Python Type Casting
Type casting, also known as type conversion, is the process of changing a variable's data type to another compatible type. It may be done explicitly or handled implicitly.
1. Explicit Type Casting:
Explicit type conversion is performed using built-in functions, often when one type may be suitable to convert to another type variable even if underlying types are compatible where explicit syntax useful for clarifying that change within coding logic such as for readability purposes
like int(), float(), str(), list(), tuple(), set(), etc. You tell Python to convert a variable value from type to a different type, rather than that assignment following from other coding steps
x = 10 # Integer
y = 3.14 # Float
z = "20" # String (even though only number-looking characters: type remains string due to how it is initialized with quotation syntax initially)
a = float(x) # a is now 10.0 (float)
b = int(y) # b is now 3 (integer – any fraction after decimal truncated without rounding here as shown)
c = int(z) # Converts String z ("20") into integer (c=20), unlike a sum in later operations that assumes conversion before calculation where often would then convert implicitly if that works, but without triggering error. E.g. using 'str' or similar functions or within string assignments where other values may end up being typecasted due to string concatenation syntax which differs notably for this purpose. It does implicit typecasting without warning or specifying to make it clear: str vs int can work or int alone for float type etc. but will do so within calculation sequence that is harder to grasp than explicit change, or to differentiate later. For instance, int could represent string conversion as shown if implicit while an attempt with same value in floating case could fail if type conflicts. By using appropriate functions these nuances are eliminated: any issues during testing stand out due to explicit usage. Implicit approach works if only numbers provided inside but you cannot distinguish from original string data since result as a number hides its meaning which would become critical to disentangle after such logic combined. Using these examples instead provides some means to differentiate steps where a variable starts as 'str' type but changes, vs remains as numerical if appropriate after some prior casting. Explicit approach keeps these clearer since always separate assignments if they appear while you retain original string and only perform implicit type casting within a given statement
print(type(a)) # Output:
print(type(b)) # Output:
print(type(c)) # Output:
2. Implicit Type Casting:
Implicit or automatic type conversion: no special type conversion instruction. Example below casts an integer 25 type into floating during that math step in 2nd statement. Done often behind scenes and useful since simplifies handling types within a statement rather than requiring two statements or nested code blocks or temporary holding variables
Useful mostly if types compatible so explicit steps seem unnecessary. When a data type change does occur though there is usually a way to check which change happens as long as a step causes such effects during logic testing, although exact changes might sometimes involve special mechanisms not clear immediately especially when using compound or nested objects
integer_number = 25
floating_number = 25.0
sum_result = integer_number + floating_number # Integer is implicitly converted to float before addition, as it can remain valid without truncation. if some truncation, as done via an int assignment this doesn't introduce issues implicitly and no separate explicit check or explicit prior assignments in those statements either unless those considered somehow safer if they change before any assignment in contrast
print(type(sum_result)) #Output:
print (sum_result) # result 50.0 printed here in the floating format without requiring further specification due to type-casting in 2nd prior statement after implicit check to confirm conversion
3. Type Errors in Type Casting
Type casting introduces the risk of TypeError when a target data type and its value type being casted differ incompatible such as string “hello” into an integer type or in reverse: casting float number “4.75” with decimals into an integer target using int(). If such truncation desired for operations such as sum without remainder an alternate variable might sometimes suffice unless the target variable in scope requires this, like when that type checked to determine how an assigned variable is to be handled later
If types fully incompatible errors during testing will identify, which might depend on sequence steps within tests for more complex nested typecasts (e.g. str inside a dict being type-converted might differ due to context or not affect due to dict, if same cast applied inside list, if same value assigned within list with and without quoting since one implies explicit typecast required and other no typecasting applied because same type retained)
str_num = "hello" # String cannot convert
#int_casted = int(str_num)
float_num_decimal= 4.75 #Valid conversion
int_casted_valid= int(float_num_decimal) #4 (note result shows decimals removed in conversion from float -> int if casting performed explicitly)