Before you can use the interpolate() method to smooth the outliers in a DataFrame, you need to

Homework Help: Questions and Answers: Before you can use the interpolate() method to smooth the outliers in a DataFrame, you need to

a) forward fill the outliers
b) back fill the outliers
c) change the outliers to missing values
d) convert the outliers to **

Answer:

First, let’s understand interpolate()Method:

The interpolate() method in pandas is used to fill missing data in a DataFrame. It works by estimating intermediate values based on neighboring data points.

Role of Outliers: Outliers are extreme values that can affect the accuracy of data analysis. Before using interpolate() to smooth these outliers, they need to be identified and treated.

Given Options: Step by Step Answering

a) Forward fill the outliers

• Forward filling (ffill) is a technique used to propagate the last valid value to the next missing one. However, it’s not specific to dealing with outliers for interpolation.

b) Back fill the outliers

• Back filling (bfill) fills missing values using subsequent values. This is also not directly related to handling outliers for interpolation.

c) Change the outliers to missing values

• To interpolate outliers, they first need to be replaced by NaN or missing values, which the interpolate() method can then handle.

d) Convert the outliers to **:

• This option is incomplete and not relevant.

Final Answer:

Based on the above analysis, the correct answer is:

c) change the outliers to missing values

Before using interpolate(), you must first convert the outliers to missing values (NaN). This is because interpolation is specifically designed to work with missing values, not existing values. Once the outliers are converted to NaN, interpolate() can then estimate new values for these positions based on the surrounding valid data points, creating a smoother dataset.

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