How to access a field of a namedtuple using a variable for the field name?

Question

I can access elements of a named tuple by name as follows(*):

from collections import namedtuple
Car = namedtuple(\'Car\', \'color mileage\')
my_car = Car(\'         


        

Answer 1

since python version 3.6 one could inherit from typing.NamedTuple

class HistoryItem(tp.NamedTuple):
    inp: str
    tsb: float
    rtn: int
    frequency: int = None

    def __getitem__(self, item):
        if isinstance(item, str):
            return getattr(self, item)
        return tp.NamedTuple.__getitem__(self, item)
        # return super().__getitem__(item)

    def get(self, item, default=None):
        try:
            return self[item]
        except (KeyError, AttributeError):
            return default

then both item[num] or item["fld_name"] will work

Answer 2

The 'getattr' answer works, but there is another option which is slightly faster.

idx = {name: i for i, name in enumerate(list(df), start=1)}
for row in df.itertuples(name=None):
   example_value = row[idx['product_price']]

Explanation

Make a dictionary mapping the column names to the row position. Call 'itertuples' with "name=None". Then access the desired values in each tuple using the indexes obtained using the column name from the dictionary.

1. Make a dictionary to find the indexes.

idx = {name: i for i, name in enumerate(list(df), start=1)}

2. Use the dictionary to access the desired values by name in the row tuples

for row in df.itertuples(name=None):
   example_value = row[idx['product_price']]

Note: Use start=0 in enumerate if you call itertuples with index=False

Here is a working example showing both methods and the timing of both methods.

import numpy as np
import pandas as pd
import timeit

data_length = 3 * 10**5
fake_data = {
    "id_code": list(range(data_length)),
    "letter_code": np.random.choice(list('abcdefgz'), size=data_length),
    "pine_cones": np.random.randint(low=1, high=100, size=data_length),
    "area": np.random.randint(low=1, high=100, size=data_length),
    "temperature": np.random.randint(low=1, high=100, size=data_length),
    "elevation": np.random.randint(low=1, high=100, size=data_length),
}
df = pd.DataFrame(fake_data)


def iter_with_idx():
    result_data = []
    
    idx = {name: i for i, name in enumerate(list(df), start=1)}
    
    for row in df.itertuples(name=None):
        
        row_calc = row[idx['pine_cones']] / row[idx['area']]
        result_data.append(row_calc)
        
    return result_data

      
def iter_with_getaatr():
    
    result_data = []
    for row in df.itertuples():
        row_calc = getattr(row, 'pine_cones') / getattr(row, 'area')
        result_data.append(row_calc)
        
    return result_data
    

dict_idx_method = timeit.timeit(iter_with_idx, number=100)
get_attr_method = timeit.timeit(iter_with_getaatr, number=100)

print(f'Dictionary index Method {dict_idx_method:0.4f} seconds')
print(f'Get attribute method {get_attr_method:0.4f} seconds')

Result:

Dictionary index Method 49.1814 seconds
Get attribute method 80.1912 seconds

I assume the difference is due to lower overhead in creating a tuple vs a named tuple and also lower overhead in accessing it by the index rather than getattr but both of those are just guesses. If anyone knows better please comment.

I have not explored how the number of columns vs number of rows effects the timing results.

Answer 3

Another way of accessing them can be:

field_idx = my_car._fields.index(field)
my_car[field_idx]

Extract index of the field and then use it to index the namedtuple.

Answer 4

You can use getattr

getattr(my_car, field)