import numpy as np

names

names = np.array(['Apple Inc', 'Coca-Cola', 'Walmart'])
negative_index = np.array([-1,-2])

Use slicing on list names

names_subset = names[negative_index]
print(names_subset)

注意哪里中括号哪里小括号，哪里逗号哪里冒号
这个做法 可以先显示最后一个 再显示倒数第二个
结果： ['Walmart' 'Coca-Cola']

For lists, useful functions include max() and min(), which identify the maximum or minimum value in a list. A useful list method is .sort() which sorts the elements in a list.

prices = [159.54, 37.13, 71.17]
price_max = max(prices) max是一个函数
但是method功能的话 是prices_min = prices.sort() 放在点变量的后面

names = ['Apple Inc', 'Coca-Cola', 'Walmart']

    names.append('Amazon.com')
    print(names)

['Apple Inc', 'Coca-Cola', 'Walmart', 'Amazon.com'] 是加一个元素，元素的总数量增加了

.extend() 可以在后面添加多个元素

.index 返还的是 index数字

    np.pmt() 企业年金

rf = pd.DataFrame.from_dict(tt, orient="index"); 
rf.to_csv("LocalDrive\\Sales.csv")

计算加权平均数~~

# Create the combined list for sales and probability
sales_probability = ['0|0.05', '200|0.1', '300|0.4', '500|0.2', '800|0.25'] 
weighted_probability = 0

# Create a for loop to calculate the weighted probability
for pair in sales_probability:
parts = pair.split('|')
weighted_probability += float(parts[0]) * float(parts[1])

# Print the weighted probability result
print("The weighted probability is {}.".format(weighted_probability))