Python pandas: How to efficiently aggregate categorical variables?

Question

I have data with an id and a value, like this:

df = pd.DataFrame({
    "id": [1,1,1,1,2,2,2,3,3],
    "value": ["a", "a", "a", "b", "a", "b", "c", "c", "c"]
})
df

As seen above, the id can occure multiple times. I want to aggregate the data from each id into one row, to get something like this:

I have code that accomplishes that in two steps. First I add the count to each row:

counts = df.groupby("id")["value"].value_counts()
def get_counts(x, instance):
    try:
        return counts.loc[x["id"], instance]
    except:
        return 0

for i in df["value"].unique():
    df["value_count_" + i] = df.apply(lambda x: get_counts(x, i), axis=1)
df

Then I drop the value column and then the duplicates:

df.drop(columns="value").drop_duplicates()

However, although this works, it takes a lot of time for larger datasets and I'm sure there is a more efficient way to get the counts in the first step.

Any ideas?

Thanks in advance!