What does ‘ValueError: cannot reindex from a duplicate axis’ mean?

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I am getting a ValueError: cannot reindex from a duplicate axis when I am trying to set an index to a certain value. I tried to reproduce this with a simple example, but I could not do it.

Here is my session inside of ipdb trace. I have a DataFrame with string index, and integer columns, float values. However when I try to create sum index for sum of all columns I am getting ValueError: cannot reindex from a duplicate axis error. I created a small DataFrame with the same characteristics, but was not able to reproduce the problem, what could I be missing?

I don"t really understand what ValueError: cannot reindex from a duplicate axismeans, what does this error message mean? Maybe this will help me diagnose the problem, and this is most answerable part of my question.

ipdb> type(affinity_matrix)
<class "pandas.core.frame.DataFrame">
ipdb> affinity_matrix.shape
(333, 10)
ipdb> affinity_matrix.columns
Int64Index([9315684, 9315597, 9316591, 9320520, 9321163, 9320615, 9321187, 9319487, 9319467, 9320484], dtype="int64")
ipdb> affinity_matrix.index
Index([u"001", u"002", u"003", u"004", u"005", u"008", u"009", u"010", u"011", u"014", u"015", u"016", u"018", u"020", u"021", u"022", u"024", u"025", u"026", u"027", u"028", u"029", u"030", u"032", u"033", u"034", u"035", u"036", u"039", u"040", u"041", u"042", u"043", u"044", u"045", u"047", u"047", u"048", u"050", u"053", u"054", u"055", u"056", u"057", u"058", u"059", u"060", u"061", u"062", u"063", u"065", u"067", u"068", u"069", u"070", u"071", u"072", u"073", u"074", u"075", u"076", u"077", u"078", u"080", u"082", u"083", u"084", u"085", u"086", u"089", u"090", u"091", u"092", u"093", u"094", u"095", u"096", u"097", u"098", u"100", u"101", u"103", u"104", u"105", u"106", u"107", u"108", u"109", u"110", u"111", u"112", u"113", u"114", u"115", u"116", u"117", u"118", u"119", u"121", u"122", ...], dtype="object")

ipdb> affinity_matrix.values.dtype
dtype("float64")
ipdb> "sums" in affinity_matrix.index
False

Here is the error:

ipdb> affinity_matrix.loc["sums"] = affinity_matrix.sum(axis=0)
*** ValueError: cannot reindex from a duplicate axis

I tried to reproduce this with a simple example, but I failed

In [32]: import pandas as pd

In [33]: import numpy as np

In [34]: a = np.arange(35).reshape(5,7)

In [35]: df = pd.DataFrame(a, ["x", "y", "u", "z", "w"], range(10, 17))

In [36]: df.values.dtype
Out[36]: dtype("int64")

In [37]: df.loc["sums"] = df.sum(axis=0)

In [38]: df
Out[38]: 
      10  11  12  13  14  15   16
x      0   1   2   3   4   5    6
y      7   8   9  10  11  12   13
u     14  15  16  17  18  19   20
z     21  22  23  24  25  26   27
w     28  29  30  31  32  33   34
sums  70  75  80  85  90  95  100

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Meaning of @classmethod and @staticmethod for beginner?

5 answers

user1632861 By user1632861

Could someone explain to me the meaning of @classmethod and @staticmethod in python? I need to know the difference and the meaning.

As far as I understand, @classmethod tells a class that it"s a method which should be inherited into subclasses, or... something. However, what"s the point of that? Why not just define the class method without adding @classmethod or @staticmethod or any @ definitions?

tl;dr: when should I use them, why should I use them, and how should I use them?

1726

Answer #1

Though classmethod and staticmethod are quite similar, there"s a slight difference in usage for both entities: classmethod must have a reference to a class object as the first parameter, whereas staticmethod can have no parameters at all.

Example

class Date(object):

    def __init__(self, day=0, month=0, year=0):
        self.day = day
        self.month = month
        self.year = year

    @classmethod
    def from_string(cls, date_as_string):
        day, month, year = map(int, date_as_string.split("-"))
        date1 = cls(day, month, year)
        return date1

    @staticmethod
    def is_date_valid(date_as_string):
        day, month, year = map(int, date_as_string.split("-"))
        return day <= 31 and month <= 12 and year <= 3999

date2 = Date.from_string("11-09-2012")
is_date = Date.is_date_valid("11-09-2012")

Explanation

Let"s assume an example of a class, dealing with date information (this will be our boilerplate):

class Date(object):

    def __init__(self, day=0, month=0, year=0):
        self.day = day
        self.month = month
        self.year = year

This class obviously could be used to store information about certain dates (without timezone information; let"s assume all dates are presented in UTC).

Here we have __init__, a typical initializer of Python class instances, which receives arguments as a typical instancemethod, having the first non-optional argument (self) that holds a reference to a newly created instance.

Class Method

We have some tasks that can be nicely done using classmethods.

Let"s assume that we want to create a lot of Date class instances having date information coming from an outer source encoded as a string with format "dd-mm-yyyy". Suppose we have to do this in different places in the source code of our project.

So what we must do here is:

  1. Parse a string to receive day, month and year as three integer variables or a 3-item tuple consisting of that variable.
  2. Instantiate Date by passing those values to the initialization call.

This will look like:

day, month, year = map(int, string_date.split("-"))
date1 = Date(day, month, year)

For this purpose, C++ can implement such a feature with overloading, but Python lacks this overloading. Instead, we can use classmethod. Let"s create another "constructor".

    @classmethod
    def from_string(cls, date_as_string):
        day, month, year = map(int, date_as_string.split("-"))
        date1 = cls(day, month, year)
        return date1

date2 = Date.from_string("11-09-2012")

Let"s look more carefully at the above implementation, and review what advantages we have here:

  1. We"ve implemented date string parsing in one place and it"s reusable now.
  2. Encapsulation works fine here (if you think that you could implement string parsing as a single function elsewhere, this solution fits the OOP paradigm far better).
  3. cls is an object that holds the class itself, not an instance of the class. It"s pretty cool because if we inherit our Date class, all children will have from_string defined also.

Static method

What about staticmethod? It"s pretty similar to classmethod but doesn"t take any obligatory parameters (like a class method or instance method does).

Let"s look at the next use case.

We have a date string that we want to validate somehow. This task is also logically bound to the Date class we"ve used so far, but doesn"t require instantiation of it.

Here is where staticmethod can be useful. Let"s look at the next piece of code:

    @staticmethod
    def is_date_valid(date_as_string):
        day, month, year = map(int, date_as_string.split("-"))
        return day <= 31 and month <= 12 and year <= 3999

    # usage:
    is_date = Date.is_date_valid("11-09-2012")

So, as we can see from usage of staticmethod, we don"t have any access to what the class is---it"s basically just a function, called syntactically like a method, but without access to the object and its internals (fields and another methods), while classmethod does.

1726

Answer #2

Rostyslav Dzinko"s answer is very appropriate. I thought I could highlight one other reason you should choose @classmethod over @staticmethod when you are creating an additional constructor.

In the example above, Rostyslav used the @classmethod from_string as a Factory to create Date objects from otherwise unacceptable parameters. The same can be done with @staticmethod as is shown in the code below:

class Date:
  def __init__(self, month, day, year):
    self.month = month
    self.day   = day
    self.year  = year


  def display(self):
    return "{0}-{1}-{2}".format(self.month, self.day, self.year)


  @staticmethod
  def millenium(month, day):
    return Date(month, day, 2000)

new_year = Date(1, 1, 2013)               # Creates a new Date object
millenium_new_year = Date.millenium(1, 1) # also creates a Date object. 

# Proof:
new_year.display()           # "1-1-2013"
millenium_new_year.display() # "1-1-2000"

isinstance(new_year, Date) # True
isinstance(millenium_new_year, Date) # True

Thus both new_year and millenium_new_year are instances of the Date class.

But, if you observe closely, the Factory process is hard-coded to create Date objects no matter what. What this means is that even if the Date class is subclassed, the subclasses will still create plain Date objects (without any properties of the subclass). See that in the example below:

class DateTime(Date):
  def display(self):
      return "{0}-{1}-{2} - 00:00:00PM".format(self.month, self.day, self.year)


datetime1 = DateTime(10, 10, 1990)
datetime2 = DateTime.millenium(10, 10)

isinstance(datetime1, DateTime) # True
isinstance(datetime2, DateTime) # False

datetime1.display() # returns "10-10-1990 - 00:00:00PM"
datetime2.display() # returns "10-10-2000" because it"s not a DateTime object but a Date object. Check the implementation of the millenium method on the Date class for more details.

datetime2 is not an instance of DateTime? WTF? Well, that"s because of the @staticmethod decorator used.

In most cases, this is undesired. If what you want is a Factory method that is aware of the class that called it, then @classmethod is what you need.

Rewriting Date.millenium as (that"s the only part of the above code that changes):

@classmethod
def millenium(cls, month, day):
    return cls(month, day, 2000)

ensures that the class is not hard-coded but rather learnt. cls can be any subclass. The resulting object will rightly be an instance of cls.
Let"s test that out:

datetime1 = DateTime(10, 10, 1990)
datetime2 = DateTime.millenium(10, 10)

isinstance(datetime1, DateTime) # True
isinstance(datetime2, DateTime) # True


datetime1.display() # "10-10-1990 - 00:00:00PM"
datetime2.display() # "10-10-2000 - 00:00:00PM"

The reason is, as you know by now, that @classmethod was used instead of @staticmethod

1726

Answer #3

@classmethod means: when this method is called, we pass the class as the first argument instead of the instance of that class (as we normally do with methods). This means you can use the class and its properties inside that method rather than a particular instance.

@staticmethod means: when this method is called, we don"t pass an instance of the class to it (as we normally do with methods). This means you can put a function inside a class but you can"t access the instance of that class (this is useful when your method does not use the instance).

We hope this article has helped you to resolve the problem. Apart from What does ‘ValueError: cannot reindex from a duplicate axis’ mean?, check other diag-related topics.

Want to excel in Python? See our review of the best Python online courses 2022. If you are interested in Data Science, check also how to learn programming in R.

By the way, this material is also available in other languages:



Cornwall Schteiner

London | 2022-12-08

Maybe there are another answers? What What does ‘ValueError: cannot reindex from a duplicate axis’ mean? exactly means?. Will get back tomorrow with feedback

Cornwall Krasiko

Singapore | 2022-12-08

I was preparing for my coding interview, thanks for clarifying this - What does ‘ValueError: cannot reindex from a duplicate axis’ mean? in Python is not the simplest one. Checked yesterday, it works!

Walter Gonzalez

Paris | 2022-12-08

Maybe there are another answers? What What does ‘ValueError: cannot reindex from a duplicate axis’ mean? exactly means?. I just hope that will not emerge anymore

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