Comments (5)
Note that the DataFrame table repr doesn't show the underlying Rust types, it shows the short DataType names.
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Note that the DataFrame table repr doesn't show the underlying Rust types, it shows the short DataType names.
I am still confused. What is "short DataType names" ? And are they String or &str ?
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And, are the following two series different ?
let s1 = Series::new("a", ["abc", "abc", "abc"]);
let s1 = Series::new("a", ["abc".to_string(), "abc".to_string(), "abc".to_string()]);
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The type of string used to build the Series does not change the resulting Series dtype
(DataType) as it is not a container of Rust primitive types; it has a String DataType of its own1 (and there is no need to call to_string()
on each element):
>>> s1.dtype()
DataType::String
>>> format!("{}", s1.dtype())
"str"
I'd suggest looking at the introductory Rust API documentation and the User Guide to get some insight into the key Polars data structures and types (including Series
and DataType
) 👍
Docs:
- API overview, data structures: https://docs.rs/polars/latest/polars/#data-structures
- User guide, data types: https://docs.pola.rs/user-guide/concepts/data-types/overview/
Footnotes
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The issue tracker is not meant for questions like this. Please ask on Discord or on Stack Overflow.
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Related Issues (20)
- `collect_async` is blocking HOT 11
- Broadcasting issue HOT 1
- High memory usage for `scan_csv()->head()` on compressed CSV file HOT 7
- JSONL parsing fails when line is just `{}` HOT 2
- `pl.col('a').is_in(['val1', None])` does not return true for null cells in col HOT 2
- read_csv_batched new_columns parameter does nothing
- Add arg to `pl.col(...).is_in(...)` - `respect_none_values: bool = False`
- feat: Reuse data buffers when casting `Arrow (Large)Utf8` to `Utf8View`.
- `.struct[idx]` inside `df.filter()` PanicException / not implemented
- `pl.all().filter` PanicException
- unnest should have a SUFFIX or PREFIX option
- Cannot add a Series to an empty DataFrame only if the Series lengh is 1
- `read_database*` methods are slow (Oracle database). HOT 4
- Parquet read with `streaming=True` shifts/drops rows HOT 6
- Add "ignore" args to `df1.frame_equals(df2)` - Add `ignore_col_order`, `ignore_sort`
- Option to use fixed point floats HOT 2
- multi comment_prefix support while parsing csv HOT 1
- Meet and Beat Pandas' Support for Nested DataFrames and Arrays HOT 4
- `join_where` ColumnNotFoundError if predicate only uses columns from one side HOT 1
- polars.testing assert_frame_equal raises AssertionError on identical dataframes HOT 4
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