0.1.7

README.md

@@ -19,6 +19,11 @@ Then, you'll need to install `polars-business`. Currently, you can do this via P
 $ pip install polars-business
 ```
 
+To use it, you'll need to `import polars_business`, and then you'll be a `.business` accessor
+on your expressions!
+
+Currently there's only a single function: `advance_n_days`.
+
 Example
 -------
 
@@ -35,3 +40,22 @@ df = pl.DataFrame({
 
 print(df.with_columns(dates_shifted=pl.col('dates').business.advance_n_days(n=5)))
 ```
+
+Note
+----
+Currently, only `pl.Date` datatype is supported.
+
+What to expected
+----------------
+The following will hopefully come relatively soon:
+- support for `Datetime`s
+- support for custom holiday calendars
+- support for rolling forwards/backwards to the next
+  valid business date (if not already on one)
+
+Ideas for future development:
+- business date range
+- support for custom mask
+
+
+Currently there's only a single function: `advance_n_days`.

src/polars_business/polars_business/__init__.py

@@ -4,7 +4,7 @@
 
 lib = _get_shared_lib_location(__file__)
 
-__version__ = "0.1.6"
+__version__ = "0.1.7"
 
 
 @pl.api.register_expr_namespace("business")
@@ -14,8 +14,8 @@ def __init__(self, expr: pl.Expr):
 
 
     def advance_n_days(self, n) -> pl.Expr:
-        if not (isinstance(n, int) and n > 0):
-            raise ValueError("only positive integers are currently supported for `n`")
+        # if not (isinstance(n, int) and n > 0):
+        #     raise ValueError("only positive integers are currently supported for `n`")
 
         return self._expr._register_plugin(
             lib=lib,

src/polars_business/pyproject.toml

@@ -10,7 +10,7 @@ classifiers = [
   "Programming Language :: Python :: Implementation :: CPython",
   "Programming Language :: Python :: Implementation :: PyPy",
 ]
-version = "0.1.6"
+version = "0.1.7"
 authors = [
   { name="Marco Gorelli", email="[email protected]" },
 ]

src/polars_business/src/expressions.rs

@@ -6,23 +6,23 @@ fn advance_n_days(inputs: &[Series]) -> PolarsResult<Series> {
     let ca = inputs[0].i32()?;
     let n = inputs[1].i32()?.get(0).unwrap();
 
-    let out = ca.apply_values(
-        |mut x|{
-        let mut weekday = (x - 4) % 7;
+    let out = ca.try_apply(
+        |x|{
+        let weekday = (x - 4) % 7;
 
-        // If on weekend, roll backwards to previous
-        // valid date (following pandas here).
         if weekday == 5 {
-            x -= 1;
-            weekday = 4;
+            polars_bail!(ComputeError: "Saturday is not a business date, cannot advance. `roll` argument coming soon.")
         } else if weekday == 6 {
-            x -= 2;
-            weekday = 4;
+            polars_bail!(ComputeError: "Sunday is not a business date, cannot advance. `roll` argument coming soon.")
         }
 
-        let n_days = n + (n + weekday) / 5 * 2;
-        x + n_days
+        let n_days = if n >= 0 {
+            n + (n + weekday) / 5 * 2
+        } else {
+            -(-n + (-n + 4-weekday) / 5 * 2)
+        };
+        Ok(x + n_days)
         }
-    );
+    )?;
     Ok(out.cast(&DataType::Date).unwrap().into_series())
 }

src/run.py

@@ -8,8 +8,8 @@
 })
 df = df.filter(pl.col('dates').dt.weekday() <6)
 
-print(df.head().with_columns(dates_shifted=pl.col('dates').business.advance_n_days(n=3))[:5])
-print(df.head().with_columns(dates_shifted=pl.Series(np.busday_offset(df.head()['dates'], 3)))[:5]) 
+print(df.head().with_columns(dates_shifted=pl.col('dates').business.advance_n_days(n=-3))[:5])
+print(df.head().with_columns(dates_shifted=pl.Series(np.busday_offset(df.head()['dates'], -3)))[:5]) 
 
 import pandas as pd
 dfpd = df.to_pandas()

tests/test_business_offsets.py

@@ -6,12 +6,12 @@
 from hypothesis import given, assume
 
 import polars as pl
-from polars_business import BusinessDayTools
+import polars_business
 
 
 @given(
     date=st.dates(min_value=dt.date(2000, 1, 1), max_value=dt.date(9999, 12, 31)),
-    n=st.integers(min_value=1, max_value=30),
+    n=st.integers(min_value=-30, max_value=30),
 )
 def test_against_np_busday_offset(date: dt.date, n: int) -> None:
     assume(date.weekday() < 5)
@@ -22,9 +22,11 @@ def test_against_np_busday_offset(date: dt.date, n: int) -> None:
 
 @given(
     date=st.dates(min_value=dt.date(2000, 1, 1), max_value=dt.date(9999, 12, 31)),
-    n=st.integers(min_value=1, max_value=30),
+    n=st.integers(min_value=-30, max_value=30),
 )
 def test_against_pandas_bday_offset(date: dt.date, n: int) -> None:
+    assume(date.weekday() < 5)
+    result = pl.DataFrame({'ts': [date]}).select(pl.col('ts').business.advance_n_days(n=n))['ts'].item()
     result = pl.DataFrame({'ts': [date]}).select(pl.col('ts').business.advance_n_days(n=n))['ts'].item()
     expected = pd.Timestamp(date) + pd.tseries.offsets.BusinessDay(n)
     assert pd.Timestamp(result) == expected