# Generate random data

start_date = dt.datetime(2013, 1, 1)
end_date = dt.datetime(2023, 5, 18)

delta = end_date - start_date

random_days = np.sort(np.random.randint(0, delta.days, 1000))

random_dates = start_date + np.array(
    [dt.timedelta(days=int(day)) for day in random_days]
)

random_values = random.choices(["A", "B", "C"], k=1000)

df = pd.DataFrame({"date": random_dates, "value": random_values})

# Get year and half-year periods

df["year"] = df["date"].dt.year

first_half = df.date.astype("datetime64[Y]")
second_half = first_half + np.timedelta64(6, "M")
second_half = second_half.astype("datetime64[M]")

df["half-year"] = np.where(df["date"].dt.month <= 6, first_half, second_half)

# Count by period and value, here we used year
counts = df.groupby(["year", "value"])["date"].count().reset_index()
# Pivot for more convenient format
pvt = counts.pivot(index="year", columns="value", values="date")

# Stacked bar plot
pvt.plot(kind = 'bar', stacked = True)