docs: add data visualization samples for public doc

samples/snippets/data_visualization_test.py

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+# Copyright 2025 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (t
+# you may not use this file except in compliance wi
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in
+# distributed under the License is distributed on a
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, eit
+# See the License for the specific language governi
+# limitations under the License.
+
+
+def test_data_visualization() -> None:
+    # [START bigquery_dataframes_data_visualization_penguin_histogram]
+    import bigframes.pandas as bpd
+
+    penguins = bpd.read_gbq("bigquery-public-data.ml_datasets.penguins")
+    penguins["culmen_depth_mm"].plot.hist(bins=40)
+    # [END bigquery_dataframes_data_visualization_penguin_histogram]
+
+    # [START bigquery_dataframes_data_visualization_noaa_line_chart]
+    import bigframes.pandas as bpd
+
+    noaa_surface = bpd.read_gbq("bigquery-public-data.noaa_gsod.gsod2021")
+
+    # Calculate median temperature for each day
+    noaa_surface_median_temps = noaa_surface[["date", "temp"]].groupby("date").median()
+
+    noaa_surface_median_temps.plot.line()
+    # [END bigquery_dataframes_data_visualization_noaa_line_chart]
+
+    # [START bigquery_dataframes_data_visualization_usa_names_area_chart]
+    import bigframes.pandas as bpd
+
+    usa_names = bpd.read_gbq("bigquery-public-data.usa_names.usa_1910_2013")
+
+    # Count the occurences of the target names each year. The result is a dataframe with a multi-index.
+    name_counts = (
+        usa_names[usa_names["name"].isin(("Mary", "Emily", "Lisa"))]
+        .groupby(("year", "name"))["number"]
+        .sum()
+    )
+
+    # Flatten the index of the dataframe so that the counts for each name has their own columns.
+    name_counts = name_counts.unstack(level=1).fillna(0)
+
+    name_counts.plot.area(stacked=False, alpha=0.5)
+    # [END bigquery_dataframes_data_visualization_usa_names_area_chart]
+
+    # [START bigquery_dataframes_data_visualization_penguin_bar_chart]
+    import bigframes.pandas as bpd
+
+    penguins = bpd.read_gbq("bigquery-public-data.ml_datasets.penguins")
+
+    penguin_count_by_sex = (
+        penguins[penguins["sex"].isin(("MALE", "FEMALE"))]
+        .groupby("sex")["species"]
+        .count()
+    )
+    penguin_count_by_sex.plot.bar()
+    # [END bigquery_dataframes_data_visualization_penguin_bar_chart]
+
+    # [START bigquery_dataframes_data_visualization_taxi_scatter_plot]
+    import bigframes.pandas as bpd
+
+    taxi_trips = bpd.read_gbq(
+        "bigquery-public-data.new_york_taxi_trips.tlc_yellow_trips_2021"
+    ).dropna()
+
+    # Data Cleaning
+    taxi_trips = taxi_trips[
+        taxi_trips["trip_distance"].between(0, 10, inclusive="right")
+    ]
+    taxi_trips = taxi_trips[taxi_trips["fare_amount"].between(0, 50, inclusive="right")]
+
+    # If you are using partial ordering mode, you will also need to assign an order to your dataset.
+    # Otherwise, the next line can be skipped.
+    taxi_trips = taxi_trips.sort_values("pickup_datetime")
+
+    taxi_trips.plot.scatter(x="trip_distance", y="fare_amount", alpha=0.5)
+    # [END bigquery_dataframes_data_visualization_taxi_scatter_plot]
+
+    # [START bigquery_dataframes_data_visualization_noaa_sampling_n]
+    import bigframes.pandas as bpd
+
+    noaa_surface = bpd.read_gbq("bigquery-public-data.noaa_gsod.gsod2021")
+
+    # Calculate median temperature for each day
+    noaa_surface_median_temps = noaa_surface[["date", "temp"]].groupby("date").median()
+
+    noaa_surface_median_temps.plot.line(sampling_n=40)
+    # [END bigquery_dataframes_data_visualization_noaa_sampling_n]
+
+    # [START bigquery_dataframes_data_visualization_usa_names_subplots]
+    import bigframes.pandas as bpd
+
+    usa_names = bpd.read_gbq("bigquery-public-data.usa_names.usa_1910_2013")
+
+    # Count the occurences of the target names each year. The result is a dataframe with a multi-index.
+    name_counts = (
+        usa_names[usa_names["name"].isin(("Mary", "Emily", "Lisa"))]
+        .groupby(("year", "name"))["number"]
+        .sum()
+    )
+
+    # Flatten the index of the dataframe so that the counts for each name has their own columns.
+    name_counts = name_counts.unstack(level=1).fillna(0)
+
+    name_counts.plot.area(subplots=True, alpha=0.5)
+    # [END bigquery_dataframes_data_visualization_usa_names_subplots]
+
+    # [START bigquery_dataframes_data_visualization_taxi_scatter_multidimension]
+    import bigframes.pandas as bpd
+
+    taxi_trips = bpd.read_gbq(
+        "bigquery-public-data.new_york_taxi_trips.tlc_yellow_trips_2021"
+    ).dropna()
+
+    # Data Cleaning
+    taxi_trips = taxi_trips[
+        taxi_trips["trip_distance"].between(0, 10, inclusive="right")
+    ]
+    taxi_trips = taxi_trips[taxi_trips["fare_amount"].between(0, 50, inclusive="right")]
+
+    # If you are using partial ordering mode, you also need to assign an order to your dataset.
+    # Otherwise, the next line can be skipped.
+    taxi_trips = taxi_trips.sort_values("pickup_datetime")
+
+    taxi_trips["passenger_count_scaled"] = taxi_trips["passenger_count"] * 30
+
+    taxi_trips.plot.scatter(
+        x="trip_distance",
+        xlabel="trip distance (miles)",
+        y="fare_amount",
+        ylabel="fare amount (usd)",
+        alpha=0.5,
+        s="passenger_count_scaled",
+        label="passenger_count",
+        c="tip_amount",
+        cmap="jet",
+        colorbar=True,
+        legend=True,
+        figsize=(15, 7),
+        sampling_n=1000,
+    )
+    # [END bigquery_dataframes_data_visualization_taxi_scatter_multidimension]