polars_core/frame/row/
transpose.rs

1use std::borrow::Cow;
2
3use either::Either;
4
5use super::*;
6
7impl DataFrame {
8    pub(crate) fn transpose_from_dtype(
9        &self,
10        dtype: &DataType,
11        keep_names_as: Option<PlSmallStr>,
12        names_out: &[PlSmallStr],
13    ) -> PolarsResult<DataFrame> {
14        let new_width = self.height();
15        let new_height = self.width();
16        // Allocate space for the transposed columns, putting the "row names" first if needed
17        let mut cols_t = match keep_names_as {
18            None => Vec::<Column>::with_capacity(new_width),
19            Some(name) => {
20                let mut tmp = Vec::<Column>::with_capacity(new_width + 1);
21                tmp.push(
22                    StringChunked::from_iter_values(
23                        name,
24                        self.get_column_names_owned().into_iter(),
25                    )
26                    .into_column(),
27                );
28                tmp
29            },
30        };
31
32        let cols = &self.columns;
33        match dtype {
34            #[cfg(feature = "dtype-i8")]
35            DataType::Int8 => numeric_transpose::<Int8Type>(cols, names_out, &mut cols_t),
36            #[cfg(feature = "dtype-i16")]
37            DataType::Int16 => numeric_transpose::<Int16Type>(cols, names_out, &mut cols_t),
38            DataType::Int32 => numeric_transpose::<Int32Type>(cols, names_out, &mut cols_t),
39            DataType::Int64 => numeric_transpose::<Int64Type>(cols, names_out, &mut cols_t),
40            #[cfg(feature = "dtype-u8")]
41            DataType::UInt8 => numeric_transpose::<UInt8Type>(cols, names_out, &mut cols_t),
42            #[cfg(feature = "dtype-u16")]
43            DataType::UInt16 => numeric_transpose::<UInt16Type>(cols, names_out, &mut cols_t),
44            DataType::UInt32 => numeric_transpose::<UInt32Type>(cols, names_out, &mut cols_t),
45            DataType::UInt64 => numeric_transpose::<UInt64Type>(cols, names_out, &mut cols_t),
46            DataType::Float32 => numeric_transpose::<Float32Type>(cols, names_out, &mut cols_t),
47            DataType::Float64 => numeric_transpose::<Float64Type>(cols, names_out, &mut cols_t),
48            #[cfg(feature = "object")]
49            DataType::Object(_) => {
50                // this requires to support `Object` in Series::iter which we don't yet
51                polars_bail!(InvalidOperation: "Object dtype not supported in 'transpose'")
52            },
53            _ => {
54                let phys_dtype = dtype.to_physical();
55                let mut buffers = (0..new_width)
56                    .map(|_| {
57                        let buf: AnyValueBufferTrusted = (&phys_dtype, new_height).into();
58                        buf
59                    })
60                    .collect::<Vec<_>>();
61
62                let columns = self
63                    .materialized_column_iter()
64                    // first cast to supertype before casting to physical to ensure units are correct
65                    .map(|s| s.cast(dtype).unwrap().cast(&phys_dtype).unwrap())
66                    .collect::<Vec<_>>();
67
68                // this is very expensive. A lot of cache misses here.
69                // This is the part that is performance critical.
70                for s in columns {
71                    polars_ensure!(s.dtype() == &phys_dtype, ComputeError: "cannot transpose with supertype: {}", dtype);
72                    s.iter().zip(buffers.iter_mut()).for_each(|(av, buf)| {
73                        // SAFETY: we checked the type and we borrow
74                        unsafe {
75                            buf.add_unchecked_borrowed_physical(&av);
76                        }
77                    });
78                }
79                cols_t.extend(buffers.into_iter().zip(names_out).map(|(buf, name)| {
80                    // SAFETY: we are casting back to the supertype
81                    let mut s = unsafe { buf.into_series().cast_unchecked(dtype).unwrap() };
82                    s.rename(name.clone());
83                    s.into()
84                }));
85            },
86        };
87        Ok(unsafe { DataFrame::new_no_checks(new_height, cols_t) })
88    }
89
90    pub fn transpose(
91        &mut self,
92        keep_names_as: Option<&str>,
93        new_col_names: Option<Either<String, Vec<String>>>,
94    ) -> PolarsResult<DataFrame> {
95        let new_col_names = match new_col_names {
96            None => None,
97            Some(Either::Left(v)) => Some(Either::Left(v.into())),
98            Some(Either::Right(v)) => Some(Either::Right(
99                v.into_iter().map(Into::into).collect::<Vec<_>>(),
100            )),
101        };
102
103        self.transpose_impl(keep_names_as, new_col_names)
104    }
105    /// Transpose a DataFrame. This is a very expensive operation.
106    pub fn transpose_impl(
107        &mut self,
108        keep_names_as: Option<&str>,
109        new_col_names: Option<Either<PlSmallStr, Vec<PlSmallStr>>>,
110    ) -> PolarsResult<DataFrame> {
111        // We must iterate columns as [`AnyValue`], so we must be contiguous.
112        self.as_single_chunk_par();
113
114        let mut df = Cow::Borrowed(self); // Can't use self because we might drop a name column
115        let names_out = match new_col_names {
116            None => (0..self.height())
117                .map(|i| format_pl_smallstr!("column_{i}"))
118                .collect(),
119            Some(cn) => match cn {
120                Either::Left(name) => {
121                    let new_names = self.column(name.as_str()).and_then(|x| x.str())?;
122                    polars_ensure!(new_names.null_count() == 0, ComputeError: "Column with new names can't have null values");
123                    df = Cow::Owned(self.drop(name.as_str())?);
124                    new_names
125                        .into_no_null_iter()
126                        .map(PlSmallStr::from_str)
127                        .collect()
128                },
129                Either::Right(names) => {
130                    polars_ensure!(names.len() == self.height(), ShapeMismatch: "Length of new column names must be the same as the row count");
131                    names
132                },
133            },
134        };
135        if let Some(cn) = keep_names_as {
136            // Check that the column name we're using for the original column names is unique before
137            // wasting time transposing
138            polars_ensure!(names_out.iter().all(|a| a.as_str() != cn), Duplicate: "{} is already in output column names", cn)
139        }
140        polars_ensure!(
141            df.height() != 0 && df.width() != 0,
142            NoData: "unable to transpose an empty DataFrame"
143        );
144        let dtype = df.get_supertype().unwrap()?;
145        df.transpose_from_dtype(&dtype, keep_names_as.map(PlSmallStr::from_str), &names_out)
146    }
147}
148
149#[inline]
150unsafe fn add_value<T: NumericNative>(
151    values_buf_ptr: usize,
152    col_idx: usize,
153    row_idx: usize,
154    value: T,
155) {
156    let vec_ref: &mut Vec<Vec<T>> = &mut *(values_buf_ptr as *mut Vec<Vec<T>>);
157    let column = vec_ref.get_unchecked_mut(col_idx);
158    let el_ptr = column.as_mut_ptr();
159    *el_ptr.add(row_idx) = value;
160}
161
162// This just fills a pre-allocated mutable series vector, which may have a name column.
163// Nothing is returned and the actual DataFrame is constructed above.
164pub(super) fn numeric_transpose<T: PolarsNumericType>(
165    cols: &[Column],
166    names_out: &[PlSmallStr],
167    cols_t: &mut Vec<Column>,
168) {
169    let new_width = cols[0].len();
170    let new_height = cols.len();
171
172    let has_nulls = cols.iter().any(|s| s.null_count() > 0);
173
174    let mut values_buf: Vec<Vec<T::Native>> = (0..new_width)
175        .map(|_| Vec::with_capacity(new_height))
176        .collect();
177    let mut validity_buf: Vec<_> = if has_nulls {
178        // we first use bools instead of bits, because we can access these in parallel without aliasing
179        (0..new_width).map(|_| vec![true; new_height]).collect()
180    } else {
181        (0..new_width).map(|_| vec![]).collect()
182    };
183
184    // work with *mut pointers because we it is UB write to &refs.
185    let values_buf_ptr = &mut values_buf as *mut Vec<Vec<T::Native>> as usize;
186    let validity_buf_ptr = &mut validity_buf as *mut Vec<Vec<bool>> as usize;
187
188    POOL.install(|| {
189        cols.iter()
190            .map(Column::as_materialized_series)
191            .enumerate()
192            .for_each(|(row_idx, s)| {
193                let s = s.cast(&T::get_static_dtype()).unwrap();
194                let ca = s.unpack::<T>().unwrap();
195
196                // SAFETY:
197                // we access in parallel, but every access is unique, so we don't break aliasing rules
198                // we also ensured we allocated enough memory, so we never reallocate and thus
199                // the pointers remain valid.
200                if has_nulls {
201                    for (col_idx, opt_v) in ca.iter().enumerate() {
202                        match opt_v {
203                            None => unsafe {
204                                let validity_vec: &mut Vec<Vec<bool>> =
205                                    &mut *(validity_buf_ptr as *mut Vec<Vec<bool>>);
206                                let column = validity_vec.get_unchecked_mut(col_idx);
207                                let el_ptr = column.as_mut_ptr();
208                                *el_ptr.add(row_idx) = false;
209                                // we must initialize this memory otherwise downstream code
210                                // might access uninitialized memory when the masked out values
211                                // are changed.
212                                add_value(values_buf_ptr, col_idx, row_idx, T::Native::default());
213                            },
214                            Some(v) => unsafe {
215                                add_value(values_buf_ptr, col_idx, row_idx, v);
216                            },
217                        }
218                    }
219                } else {
220                    for (col_idx, v) in ca.into_no_null_iter().enumerate() {
221                        unsafe {
222                            let column: &mut Vec<Vec<T::Native>> =
223                                &mut *(values_buf_ptr as *mut Vec<Vec<T::Native>>);
224                            let el_ptr = column.get_unchecked_mut(col_idx).as_mut_ptr();
225                            *el_ptr.add(row_idx) = v;
226                        }
227                    }
228                }
229            })
230    });
231
232    let par_iter = values_buf
233        .into_par_iter()
234        .zip(validity_buf)
235        .zip(names_out)
236        .map(|((mut values, validity), name)| {
237            // SAFETY:
238            // all values are written we can now set len
239            unsafe {
240                values.set_len(new_height);
241            }
242
243            let validity = if has_nulls {
244                let validity = Bitmap::from_trusted_len_iter(validity.iter().copied());
245                if validity.unset_bits() > 0 {
246                    Some(validity)
247                } else {
248                    None
249                }
250            } else {
251                None
252            };
253
254            let arr = PrimitiveArray::<T::Native>::new(
255                T::get_static_dtype().to_arrow(CompatLevel::newest()),
256                values.into(),
257                validity,
258            );
259            ChunkedArray::<T>::with_chunk(name.clone(), arr).into_column()
260        });
261    POOL.install(|| cols_t.par_extend(par_iter));
262}
263
264#[cfg(test)]
265mod test {
266    use super::*;
267
268    #[test]
269    fn test_transpose() -> PolarsResult<()> {
270        let mut df = df![
271            "a" => [1, 2, 3],
272            "b" => [10, 20, 30],
273        ]?;
274
275        let out = df.transpose(None, None)?;
276        let expected = df![
277            "column_0" => [1, 10],
278            "column_1" => [2, 20],
279            "column_2" => [3, 30],
280
281        ]?;
282        assert!(out.equals_missing(&expected));
283
284        let mut df = df![
285            "a" => [Some(1), None, Some(3)],
286            "b" => [Some(10), Some(20), None],
287        ]?;
288        let out = df.transpose(None, None)?;
289        let expected = df![
290            "column_0" => [1, 10],
291            "column_1" => [None, Some(20)],
292            "column_2" => [Some(3), None],
293
294        ]?;
295        assert!(out.equals_missing(&expected));
296
297        let mut df = df![
298            "a" => ["a", "b", "c"],
299            "b" => [Some(10), Some(20), None],
300        ]?;
301        let out = df.transpose(None, None)?;
302        let expected = df![
303            "column_0" => ["a", "10"],
304            "column_1" => ["b", "20"],
305            "column_2" => [Some("c"), None],
306
307        ]?;
308        assert!(out.equals_missing(&expected));
309        Ok(())
310    }
311}
polars_core/frame/row/transpose.rs

polars_core/frame/row/
transpose.rs
