1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
use super::*;
use crate::csv::read_impl::{
    to_batched_owned_mmap, to_batched_owned_read, BatchedCsvReaderMmap, BatchedCsvReaderRead,
    OwnedBatchedCsvReader, OwnedBatchedCsvReaderMmap,
};
use crate::csv::utils::infer_file_schema;

#[derive(Copy, Clone, Debug, Eq, PartialEq, Hash)]
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
pub enum CsvEncoding {
    /// Utf8 encoding
    Utf8,
    /// Utf8 encoding and unknown bytes are replaced with �
    LossyUtf8,
}

#[derive(Clone, Debug, Eq, PartialEq, Hash)]
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
pub enum NullValues {
    /// A single value that's used for all columns
    AllColumnsSingle(String),
    /// Multiple values that are used for all columns
    AllColumns(Vec<String>),
    /// Tuples that map column names to null value of that column
    Named(Vec<(String, String)>),
}

#[derive(Clone, Debug, Eq, PartialEq, Hash)]
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
pub enum CommentPrefix {
    /// A single byte character that indicates the start of a comment line.
    Single(u8),
    /// A string that indicates the start of a comment line.
    /// This allows for multiple characters to be used as a comment identifier.
    Multi(String),
}

impl CommentPrefix {
    /// Creates a new `CommentPrefix` for the `Single` variant.
    pub fn new_single(c: u8) -> Self {
        CommentPrefix::Single(c)
    }

    /// Creates a new `CommentPrefix`. If `Multi` variant is used and the string is longer
    /// than 5 characters, it will return `None`.
    pub fn new_multi(s: String) -> Option<Self> {
        if s.len() <= 5 {
            Some(CommentPrefix::Multi(s))
        } else {
            None
        }
    }
}

pub(super) enum NullValuesCompiled {
    /// A single value that's used for all columns
    AllColumnsSingle(String),
    // Multiple null values that are null for all columns
    AllColumns(Vec<String>),
    /// A different null value per column, computed from `NullValues::Named`
    Columns(Vec<String>),
}

impl NullValuesCompiled {
    pub(super) fn apply_projection(&mut self, projections: &[usize]) {
        if let Self::Columns(nv) = self {
            let nv = projections
                .iter()
                .map(|i| std::mem::take(&mut nv[*i]))
                .collect::<Vec<_>>();

            *self = NullValuesCompiled::Columns(nv);
        }
    }

    /// # Safety
    ///
    /// The caller must ensure that `index` is in bounds
    pub(super) unsafe fn is_null(&self, field: &[u8], index: usize) -> bool {
        use NullValuesCompiled::*;
        match self {
            AllColumnsSingle(v) => v.as_bytes() == field,
            AllColumns(v) => v.iter().any(|v| v.as_bytes() == field),
            Columns(v) => {
                debug_assert!(index < v.len());
                v.get_unchecked(index).as_bytes() == field
            },
        }
    }
}

impl NullValues {
    pub(super) fn compile(self, schema: &Schema) -> PolarsResult<NullValuesCompiled> {
        Ok(match self {
            NullValues::AllColumnsSingle(v) => NullValuesCompiled::AllColumnsSingle(v),
            NullValues::AllColumns(v) => NullValuesCompiled::AllColumns(v),
            NullValues::Named(v) => {
                let mut null_values = vec!["".to_string(); schema.len()];
                for (name, null_value) in v {
                    let i = schema.try_index_of(&name)?;
                    null_values[i] = null_value;
                }
                NullValuesCompiled::Columns(null_values)
            },
        })
    }
}

/// Create a new DataFrame by reading a csv file.
///
/// # Example
///
/// ```
/// use polars_core::prelude::*;
/// use polars_io::prelude::*;
/// use std::fs::File;
///
/// fn example() -> PolarsResult<DataFrame> {
///     CsvReader::from_path("iris.csv")?
///             .has_header(true)
///             .finish()
/// }
/// ```
#[must_use]
pub struct CsvReader<'a, R>
where
    R: MmapBytesReader,
{
    /// File or Stream object
    reader: R,
    /// Stop reading from the csv after this number of rows is reached
    n_rows: Option<usize>,
    // used by error ignore logic
    max_records: Option<usize>,
    skip_rows_before_header: usize,
    /// Optional indexes of the columns to project
    projection: Option<Vec<usize>>,
    /// Optional column names to project/ select.
    columns: Option<Vec<String>>,
    separator: Option<u8>,
    pub(crate) schema: Option<SchemaRef>,
    encoding: CsvEncoding,
    n_threads: Option<usize>,
    path: Option<PathBuf>,
    schema_overwrite: Option<SchemaRef>,
    dtype_overwrite: Option<&'a [DataType]>,
    sample_size: usize,
    chunk_size: usize,
    comment_prefix: Option<CommentPrefix>,
    null_values: Option<NullValues>,
    predicate: Option<Arc<dyn PhysicalIoExpr>>,
    quote_char: Option<u8>,
    skip_rows_after_header: usize,
    try_parse_dates: bool,
    row_index: Option<RowIndex>,
    /// Aggregates chunk afterwards to a single chunk.
    rechunk: bool,
    raise_if_empty: bool,
    truncate_ragged_lines: bool,
    missing_is_null: bool,
    low_memory: bool,
    has_header: bool,
    ignore_errors: bool,
    eol_char: u8,
}

impl<'a, R> CsvReader<'a, R>
where
    R: 'a + MmapBytesReader,
{
    /// Skip these rows after the header
    pub fn with_skip_rows_after_header(mut self, offset: usize) -> Self {
        self.skip_rows_after_header = offset;
        self
    }

    /// Add a row index column.
    pub fn with_row_index(mut self, row_index: Option<RowIndex>) -> Self {
        self.row_index = row_index;
        self
    }

    /// Sets the chunk size used by the parser. This influences performance
    pub fn with_chunk_size(mut self, chunk_size: usize) -> Self {
        self.chunk_size = chunk_size;
        self
    }

    /// Set  [`CsvEncoding`]
    pub fn with_encoding(mut self, enc: CsvEncoding) -> Self {
        self.encoding = enc;
        self
    }

    /// Try to stop parsing when `n` rows are parsed. During multithreaded parsing the upper bound `n` cannot
    /// be guaranteed.
    pub fn with_n_rows(mut self, num_rows: Option<usize>) -> Self {
        self.n_rows = num_rows;
        self
    }

    /// Continue with next batch when a ParserError is encountered.
    pub fn with_ignore_errors(mut self, ignore: bool) -> Self {
        self.ignore_errors = ignore;
        self
    }

    /// Set the CSV file's schema. This only accepts datatypes that are implemented
    /// in the csv parser and expects a complete Schema.
    ///
    /// It is recommended to use [with_dtypes](Self::with_dtypes) instead.
    pub fn with_schema(mut self, schema: Option<SchemaRef>) -> Self {
        self.schema = schema;
        self
    }

    /// Skip the first `n` rows during parsing. The header will be parsed at `n` lines.
    pub fn with_skip_rows(mut self, skip_rows: usize) -> Self {
        self.skip_rows_before_header = skip_rows;
        self
    }

    /// Rechunk the DataFrame to contiguous memory after the CSV is parsed.
    pub fn with_rechunk(mut self, rechunk: bool) -> Self {
        self.rechunk = rechunk;
        self
    }

    /// Set whether the CSV file has headers
    pub fn has_header(mut self, has_header: bool) -> Self {
        self.has_header = has_header;
        self
    }

    /// Set the CSV file's column separator as a byte character
    pub fn with_separator(mut self, separator: u8) -> Self {
        self.separator = Some(separator);
        self
    }

    /// Set the comment prefix for this instance. Lines starting with this prefix will be ignored.
    pub fn with_comment_prefix(mut self, comment_prefix: Option<&str>) -> Self {
        self.comment_prefix = comment_prefix.map(|s| {
            if s.len() == 1 && s.chars().next().unwrap().is_ascii() {
                CommentPrefix::Single(s.as_bytes()[0])
            } else {
                CommentPrefix::Multi(s.to_string())
            }
        });
        self
    }

    /// Sets the comment prefix from `CsvParserOptions` for internal initialization.
    pub fn _with_comment_prefix(mut self, comment_prefix: Option<CommentPrefix>) -> Self {
        self.comment_prefix = comment_prefix;
        self
    }

    pub fn with_end_of_line_char(mut self, eol_char: u8) -> Self {
        self.eol_char = eol_char;
        self
    }

    /// Set values that will be interpreted as missing/ null. Note that any value you set as null value
    /// will not be escaped, so if quotation marks are part of the null value you should include them.
    pub fn with_null_values(mut self, null_values: Option<NullValues>) -> Self {
        self.null_values = null_values;
        self
    }

    /// Treat missing fields as null.
    pub fn with_missing_is_null(mut self, missing_is_null: bool) -> Self {
        self.missing_is_null = missing_is_null;
        self
    }

    /// Overwrite the schema with the dtypes in this given Schema. The given schema may be a subset
    /// of the total schema.
    pub fn with_dtypes(mut self, schema: Option<SchemaRef>) -> Self {
        self.schema_overwrite = schema;
        self
    }

    /// Overwrite the dtypes in the schema in the order of the slice that's given.
    /// This is useful if you don't know the column names beforehand
    pub fn with_dtypes_slice(mut self, dtypes: Option<&'a [DataType]>) -> Self {
        self.dtype_overwrite = dtypes;
        self
    }

    /// Set the CSV reader to infer the schema of the file
    ///
    /// # Arguments
    /// * `max_records` - Maximum number of rows read for schema inference.
    ///                   Setting this to `None` will do a full table scan (slow).
    pub fn infer_schema(mut self, max_records: Option<usize>) -> Self {
        // used by error ignore logic
        self.max_records = max_records;
        self
    }

    /// Set the reader's column projection. This counts from 0, meaning that
    /// `vec![0, 4]` would select the 1st and 5th column.
    pub fn with_projection(mut self, projection: Option<Vec<usize>>) -> Self {
        self.projection = projection;
        self
    }

    /// Columns to select/ project
    pub fn with_columns(mut self, columns: Option<Vec<String>>) -> Self {
        self.columns = columns;
        self
    }

    /// Set the number of threads used in CSV reading. The default uses the number of cores of
    /// your cpu.
    ///
    /// Note that this only works if this is initialized with `CsvReader::from_path`.
    /// Note that the number of cores is the maximum allowed number of threads.
    pub fn with_n_threads(mut self, n: Option<usize>) -> Self {
        self.n_threads = n;
        self
    }

    /// The preferred way to initialize this builder. This allows the CSV file to be memory mapped
    /// and thereby greatly increases parsing performance.
    pub fn with_path<P: Into<PathBuf>>(mut self, path: Option<P>) -> Self {
        self.path = path.map(|p| p.into());
        self
    }

    /// Sets the size of the sample taken from the CSV file. The sample is used to get statistic about
    /// the file. These statistics are used to try to optimally allocate up front. Increasing this may
    /// improve performance.
    pub fn sample_size(mut self, size: usize) -> Self {
        self.sample_size = size;
        self
    }

    /// Raise an error if CSV is empty (otherwise return an empty frame)
    pub fn raise_if_empty(mut self, toggle: bool) -> Self {
        self.raise_if_empty = toggle;
        self
    }

    /// Reduce memory consumption at the expense of performance
    pub fn low_memory(mut self, toggle: bool) -> Self {
        self.low_memory = toggle;
        self
    }

    /// Set the `char` used as quote char. The default is `b'"'`. If set to `[None]` quoting is disabled.
    pub fn with_quote_char(mut self, quote_char: Option<u8>) -> Self {
        self.quote_char = quote_char;
        self
    }

    /// Automatically try to parse dates/ datetimes and time. If parsing fails, columns remain of dtype `[DataType::String]`.
    pub fn with_try_parse_dates(mut self, toggle: bool) -> Self {
        self.try_parse_dates = toggle;
        self
    }

    pub fn with_predicate(mut self, predicate: Option<Arc<dyn PhysicalIoExpr>>) -> Self {
        self.predicate = predicate;
        self
    }

    /// Truncate lines that are longer than the schema.
    pub fn truncate_ragged_lines(mut self, toggle: bool) -> Self {
        self.truncate_ragged_lines = toggle;
        self
    }
}

impl<'a> CsvReader<'a, File> {
    /// This is the recommended way to create a csv reader as this allows for fastest parsing.
    pub fn from_path<P: Into<PathBuf>>(path: P) -> PolarsResult<Self> {
        let path = resolve_homedir(&path.into());
        let f = polars_utils::open_file(&path)?;
        Ok(Self::new(f).with_path(Some(path)))
    }
}

impl<'a, R: MmapBytesReader + 'a> CsvReader<'a, R> {
    fn core_reader<'b>(
        &'b mut self,
        schema: Option<SchemaRef>,
        to_cast: Vec<Field>,
    ) -> PolarsResult<CoreReader<'b>>
    where
        'a: 'b,
    {
        let reader_bytes = get_reader_bytes(&mut self.reader)?;
        CoreReader::new(
            reader_bytes,
            self.n_rows,
            self.skip_rows_before_header,
            std::mem::take(&mut self.projection),
            self.max_records,
            self.separator,
            self.has_header,
            self.ignore_errors,
            self.schema.clone(),
            std::mem::take(&mut self.columns),
            self.encoding,
            self.n_threads,
            schema,
            self.dtype_overwrite,
            self.sample_size,
            self.chunk_size,
            self.low_memory,
            std::mem::take(&mut self.comment_prefix),
            self.quote_char,
            self.eol_char,
            std::mem::take(&mut self.null_values),
            self.missing_is_null,
            std::mem::take(&mut self.predicate),
            to_cast,
            self.skip_rows_after_header,
            std::mem::take(&mut self.row_index),
            self.try_parse_dates,
            self.raise_if_empty,
            self.truncate_ragged_lines,
        )
    }

    fn prepare_schema_overwrite(
        &self,
        overwriting_schema: &Schema,
    ) -> PolarsResult<(Schema, Vec<Field>, bool)> {
        // This branch we check if there are dtypes we cannot parse.
        // We only support a few dtypes in the parser and later cast to the required dtype
        let mut to_cast = Vec::with_capacity(overwriting_schema.len());

        let mut _has_categorical = false;
        let mut _err: Option<PolarsError> = None;

        #[allow(unused_mut)]
        let schema = overwriting_schema
            .iter_fields()
            .filter_map(|mut fld| {
                use DataType::*;
                match fld.data_type() {
                    Time => {
                        to_cast.push(fld);
                        // let inference decide the column type
                        None
                    },
                    #[cfg(feature = "dtype-categorical")]
                    Categorical(_, _) => {
                        _has_categorical = true;
                        Some(fld)
                    },
                    #[cfg(feature = "dtype-decimal")]
                    Decimal(precision, scale) => match (precision, scale) {
                        (_, Some(_)) => {
                            to_cast.push(fld.clone());
                            fld.coerce(String);
                            Some(fld)
                        },
                        _ => {
                            _err = Some(PolarsError::ComputeError(
                                "'scale' must be set when reading csv column as Decimal".into(),
                            ));
                            None
                        },
                    },
                    _ => Some(fld),
                }
            })
            .collect::<Schema>();

        if let Some(err) = _err {
            Err(err)
        } else {
            Ok((schema, to_cast, _has_categorical))
        }
    }

    pub fn batched_borrowed_mmap(&'a mut self) -> PolarsResult<BatchedCsvReaderMmap<'a>> {
        if let Some(schema) = self.schema_overwrite.as_deref() {
            let (schema, to_cast, has_cat) = self.prepare_schema_overwrite(schema)?;
            let schema = Arc::new(schema);

            let csv_reader = self.core_reader(Some(schema), to_cast)?;
            csv_reader.batched_mmap(has_cat)
        } else {
            let csv_reader = self.core_reader(self.schema.clone(), vec![])?;
            csv_reader.batched_mmap(false)
        }
    }
    pub fn batched_borrowed_read(&'a mut self) -> PolarsResult<BatchedCsvReaderRead<'a>> {
        if let Some(schema) = self.schema_overwrite.as_deref() {
            let (schema, to_cast, has_cat) = self.prepare_schema_overwrite(schema)?;
            let schema = Arc::new(schema);

            let csv_reader = self.core_reader(Some(schema), to_cast)?;
            csv_reader.batched_read(has_cat)
        } else {
            let csv_reader = self.core_reader(self.schema.clone(), vec![])?;
            csv_reader.batched_read(false)
        }
    }
}

impl<'a> CsvReader<'a, Box<dyn MmapBytesReader>> {
    pub fn batched_mmap(
        mut self,
        schema: Option<SchemaRef>,
    ) -> PolarsResult<OwnedBatchedCsvReaderMmap> {
        match schema {
            Some(schema) => Ok(to_batched_owned_mmap(self, schema)),
            None => {
                let reader_bytes = get_reader_bytes(&mut self.reader)?;

                let (inferred_schema, _, _) = infer_file_schema(
                    &reader_bytes,
                    self.separator.unwrap_or(b','),
                    self.max_records,
                    self.has_header,
                    None,
                    &mut self.skip_rows_before_header,
                    self.skip_rows_after_header,
                    self.comment_prefix.as_ref(),
                    self.quote_char,
                    self.eol_char,
                    self.null_values.as_ref(),
                    self.try_parse_dates,
                    self.raise_if_empty,
                    &mut self.n_threads,
                )?;
                let schema = Arc::new(inferred_schema);
                Ok(to_batched_owned_mmap(self, schema))
            },
        }
    }
    pub fn batched_read(
        mut self,
        schema: Option<SchemaRef>,
    ) -> PolarsResult<OwnedBatchedCsvReader> {
        match schema {
            Some(schema) => Ok(to_batched_owned_read(self, schema)),
            None => {
                let reader_bytes = get_reader_bytes(&mut self.reader)?;

                let (inferred_schema, _, _) = infer_file_schema(
                    &reader_bytes,
                    self.separator.unwrap_or(b','),
                    self.max_records,
                    self.has_header,
                    None,
                    &mut self.skip_rows_before_header,
                    self.skip_rows_after_header,
                    self.comment_prefix.as_ref(),
                    self.quote_char,
                    self.eol_char,
                    self.null_values.as_ref(),
                    self.try_parse_dates,
                    self.raise_if_empty,
                    &mut self.n_threads,
                )?;
                let schema = Arc::new(inferred_schema);
                Ok(to_batched_owned_read(self, schema))
            },
        }
    }
}

impl<'a, R> SerReader<R> for CsvReader<'a, R>
where
    R: MmapBytesReader + 'a,
{
    /// Create a new CsvReader from a file/ stream
    fn new(reader: R) -> Self {
        CsvReader {
            reader,
            rechunk: true,
            n_rows: None,
            max_records: Some(128),
            skip_rows_before_header: 0,
            projection: None,
            separator: None,
            has_header: true,
            ignore_errors: false,
            schema: None,
            columns: None,
            encoding: CsvEncoding::Utf8,
            n_threads: None,
            path: None,
            schema_overwrite: None,
            dtype_overwrite: None,
            sample_size: 1024,
            chunk_size: 1 << 18,
            low_memory: false,
            comment_prefix: None,
            eol_char: b'\n',
            null_values: None,
            missing_is_null: true,
            predicate: None,
            quote_char: Some(b'"'),
            skip_rows_after_header: 0,
            try_parse_dates: false,
            row_index: None,
            raise_if_empty: true,
            truncate_ragged_lines: false,
        }
    }

    /// Read the file and create the DataFrame.
    fn finish(mut self) -> PolarsResult<DataFrame> {
        let rechunk = self.rechunk;
        let schema_overwrite = self.schema_overwrite.clone();
        let low_memory = self.low_memory;

        #[cfg(feature = "dtype-categorical")]
        let mut _cat_lock = None;

        let mut df = if let Some(schema) = schema_overwrite.as_deref() {
            let (schema, to_cast, _has_cat) = self.prepare_schema_overwrite(schema)?;

            #[cfg(feature = "dtype-categorical")]
            if _has_cat {
                _cat_lock = Some(polars_core::StringCacheHolder::hold())
            }

            let mut csv_reader = self.core_reader(Some(Arc::new(schema)), to_cast)?;
            csv_reader.as_df()?
        } else {
            #[cfg(feature = "dtype-categorical")]
            {
                let has_cat = self
                    .schema
                    .clone()
                    .map(|schema| {
                        schema
                            .iter_dtypes()
                            .any(|dtype| matches!(dtype, DataType::Categorical(_, _)))
                    })
                    .unwrap_or(false);
                if has_cat {
                    _cat_lock = Some(polars_core::StringCacheHolder::hold())
                }
            }
            let mut csv_reader = self.core_reader(self.schema.clone(), vec![])?;
            csv_reader.as_df()?
        };

        // Important that this rechunk is never done in parallel.
        // As that leads to great memory overhead.
        if rechunk && df.n_chunks() > 1 {
            if low_memory {
                df.as_single_chunk();
            } else {
                df.as_single_chunk_par();
            }
        }

        #[cfg(feature = "temporal")]
        // only needed until we also can parse time columns in place
        if self.try_parse_dates {
            // determine the schema that's given by the user. That should not be changed
            let fixed_schema = match (schema_overwrite, self.dtype_overwrite) {
                (Some(schema), _) => schema,
                (None, Some(dtypes)) => {
                    let schema = dtypes
                        .iter()
                        .zip(df.get_column_names())
                        .map(|(dtype, name)| Field::new(name, dtype.clone()))
                        .collect::<Schema>();

                    Arc::new(schema)
                },
                _ => Arc::default(),
            };
            df = parse_dates(df, &fixed_schema)
        }
        Ok(df)
    }
}

#[cfg(feature = "temporal")]
fn parse_dates(mut df: DataFrame, fixed_schema: &Schema) -> DataFrame {
    use polars_core::POOL;

    let cols = unsafe { std::mem::take(df.get_columns_mut()) }
        .into_par_iter()
        .map(|s| {
            match s.dtype() {
                DataType::String => {
                    let ca = s.str().unwrap();
                    // don't change columns that are in the fixed schema.
                    if fixed_schema.index_of(s.name()).is_some() {
                        return s;
                    }

                    #[cfg(feature = "dtype-time")]
                    if let Ok(ca) = ca.as_time(None, false) {
                        return ca.into_series();
                    }
                    s
                },
                _ => s,
            }
        });
    let cols = POOL.install(|| cols.collect::<Vec<_>>());

    unsafe { DataFrame::new_no_checks(cols) }
}