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Python Tokens

Tokens  or Lexical unit are the smallest buidling blocks of a program. Tokens are the equivalent of alphabets , grammer and tenses of a English language. Python has the following tokens:- Keywords Identifiers(Names) Literals Operators Punctuators a. Keywords - are the reserved words that has a special meaning and should be used only where its meant to be. Example:- False, True, for, while, None, break, if, elif, else b. Identifiers(Names) - The names given to different parts of the program like variables, function, objects, classes etc. eg:- a = 2, add(), here a and add are identifier names Rules for framing identifier names. Identifier name should be a combination of letters(a-z, A-Z) and digits(0-9). eg:- Valid names - abc123, abc, xy_123, xy_ Invalid names - abc#12, age$  First character must be a letter or underscore. eg:- Valid names- _123, a_123    Invalid names - 1abc Upper and lower case are different. eg:- ABC and abc are both different identifiers Xyz and
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Python Tokens - Operators

Operators - Tokens that trigger computation when applied to variables and other objects in a expression. Variable and expression to which the operators are applied are called operands. Example:- 2+3 here 2,3 are operands and + is operator. 1. Unary Operators -  It operates on only one operand. Example:- Operator Name Example + Unary Plus +10, 23E+2 - Unary Minus -4.3, -2.3E-1 ~ Bitwise complement ~3 not Logical negation not 2 2. Binary Operators - It requires two operators to operate upon. Operator Name Example + Addition 2+3 = 5 - Subtraction 2-3=-1 * Multiplication 2*3=6 / Division 2/3 = 0.66666666 % Remainder 2%3 = 2 ** Exponent 2**3 = 8 // Floor division 55//2 = 27
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Descriptive Statistics - count & sum

F. Count() - counts the non-NA entries for each row and column. Values None, Nat, NaN are considered as NA in pandas. Example:-  1. import pandas as pd df2 = pd.DataFrame({2016:{'q1':500,'q2':500,'q3':47000,'q4':49000},2017:{'q1':'A','q2':'A','q3':'A','q4':'D'},2018:{'q1':54500,'q2':51000},2019:{'q1':True,'q2':'False'}}) print(df2.count()) Output:- 2016    4 2017    4 2018    2 2019    2 2.  import pandas as pd df2 = pd.DataFrame({2016:{'q1':500,'q2':500,'q3':47000,'q4':49000},2017:{'q1':'A','q2':'A','q3':'A','q4':'D'},2018:{'q1':54500,'q2':51000},2019:{'q1':True,'q2':'False'}}) print(df2.count(numeric_only=True)) Output:- 2016 4 2018 2 G. Sum() - Returns the sum of the values for
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