資料結構1

方法一

nba_div_1 = "大西洋組"

nba_div_2 = "中央組" 

nba_div_3 = "東南組" 

nba_div_4 = "西北組" 

nba_div_5 = "太平洋組"
nba_div_6 = "西南組"

方法二

nba_divs = ["大西洋組", "中央組", "東南組", "西北組", "太平洋組", "西南組"]

print(nba_divs[0])
print(nba_divs[1])

list

# as of 2016-12-13

team_name = "金州勇士隊" 

wins = 21 

losses = 4 

win_percentage = 0.84 

is_on_winning_streak = True 

golden_state_warriors = [team_name, wins, losses, win_percentage, is_on_winning_streak]

print(type(golden_state_warriors))

print(type(golden_state_warriors[0]))

tuple
nba_divs_tuple 不能做新增(.insert)或修改

dic

# as of 2016-12-13

team_name = "金州勇士隊"

wins = 21 

losses = 4 

win_percentage = 0.84 

is_on_winning_streak = True 

golden_state_warriors = { "team_name": team_name, "wins": wins, "losses": losses, "win_percentage": win_percentage, "is_on_winning_streak": is_on_winning_streak }

type(golden_state_warriors["team_name"])

ndarray

1. Python 的 list 無法使用 element-wise 運算

import numpy # 引入 numpy 套件

km_list = [21, 42.195]

km_array = numpy.array(km_list)

print(km_array)

print(type(km_array))

km_to_mile = 0.621371192

mile_array = km_array * km_to_mile

print(mile_array)

2. ndarray 只能容許一個資料類型

同時儲存有數值，布林值，會被自動轉換為數值
同時儲存有數值，布林值與文字，會被自動轉換為文字

import numpy as np

my_list = [1, True]

#my_list = [1, True, "False"]

my_np_array = np.array(my_list)

print(type(my_list[1]))

print(type(my_np_array[1]))

print(my_np_array.dtype)

3. ndarray 可以建立矩陣

import numpy as np 

my_np_array = np.array([1, 2, 3, 4]) 

print(my_np_array) 

print(my_np_array ** 2) 

print("---") # 分隔一下 

my_2d_array = np.array([[1, 3], [2, 4]]) 

print(my_2d_array) 

print(my_2d_array ** 2)

4. 選擇 ndarray 的元素
用索引值選擇

import numpy as np

my_np_array = np.array([1, 2, 3, 4]) 

print(my_np_array[0]) 

my_2d_array = np.array([[1, 3], [2, 4]]) 

print(my_2d_array[1, 1])

用布林值選擇
import numpy as np


my_np_array = np.array([1, 2, 3, 4])
filter = my_np_array >= 3
filter

out: array([False, False, True, True], dtype=bool)

5. 了解外觀的屬性

.size
.shape

import numpy as np

my_np_array = np.array([1, 2, 3, 4]) 

print(my_np_array.size) 

print(my_np_array.shape) 

print("---") # 分隔一下 my_2d_array = np.array([[1, 3], [2, 4]]) 

print(my_2d_array.size) 

print(my_2d_array.shape)