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use super::GroupBy;
use crate::prelude::*;
use rayon::prelude::*;
use crate::frame::groupby::hashing::HASHMAP_INIT_SIZE;
use crate::POOL;
#[derive(Copy, Clone)]
pub enum PivotAgg {
First,
Sum,
Min,
Max,
Mean,
Median,
Count,
Last,
}
impl DataFrame {
pub fn pivot<I0, S0, I1, S1, I2, S2>(
&self,
values: I0,
index: I1,
columns: I2,
agg_fn: PivotAgg,
sort_columns: bool,
) -> Result<DataFrame>
where
I0: IntoIterator<Item = S0>,
S0: AsRef<str>,
I1: IntoIterator<Item = S1>,
S1: AsRef<str>,
I2: IntoIterator<Item = S2>,
S2: AsRef<str>,
{
let values = values
.into_iter()
.map(|s| s.as_ref().to_string())
.collect::<Vec<_>>();
let index = index
.into_iter()
.map(|s| s.as_ref().to_string())
.collect::<Vec<_>>();
let columns = columns
.into_iter()
.map(|s| s.as_ref().to_string())
.collect::<Vec<_>>();
self.pivot_impl(&values, &index, &columns, agg_fn, sort_columns, false)
}
pub fn pivot_stable<I0, S0, I1, S1, I2, S2>(
&self,
values: I0,
index: I1,
columns: I2,
agg_fn: PivotAgg,
sort_columns: bool,
) -> Result<DataFrame>
where
I0: IntoIterator<Item = S0>,
S0: AsRef<str>,
I1: IntoIterator<Item = S1>,
S1: AsRef<str>,
I2: IntoIterator<Item = S2>,
S2: AsRef<str>,
{
let values = values
.into_iter()
.map(|s| s.as_ref().to_string())
.collect::<Vec<_>>();
let index = index
.into_iter()
.map(|s| s.as_ref().to_string())
.collect::<Vec<_>>();
let columns = columns
.into_iter()
.map(|s| s.as_ref().to_string())
.collect::<Vec<_>>();
self.pivot_impl(&values, &index, &columns, agg_fn, sort_columns, true)
}
fn compute_col_idx(
&self,
column: &str,
groups: &GroupsProxy,
) -> Result<(Vec<IdxSize>, Series)> {
let column_s = self.column(column)?;
let column_agg = unsafe { column_s.agg_first(groups) };
let column_agg_physical = column_agg.to_physical_repr();
let mut col_to_idx = PlHashMap::with_capacity(HASHMAP_INIT_SIZE);
let mut idx = 0 as IdxSize;
let col_locations = column_agg_physical
.iter()
.map(|v| {
let idx = *col_to_idx.entry(v).or_insert_with(|| {
let old_idx = idx;
idx += 1;
old_idx
});
idx
})
.collect();
drop(col_to_idx);
Ok((col_locations, column_agg))
}
fn compute_row_idx(
&self,
index: &[String],
groups: &GroupsProxy,
count: usize,
) -> Result<(Vec<IdxSize>, usize, Option<Vec<Series>>)> {
let (row_locations, n_rows, row_index) = if index.len() == 1 {
let index_s = self.column(&index[0])?;
let index_agg = unsafe { index_s.agg_first(groups) };
let index_agg_physical = index_agg.to_physical_repr();
let mut row_to_idx =
PlIndexMap::with_capacity_and_hasher(HASHMAP_INIT_SIZE, Default::default());
let mut idx = 0 as IdxSize;
let row_locations = index_agg_physical
.iter()
.map(|v| {
let idx = *row_to_idx.entry(v).or_insert_with(|| {
let old_idx = idx;
idx += 1;
old_idx
});
idx
})
.collect::<Vec<_>>();
let row_index = match count {
0 => {
let s = Series::new(
&index[0],
row_to_idx.into_iter().map(|(k, _)| k).collect::<Vec<_>>(),
);
let s = s.cast(index_s.dtype()).unwrap();
Some(vec![s])
}
_ => None,
};
(row_locations, idx as usize, row_index)
} else {
let index_s = self.columns(index)?;
let index_agg_physical = index_s
.iter()
.map(|s| unsafe { s.agg_first(groups).to_physical_repr().into_owned() })
.collect::<Vec<_>>();
let mut iters = index_agg_physical
.iter()
.map(|s| s.iter())
.collect::<Vec<_>>();
let mut row_to_idx =
PlIndexMap::with_capacity_and_hasher(HASHMAP_INIT_SIZE, Default::default());
let mut idx = 0 as IdxSize;
let mut row_locations = Vec::with_capacity(groups.len());
loop {
match iters
.iter_mut()
.map(|it| it.next())
.collect::<Option<Vec<_>>>()
{
None => break,
Some(items) => {
let idx = *row_to_idx.entry(items).or_insert_with(|| {
let old_idx = idx;
idx += 1;
old_idx
});
row_locations.push(idx)
}
}
}
let row_index = match count {
0 => Some(
index
.iter()
.enumerate()
.map(|(i, name)| {
let s = Series::new(
name,
row_to_idx
.iter()
.map(|(k, _)| {
debug_assert!(i < k.len());
unsafe { k.get_unchecked(i).clone() }
})
.collect::<Vec<_>>(),
);
s.cast(index_s[i].dtype()).unwrap()
})
.collect::<Vec<_>>(),
),
_ => None,
};
(row_locations, idx as usize, row_index)
};
Ok((row_locations, n_rows, row_index))
}
fn pivot_impl(
&self,
values: &[String],
index: &[String],
columns: &[String],
agg_fn: PivotAgg,
sort_columns: bool,
stable: bool,
) -> Result<DataFrame> {
if index.is_empty() {
return Err(PolarsError::ComputeError(
"index cannot be zero length".into(),
));
}
let mut final_cols = vec![];
let mut count = 0;
let out: Result<()> = POOL.install(|| {
for column in columns {
let mut groupby = index.to_vec();
groupby.push(column.clone());
let groups = self.groupby_stable(groupby)?.groups;
if !stable {
println!("unstable pivot not yet supported, using stable pivot");
};
let (col, row) = POOL.join(
|| self.compute_col_idx(column, &groups),
|| self.compute_row_idx(index, &groups, count),
);
let (col_locations, column_agg) = col?;
let (row_locations, n_rows, mut row_index) = row?;
for value_col in values {
let value_col = self.column(value_col)?;
use PivotAgg::*;
let value_agg = unsafe {
match agg_fn {
Sum => value_col.agg_sum(&groups),
Min => value_col.agg_min(&groups),
Max => value_col.agg_max(&groups),
Last => value_col.agg_last(&groups),
First => value_col.agg_first(&groups),
Mean => value_col.agg_mean(&groups),
Median => value_col.agg_median(&groups),
Count => groups.group_count().into_series(),
}
};
let headers = column_agg.unique_stable()?.cast(&DataType::Utf8)?;
let headers = headers.utf8().unwrap();
let n_cols = headers.len();
let mut buf = vec![AnyValue::Null; n_rows * n_cols];
let value_agg_phys = value_agg.to_physical_repr();
for ((row_idx, col_idx), val) in row_locations
.iter()
.zip(&col_locations)
.zip(value_agg_phys.iter())
{
unsafe {
let idx = *row_idx as usize + *col_idx as usize * n_rows;
debug_assert!(idx < buf.len());
*buf.get_unchecked_mut(idx) = val;
}
}
let headers_iter = headers.par_iter_indexed();
let mut cols = (0..n_cols)
.into_par_iter()
.zip(headers_iter)
.map(|(i, opt_name)| {
let offset = i * n_rows;
let avs = &buf[offset..offset + n_rows];
let name = opt_name.unwrap_or("null");
let mut out = Series::new(name, avs);
finish_logical_type(&mut out, value_agg.dtype());
out
})
.collect::<Vec<_>>();
if sort_columns {
cols.sort_unstable_by(|a, b| a.name().partial_cmp(b.name()).unwrap());
}
let cols = if count == 0 {
let mut final_cols = row_index.take().unwrap();
final_cols.extend(cols);
final_cols
} else {
cols
};
count += 1;
final_cols.extend_from_slice(&cols);
}
}
Ok(())
});
let _ = out?;
Ok(DataFrame::new_no_checks(final_cols))
}
}
impl<'df> GroupBy<'df> {
#[cfg_attr(docsrs, doc(cfg(feature = "rows")))]
pub fn pivot(&mut self, columns: impl IntoVec<String>, values: impl IntoVec<String>) -> Pivot {
let columns = columns.into_vec();
let values = values.into_vec();
Pivot {
gb: self,
columns,
values,
}
}
}
#[cfg_attr(docsrs, doc(cfg(feature = "rows")))]
pub struct Pivot<'df> {
gb: &'df GroupBy<'df>,
columns: Vec<String>,
values: Vec<String>,
}
fn finish_logical_type(column: &mut Series, dtype: &DataType) {
*column = column.cast(dtype).unwrap();
}
impl<'df> Pivot<'df> {
fn execute(&self, agg: PivotAgg) -> Result<DataFrame> {
println!("This pivot syntax is deprecated. Consider using DataFrame::pivot");
let index = self
.gb
.selected_keys
.iter()
.map(|s| s.name().to_string())
.collect::<Vec<_>>();
self.gb
.df
.pivot_impl(&self.values, &index, &self.columns, agg, true, false)
}
pub fn count(&self) -> Result<DataFrame> {
self.execute(PivotAgg::Count)
}
pub fn first(&self) -> Result<DataFrame> {
self.execute(PivotAgg::First)
}
pub fn sum(&self) -> Result<DataFrame> {
self.execute(PivotAgg::Sum)
}
pub fn min(&self) -> Result<DataFrame> {
self.execute(PivotAgg::Min)
}
pub fn max(&self) -> Result<DataFrame> {
self.execute(PivotAgg::Max)
}
pub fn mean(&self) -> Result<DataFrame> {
self.execute(PivotAgg::Mean)
}
pub fn median(&self) -> Result<DataFrame> {
self.execute(PivotAgg::Median)
}
pub fn last(&self) -> Result<DataFrame> {
self.execute(PivotAgg::Last)
}
}