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 Iterating over pandas.df to check for values out of range

You do mean: print or really Print with uppercase
sry, thought I had corrected that..
And now, does your program runs well ?
well yes, but originally it was the for loop which was the problem...

Now I figured out how to iterate over the values in the data. But the code is not modifying the "memory" index array ( I called this array -Valid-) properly, with zeros for every occurence of an invalid value. The memory array( the array I called -Valid-) turns all the columns values from 1 to zero, instead of zero for the respective row and column index..

The following runs without python errors for me;

import numpy as np
import chardet
import pandas as pd

# Open and read csv file 
with open('testfilex1w.csv', 'rb') as f:
    #detect encoding of csv file, assigning encoding to -Result-
    Result = chardet.detect(
# Use panda to read csv file relative to above detected encoding    
Data = pd.read_csv('testfilex1w.csv', encoding=Result['encoding'], header= None)

#Display duplicates (student ids) found in pd.dataframe -Data-
print(Data[Data.duplicated([0], keep=False)])

# Drop duplicated rows based on IDs[0] and Names[1], IDS and Names are defined
# below

Data = Data.drop_duplicates([0], keep='last')
Data = Data.drop_duplicates([1], keep='last')

# Compute number of rows and columns of original data
Columns = len(Data.columns)
Rows    = len(Data.index)

# Create a selection of columns to group data
Ids = Data.iloc[:, 0]
Names = Data.iloc[:, 1]

# The amount of grades columns is unknown in the assignment, I must therefore
# create code that will work with x amount of columns
Grades = Data.iloc[:, range(2,Columns)]

# Compute number of rows and columns of dupliate ridden grades
Gradecols = len(Grades.columns)
Graderows =len(Grades.index)

Elements = (Gradecols * Graderows)

# Create a "memory" array of ones for indexing, in order to remove rows from
# original grades data by modifying this array with zeros based on below for/ if loop.
Valid = pd.DataFrame(np.ones((len(Grades.index),len(Grades.columns))))

# Create variable to keep track of rows, where invalid data might exist

# Create matrix array of grades... because I cant figure out how to iterate over
# a pandas dataframe :(
Gradesnp = Grades.values

# For loop with if statements in order to find occurences of values out of 
# range and modify the memory array with zeros so that I can remove out of range
# values from orginal grades data.
for row in Gradesnp:
    for item in row:
        if(item < -3.0 or item > 12.0):
            print("Invalid grade found! Grade was {} in Line {}.".format(item, Rowcount))
            #modify -Valid- array per index, with zero if the if statement is satisfied.

# Through the use of Boolean indexing, the data array is modified by the 
# memory vector(-Valid-) so that values corresponding to 0 are excluded and values 
#corresponding to 1 are included.
For me too.

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