Python lesson
Missing values
One revenue is missing: Grace's. Left alone, it's skipped by sums and averages without any warning.
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Empty cells hide in most real files. pandas shows them as NaN, and .isna() finds them:
print(df["Revenue"].isna().sum())1Key points
.isna()True for every missing value.
.isna().sum()How many are missing: each True counts as 1.
.fillna(0)Fill the missing values with something, like 0.
Put together: df["Revenue"].isna().sum() to count them, df["Revenue"] = df["Revenue"].fillna(0) to fill them.
Practice
1. Build a line that shows how many revenue values are missing.
df["Revenue"].isna().sum()2. Build a line that fills the missing revenue with 0.
df["Revenue"] = df["Revenue"].fillna(0)3. Show the rows where revenue is missing.
df[df["Revenue"].isna()]4. Fill the missing revenue with the average revenue.
df["Revenue"] = df["Revenue"].fillna(df["Revenue"].mean())5. Fill the missing revenue with 0, then show how many are still missing.
df["Revenue"] = df["Revenue"].fillna(0)
df["Revenue"].isna().sum()Learn it by doing. This lesson is a short hands-on level on Bampaa: see it work, then build and write it yourself, with instant feedback.
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