Data structure in Python

Author

Tony Duan

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

Code

a= True
type(a)

bool

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)

1.2.2 first 2 on the list

Code

fruits[:2]

['orange', 'apple']

1.2.3 last 2 on the list

Code

fruits[-2:]

['banana', 'apple']

1.2.4 find how many time in the list with `count()`

Code

fruits.count('apple')

1.2.5 find locaiton of on the list with `index()`

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

Code

fruits.index('apple')

all ‘apple’ in the list

Code

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

[1, 5, 7]

1.2.6 reverse the list

Code

fruits.reverse()
fruits

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

1.2.7 sort the list

Code

fruits.sort()
fruits

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

1.2.8 add element on the list

Code

fruits.append('grape')
fruits

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

1.2.9 drop last element

Code

fruits.pop()

fruits

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

1.2.10 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]

1.2.11 list to Tuples

Code

tuple(squares)

(0, 1, 4, 9, 16, 25, 36, 49, 64, 81)

1.2.12 list to set

Code

set(squares)

{0, 1, 4, 9, 16, 25, 36, 49, 64, 81}

1.2.13 list to dictionary

1.2.13.1 one list to dictionary

Code

list=['a', 1, 'b', 2, 'c', 3]

def convert(lst):
   res_dict = {}
   for i in range(0, len(lst), 2):
       res_dict[lst[i]] = lst[i + 1]
   return res_dict
 
convert(list)

{'a': 1, 'b': 2, 'c': 3}

1.2.13.2 two list to dictionary

Code

import itertools

keys = ('name', 'age', 'food')

values = ('Monty', 42, 'spam')

dict(zip(keys, values))

{'name': 'Monty', 'age': 42, 'food': 'spam'}

1.3 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.

1.4 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

1.5 Dictionaries

Code

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

tel

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

Code

type(tel)

dict

Code

tel['jack']

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]]

2.1 numpy Array

Code

import numpy as np

A2 = np.array([[1, 2, 3], [3, 4, 5]])
print(A2)

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

Code

type(A2)

numpy.ndarray

2.2 shape

Code

A2.shape

(2, 3)

2.3 row number

Code

len(A2)

2.4 total elements

Code

A2.size

2.5 dimension

Code

A2.ndim

2.6 count numpy.ndarray

Code

import collections, numpy
a = numpy.array([0, 3, 0, 4])
collections.Counter(a)

Counter({np.int64(0): 2, np.int64(3): 1, np.int64(4): 1})

2.6.1 convert list into numpy array

Code

A = [
  [1, 4, 5, 12], 
  [-5, 8, 9, 0],
  [-6, 7, 11, 19],
  [1, 4, 5, 12], 
  [-5, 8, 9, 0],
  [-6, 7, 11, 19],
  [1, 4, 5, 12], 
  [-5, 8, 9, 0],
  [-6, 7, 11, 19]
  ]
    
A3 = np.array(A)

print(A3)

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

2.6.2 selection

2.6.2.1 first 5 row

Code

A[:5]

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

2.6.2.2 lst 5 row

Code

A[:-5]

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

2.6.2.3 first row

Code

A[0]

[1, 4, 5, 12]

2.6.2.4 first column

Code

A2[:,0]

array([1, 3])

2.6.2.5 first row and first column element

Code

A2[0,0]

np.int64(1)

Code

A2.dtype

dtype('int64')

2.6.2.6 2 row and 3 column

Code

A2[1,2]

np.int64(5)

2.6.2.7 filter

2.6.2.7.1 filter all

Code

print(A3)

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

Code

A3>4

array([[False, False,  True,  True],
       [False,  True,  True, False],
       [False,  True,  True,  True],
       [False, False,  True,  True],
       [False,  True,  True, False],
       [False,  True,  True,  True],
       [False, False,  True,  True],
       [False,  True,  True, False],
       [False,  True,  True,  True]])

Code

A3[A3>4]

array([ 5, 12,  8,  9,  7, 11, 19,  5, 12,  8,  9,  7, 11, 19,  5, 12,  8,
        9,  7, 11, 19])

2.6.2.7.2 filter row

Code

A3

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

filter secound column>5

Code

filter_val=(A3>5)[:,2]

which only keep 2,3 row.

Code

A3[filter_val,0:]

array([[-5,  8,  9,  0],
       [-6,  7, 11, 19],
       [-5,  8,  9,  0],
       [-6,  7, 11, 19],
       [-5,  8,  9,  0],
       [-6,  7, 11, 19]])

2.6.3 create numpy array

2.6.3.1 create eye

Code

np.eye(3)

array([[1., 0., 0.],
       [0., 1., 0.],
       [0., 0., 1.]])

2.6.3.2 create zero

Code

np.zeros((2,3))

array([[0., 0., 0.],
       [0., 0., 0.]])

2.6.3.3 create ones

Code

np.ones((2,3))

array([[1., 1., 1.],
       [1., 1., 1.]])

2.6.4 compare

Code

# Creating Array
a = np.array([1,2,3,4]) 
b = np.array([3,2,5,6])

Code

# Comparing two arrays
np.greater(a, b)

array([False, False, False, False])

Code

a >= b

array([False,  True, False, False])

Code

# Comparing two arrays
np.less(a, b)

array([ True, False,  True,  True])

Code

# Comparing two arrays
np.equal(a, b)

array([False,  True, False, False])

2.6.5 reshape

Code

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

a

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

2.6.6 calculation

Code

b=a*a
b

array([[ 0,  1,  4],
       [ 9, 16, 25],
       [36, 49, 64]])

Code

b=a+a
b

array([[ 0,  2,  4],
       [ 6,  8, 10],
       [12, 14, 16]])

2.6.7 numpy array to dataframe

Code

import pandas as pd
df = pd.DataFrame(b, columns=['Column_A', 'Column_B', 'Column_C'])

df

	Column_A	Column_B	Column_C
0	0	2	4
1	6	8	10
2	12	14	16

3 pandas series

4 Reference

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

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

1 bulid-in data Structures

1.1 singular

1.2 list

1.2.1 find length of the list with len()

1.2.2 first 2 on the list

1.2.3 last 2 on the list

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

1.2.5 find locaiton of on the list with index()

1.2.6 reverse the list

1.2.7 sort the list

1.2.8 add element on the list

1.2.9 drop last element

1.2.10 List Comprehensions

1.2.11 list to Tuples

1.2.12 list to set

1.2.13 list to dictionary

1.2.13.1 one list to dictionary

1.2.13.2 two list to dictionary

1.3 Tuples

1.4 Sets

1.5 Dictionaries

2 numpy data Structures(matrix in python)

2.1 numpy Array

2.2 shape

2.3 row number

2.4 total elements

2.5 dimension

2.6 count numpy.ndarray

2.6.1 convert list into numpy array

2.6.2 selection

2.6.2.1 first 5 row

2.6.2.2 lst 5 row

2.6.2.3 first row

2.6.2.4 first column

2.6.2.5 first row and first column element

2.6.2.6 2 row and 3 column

2.6.2.7 filter

2.6.2.7.1 filter all

2.6.2.7.2 filter row

2.6.3 create numpy array

2.6.3.1 create eye

2.6.3.2 create zero

2.6.3.3 create ones

2.6.4 compare

2.6.5 reshape

2.6.6 calculation

2.6.7 numpy array to dataframe

3 pandas series

4 Reference

1.2.1 find length of the list with `len()`

1.2.4 find how many time in the list with `count()`

1.2.5 find locaiton of on the list with `index()`