data type and data structure in Python

1 bulid-in data Structures

1.1 singular

Code

a=1
type(a)

int

Code

a=1.3
type(a)

float

Code

a='hell'
type(a)

str

1.2 list

Code

a=[1,2,3]

a

[1, 2, 3]

Code

type(a) 

list

Code

fruits = ['orange', 'apple', 'pear', 'banana', 'kiwi', 'apple', 'banana','apple']

1.2.1 find length of the list with len()

Code

len(fruits)

8

1.2.2 find how many time in the list with count()

Code

fruits.count('apple')

3

1.2.3 find locaiton of on the list with index()

show the first ‘apple’ index. python list start at 0

Code

fruits.index('apple')

1

all ‘apple’ in the list

Code

[index for index, value in enumerate(fruits) if value == 'apple']

[1, 5, 7]

1.3 reverse the list

Code

fruits.reverse()
fruits

['apple', 'banana', 'apple', 'kiwi', 'banana', 'pear', 'apple', 'orange']

1.3.1 sort the list

Code

fruits.sort()
fruits

['apple', 'apple', 'apple', 'banana', 'banana', 'kiwi', 'orange', 'pear']

1.3.2 add element on the list

Code

fruits.append('grape')
fruits

['apple',
 'apple',
 'apple',
 'banana',
 'banana',
 'kiwi',
 'orange',
 'pear',
 'grape']

1.4 drop last element

Code

fruits.pop()

fruits

['apple', 'apple', 'apple', 'banana', 'banana', 'kiwi', 'orange', 'pear']

1.4.1 List Comprehensions

using loop:

Code

squares = []
for x in range(10):
  squares.append(x**2)
  
squares

[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

using List Comprehensions

Code

squares = [x**2 for x in range(10)]
squares

[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

1.5 Tuples

Code

fruits = ('orange', 'apple', 'pear', 'banana', 'kiwi', 'apple', 'banana','apple')

fruits

('orange', 'apple', 'pear', 'banana', 'kiwi', 'apple', 'banana', 'apple')

Code

type(fruits)

tuple

tuple can not be modified.

1.6 Sets

A set is an unordered collection with no duplicate elements.

Code

basket = {'apple', 'orange', 'apple', 'pear', 'orange', 'banana'}

basket

{'apple', 'banana', 'orange', 'pear'}

Code

type(basket)

set

1.7 Dictionaries

Code

tel = {'jack': 4098, 'sape': 4139}

tel

{'jack': 4098, 'sape': 4139}

Code

type(tel)

dict

Code

tel['jack']

4098

2 numpy data Structures(matrix in python)

NumPy is the fundamental package for scientific computing in Python. It is a Python library that provides a multidimensional array object

Python doesn’t have a built-in type for matrices. However, we can treat a list of a list as a matrix

Code

A = [[1, 4, 5, 12], 
    [-5, 8, 9, 0],
    [-6, 7, 11, 19]]
    
A

[[1, 4, 5, 12], [-5, 8, 9, 0], [-6, 7, 11, 19]]

numpy Array

Code

import numpy as np

A = np.array([[1, 2, 3], [3, 4, 5]])
print(A)

[[1 2 3]
 [3 4 5]]

Code

type(A)

numpy.ndarray

2.0.1 selection

first row

Code

A[0]

array([1, 2, 3])

first column

Code

A[:,0]

array([1, 3])

first row and first column element

Code

A[0,0]

1

Code

A.dtype

dtype('int64')

2 row and 3 column

Code

A.shape

(2, 3)

2.0.2 reshape

Code

a=np.arange(9).reshape(3, 3)

a

array([[0, 1, 2],
       [3, 4, 5],
       [6, 7, 8]])

3 pandas data Structures(dataframe in python)

4 Reference

https://docs.python.org/3/tutorial/datastructures.html#

https://numpy.org/doc/stable/user/basics.rec.html