Smart writing when adding machine learning statistics as features

This is how to write code that generates a statistic in the row direction of numerical data as a feature.

Common feature generation

First of all, how to write well

#How to add one by one
df["sum"] = df.sum(axis=1)
df["max"] = df.max(axis=1)
df["min"] = df.min(axis=1)
df["mean"] = df.mean(axis=1)
df["median"] = df.median(axis=1)
df["mad"] = df.mad(axis=1)
df["var"] = df.var(axis=1)
df["std"] = df.std(axis=1)
df["skew"] = df.skew(axis=1)
df["kurt"] = df.kurt(axis=1)

df.head()

This is fine, but it's a bit uncomfortable considering expandability and maintenance.

Smart feature generation

func_list =["sum", "max", "min", "mean", "median", "mad",
            "var", "std", "skew", "kurt"]

for func in func_list:
    df[func] = df.apply(func, axis=1)
    

You can also use Lambda functions

f_diff = lambda x: df["xxx"]-df["yyy"]

func_list =["sum", "max", "min", "mean", "median", "mad",
            "var", "std", "skew", "kurt", f_diff]

for func in func_list:
    df[func] = df.apply(func, axis=1)
    

In machine learning, there are many ad hoc experiments, so "write-down" code tends to be mass-produced.

• Actually, looking at Kaggle's kernel etc., I feel that there is a lot of dirty code.

Writing code that is as clean as possible makes it easier to reuse and more extensible.