python coding (Pandas: Series) Perform the following tasks with pandas Series: a) Create a Series from the list [7,11,13,17].

1-24 Complete the following code. Ignore any highlighted answers, those are all not correct. thank you

radius_mean gives the average size of the cells in terms of the radius of circles drawn around each cell in the biopsy. texture_mean provides the amount of variation in light to dark values in an image of the cells. sb.regplot (data = cancer, x = "radius_mean", y = "texture_mean", "lowess = True, line_kws = {"color": "orange"}) --INSERT-- 40 texture_mean 35 20 15 10 10 15 20 radius_mean 25 Provide a brief explanation of what is displayed in this plot. Be sure to comment on the orange line and how it expresses the conditional mean of "texture_mean" for different values of "radius_mean."

# Create Custom Transformer Create a custom transformer, just as we did in the lecture video entitled "Custom Transformers", that performs two computations: 1. Adds an attribute to the end of the numerical data (i.e. new last column) that is equal to $\frac{x_1^3}{x_5}$ for each observation.  In other words, for each instance, you will cube the $x_1$ column and then divide by the $x_5$ column. 2. Drops the entire $x_4$ feature column if the passed function argument `drop_x4` is `True` and doesn't drop the column if `drop_x4` is `False`. (See further instructions below.) You must name your custom transformer class `Assignment4Transformer`. Your class should include an input parameter called `drop_x4` with a default value of `True` that deletes the $x_4$ feature column when its value is `True`, but preserves the $x_4$ feature column when you pass a value of `False`. This transformer will be used in a pipeline. In that pipeline, an imputer will be run *before* this transformer. Keep…