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A list as a data structure that consists of a sequence of elements. In Python, each element can be of any type, including a list.

Because we are interested in data science computations (e.g., using numpy, etc.) all lists in these examples will be elements of all integers or all floats.

The example Python list is the following.

Note that Python (like many modern languages, but not JSON data) allows a comma after the last element of the list.

A number of different ways of processing the last are now presented. Some are more machine efficient and/or programmer efficient than other ways.

• In a Python list, a comma in allowed as an element terminator.
• In a JSON list, a comma is used as an element separator.

Here is the Python code.
list1 = [2,3,5,7,11,] len1 = len(list1) pos1 = 0 while pos1 != len1: value1 = list1[pos1] print("{0:d}. {1:d}".format(pos1+1,value1)) pos1 += 1
Here is the output of the Python code.
1. 2 2. 3 3. 5 4. 7 5. 11

In Python, the for loop is not like a for loop in C, Java, etc. The Python for is more like a for each loop in some other languages.

Here is the Python code.
list1 = [2,3,5,7,11,] for pos1 in range(len(list1)): value1 = list1[pos1] print("{0:d}. {1:d}".format(pos1+1,value1))
Here is the output of the Python code.
1. 2 2. 3 3. 5 4. 7 5. 11

The Python for construct iterates over a list (or range, etc.).

Here is the Python code.
list1 = [2,3,5,7,11,] pos1 = 0 for value1 in list1: value1 = list1[pos1] print("{0:d}. {1:d}".format(pos1+1,value1)) pos1 += 1
Here is the output of the Python code.
1. 2 2. 3 3. 5 4. 7 5. 11

The Python for enumerate returns both the position and the element.

Here is the Python code.
list1 = [2,3,5,7,11,] for pos1, value1 in enumerate(list1): print("{0:d}. {1:d}".format(pos1+1,value1))
Here is the output of the Python code.
1. 2 2. 3 3. 5 4. 7 5. 11

In Python, an array is a structured data type of elements of the same type that has constant time access via an integer index.

In Python, a list is a structured data type of elements of differing types that has non-constant time access via an integer index.

The Python for enumerate returns both the position and the element.

Here is the Python code.
import numpy as np list1 = [2,3,5,7,11,] array1 = np.asarray(list1,dtype=np.int32) for pos1, value1 in enumerate(array1): print("{0:d}. {1:d}".format(pos1+1,value1))
Here is the output of the Python code.
1. 2 2. 3 3. 5 4. 7 5. 11

The Python for enumerate returns both the position and the element.

Here is the Python code.
import numpy as np list1 = [2,3,5,7,11,] array1 = np.asarray(list1,dtype=np.float64) for pos1, value1 in enumerate(array1): print("{0:d}. {1:2.1f}".format(pos1+1,value1))
Here is the output of the Python code.
1. 2.0 2. 3.0 3. 5.0 4. 7.0 5. 11.0

Some common numpy data types are the following.

• float64: double precision float: sign bit, 11 bits exponent, 52 bits mantissa
• int8 - byte (-128 to 127)
• int16 - integer (-32,768 to 32,767)
• int32 -i Integer (-2,147,483,648 to 2,147,483,647)
• int64 - integer (-9,223,372,036,854,775,808 to 9,223,372,036,854,775,807)
• uint8 - unsigned integer (0 to 255)
• uint16 - unsigned integer (0 to 65,535)
• uint32 - unsigned integer (0 to 4,294,967,295)
• uint64 - unsigned integer (0 to 18,446,744,073,709,551,615)

It is often best if you explicitly set the type to be used instead of taking a default type which could vary from platform to platform.

There are also complex types (not covered here).

In general, always use the largest/biggest precision available for floating point computations.

In the specific case of NumPy, one would not use float32 unless one had a really good reason. Instead, one would use float64.

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