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132 | def plot_age_pyramid(
person: DataFrame,
datetime_ref: datetime = None,
filename: str = None,
savefig: bool = False,
return_vector: bool = False,
) -> Tuple[alt.Chart, Series]:
"""Plot an age pyramid from a 'person' pandas DataFrame.
Parameters
----------
person : pd.DataFrame (ks.DataFrame not supported),
The person table. Must have the following columns:
- `birth_datetime`, dtype : datetime or str
- `person_id`, dtype : any
- `gender_source_value`, dtype : str, {'m', 'f'}
datetime_ref : datetime,
The reference date to compute population age from.
If set to None, datetime.today() will be used instead.
savefig : bool,
If set to True, filename must be set.
The plot will be saved at the specified filename.
filename : Optional[str],
The path to save figure at.
Returns
-------
chart : alt.Chart,
If savefig set to True, returns None.
group_gender_age : Series,
The total number of patients grouped by gender and binned age.
"""
check_columns(person, ["person_id", "birth_datetime", "gender_source_value"])
if savefig:
if filename is None:
raise ValueError("You have to set a filename")
if not isinstance(filename, str):
raise ValueError(f"'filename' type must be str, got {type(filename)}")
person_ = person.copy()
if datetime_ref is None:
today = datetime.today()
else:
today = pd.to_datetime(datetime_ref)
# TODO: replace with from ..utils.datetime_helpers.substract_datetime
deltas = today - person_["birth_datetime"]
if bd.is_pandas(person_):
deltas = deltas.dt.total_seconds()
person_["age"] = deltas / (365 * 24 * 3600)
person_ = person_.query("age > 0.0")
bins = np.arange(0, 100, 10)
labels = [f"{left}-{right}" for left, right in zip(bins[:-1], bins[1:])]
person_["age_bins"] = bd.cut(person_["age"], bins=bins, labels=labels)
person_["age_bins"] = (
person_["age_bins"].astype(str).str.lower().str.replace("nan", "90+")
)
person_ = person_.loc[person_["gender_source_value"].isin(["m", "f"])]
group_gender_age = person_.groupby(["gender_source_value", "age_bins"])[
"person_id"
].count()
# Convert to pandas to ease plotting.
# Since we have aggregated the data, this operation won't crash.
group_gender_age = bd.to_pandas(group_gender_age)
male = group_gender_age["m"].reset_index()
female = group_gender_age["f"].reset_index()
left = (
alt.Chart(male)
.mark_bar()
.encode(
y=alt.Y("age_bins", axis=None, sort=alt.SortOrder("descending")),
x=alt.X("person_id", sort=alt.SortOrder("descending")),
)
.properties(title="Male")
)
right = (
alt.Chart(female)
.mark_bar(color="coral")
.encode(
y=alt.Y("age_bins", axis=None, sort=alt.SortOrder("descending")),
x=alt.X("person_id", title="N"),
)
.properties(title="Female")
)
middle = (
alt.Chart(male)
.mark_text()
.encode(
y=alt.Text("age_bins", axis=None, sort=alt.SortOrder("descending")),
text=alt.Y("age_bins"),
)
)
chart = alt.concat(left, middle, right, spacing=5)
if savefig:
chart.save(filename)
if return_vector:
return group_gender_age
if return_vector:
return chart, group_gender_age
return chart
|