Python中的Pandas详解-3-算数运算_pandas sqrt-优快云博客

对Pandas Series执行算术运算

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和 NumPy ndarray 一样，我们可以对 Pandas Series 执行元素级算术运算。我们将了解 Pandas Series 和单个数字之间的算术运算。我们创建一个新的 Pandas Series，用于存储只有水果的购物清单。

# We create a Pandas Series that stores a grocery list of just fruits
fruits= pd.Series(data = [10, 6, 3,], index = ['apples', 'oranges', 'bananas'])

# We display the fruits Pandas Series
fruits

apples         10
oranges        6
bananas       3
dtype: int64

我们现在可以通过执行基本的算术运算，修改 fruits 中的数据。我们来看一些示例：

# We print fruits for reference
print('Original grocery list of fruits:\n ', fruits)

# We perform basic element-wise operations using arithmetic symbols
print()
print('fruits + 2:\n', fruits + 2) # We add 2 to each item in fruits
print()
print('fruits - 2:\n', fruits - 2) # We subtract 2 to each item in fruits
print()
print('fruits * 2:\n', fruits * 2) # We multiply each item in fruits by 2 
print()
print('fruits / 2:\n', fruits / 2) # We divide each item in fruits by 2
print()

Original grocery list of fruits:
apples         10
oranges        6
bananas       3
dtype: int64

fruits + 2:
apples         12
oranges        8
bananas       5
dtype: int64

fruits - 2:
apples           8
oranges        4
bananas       1
dtype: int64

fruits * 2:
apples         20
oranges      12
bananas       6
dtype: int64

fruits / 2:
apples           5.0
oranges        3.0
bananas       1.5
dtype: float64

我们还可以对 Pandas Series 中的所有元素应用 NumPy 中的数学函数，例如 sqrt(x)。

# We import NumPy as np to be able to use the mathematical functions
import numpy as np

# We print fruits for reference
print('Original grocery list of fruits:\n', fruits)

# We apply different mathematical functions to all elements of fruits
print()
print('EXP(X) = \n', np.exp(fruits))
print() 
print('SQRT(X) =\n', np.sqrt(fruits))
print()
print('POW(X,2) =\n',np.power(fruits,2)) # We raise all elements of fruits to the power of 2

Original grocery list of fruits:
apples         10
oranges        6
bananas       3
dtype: int64

EXP(X) =
apples        22026.465795
oranges         403.428793
bananas          20.085537
dtype: float64

SQRT(X) =
apples            3.162278
oranges         2.449490
bananas        1.732051
dtype: float64

POW(X,2) =
apples         100
oranges        36
bananas         9
dtype: int64

Pandas 还允许我们仅对 fruits 购物清单中的部分条目应用算术运算。我们来看一些示例：

# We print fruits for reference
print('Original grocery list of fruits:\n ', fruits)
print()

# We add 2 only to the bananas
print('Amount of bananas + 2 = ', fruits['bananas'] + 2)
print()

# We subtract 2 from apples
print('Amount of apples - 2 = ', fruits.iloc[0] - 2)
print()

# We multiply apples and oranges by 2
print('We double the amount of apples and oranges:\n', fruits[['apples', 'oranges']] * 2)
print()

# We divide apples and oranges by 2
print('We half the amount of apples and oranges:\n', fruits.loc[['apples', 'oranges']] / 2)

Original grocery list of fruits:
apples         10
oranges        6
bananas       3
dtype: int64

Amount of bananas + 2 = 5

Amount of apples - 2 = 8

We double the amount of apples and oranges:
apples         20
oranges      12
dtype: int64

We half the amount of apples and oranges:
apples         5.0
oranges      3.0
dtype: float64

你还可以对具有混合数据类型的 Pandas Series 应用算术运算，前提是该算术运算适合 Series 中的所有数据类型，否则会出错。我们来看看将购物清单乘以 2 会发生什么

# We multiply our grocery list by 2
groceries * 2

eggs                 60
apples             12
milk         YesYes
bread        NoNo
dtype: object

可以看出，在上述示例中，我们乘以了 2，Pandas 使每个条目的数据翻倍，包括字符串。Pandas 能够这么操作是因为，乘法运算 * 对数字和字符串来说都可行。如果你要应用对数字有效但是对字符串无效的运算，例如 /，则会出错。如果 Pandas Series 中有混合类型的数据，确保对于所有的元素数据类型，这些算术运算都有效。

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