixed = pd.Series(['1', 2, '3', 4, 'five'])

1. dr.as_integer()¶

In [2]:

print(dr.as_integer(s_float))

print(dr.as_integer(s_float))

0    1
1    2
2    3
3    4
4    5
dtype: int64

In [6]:

try:
    print(dr.as_integer(s_str))
except Exception as e:
    logger.error(e)

try: print(dr.as_integer(s_str)) except Exception as e: logger.error(e)

2026-08-22 13:53:56.246 | ERROR    | __main__:<module>:4 - invalid literal for int() with base 10: '4.6'

In [7]:

try:
    print(dr.as_integer(s_mixed))
except Exception as e:
    logger.error(e)

try: print(dr.as_integer(s_mixed)) except Exception as e: logger.error(e)

2026-08-22 13:54:16.268 | ERROR    | __main__:<module>:4 - invalid literal for int() with base 10: 'five'

2. dr.as_double()¶

In [8]:

print(dr.as_double(s_float))

print(dr.as_double(s_float))

0    1.0
1    2.5
2    3.7
3    4.2
4    5.9
dtype: float64

In [9]:

print(dr.as_double(s_str))

print(dr.as_double(s_str))

0    1.0
1    2.0
2    3.0
3    4.6
4    5.7
dtype: float64

In [10]:

try:
    print(dr.as_double(s_mixed))
except Exception as e:
    logger.error(e)

try: print(dr.as_double(s_mixed)) except Exception as e: logger.error(e)

2026-08-22 13:55:04.245 | ERROR    | __main__:<module>:4 - could not convert string to float: 'five'

3. dr.as_numeric()¶

In [11]:

print(dr.as_numeric(s_float))

print(dr.as_numeric(s_float))

0    1
1    2
2    3
3    4
4    5
dtype: int64

In [12]:

print(dr.as_numeric(s_str))

print(dr.as_numeric(s_str))

0    1.0
1    2.0
2    3.0
3    4.6
4    5.7
dtype: float64

In [13]:

try:
    print(dr.as_numeric(s_mixed))
except Exception as e:
    logger.error(e)

try: print(dr.as_numeric(s_mixed)) except Exception as e: logger.error(e)

2026-08-22 13:56:34.230 | ERROR    | __main__:<module>:4 - Cannot convert 0       1
1       2
2       3
3       4
4    five
dtype: object to numeric

4. Use Pandas conversion methods or functions¶

In [15]:

df_demo = pd.DataFrame({
    'A': ['1', '2', '3'],
    'B': ['4.1', '5.2', '6.3']
})

df_demo = pd.DataFrame({ 'A': ['1', '2', '3'], 'B': ['4.1', '5.2', '6.3'] })

In [16]:

##-----------------------##
## Using Series.astype() ##
##-----------------------##

print(
    df_demo
    >> dr.mutate(
        A = f.A.astype(int),
        B = f.B.astype(float)
    )
)

##-----------------------## ## Using Series.astype() ## ##-----------------------## print( df_demo >> dr.mutate( A = f.A.astype(int), B = f.B.astype(float) ) )

        A         B
  <int64> <float64>
0       1       4.1
1       2       5.2
2       3       6.3

In [18]:

##-----------------------##
## Using pd.to_numeric() ##
##-----------------------##

print(
    df_demo
    >> dr.pipe(lambda f: f >> dr.mutate(
            A = pd.to_numeric(f.A, errors='raise'),
            B = pd.to_numeric(f.B, errors='raise')
        )
    )
)

##-----------------------## ## Using pd.to_numeric() ## ##-----------------------## print( df_demo >> dr.pipe(lambda f: f >> dr.mutate( A = pd.to_numeric(f.A, errors='raise'), B = pd.to_numeric(f.B, errors='raise') ) ) )

        A         B
  <int64> <float64>
0       1       4.1
1       2       5.2
2       3       6.3