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Method TypeInferrer

python/pyarrow/src/arrow/python/inference.cc:389–417  ·  view source on GitHub ↗

\param validate_interval the number of elements to observe before checking whether the data is mixed type or has other problems. This helps avoid excess computation for each element while also making sure we "bail out" early with long sequences that may have problems up front \param make_unions permit mixed-type data by creating union types (not yet implemented)

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387 // \param make_unions permit mixed-type data by creating union types (not yet
388 // implemented)
389 explicit TypeInferrer(bool pandas_null_sentinels = false,
390 int64_t validate_interval = 100, bool make_unions = false)
391 : pandas_null_sentinels_(pandas_null_sentinels),
392 validate_interval_(validate_interval),
393 make_unions_(make_unions),
394 total_count_(0),
395 none_count_(0),
396 bool_count_(0),
397 int_count_(0),
398 date_count_(0),
399 time_count_(0),
400 timestamp_micro_count_(0),
401 duration_count_(0),
402 float_count_(0),
403 binary_count_(0),
404 unicode_count_(0),
405 decimal_count_(0),
406 list_count_(0),
407 struct_count_(0),
408 arrow_scalar_count_(0),
409 numpy_dtype_count_(0),
410 interval_count_(0),
411 uuid_count_(0),
412 max_decimal_metadata_(std::numeric_limits<int32_t>::min(),
413 std::numeric_limits<int32_t>::min()),
414 decimal_type_() {
415 ARROW_CHECK_OK(internal::ImportDecimalType(&decimal_type_));
416 ARROW_CHECK_OK(ImportPresentIntervalTypes(&interval_types_));
417 }
418
419 /// \param[in] obj a Python object in the sequence
420 /// \param[out] keep_going if sufficient information has been gathered to

Callers

nothing calls this directly

Calls 2

ImportDecimalTypeFunction · 0.85

Tested by

no test coverage detected