How to "attach" functionality to objects in Python e.g. to pandas DataFrame?

user_1142881 0
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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 e.g. a pandas DataFrame without explicitly calling utility functions and passing the DataFrame as an argument i.e. 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 e.g. Axes and Dtype.

In Scala one can define an implicit class to attach functionality to an otherwise unavailable or too-complex-to-initialize object e.g. 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.

pandas scala python
2 Answers

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)
user_28272620

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
>>
user_28237550
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