https://pola-rs.github.io/polars/py-polars/html/reference/api/polars.DataFrame.join.html
Can I specify the default NULL value for outer joins? Like 0?
The join method does not currently have an option for setting a default value for nulls. However, there is an easy way to accomplish this.
Let's say we have this data:
import polars as pl
df1 = pl.DataFrame({"key": ["a", "b", "d"], "var1": [1, 1, 1]})
df2 = pl.DataFrame({"key": ["a", "b", "c"], "var2": [2, 2, 2]})
df1.join(df2, on="key", how="outer")
shape: (4, 3)
┌─────┬──────┬──────┐
│ key ┆ var1 ┆ var2 │
│ --- ┆ --- ┆ --- │
│ str ┆ i64 ┆ i64 │
╞═════╪══════╪══════╡
│ a ┆ 1 ┆ 2 │
├╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌╌┤
│ b ┆ 1 ┆ 2 │
├╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌╌┤
│ c ┆ null ┆ 2 │
├╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌╌┤
│ d ┆ 1 ┆ null │
└─────┴──────┴──────┘
To create a different value for the null values, simply use this:
df1.join(df2, on="key", how="outer").with_column(pl.all().fill_null(0))
shape: (4, 3)
┌─────┬──────┬──────┐
│ key ┆ var1 ┆ var2 │
│ --- ┆ --- ┆ --- │
│ str ┆ i64 ┆ i64 │
╞═════╪══════╪══════╡
│ a ┆ 1 ┆ 2 │
├╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌╌┤
│ b ┆ 1 ┆ 2 │
├╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌╌┤
│ c ┆ 0 ┆ 2 │
├╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌╌┤
│ d ┆ 1 ┆ 0 │
└─────┴──────┴──────┘
Related
I would like to do an ordinal encoding of a column. Pandas has the nice and convenient method of pd.factorize(), however, I would like to achieve the same in polars.
df = pl.DataFrame({"a": [5, 8, 10], "b": ["hi", "hello", "hi"]})
┌─────┬───────┐
│ a ┆ b │
│ --- ┆ --- │
│ i64 ┆ str │
╞═════╪═══════╡
│ 5 ┆ hi │
├╌╌╌╌╌┼╌╌╌╌╌╌╌┤
│ 8 ┆ hello │
├╌╌╌╌╌┼╌╌╌╌╌╌╌┤
│ 10 ┆ hi │
└─────┴───────┘
desired result:
┌─────┬─────┐
│ a ┆ b │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 0 ┆ 0 │
├╌╌╌╌╌┼╌╌╌╌╌┤
│ 1 ┆ 1 │
├╌╌╌╌╌┼╌╌╌╌╌┤
│ 2 ┆ 0 │
└─────┴─────┘
You can join with a dummy DataFrame that contains the unique values and the ordinal encoding you are interested in:
df = pl.DataFrame({"a": [5, 8, 10], "b": ["hi", "hello", "hi"]})
unique = df.select(
pl.col("b").unique(maintain_order=True)
).with_row_count(name="ordinal")
df.join(unique, on="b")
Or you could "misuse" the fact that categorical values are backed by u32 integers.
df.with_column(
pl.col("b").cast(pl.Categorical).to_physical().alias("ordinal")
)
Both methods output:
shape: (3, 3)
┌─────┬───────┬─────────┐
│ a ┆ b ┆ ordinal │
│ --- ┆ --- ┆ --- │
│ i64 ┆ str ┆ u32 │
╞═════╪═══════╪═════════╡
│ 5 ┆ hi ┆ 0 │
├╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌┤
│ 8 ┆ hello ┆ 1 │
├╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌┤
│ 10 ┆ hi ┆ 0 │
└─────┴───────┴─────────┘
Here's another way to do it although I doubt it's better than the dummy Dataframe from #ritchie46
df.with_columns([pl.col('b').unique().list().alias('uniq'),
pl.col('b').unique().list().arr.eval(pl.element().rank()).alias('uniqid')]).explode(['uniq','uniqid']).filter(pl.col('b')==pl.col('uniq')).select(pl.exclude('uniq')).with_column(pl.col('uniqid')-1)
There's almost certainly a way to improve this but basically it creates a new column called uniq which is a list of all the unique values of the column as well as uniqid which (I think, and seems to be) the 1-index based order of the values. It then explodes those creating a row for ever value in uniq and then filters out the ones rows that don't equal the column b. Since rank gives the 1-index (rather than 0-index) you have to subtract 1 and exclude the uniq column that we don't care about since it's the same as b.
If the order is not important you could use .rank(method="dense")
>>> df.select(pl.all().rank(method="dense") - 1)
shape: (3, 2)
┌─────┬─────┐
│ a ┆ b │
│ --- ┆ --- │
│ u32 ┆ u32 │
╞═════╪═════╡
│ 0 ┆ 1 │
├╌╌╌╌╌┼╌╌╌╌╌┤
│ 1 ┆ 0 │
├╌╌╌╌╌┼╌╌╌╌╌┤
│ 2 ┆ 1 │
└─────┴─────┘
If it is - you could:
>>> (
... df.with_row_count()
... .with_columns([
... pl.col("row_nr").first()
... .over(col)
... .rank(method="dense")
... .alias(col) - 1
... for col in df.columns
... ])
... .drop("row_nr")
... )
shape: (3, 2)
┌─────┬─────┐
│ a ┆ b │
│ --- ┆ --- │
│ u32 ┆ u32 │
╞═════╪═════╡
│ 0 ┆ 0 │
├╌╌╌╌╌┼╌╌╌╌╌┤
│ 1 ┆ 1 │
├╌╌╌╌╌┼╌╌╌╌╌┤
│ 2 ┆ 0 │
└─────┴─────┘
Given the following dataframe, I would like to group by "foo", sort on "bar", and then keep the whole row.
df = pl.DataFrame(
{
"foo": [1, 1, 1, 2, 2, 2, 3],
"bar": [5, 7, 6, 4, 2, 3, 1],
"baz": [1, 2, 3, 4, 5, 6, 7],
}
)
df_desired = pl.DataFrame({"foo": [1, 2, 3], "bar": [5, 2, 1], "baz": [1,5,7]})
>>> df_desired
shape: (3, 3)
┌─────┬─────┬─────┐
│ foo ┆ bar ┆ baz │
│ --- ┆ --- ┆ --- │
│ i64 ┆ i64 ┆ i64 │
╞═════╪═════╪═════╡
│ 1 ┆ 5 ┆ 1 │
├╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌┤
│ 2 ┆ 2 ┆ 5 │
├╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌┤
│ 3 ┆ 1 ┆ 7 │
└─────┴─────┴─────┘
I can do this by sorting beforehand, but this is expensive compared to sorting the group:
df_solution = df.sort("bar").groupby("foo", maintain_order=True).first().sort(by="foo")
assert df_desired.frame_equal(df_solution)
I can sort by "foo" in the aggregation, as in this SO answer:
>>> df.groupby("foo").agg(pl.col("bar").sort().first()).sort(by="foo")
shape: (3, 2)
┌─────┬─────┐
│ foo ┆ bar │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 1 ┆ 5 │
├╌╌╌╌╌┼╌╌╌╌╌┤
│ 2 ┆ 2 │
├╌╌╌╌╌┼╌╌╌╌╌┤
│ 3 ┆ 1 │
└─────┴─────┘
but then I only get that column. How do I also keep "baz"'s row value? Any additional entries to .agg([]) are independent of the new pl.col("bar").sort().
You could use .unique() instead of .groupby() after the .sort()
>>> df.sort(by="bar").unique(subset="foo")
shape: (3, 3)
┌─────┬─────┬─────┐
│ foo ┆ bar ┆ baz │
│ --- ┆ --- ┆ --- │
│ i64 ┆ i64 ┆ i64 │
╞═════╪═════╪═════╡
│ 3 ┆ 1 ┆ 7 │
├╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌┤
│ 2 ┆ 2 ┆ 5 │
├╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌┤
│ 1 ┆ 5 ┆ 1 │
└─────┴─────┴─────┘
For .groupby().agg() you can get the index of the row with pl.col("bar").arg_min()
You can pass this to pl.all().take() to return all columns.
>>> df.groupby("foo").agg(pl.all().take(pl.col("bar").arg_min()))
shape: (3, 3)
┌─────┬─────┬─────┐
│ foo ┆ bar ┆ baz │
│ --- ┆ --- ┆ --- │
│ i64 ┆ i64 ┆ i64 │
╞═════╪═════╪═════╡
│ 3 ┆ 1 ┆ 7 │
├╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌┤
│ 2 ┆ 2 ┆ 5 │
├╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌┤
│ 1 ┆ 5 ┆ 1 │
└─────┴─────┴─────┘
UPDATE:
Can also be written as .sort_by().first()
>>> df.groupby("foo").agg(pl.all().sort_by("bar").first())
shape: (3, 3)
┌─────┬─────┬─────┐
│ foo ┆ bar ┆ baz │
│ --- ┆ --- ┆ --- │
│ i64 ┆ i64 ┆ i64 │
╞═════╪═════╪═════╡
│ 1 ┆ 5 ┆ 1 │
├╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌┤
│ 3 ┆ 1 ┆ 7 │
├╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌┤
│ 2 ┆ 2 ┆ 5 │
└─────┴─────┴─────┘
I have a dataframe like this:
import polars as pl
orig_df = pl.DataFrame({
"primary_key": [1, 1, 2, 2, 3, 3],
"simple_foreign_keys": [1, 1, 2, 4, 4, 4],
"fancy_foreign_keys": [[1], [1], [2], [1], [3, 4], [3, 4]],
})
I have some logic that computes when the secondary column changes, then additional logic to rank those changes per primary column. It works fine on the simple data type:
foreign_key_changed = pl.col('simple_foreign_keys') != pl.col('simple_foreign_keys').shift_and_fill(1, 0)
df = orig_df.sort('primary_key').with_column(foreign_key_changed.cumsum().alias('raw_changes'))
print(df)
shape: (6, 4)
┌─────────────┬─────────────────────┬────────────────────┬─────────────┐
│ primary_key ┆ simple_foreign_keys ┆ fancy_foreign_keys ┆ raw_changes │
│ --- ┆ --- ┆ --- ┆ --- │
│ i64 ┆ i64 ┆ list[i64] ┆ u32 │
╞═════════════╪═════════════════════╪════════════════════╪═════════════╡
│ 1 ┆ 1 ┆ [1] ┆ 1 │
├╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 1 ┆ 1 ┆ [1] ┆ 1 │
├╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 2 ┆ 2 ┆ [2] ┆ 2 │
├╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 2 ┆ 4 ┆ [1] ┆ 3 │
├╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 3 ┆ 4 ┆ [3, 4] ┆ 3 │
├╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 3 ┆ 4 ┆ [3, 4] ┆ 3 │
└─────────────┴─────────────────────┴────────────────────┴─────────────┘
df = orig_df.sort('primary_key').with_column(foreign_key_changed.cumsum().rank('dense').over('primary_key').alias('ranked_changes'))
print(df)
shape: (6, 4)
┌─────────────┬─────────────────────┬────────────────────┬────────────────┐
│ primary_key ┆ simple_foreign_keys ┆ fancy_foreign_keys ┆ ranked_changes │
│ --- ┆ --- ┆ --- ┆ --- │
│ i64 ┆ i64 ┆ list[i64] ┆ u32 │
╞═════════════╪═════════════════════╪════════════════════╪════════════════╡
│ 1 ┆ 1 ┆ [1] ┆ 1 │
├╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 1 ┆ 1 ┆ [1] ┆ 1 │
├╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 2 ┆ 2 ┆ [2] ┆ 1 │
├╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 2 ┆ 4 ┆ [1] ┆ 2 │
├╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 3 ┆ 4 ┆ [3, 4] ┆ 1 │
├╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 3 ┆ 4 ┆ [3, 4] ┆ 1 │
└─────────────┴─────────────────────┴────────────────────┴────────────────┘
But if I try the exact same logic on the List column, it blows up on me. Note that the intermediate columns (the cumsum, the rank) are still plain integers:
foreign_key_changed = pl.col('fancy_foreign_keys') != pl.col('fancy_foreign_keys').shift_and_fill(1, [])
df = orig_df.sort('primary_key').with_column(foreign_key_changed.cumsum().alias('raw_changes'))
print(df)
shape: (6, 4)
┌─────────────┬─────────────────────┬────────────────────┬─────────────┐
│ primary_key ┆ simple_foreign_keys ┆ fancy_foreign_keys ┆ raw_changes │
│ --- ┆ --- ┆ --- ┆ --- │
│ i64 ┆ i64 ┆ list[i64] ┆ u32 │
╞═════════════╪═════════════════════╪════════════════════╪═════════════╡
│ 1 ┆ 1 ┆ [1] ┆ 1 │
├╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 1 ┆ 1 ┆ [1] ┆ 1 │
├╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 2 ┆ 2 ┆ [2] ┆ 2 │
├╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 2 ┆ 4 ┆ [1] ┆ 3 │
├╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 3 ┆ 4 ┆ [3, 4] ┆ 4 │
├╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 3 ┆ 4 ┆ [3, 4] ┆ 4 │
└─────────────┴─────────────────────┴────────────────────┴─────────────┘
df = orig_df.sort('primary_key').with_column(foreign_key_changed.cumsum().rank('dense').over('primary_key').alias('ranked_changes'))
thread '<unnamed>' panicked at 'implementation error, cannot get ref List(Null) from Int64', /Users/runner/work/polars/polars/polars/polars-core/src/series/mod.rs:945:13
note: run with `RUST_BACKTRACE=1` environment variable to display a backtrace
Traceback (most recent call last):
File "polars_example.py", line 18, in <module>
df = orig_df.sort('primary_key').with_column(foreign_key_changed.cumsum().rank('dense').over('primary_key').alias('ranked_changes'))
File "venv/lib/python3.9/site-packages/polars/internals/frame.py", line 4144, in with_column
return self.with_columns([column])
File "venv/lib/python3.9/site-packages/polars/internals/frame.py", line 5308, in with_columns
self.lazy()
File "venv/lib/python3.9/site-packages/polars/internals/lazy_frame.py", line 652, in collect
return self._dataframe_class._from_pydf(ldf.collect())
pyo3_runtime.PanicException: implementation error, cannot get ref List(Null) from Int64
Am I doing this wrong? Is there some massaging that has to happen with the types?
This is with polars 0.13.62.
Any way to make the dynamic polars expressions not break with errors?
Currently I'm just excluding the columns by type, but just wondering if there is a better way.
For example, i have a df coming from parquet, if i just execute an expression on all columns it might break for certain types. Instead I want to contain these errors and possibly return a default value like None or -1 or something else.
import polars as pl
df = pl.scan_parquet("/path/to/data/*.parquet")
print(df.schema)
# Prints: {'date_time': <class 'polars.datatypes.Datetime'>, 'incident': <class 'polars.datatypes.Utf8'>, 'address': <class 'polars.datatypes.Utf8'>, 'city': <class 'polars.datatypes.Utf8'>, 'zipcode': <class 'polars.datatypes.Int32'>}
Now if i form generic expression on top of this, there are chances it may fail. For example,
# Finding positive count across all columns
# Fails due to: exceptions.ComputeError: cannot compare Utf8 with numeric data
print(df.select((pl.all() > 0).count().prefix("__positive_count_")).collect())
# Finding positive count across all columns
# Fails due to: pyo3_runtime.PanicException: 'unique_counts' not implemented for datetime[ns] data types
print(df.select(pl.all().unique_counts().prefix("__unique_count_")).collect())
# Finding positive count across all columns
# Fails due to: exceptions.SchemaError: Series dtype Int32 != utf8
# Note: this could have been avoided by doing an explict cast to string first
print(df.select((pl.all().str.lengths() > 0).count().prefix("__empty_count_")).collect())
I'll keep to things that work in lazy mode, as it appears that you are working in lazy mode with Parquet files.
Let's use this data as an example:
import polars as pl
from datetime import datetime
df = pl.DataFrame(
{
"col_int": [-2, -2, 0, 2, 2],
"col_float": [-20.0, -10, 10, 20, 20],
"col_date": pl.date_range(datetime(2020, 1, 1), datetime(2020, 5, 1), "1mo"),
"col_str": ["str1", "str2", "", None, "str5"],
"col_bool": [True, False, False, True, False],
}
).lazy()
df.collect()
shape: (5, 5)
┌─────────┬───────────┬─────────────────────┬─────────┬──────────┐
│ col_int ┆ col_float ┆ col_date ┆ col_str ┆ col_bool │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ i64 ┆ f64 ┆ datetime[ns] ┆ str ┆ bool │
╞═════════╪═══════════╪═════════════════════╪═════════╪══════════╡
│ -2 ┆ -20.0 ┆ 2020-01-01 00:00:00 ┆ str1 ┆ true │
├╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ -2 ┆ -10.0 ┆ 2020-02-01 00:00:00 ┆ str2 ┆ false │
├╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 0 ┆ 10.0 ┆ 2020-03-01 00:00:00 ┆ ┆ false │
├╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 2 ┆ 20.0 ┆ 2020-04-01 00:00:00 ┆ null ┆ true │
├╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┤
│ 2 ┆ 20.0 ┆ 2020-05-01 00:00:00 ┆ str5 ┆ false │
└─────────┴───────────┴─────────────────────┴─────────┴──────────┘
Using the col Expression
One feature of the col expression is that you can supply a datatype, or even a list of datatypes. For example, if we want to contain our queries to floats, we can do the following:
df.select((pl.col(pl.Float64) > 0).sum().suffix("__positive_count_")).collect()
shape: (1, 1)
┌────────────────────────────┐
│ col_float__positive_count_ │
│ --- │
│ u32 │
╞════════════════════════════╡
│ 3 │
└────────────────────────────┘
(Note: (pl.col(...) > 0) creates a series of boolean values that need to be summed, not counted)
To include more than one datatype, you can supply a list of datatypes to col.
df.select(
(pl.col([pl.Int64, pl.Float64]) > 0).sum().suffix("__positive_count_")
).collect()
shape: (1, 2)
┌──────────────────────────┬────────────────────────────┐
│ col_int__positive_count_ ┆ col_float__positive_count_ │
│ --- ┆ --- │
│ u32 ┆ u32 │
╞══════════════════════════╪════════════════════════════╡
│ 2 ┆ 3 │
└──────────────────────────┴────────────────────────────┘
You can also combine these into the same select statement if you'd like.
df.select(
[
(pl.col(pl.Utf8).str.lengths() == 0).sum().suffix("__empty_count"),
pl.col(pl.Utf8).is_null().sum().suffix("__null_count"),
(pl.col([pl.Float64, pl.Int64]) > 0).sum().suffix("_positive_count"),
]
).collect()
shape: (1, 4)
┌──────────────────────┬─────────────────────┬──────────────────────────┬────────────────────────┐
│ col_str__empty_count ┆ col_str__null_count ┆ col_float_positive_count ┆ col_int_positive_count │
│ --- ┆ --- ┆ --- ┆ --- │
│ u32 ┆ u32 ┆ u32 ┆ u32 │
╞══════════════════════╪═════════════════════╪══════════════════════════╪════════════════════════╡
│ 1 ┆ 1 ┆ 3 ┆ 2 │
└──────────────────────┴─────────────────────┴──────────────────────────┴────────────────────────┘
The Cookbook has a handy list of datatypes.
Using the exclude expression
Another handy trick is to use the exclude expression. With this, we can select all columns except columns of certain datatypes. For example:
df.select(
[
pl.exclude(pl.Utf8).max().suffix("_max"),
pl.exclude([pl.Utf8, pl.Boolean]).min().suffix("_min"),
]
).collect()
shape: (1, 7)
┌─────────────┬───────────────┬─────────────────────┬──────────────┬─────────────┬───────────────┬─────────────────────┐
│ col_int_max ┆ col_float_max ┆ col_date_max ┆ col_bool_max ┆ col_int_min ┆ col_float_min ┆ col_date_min │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ i64 ┆ f64 ┆ datetime[ns] ┆ u32 ┆ i64 ┆ f64 ┆ datetime[ns] │
╞═════════════╪═══════════════╪═════════════════════╪══════════════╪═════════════╪═══════════════╪═════════════════════╡
│ 2 ┆ 20.0 ┆ 2020-05-01 00:00:00 ┆ 1 ┆ -2 ┆ -20.0 ┆ 2020-01-01 00:00:00 │
└─────────────┴───────────────┴─────────────────────┴──────────────┴─────────────┴───────────────┴─────────────────────┘
Unique counts
One caution: unique_counts results in Series of varying lengths.
df.select(pl.col("col_int").unique_counts().prefix(
"__unique_count_")).collect()
shape: (3, 1)
┌────────────────────────┐
│ __unique_count_col_int │
│ --- │
│ u32 │
╞════════════════════════╡
│ 2 │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 1 │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 2 │
└────────────────────────┘
df.select(pl.col("col_float").unique_counts().prefix(
"__unique_count_")).collect()
shape: (4, 1)
┌──────────────────────────┐
│ __unique_count_col_float │
│ --- │
│ u32 │
╞══════════════════════════╡
│ 1 │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 1 │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 1 │
├╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤
│ 2 │
└──────────────────────────┘
As such, these should not be combined into the same results. Each column/Series of a DataFrame must have the same length.
What would be the most idiomatic (and efficient) way to add a column in front of a polars data frame? Same thing like .with_column but add it at index 0?
You can select in the order you want your new DataFrame.
df = pl.DataFrame({
"a": [1, 2, 3],
"b": [True, None, False]
})
df.select([
pl.lit("foo").alias("z"),
pl.all()
])
shape: (3, 3)
┌─────┬─────┬───────┐
│ z ┆ a ┆ b │
│ --- ┆ --- ┆ --- │
│ str ┆ i64 ┆ bool │
╞═════╪═════╪═══════╡
│ foo ┆ 1 ┆ true │
├╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌┤
│ foo ┆ 2 ┆ null │
├╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌┤
│ foo ┆ 3 ┆ false │
└─────┴─────┴───────┘