Polars: how to add a column with numerical?

Question

In pandas, we can just assign directly:

import pandas as pd

df = pd.DataFrame({"a": [1, 2]})

# add a single value
df["b"] = 3

# add an existing Series
df["c"] = pd.Series([4, 5])

   a  b  c
0  1  3  4
1  2  3  5

Notice that the new numerical Series is not in the original df, it is a result of some computation.

How do we do the same thing in polars?

import polars as pl

df = pl.DataFrame({"a": [1, 2]})

df = df.with_columns(...) # ????

Answer 1

Let's start with this DataFrame:

import polars as pl

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

shape: (5, 1)
┌──────┐
│ col1 │
│ ---  │
│ i64  │
╞══════╡
│ 1    │
│ 2    │
│ 3    │
│ 4    │
│ 5    │
└──────┘

To add a scalar (single value)

Use polars.lit.

my_scalar = -1
df.with_columns(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_columns(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_columns(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_columns(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_columns(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_columns(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          │
└──────┴─────────────┘