So now let us move on to the removing known later characters know certain characters such as numbers
or punctuation marks, will cause overvote detection to feel because words won't look exactly as they
are spelled in our dictionary fight.
For example, see if you have the last word, as you find that is you don't.
And we didn't remove the dot at the end of the string so it wouldn't be counted as an English word because
you that is vital.
You always dealt with the period in a dictionary.
So to avoid such misinterpretations, numbers and punctuation marks needs to be removed.
OK, so the previously explained our Get English word count function calls the function remove known
letters is now on a string to remove any number and punctuation characters from it.
So if we look at the removal on letter astar, which takes a message start as a parameter, OK, yachties
letters only Astana's blank dictionary of the blank list.
Then from symbol one in the message Istria if symbol one in the letters in space hestia.
We are saying that those onliest append symbol one.
OK, now over here we are creating a blank list that goes on last year and after which we use a for
loop to loop over each character in the message is an argument and next before loop checks, whether
the character exists in the string letters and this is then we check if the character is a number or
a punctuation mark.
OK, if it won't exist in letters and space is the string and will be added to the list if the character
doesn't exist in the string, it's added to the end of the list using the method over here.
OK, now let's have a look at next.
That is the append the list method.
OK, now when we add a value to the end of the list, we are appointing a value to the list in that
case.
So this is done with list so frequently in Python that they didn't append the list method that takes
a single argument to obtain to the end of the list.
Now, for example, just to check this up in Matorin, the interactive shall, if we go back here and
here, we create a list, one that's equal to blank.
OK, after this, we are seeing list one dot of pink and we give civil fighting within the last one,
it brings the value that is vital.
Again, we see at least one dot up and here we are hacking.
And again, we see that in the value of list one.
So it Prince Python and then hacking that was added.
So after we have created an empty list, name the last one we can enter to list one dot up in a particular
string to add a string value to the list.
Then we can enter again, list one to return the value stored in business, which is right now Python.
If you again use append to the list one to at the end of the list.
Now list one, we are getting Python and hacking both.
So similarly, we also use the only method to add the items to the letters only SDR list we created
in our code earlier.
OK, so whenever we select those only as append and assemble one in the line, it basically goes on
upending the values, then succumbing to creating a string of letters.
Let's go back to our program here.
And after finishing the loop over here, that is in removing all Asgeir and the append one after this.
OK, now letters onliest should be a list for each letter.
And this character from the original message is now because a list of one character string isn't useful
for finding the English word.
We have joined the character string in the letters on Steer into one string and Rutan's that so that
we have Poolesville written or blank, then go to join letters only now to concatenate the list items
in letters only a year into one lobstering we call the joint string Martorell LeBlanc's string that
is open plus single apostrophe and this joins the string in the letters only start with a blank string
between them and this string value is then returned as a remove non letter SDR functions return value.
OK, now coming to the dictating English votes or detecting English words.
Now when a message is encrypted, all of which is decrypted OK with a Ronke, it will often produce
far more non letter and non space, correct?
Those that are found in a typical English message.
All the votes it produces will often be random and not found in the dictionary of English words.
So if we go for a definition of is English check function, we can check for both of these issues in
the given strength, not here in this function.
We have created this or set up this to accept the strong argument that this message is suggestion and
return a boolean value of proof when the string is English text and when it is not.
Now this function has three parameters.
One is messages are verbose and SDR that is 20 and letterboxing, which is eighty five.
And the first parameter contains a string Kubitschek second and third parameter set default percentages
for votes and letters which the string must contain in order to be confirmed as English.
OK, now a percentage is just a number between zero one hundred.
That shows how much of something is a proportional to total number of those things.
So we'll explore how to use this default arguments and calculate percentage in the polling station now.
Now, using the default argument, sometimes a function will always have the same values pass to it
when it is scored instead of including these.
For every function call, you can specify a default argument in the functions definition statement itself.
Like here we have defined three parameters with default arguments of twenty and eighty five provided
for four percent a year and Letha percent respectively.
Now this is English check function.
Can we call with one, two, three arguments?
If no arguments are parsed for one percent or two percent a year, then the values assigned to these
parameters will be the default arguments.
OK, now the default arguments defined what percent of the message ASDF string needs to be made up of
real English votes for is English.
All these English to predetermine that message just yet is an English string.
And what percent of message needs to be made up of Letrozole spaces instead of numbers or punctuation
marks?
For example, if is English check is called with only one argument before arguments are used for four
percent, which means twenty percent of string needs to be made up of English words and eighty five
percent of the string needs to be made up of letters.
Now, these percentages work for detecting your English in most cases, but you might.
I want to try a document combinations in specific cases, one is English, which is basically called
needs some more losers, more restrictive tests.
So in those situations, a program can just pass arguments for even four percent this year and let the
person this year instead of using the default ones.