Using pandas library

[ramitaherwahdan1978]

Hi,

I am a teacher and we are using python and pandas data structure. I am having trouble with this code:

import pandas as pd
numbers = pd.DataFrame([[3],[8],[4],[9],[14],[17]])
numbers.index = ["Odd", "Even", "Even", "Odd", "Even", "Odd"]
# check if 0 or 1
for i in numbers:
    i = numbers % 2
    print(i)
    if i == 0:
        numbers[1] = ["Y"]
    else:
        numbers[1] = ["N"]

I get i printed correctly but when i run the if statements and trying to add new column data i get this error:

Traceback (most recent call last):
File "C:/Users/ramit/PycharmProjects/grade10A1/lesson.py", line 15, in <module>
if i == 0:
File "C:\Users\ramit\PycharmProjects\grade10A1\venv\lib\site-packages\pandas\core\generic.py", line 1326, in __nonzero__
raise ValueError(
ValueError: The truth value of a DataFrame is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

Please help,
Thanks.

[bowlofred]

You're overwriting the for loop variable i within the loop. You never use i as an index into the dataframe.

[snippsat]

Also when using Pandas is looping through rows/columns rarely the best solution.
In fact Pandas even has a red warning about this.

Iterating through pandas objects is generally slow.
In many cases, iterating manually over the rows is not needed and can be avoided (using) a vectorized solution,
many operations can be performed using built-in methods or NumPy functions, (boolean) indexing.

So as example with your code,could eg use np.where.

import pandas as pd
import numpy as np

numbers = pd.DataFrame([[3],[8],[4],[9],[14],[17]], columns=['Numbers'])
numbers['Odd/Even'] = np.where(numbers['Numbers'] % 2, 'Odd', 'Even')

Output:
>>> numbers
   Numbers Odd/Even
0        3      Odd
1        8     Even
2        4     Even
3        9      Odd
4       14     Even
5       17      Odd

[ramitaherwahdan1978]

Also when using Pandas is looping through rows/columns rarely the best solution.
In fact Pandas even has a red warning about this.

Iterating through pandas objects is generally slow.
In many cases, iterating manually over the rows is not needed and can be avoided (using) a vectorized solution,
many operations can be performed using built-in methods or NumPy functions, (boolean) indexing.

So as example with your code,could eg use np.where.

import pandas as pd
import numpy as np

numbers = pd.DataFrame([[3],[8],[4],[9],[14],[17]], columns=['Numbers'])
numbers['Odd/Even'] = np.where(numbers['Numbers'] % 2, 'Odd', 'Even')

Output:
>>> numbers
   Numbers Odd/Even
0        3      Odd
1        8     Even
2        4     Even
3        9      Odd
4       14     Even
5       17      Odd

Thanks for answering but we didn't give students the numpy, is there other ways?

[jefsummers]

My understanding is that Pandas is built on Numpy (based on a google search and checking a number of sites), so if you gave them Pandas you also gave them numpy implicitly. Try it out

[jefsummers]

Did figure out something that does not use numpy.

import pandas as pd
 
numbers = pd.DataFrame([[3],[8],[4],[9],[14],[17]], columns=['Numbers'])
numbers['Odd/Even'] = numbers['Numbers'].map(lambda x : x%2)
numbers

Output:
	Numbers	Odd/Even
0	3	1
1	8	0
2	4	0
3	9	1
4	14	0
5	17	1