Web Reference: Jul 23, 2025 · In our data contains missing values in quantity, price, bought, forenoon and afternoon columns, So, We can replace missing values in the quantity column with mean, price column with a median, Bought column with standard deviation. Replace all NaN elements in column ‘A’, ‘B’, ‘C’, and ‘D’, with 0, 1, 2, and 3 respectively. Only replace the first NaN element. When filling using a DataFrame, replacement happens along the same column names and same indices. Note that column D is not affected since it is not present in df2. Multiple approaches exist for handling missing data. This section covers some of them along with their benefits and drawbacks. To better illustrate the use case, we will be using Loan Data available on DataLab along with the source code covered in the tutorial. Since the dataset does not have any missing values, we will use a subset of the data (10...
YouTube Excerpt: In this tutorial, we'll see 3 different ways we can use to
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