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docs: Make minor edits and fix typos in sample comments #1853

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20 changes: 10 additions & 10 deletions samples/snippets/data_visualization_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -26,7 +26,7 @@ def test_data_visualization() -> None:

noaa_surface = bpd.read_gbq("bigquery-public-data.noaa_gsod.gsod2021")

# Calculate median temperature for each day
# Calculate the median temperature for each day.
noaa_surface_median_temps = noaa_surface[["date", "temp"]].groupby("date").median()

noaa_surface_median_temps.plot.line()
Expand All @@ -37,14 +37,14 @@ def test_data_visualization() -> None:

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.
# Count the occurrences 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.
# Flatten the index of the DataFrame so that the counts for each name have their own columns.
name_counts = name_counts.unstack(level=1).fillna(0)

name_counts.plot.area(stacked=False, alpha=0.5)
Expand All @@ -70,13 +70,13 @@ def test_data_visualization() -> None:
"bigquery-public-data.new_york_taxi_trips.tlc_yellow_trips_2021"
).dropna()

# Data Cleaning
# 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.
# If you're 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")

Expand All @@ -88,7 +88,7 @@ def test_data_visualization() -> None:

noaa_surface = bpd.read_gbq("bigquery-public-data.noaa_gsod.gsod2021")

# Calculate median temperature for each day
# Calculate the 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)
Expand All @@ -99,14 +99,14 @@ def test_data_visualization() -> None:

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.
# Count the occurrences 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.
# Flatten the index of the DataFrame so that the counts for each name have their own columns.
name_counts = name_counts.unstack(level=1).fillna(0)

name_counts.plot.area(subplots=True, alpha=0.5)
Expand All @@ -119,13 +119,13 @@ def test_data_visualization() -> None:
"bigquery-public-data.new_york_taxi_trips.tlc_yellow_trips_2021"
).dropna()

# Data Cleaning
# 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.
# If you're 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")

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