Polars: how to add a column with numerical?

Question

in pandas：

df['new'] = a

where a is a numerical Series or just a number.
while in polars we can add a char

df.with_column(
 [
  pl.all(),
  pl.lit('str').alias('new')
 ]
)

but how to add a numerical Series or a number as a new column in polars?
Notice that the new numerical Series is not in the original df, it is a result of some computation.

Answer 1

Let's start with this DataFrame:

import polars as pl
df = pl.DataFrame(
    {
        "col1": [1, 2, 3, 4, 5],
    }
)
print(df)

shape: (5, 1)
????????
? col1 ?
? ---  ?
? i64  ?
????????
? 1    ?
????????
? 2    ?
????????
? 3    ?
????????
? 4    ?
????????
? 5    ?
????????

To add a scalar (single value)

Use polars.lit.

my_scalar = -1
df.with_column(pl.lit(my_scalar).alias("col_scalar"))

shape: (5, 2)
?????????????????????
? col1 ? col_scalar ?
? ---  ? ---        ?
? i64  ? i32        ?
?????????????????????
? 1    ? -1         ?
?????????????????????
? 2    ? -1         ?
?????????????????????
? 3    ? -1         ?
?????????????????????
? 4    ? -1         ?
?????????????????????
? 5    ? -1         ?
?????????????????????

You can also choose the datatype of the new column using the dtype keyword.

df.with_column(pl.lit(my_scalar, dtype=pl.Float64).alias("col_scalar_float"))

shape: (5, 2)
???????????????????????????
? col1 ? col_scalar_float ?
? ---  ? ---              ?
? i64  ? f64              ?
???????????????????????????
? 1    ? -1.0             ?
???????????????????????????
? 2    ? -1.0             ?
???????????????????????????
? 3    ? -1.0             ?
???????????????????????????
? 4    ? -1.0             ?
???????????????????????????
? 5    ? -1.0             ?
???????????????????????????

To add a list

To add a list of values (perhaps from some external computation), use the polars.Series constructor and provide a name to the Series constructor.

my_list = [10, 20, 30, 40, 50]
df.with_column(pl.Series(name="col_list", values=my_list))

shape: (5, 2)
???????????????????
? col1 ? col_list ?
? ---  ? ---      ?
? i64  ? i64      ?
???????????????????
? 1    ? 10       ?
???????????????????
? 2    ? 20       ?
???????????????????
? 3    ? 30       ?
???????????????????
? 4    ? 40       ?
???????????????????
? 5    ? 50       ?
???????????????????

You can use the dtype keyword to control the datatype of the new series, if needed.

df.with_column(pl.Series(name="col_list", values=my_list, dtype=pl.Float64))

shape: (5, 2)
???????????????????
? col1 ? col_list ?
? ---  ? ---      ?
? i64  ? f64      ?
???????????????????
? 1    ? 10.0     ?
???????????????????
? 2    ? 20.0     ?
???????????????????
? 3    ? 30.0     ?
???????????????????
? 4    ? 40.0     ?
???????????????????
? 5    ? 50.0     ?
???????????????????

To add a Series

If you already have a Series, you can just provide a reference to it.

my_series = pl.Series(name="my_series_name", values=[10, 20, 30, 40, 50])
df.with_column(my_series)

shape: (5, 2)
?????????????????????????
? col1 ? my_series_name ?
? ---  ? ---            ?
? i64  ? i64            ?
?????????????????????????
? 1    ? 10             ?
?????????????????????????
? 2    ? 20             ?
?????????????????????????
? 3    ? 30             ?
?????????????????????????
? 4    ? 40             ?
?????????????????????????
? 5    ? 50             ?
?????????????????????????

If your Series does not already have a name, you can provide one using the alias Expression.

my_series_no_name = pl.Series(values=[10, 20, 30, 40, 50])
df.with_column(my_series_no_name.alias('col_no_name'))

shape: (5, 2)
??????????????????????
? col1 ? col_no_name ?
? ---  ? ---         ?
? i64  ? i64         ?
??????????????????????
? 1    ? 10          ?
??????????????????????
? 2    ? 20          ?
??????????????????????
? 3    ? 30          ?
??????????????????????
? 4    ? 40          ?
??????????????????????
? 5    ? 50          ?
??????????????????????