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CLN: remove verbose and private_key references pandas-dev#34640
1 parent 650cbeb commit 7746e76

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2 files changed

+2
-15
lines changed

2 files changed

+2
-15
lines changed

pandas/io/gbq.py

-6
Original file line numberDiff line numberDiff line change
@@ -31,8 +31,6 @@ def read_gbq(
3131
credentials=None,
3232
use_bqstorage_api: Optional[bool] = None,
3333
max_results: Optional[int] = None,
34-
private_key=None,
35-
verbose=None,
3634
progress_bar_type: Optional[str] = None,
3735
) -> "DataFrame":
3836
"""
@@ -208,8 +206,6 @@ def to_gbq(
208206
location: Optional[str] = None,
209207
progress_bar: bool = True,
210208
credentials=None,
211-
verbose=None,
212-
private_key=None,
213209
) -> None:
214210
pandas_gbq = _try_import()
215211
pandas_gbq.to_gbq(
@@ -224,6 +220,4 @@ def to_gbq(
224220
location=location,
225221
progress_bar=progress_bar,
226222
credentials=credentials,
227-
verbose=verbose,
228-
private_key=private_key,
229223
)

pandas/io/parsers.py

+2-9
Original file line numberDiff line numberDiff line change
@@ -215,8 +215,6 @@
215215
Detect missing value markers (empty strings and the value of na_values). In
216216
data without any NAs, passing na_filter=False can improve the performance
217217
of reading a large file.
218-
verbose : bool, default False
219-
Indicate number of NA values placed in non-numeric columns.
220218
skip_blank_lines : bool, default True
221219
If True, skip over blank lines rather than interpreting as NaN values.
222220
parse_dates : bool or list of int or names or list of lists or dict, \
@@ -498,7 +496,6 @@ def _read(filepath_or_buffer: FilePathOrBuffer, kwds):
498496
"usecols": None,
499497
# 'iterator': False,
500498
"chunksize": None,
501-
"verbose": False,
502499
"encoding": None,
503500
"squeeze": False,
504501
"compression": None,
@@ -554,7 +551,6 @@ def parser_f(
554551
na_values=None,
555552
keep_default_na=True,
556553
na_filter=True,
557-
verbose=False,
558554
skip_blank_lines=True,
559555
# Datetime Handling
560556
parse_dates=False,
@@ -658,7 +654,6 @@ def parser_f(
658654
converters=converters,
659655
dtype=dtype,
660656
usecols=usecols,
661-
verbose=verbose,
662657
encoding=encoding,
663658
squeeze=squeeze,
664659
memory_map=memory_map,
@@ -1710,7 +1705,7 @@ def _agg_index(self, index, try_parse_dates=True):
17101705
return index
17111706

17121707
def _convert_to_ndarrays(
1713-
self, dct, na_values, na_fvalues, verbose=False, converters=None, dtypes=None
1708+
self, dct, na_values, na_fvalues, converters=None, dtypes=None
17141709
):
17151710
result = {}
17161711
for c, values in dct.items():
@@ -1780,7 +1775,7 @@ def _convert_to_ndarrays(
17801775
cvals = self._cast_types(cvals, cast_type, c)
17811776

17821777
result[c] = cvals
1783-
if verbose and na_count:
1778+
if na_count:
17841779
print(f"Filled {na_count} NA values in column {c!s}")
17851780
return result
17861781

@@ -2303,7 +2298,6 @@ def __init__(self, f, **kwds):
23032298
if "has_index_names" in kwds:
23042299
self.has_index_names = kwds["has_index_names"]
23052300

2306-
self.verbose = kwds["verbose"]
23072301
self.converters = kwds["converters"]
23082302

23092303
self.dtype = kwds["dtype"]
@@ -2588,7 +2582,6 @@ def _clean_mapping(mapping):
25882582
data,
25892583
clean_na_values,
25902584
clean_na_fvalues,
2591-
self.verbose,
25922585
clean_conv,
25932586
clean_dtypes,
25942587
)

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