In python, rename variables using parameter of a function

Question

I am creating a function. One input of this function will be a panda dataframe and one of its tasks is to do some operation with two variables of this dataframe. These two variables are not fixed and I want to have the freedom to determine them using parameters as inputs of the function fun.

For example, suppose at some moment the variables I want to use are 'var1' and 'var2' (but at another time, I may want to use others two variables). Supose that these variables take values 1,2,3,4 and I want to reduce df doing var1 == 1 and var2 == 1. My functions is like this

def fun(df , var = ['input_var1', 'input_var2'] , val):
    df = df.rename(columns={  var[1] : 'aux_var1 ', var[2]:'aux_var2'})

    # Other operations
    df  = df.loc[(df.aux_var1 == val ) & (df.aux_var2 == val )] 
    # end of operations

    # recover 
    df = df.rename(columns={ 'aux_var1': var[1] ,'aux_var2': var[2]})
    return df

When I use the function fun, I have the error

fun(df, var = ['var1','var2'], val = 1)
IndexError: list index out of range

Actually, I want to do other more complex operations and I didn't describe these operations so as not to extend the question. Perhaps the simple example above has a solution that does not need to rename the variables. But maybe this solution doesn't work with the operations I really want to do. So first, I would necessarily like to correct the error when renaming the variables. If you want to give another more elegant solution that doesn't need renaming, I appreciate that too, but I will be very grateful if besides the elegant solution, you offer me the solution about renaming.

Quickbeam2k1 · Accepted Answer · 2019-09-29 06:34:31Z

3

Python liste are zero indexed, i.e. the first element index is 0. Just change the lines:

df = df.rename(columns={  var[1] : 'aux_var1 ', var[2]:'aux_var2'})

df = df.rename(columns={ 'aux_var1': var[1] ,'aux_var2': var[2]})

to

df = df.rename(columns={  var[0] : 'aux_var1 ', var[1]:'aux_var2'})

df = df.rename(columns={ 'aux_var1': var[0] ,'aux_var2': var[1]})

respectively

answered Sep 29, 2019 at 6:34

Quickbeam2k1

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Simon · Accepted Answer · 2019-09-29 06:34:02Z

3

In this case you are accessing var[2] but a 2-element list in Python has elements 0 and 1. Element 2 does not exist and therefore accessing it is out of range.

answered Sep 29, 2019 at 6:34

Simon

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1 Comment

Simon Over a year ago

When trying to solve a complicated problem, it can be easy to overlook the simple ones that you encounter along the way. Glad to help.

Richard Nemeth · Accepted Answer · 2019-09-29 06:50:35Z

As it has been mentioned in other answers, the error you are receiving is due to the 0-indexing of Python lists, i.e. if you wish to access the first element of the list var, you do that by taking the 0 index instead of 1 index: var[0].

However to the topic of renaming, you are able to perform the filtering of pandas dataframe without any column renaming. I can see that you are accessing the column as an attribute of the dataframe, however you are able to achieve the same via utilising the __getitem__ method, which is more commonly used with square brackets, f.e. df[var[0]].

If you wish to have more generality over your function without any renaming happening, I can suggest this:

from functools import reduce

def fun(df , var, val):
    _sub = reduce(
                  lambda x, y: x & (df[y] == val), 
                  var, 
                  pd.Series([True]*df.shape[0])
                 )
    return df[_sub]

This will work with any number of input column variables. Hope this will serve as an inspiration to your more complicated operations you intend to do.

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