vladborovtsov commented on code in PR #47663:
URL: https://github.com/apache/arrow/pull/47663#discussion_r2613884632
##########
python/pyarrow/_csv.pyx:
##########
@@ -807,6 +810,59 @@ cdef class ConvertOptions(_Weakrefable):
fast: bool
----
fast: [[true,true,false,false,null]]
+
+ Set a default column type for all columns (disables type inference):
+
+ >>> convert_options = csv.ConvertOptions(default_column_type=pa.string())
+ >>> csv.read_csv(io.BytesIO(s.encode()), convert_options=convert_options)
+ pyarrow.Table
+ animals: string
+ n_legs: string
+ entry: string
+ fast: string
+ ----
+ animals: [["Flamingo","Horse","Brittle stars","Centipede",""]]
+ n_legs: [["2","4","5","100","6"]]
+ entry: [["01/03/2022","02/03/2022","03/03/2022","04/03/2022","05/03/2022"]]
+ fast: [["Yes","Yes","No","No",""]]
+
+ Combine default_column_type with column_types (specific column types
override default):
+
+ >>> convert_options = csv.ConvertOptions(
+ ... column_types={"n_legs": pa.int64(), "fast":
pa.bool_()},
+ ... default_column_type=pa.string(),
+ ... true_values=["Yes"],
+ ... false_values=["No"])
+ >>> csv.read_csv(io.BytesIO(s.encode()), convert_options=convert_options)
+ pyarrow.Table
+ animals: string
+ n_legs: int64
+ entry: string
+ fast: bool
+ ----
+ animals: [["Flamingo","Horse","Brittle stars","Centipede",""]]
+ n_legs: [[2,4,5,100,6]]
+ entry: [["01/03/2022","02/03/2022","03/03/2022","04/03/2022","05/03/2022"]]
+ fast: [[true,true,false,false,null]]
+
+ Use default_column_type with selective column_types for mixed type
conversion:
Review Comment:
Yes it is. This third example specifies that `n_legs` column must be
integer, while `default_column_type` is string. While the example above
specifies only `default_column_type` and you can see that obvious integer value
represented as string.
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