Pandas数值运算¶

复习numpy 数值计算¶

In [6]:

import pandas as pd
import numpy as np

import pandas as pd import numpy as np

In [8]:

rng = np.random.RandomState(42)
ser = pd.Series(rng.randint(0, 10, 4))
ser

rng = np.random.RandomState(42) ser = pd.Series(rng.randint(0, 10, 4)) ser

Out[8]:

0    6
1    3
2    7
3    4
dtype: int32

In [10]:

df = pd.DataFrame(rng.randint(0, 10, (3, 4)),
                  columns=['A', 'B', 'C', 'D'])
df

df = pd.DataFrame(rng.randint(0, 10, (3, 4)), columns=['A', 'B', 'C', 'D']) df

Out[10]:

	A	B	C	D
0	6	9	2	6
1	7	4	3	7
2	7	2	5	4

In [12]:

np.exp(ser)

np.exp(ser)

Out[12]:

0     403.428793
1      20.085537
2    1096.633158
3      54.598150
dtype: float64

In [14]:

np.sin(df * np.pi / 4)

np.sin(df * np.pi / 4)

Out[14]:

	A	B	C	D
0	-1.000000	7.071068e-01	1.000000	-1.000000e+00
1	-0.707107	1.224647e-16	0.707107	-7.071068e-01
2	-0.707107	1.000000e+00	-0.707107	1.224647e-16

Series 计算¶

In [17]:

area = pd.Series({'Alaska': 1723337, 'Texas': 695662,
                  'California': 423967}, name='area')
population = pd.Series({'California': 38332521, 'Texas': 26448193,
                        'New York': 19651127}, name='population')

area = pd.Series({'Alaska': 1723337, 'Texas': 695662, 'California': 423967}, name='area') population = pd.Series({'California': 38332521, 'Texas': 26448193, 'New York': 19651127}, name='population')

In [19]:

population / area

population / area

Out[19]:

Alaska              NaN
California    90.413926
New York            NaN
Texas         38.018740
dtype: float64

In [25]:

A = pd.Series([2, 4, 6], index=[0, 1, 2])
B = pd.Series([1, 3, 5], index=[1, 2, 3])
A + B

A = pd.Series([2, 4, 6], index=[0, 1, 2]) B = pd.Series([1, 3, 5], index=[1, 2, 3]) A + B

Out[25]:

0    NaN
1    5.0
2    9.0
3    NaN
dtype: float64

In [27]:

A.add(B, fill_value=0)

A.add(B, fill_value=0)

Out[27]:

0    2.0
1    5.0
2    9.0
3    5.0
dtype: float64

Pandas DataFrame 数值计算¶

In [30]:

A = pd.DataFrame(rng.randint(0, 20, (2, 2)),
                 columns=list('AB'))
A

A = pd.DataFrame(rng.randint(0, 20, (2, 2)), columns=list('AB')) A

Out[30]:

	A	B
0	1	11
1	5	1

In [32]:

B = pd.DataFrame(rng.randint(0, 10, (3, 3)),
                 columns=list('BAC'))
B

B = pd.DataFrame(rng.randint(0, 10, (3, 3)), columns=list('BAC')) B

Out[32]:

	B	A	C
0	4	0	9
1	5	8	0
2	9	2	6

In [34]:

A + B

A + B

Out[34]:

	A	B	C
0	1.0	15.0	NaN
1	13.0	6.0	NaN
2	NaN	NaN	NaN

In [36]:

fill = A.stack().mean()
A.add(B, fill_value=fill)

fill = A.stack().mean() A.add(B, fill_value=fill)

Out[36]:

	A	B	C
0	1.0	15.0	13.5
1	13.0	6.0	4.5
2	6.5	13.5	10.5

Python 运算符号 与相应 Pandas 对象的方法

Python 运算符	Pandas 对象的方法
+	add()
-	sub(), subtract()
*	mul(), multiply()
/	truediv(), div(), divide()
//	floordiv()
%	mod()
**	pow()

DataFrame 与 Series混合计算¶

In [41]:

A = rng.randint(10, size=(3, 4))
A

A = rng.randint(10, size=(3, 4)) A

Out[41]:

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

In [43]:

A - A[0]

A - A[0]

Out[43]:

array([[ 0,  0,  0,  0],
       [-1, -2,  2,  4],
       [ 3, -7,  1,  4]])

In [45]:

df = pd.DataFrame(A, columns=list('QRST'))
df - df.iloc[0]

df = pd.DataFrame(A, columns=list('QRST')) df - df.iloc[0]

Out[45]:

	Q	R	S	T
0	0	0	0	0
1	-1	-2	2	4
2	3	-7	1	4

In [47]:

df.subtract(df['R'], axis=0)

df.subtract(df['R'], axis=0)

Out[47]:

	Q	R	S	T
0	-5	0	-6	-4
1	-4	0	-2	2
2	5	0	2	7