import pandas as pd
from datetime import datetime

df = pd.DataFrame(
    {
        "a": [1, -2, 3],
        "b": [4, 5, 6],
        "c": [datetime(2020, 1, 1), datetime(2025, 4, 2), datetime(2026, 12, 3)],
        "d": ["fox", "beluga", "narwhal"],
    }
)

result = df.assign(
    # The usual Series methods are supported
    a_abs=pd.col("a").abs(),
    # And can be combined
    a_centered=pd.col("a") - pd.col("a").mean(),
    a_plus_b=pd.col("a") + pd.col("b"),
    # Namespace are supported too
    c_year=pd.col("c").dt.year,
    c_month_name=pd.col("c").dt.strftime("%B"),
    d_upper=pd.col("d").str.upper(),
).loc[pd.col("a_abs") > 1]  # This works in `loc` too

print(result)

   a  b          c        d  a_abs  a_centered  a_plus_b  c_year c_month_name  d_upper
1 -2  5 2025-04-02   beluga      2   -2.666667         3    2025        April   BELUGA
2  3  6 2026-12-03  narwhal      3    2.333333         9    2026     December  NARWHAL

In [4]: pd.col('value')
Out[4]: col('value')

In [5]: pd.col('value') * pd.col('weight')
Out[5]: (col('value') * col('weight'))

In [6]: (pd.col('value') - pd.col('value').mean()) / pd.col('value').std()
Out[6]: ((col('value') - col('value').mean()) / col('value').std())

In [7]: pd.col('timestamp').dt.strftime('%B')
Out[7]: col('timestamp').dt.strftime('%B')

In [19]: df = pd.DataFrame({'a': [1,2,3], 'b': [4,5,6]})

In [20]: df.assign(**{col: lambda df: df[col] + 10 for col in ('a', 'b')})
Out[20]:
    a   b
0  14  14
1  15  15
2  16  16

In [4]: df.assign(**{col: pd.col(col) + 10 for col in ('a', 'b')})
Out[4]: 
    a   b
0  11  14
1  12  15
2  13  16