如何将功能“附加”到 Python 中的对象，例如 Pandas DataFrame？

Question

Maybe this is more of a theoretical language question rather than pandas per-se. I have a set of function extensions that I'd like to "attach" to eg a pandas DataFrame without explicitly calling utility functions and passing the DataFrame as an argument ie to have the syntactic sugar. Extending Pandas DataFrame is also not a choice because of the inaccessible types needed to define and chain the DataFrame contructor eg Axes and Dtype .

In Scala one can define an implicit class to attach functionality to an otherwise unavailable or too-complex-to-initialize object eg the String type can't be extended in Java AFAIR. For example the following attaches a function to a String type dynamically https://www.oreilly.com/library/view/scala-cookbook/9781449340292/ch01s11.html

scala> implicit class StringImprovements(s: String) {
    def increment = s.map(c => (c + 1).toChar)
}

scala> val result = "HAL".increment   
result: String = IBM

Likewise, I'd like to be able to do:

# somewhere in scope
def lexi_sort(df):
    """Lexicographically sorts the input pandas DataFrame by index and columns""" 
    df.sort_index(axis=0, level=df.index.names, inplace=True)
    df.sort_index(axis=1, level=df.columns.names, inplace=True)
    return df

df = pd.DataFrame(...)
# some magic and then ...
df.lexi_sort()

One valid possibility is to use the Decorator Pattern but I was wondering whether Python offered a less boiler-plate language alternative like Scala does.

Answer 1

In pandas, you can do:

def lexi_sort(df):
    """Lexicographically sorts the input pandas DataFrame by index and columns"""
    df.sort_index(axis=0, level=df.index.names, inplace=True)
    df.sort_index(axis=1, level=df.columns.names, inplace=True)
    return df

pd.DataFrame.lexi_sort = lexi_sort

df = pd.read_csv('dummy.csv')
df.lexi_sort()

I guess for other objects you can define a method within the class to achieve the same outcome.

class A():
    def __init__(self, df:pd.DataFrame):
        self.df = df
        self.n = 0

    def lexi_sort(self):
        """Lexicographically sorts the input pandas DataFrame by index and columns"""
        self.df.sort_index(axis=0, level=self.df.index.names, inplace=True)
        self.df.sort_index(axis=1, level=self.df.columns.names, inplace=True)
        return df

    def add_one(self):
        self.n += 1

a = A(df)
print(a.n)
a.add_one()
print(a.n)

Answer 2

Subclass DataFrame and don't do anything but add your feature .

import pd
import random,string

class Foo(pd.DataFrame):
    def lexi_sort(self):
        """Lexicographically sorts the input pandas DataFrame by index and columns""" 
        self.sort_index(axis=0, level=df.index.names, inplace=True)
        self.sort_index(axis=1, level=df.columns.names, inplace=True)

nrows = 10        
columns = ['b','d','a','c']
rows = [random.sample(string.ascii_lowercase,len(columns)) for _ in range(nrows)]
index = random.sample(string.ascii_lowercase,nrows)

df = Foo(rows,index,columns)

---

>>> df
   b  d  a  c
w  n  g  u  m
x  t  e  q  k
n  u  x  j  s
u  s  t  u  b
f  g  t  e  j
j  w  b  h  j
h  v  o  p  a
a  q  i  l  b
g  p  i  k  u
o  q  x  p  t
>>> df.lexi_sort()
>>> df
   a  b  c  d
a  l  q  b  i
f  e  g  j  t
g  k  p  u  i
h  p  v  a  o
j  h  w  j  b
n  j  u  s  x
o  p  q  t  x
u  u  s  b  t
w  u  n  m  g
x  q  t  k  e
>>