docs: remove import bigframes.pandas as bpd boilerplate from many samples

CHANGELOG.md

@@ -463,7 +463,7 @@
 
 * Address `read_csv` with both `index_col` and `use_cols` behavior inconsistency with pandas ([#1785](https://github.com/googleapis/python-bigquery-dataframes/issues/1785)) ([ba7c313](https://github.com/googleapis/python-bigquery-dataframes/commit/ba7c313c8d308e3ff3f736b60978cb7a51715209))
 * Allow KMeans model init parameter as k-means++ alias ([#1790](https://github.com/googleapis/python-bigquery-dataframes/issues/1790)) ([0b59cf1](https://github.com/googleapis/python-bigquery-dataframes/commit/0b59cf1008613770fa1433c6da395e755c86fe22))
-* Replace function now can handle bpd.NA value. ([#1786](https://github.com/googleapis/python-bigquery-dataframes/issues/1786)) ([7269512](https://github.com/googleapis/python-bigquery-dataframes/commit/7269512a28eb42029447d5380c764353278a74e1))
+* Replace function now can handle pd.NA value. ([#1786](https://github.com/googleapis/python-bigquery-dataframes/issues/1786)) ([7269512](https://github.com/googleapis/python-bigquery-dataframes/commit/7269512a28eb42029447d5380c764353278a74e1))
 
 
 ### Documentation

bigframes/bigquery/_operations/ai.py

@@ -53,7 +53,6 @@ def generate(
 
         >>> import bigframes.pandas as bpd
         >>> import bigframes.bigquery as bbq
-        >>> bpd.options.display.progress_bar = None
         >>> country = bpd.Series(["Japan", "Canada"])
         >>> bbq.ai.generate(("What's the capital city of ", country, " one word only"))
         0    {'result': 'Tokyo\\n', 'full_response': '{"cand...
@@ -155,7 +154,6 @@ def generate_bool(
 
         >>> import bigframes.pandas as bpd
         >>> import bigframes.bigquery as bbq
-        >>> bpd.options.display.progress_bar = None
         >>> df = bpd.DataFrame({
         ...     "col_1": ["apple", "bear", "pear"],
         ...     "col_2": ["fruit", "animal", "animal"]
@@ -240,7 +238,6 @@ def generate_int(
 
         >>> import bigframes.pandas as bpd
         >>> import bigframes.bigquery as bbq
-        >>> bpd.options.display.progress_bar = None
         >>> animal = bpd.Series(["Kangaroo", "Rabbit", "Spider"])
         >>> bbq.ai.generate_int(("How many legs does a ", animal, " have?"))
         0    {'result': 2, 'full_response': '{"candidates":...
@@ -322,7 +319,6 @@ def generate_double(
 
         >>> import bigframes.pandas as bpd
         >>> import bigframes.bigquery as bbq
-        >>> bpd.options.display.progress_bar = None
         >>> animal = bpd.Series(["Kangaroo", "Rabbit", "Spider"])
         >>> bbq.ai.generate_double(("How many legs does a ", animal, " have?"))
         0    {'result': 2.0, 'full_response': '{"candidates...
@@ -402,7 +398,6 @@ def if_(
 
         >>> import bigframes.pandas as bpd
         >>> import bigframes.bigquery as bbq
-        >>> bpd.options.display.progress_bar = None
         >>> us_state = bpd.Series(["Massachusetts", "Illinois", "Hawaii"])
         >>> bbq.ai.if_((us_state, " has a city called Springfield"))
         0     True
@@ -459,7 +454,6 @@ def classify(
 
         >>> import bigframes.pandas as bpd
         >>> import bigframes.bigquery as bbq
-        >>> bpd.options.display.progress_bar = None
         >>> df = bpd.DataFrame({'creature': ['Cat', 'Salmon']})
         >>> df['type'] = bbq.ai.classify(df['creature'], ['Mammal', 'Fish'])
         >>> df
@@ -517,7 +511,6 @@ def score(
 
         >>> import bigframes.pandas as bpd
         >>> import bigframes.bigquery as bbq
-        >>> bpd.options.display.progress_bar = None
         >>> animal = bpd.Series(["Tiger", "Rabbit", "Blue Whale"])
         >>> bbq.ai.score(("Rank the relative weights of ", animal, " on the scale from 1 to 3")) # doctest: +SKIP
         0    2.0

bigframes/bigquery/_operations/approx_agg.py

@@ -40,7 +40,6 @@ def approx_top_count(
 
         >>> import bigframes.pandas as bpd
         >>> import bigframes.bigquery as bbq
-        >>> bpd.options.display.progress_bar = None
         >>> s = bpd.Series(["apple", "apple", "pear", "pear", "pear", "banana"])
         >>> bbq.approx_top_count(s, number=2)
         [{'value': 'pear', 'count': 3}, {'value': 'apple', 'count': 2}]

bigframes/bigquery/_operations/array.py

@@ -40,7 +40,6 @@ def array_length(series: series.Series) -> series.Series:
 
         >>> import bigframes.pandas as bpd
         >>> import bigframes.bigquery as bbq
-        >>> bpd.options.display.progress_bar = None
 
         >>> s = bpd.Series([[1, 2, 8, 3], [], [3, 4]])
         >>> bbq.array_length(s)
@@ -78,8 +77,6 @@ def array_agg(
 
         >>> import bigframes.pandas as bpd
         >>> import bigframes.bigquery as bbq
-        >>> import numpy as np
-        >>> bpd.options.display.progress_bar = None
 
     For a SeriesGroupBy object:
 
@@ -128,8 +125,6 @@ def array_to_string(series: series.Series, delimiter: str) -> series.Series:
 
         >>> import bigframes.pandas as bpd
         >>> import bigframes.bigquery as bbq
-        >>> import numpy as np
-        >>> bpd.options.display.progress_bar = None
 
         >>> s = bpd.Series([["H", "i", "!"], ["Hello", "World"], np.nan, [], ["Hi"]])
         >>> bbq.array_to_string(s, delimiter=", ")

bigframes/bigquery/_operations/datetime.py

@@ -21,10 +21,8 @@ def unix_seconds(input: series.Series) -> series.Series:
 
     **Examples:**
 
-        >>> import pandas as pd
         >>> import bigframes.pandas as bpd
         >>> import bigframes.bigquery as bbq
-        >>> bpd.options.display.progress_bar = None
 
         >>> s = bpd.Series([pd.Timestamp("1970-01-02", tz="UTC"), pd.Timestamp("1970-01-03", tz="UTC")])
         >>> bbq.unix_seconds(s)
@@ -48,10 +46,8 @@ def unix_millis(input: series.Series) -> series.Series:
 
     **Examples:**
 
-        >>> import pandas as pd
         >>> import bigframes.pandas as bpd
         >>> import bigframes.bigquery as bbq
-        >>> bpd.options.display.progress_bar = None
 
         >>> s = bpd.Series([pd.Timestamp("1970-01-02", tz="UTC"), pd.Timestamp("1970-01-03", tz="UTC")])
         >>> bbq.unix_millis(s)
@@ -75,10 +71,8 @@ def unix_micros(input: series.Series) -> series.Series:
 
     **Examples:**
 
-        >>> import pandas as pd
         >>> import bigframes.pandas as bpd
         >>> import bigframes.bigquery as bbq
-        >>> bpd.options.display.progress_bar = None
 
         >>> s = bpd.Series([pd.Timestamp("1970-01-02", tz="UTC"), pd.Timestamp("1970-01-03", tz="UTC")])
         >>> bbq.unix_micros(s)

bigframes/bigquery/_operations/geo.py

@@ -53,7 +53,6 @@ def st_area(
         >>> import bigframes.pandas as bpd
         >>> import bigframes.bigquery as bbq
         >>> from shapely.geometry import Polygon, LineString, Point
-        >>> bpd.options.display.progress_bar = None
 
         >>> series = bigframes.geopandas.GeoSeries(
         ...         [
@@ -125,7 +124,6 @@ def st_buffer(
         >>> import bigframes.pandas as bpd
         >>> import bigframes.bigquery as bbq
         >>> from shapely.geometry import Point
-        >>> bpd.options.display.progress_bar = None
 
         >>> series = bigframes.geopandas.GeoSeries(
         ...         [
@@ -195,7 +193,6 @@ def st_centroid(
         >>> import bigframes.pandas as bpd
         >>> import bigframes.bigquery as bbq
         >>> from shapely.geometry import Polygon, LineString, Point
-        >>> bpd.options.display.progress_bar = None
 
         >>> series = bigframes.geopandas.GeoSeries(
         ...         [
@@ -250,7 +247,6 @@ def st_convexhull(
         >>> import bigframes.pandas as bpd
         >>> import bigframes.bigquery as bbq
         >>> from shapely.geometry import Polygon, LineString, Point
-        >>> bpd.options.display.progress_bar = None
 
         >>> series = bigframes.geopandas.GeoSeries(
         ...         [
@@ -312,7 +308,6 @@ def st_difference(
         >>> import bigframes.bigquery as bbq
         >>> import bigframes.geopandas
         >>> from shapely.geometry import Polygon, LineString, Point
-        >>> bpd.options.display.progress_bar = None
 
     We can check two GeoSeries against each other, row by row:
 
@@ -407,7 +402,6 @@ def st_distance(
         >>> import bigframes.bigquery as bbq
         >>> import bigframes.geopandas
         >>> from shapely.geometry import Polygon, LineString, Point
-        >>> bpd.options.display.progress_bar = None
 
     We can check two GeoSeries against each other, row by row.
 
@@ -489,7 +483,6 @@ def st_intersection(
         >>> import bigframes.bigquery as bbq
         >>> import bigframes.geopandas
         >>> from shapely.geometry import Polygon, LineString, Point
-        >>> bpd.options.display.progress_bar = None
 
     We can check two GeoSeries against each other, row by row.
 
@@ -583,7 +576,6 @@ def st_isclosed(
         >>> import bigframes.bigquery as bbq
 
         >>> from shapely.geometry import Point, LineString, Polygon
-        >>> bpd.options.display.progress_bar = None
 
         >>> series = bigframes.geopandas.GeoSeries(
         ...     [
@@ -650,7 +642,6 @@ def st_length(
         >>> import bigframes.bigquery as bbq
 
         >>> from shapely.geometry import Polygon, LineString, Point, GeometryCollection
-        >>> bpd.options.display.progress_bar = None
 
         >>> series = bigframes.geopandas.GeoSeries(
         ...         [

bigframes/bigquery/_operations/json.py

@@ -49,8 +49,6 @@ def json_set(
 
         >>> import bigframes.pandas as bpd
         >>> import bigframes.bigquery as bbq
-        >>> import numpy as np
-        >>> bpd.options.display.progress_bar = None
 
         >>> s = bpd.read_gbq("SELECT JSON '{\\\"a\\\": 1}' AS data")["data"]
         >>> bbq.json_set(s, json_path_value_pairs=[("$.a", 100), ("$.b", "hi")])
@@ -101,7 +99,6 @@ def json_extract(
 
         >>> import bigframes.pandas as bpd
         >>> import bigframes.bigquery as bbq
-        >>> bpd.options.display.progress_bar = None
 
         >>> s = bpd.Series(['{"class": {"students": [{"id": 5}, {"id": 12}]}}'])
         >>> bbq.json_extract(s, json_path="$.class")
@@ -141,7 +138,6 @@ def json_extract_array(
 
         >>> import bigframes.pandas as bpd
         >>> import bigframes.bigquery as bbq
-        >>> bpd.options.display.progress_bar = None
 
         >>> s = bpd.Series(['[1, 2, 3]', '[4, 5]'])
         >>> bbq.json_extract_array(s)
@@ -204,7 +200,6 @@ def json_extract_string_array(
 
         >>> import bigframes.pandas as bpd
         >>> import bigframes.bigquery as bbq
-        >>> bpd.options.display.progress_bar = None
 
         >>> s = bpd.Series(['[1, 2, 3]', '[4, 5]'])
         >>> bbq.json_extract_string_array(s)
@@ -272,7 +267,6 @@ def json_query(
 
         >>> import bigframes.pandas as bpd
         >>> import bigframes.bigquery as bbq
-        >>> bpd.options.display.progress_bar = None
 
         >>> s = bpd.Series(['{"class": {"students": [{"id": 5}, {"id": 12}]}}'])
         >>> bbq.json_query(s, json_path="$.class")
@@ -303,7 +297,6 @@ def json_query_array(
 
         >>> import bigframes.pandas as bpd
         >>> import bigframes.bigquery as bbq
-        >>> bpd.options.display.progress_bar = None
 
         >>> s = bpd.Series(['[1, 2, 3]', '[4, 5]'])
         >>> bbq.json_query_array(s)
@@ -355,7 +348,6 @@ def json_value(
 
         >>> import bigframes.pandas as bpd
         >>> import bigframes.bigquery as bbq
-        >>> bpd.options.display.progress_bar = None
 
         >>> s = bpd.Series(['{"name": "Jakob", "age": "6"}', '{"name": "Jakob", "age": []}'])
         >>> bbq.json_value(s, json_path="$.age")
@@ -392,7 +384,6 @@ def json_value_array(
 
         >>> import bigframes.pandas as bpd
         >>> import bigframes.bigquery as bbq
-        >>> bpd.options.display.progress_bar = None
 
         >>> s = bpd.Series(['[1, 2, 3]', '[4, 5]'])
         >>> bbq.json_value_array(s)
@@ -439,7 +430,6 @@ def to_json(
 
         >>> import bigframes.pandas as bpd
         >>> import bigframes.bigquery as bbq
-        >>> bpd.options.display.progress_bar = None
 
         >>> s = bpd.Series([1, 2, 3])
         >>> bbq.to_json(s)
@@ -473,7 +463,6 @@ def to_json_string(
 
         >>> import bigframes.pandas as bpd
         >>> import bigframes.bigquery as bbq
-        >>> bpd.options.display.progress_bar = None
 
         >>> s = bpd.Series([1, 2, 3])
         >>> bbq.to_json_string(s)
@@ -512,7 +501,6 @@ def parse_json(
 
         >>> import bigframes.pandas as bpd
         >>> import bigframes.bigquery as bbq
-        >>> bpd.options.display.progress_bar = None
 
         >>> s = bpd.Series(['{"class": {"students": [{"id": 5}, {"id": 12}]}}'])
         >>> s

bigframes/bigquery/_operations/search.py

@@ -111,7 +111,6 @@ def vector_search(
 
         >>> import bigframes.pandas as bpd
         >>> import bigframes.bigquery as bbq
-        >>> bpd.options.display.progress_bar = None
 
     DataFrame embeddings for which to find nearest neighbors. The ``ARRAY<FLOAT64>`` column
     is used as the search query:

bigframes/bigquery/_operations/sql.py

@@ -36,9 +36,6 @@ def sql_scalar(
 
         >>> import bigframes.pandas as bpd
         >>> import bigframes.bigquery as bbq
-        >>> import pandas as pd
-        >>> import pyarrow as pa
-        >>> bpd.options.display.progress_bar = None
 
         >>> s = bpd.Series(["1.5", "2.5", "3.5"])
         >>> s = s.astype(pd.ArrowDtype(pa.decimal128(38, 9)))

bigframes/bigquery/_operations/struct.py

@@ -39,7 +39,6 @@ def struct(value: dataframe.DataFrame) -> series.Series:
         >>> import bigframes.pandas as bpd
         >>> import bigframes.bigquery as bbq
         >>> import bigframes.series as series
-        >>> bpd.options.display.progress_bar = None
 
         >>> srs = series.Series([{"version": 1, "project": "pandas"}, {"version": 2, "project": "numpy"},])
         >>> df = srs.struct.explode()