sns.barplot aggregates a numeric column (mean by default). sns.countplot counts rows. Error bars on barplot are a confidence interval around that mean.

Goal

Draw mean revenue by city, grouped bars by product, and a count of rows.

Mean bars

df = pd.DataFrame(
    {
        "city": ["Nairobi", "Nairobi", "Mombasa", "Mombasa", "Kisumu", "Kisumu"] * 2,
        "product": ["A", "B"] * 6,
        "units": [12, 7, 9, 4, 11, 3, 10, 8, 6, 5, 14, 2],
        "price": [10.5, 22.0, 10.5, 22.0, 10.5, 22.0] * 2,
    }
)
df["revenue"] = df["units"] * df["price"]
sns.barplot(data=df, x="city", y="revenue")
plt.title("Mean revenue")
plt.show()

Hue

df = pd.DataFrame(
    {
        "city": ["Nairobi", "Nairobi", "Mombasa", "Mombasa", "Kisumu", "Kisumu"] * 2,
        "product": ["A", "B"] * 6,
        "units": [12, 7, 9, 4, 11, 3, 10, 8, 6, 5, 14, 2],
        "price": [10.5, 22.0, 10.5, 22.0, 10.5, 22.0] * 2,
    }
)
df["revenue"] = df["units"] * df["price"]
sns.barplot(data=df, x="city", y="revenue", hue="product")
plt.title("Mean revenue by product")
plt.show()

Sum, no error bar

df = pd.DataFrame(
    {
        "city": ["Nairobi", "Nairobi", "Mombasa", "Mombasa", "Kisumu", "Kisumu"] * 2,
        "product": ["A", "B"] * 6,
        "units": [12, 7, 9, 4, 11, 3, 10, 8, 6, 5, 14, 2],
        "price": [10.5, 22.0, 10.5, 22.0, 10.5, 22.0] * 2,
    }
)
df["revenue"] = df["units"] * df["price"]
sns.barplot(data=df, x="city", y="revenue", estimator="sum", errorbar=None)
plt.title("Total revenue")
plt.show()

Counts

df = pd.DataFrame(
    {
        "city": ["Nairobi", "Nairobi", "Mombasa", "Mombasa", "Kisumu", "Kisumu"] * 2,
        "product": ["A", "B"] * 6,
    }
)
sns.countplot(data=df, x="city", hue="product")
plt.title("Rows per city")
plt.show()

countplot has no y= — it counts rows. Use it for categories; use barplot for a numeric column you want to average or sum.

Pitfall

Default barplot is the mean, not the sum. For a sales total, pass estimator="sum" or groupby first.