Pandas库

引子

Numpy 在向量化的数值计算中表现优异

但是在处理更灵活、复杂的数据任务：

如为数据添加标签、处理缺失值、分组和透视表等方面

Numpy显得力不从心

而基于Numpy构建的Pandas库，提供了使得数据分析变得更快更简单的高级数据结构和操作工具

12.1 对象创建

12.1.1 Pandas Series对象

Series 是带标签数据的一维数组

Series对象的创建

通用结构: pd.Series(data, index=index, dtype=dtype)

data：数据，可以是列表，字典或Numpy数组

index：索引，为可选参数

dtype: 数据类型，为可选参数

1、用列表创建

• index缺省，默认为整数序列

import pandas as pd

data = pd.Series([1.5, 3, 4.5, 6])
data

0    1.5
1    3.0
2    4.5
3    6.0
dtype: float64

• 增加index

data = pd.Series([1.5, 3, 4.5, 6], index=["a", "b", "c", "d"])
data

a    1.5
b    3.0
c    4.5
d    6.0
dtype: float64

• 增加数据类型 缺省则从传入的数据自动判断

data = pd.Series([1, 2, 3, 4], index=["a", "b", "c", "d"])    
data

a    1
b    2
c    3
d    4
dtype: int64

data = pd.Series([1, 2, 3, 4], index=["a", "b", "c", "d"], dtype="float")
data

a    1.0
b    2.0
c    3.0
d    4.0
dtype: float64

注意：数据支持多种类型

data = pd.Series([1, 2, "3", 4], index=["a", "b", "c", "d"])
data

a    1
b    2
c    3
d    4
dtype: object

data["a"]

1

data["c"]

'3'

数据类型可被强制改变

data = pd.Series([1, 2, "3", 4], index=["a", "b", "c", "d"], dtype=float)
data

a    1.0
b    2.0
c    3.0
d    4.0
dtype: float64

data["c"]

3.0

data = pd.Series([1, 2, "a", 4], index=["a", "b", "c", "d"], dtype=float)
data

2、用一维numpy数组创建

import numpy as np

x = np.arange(5)
pd.Series(x)

0    0
1    1
2    2
3    3
4    4
dtype: int32

3、用字典创建

• 默认以键为index 值为data

population_dict = {"BeiJing": 2154,
                   "ShangHai": 2424,
                   "ShenZhen": 1303,
                   "HangZhou": 981 }
population = pd.Series(population_dict)    
population

BeiJing     2154
ShangHai    2424
ShenZhen    1303
HangZhou     981
dtype: int64

• 字典创建，如果指定index，则会到字典的键中筛选，找不到的，值设为NaN

population = pd.Series(population_dict, index=["BeiJing", "HangZhou", "c", "d"])    
population

BeiJing     2154.0
HangZhou     981.0
c              NaN
d              NaN
dtype: float64

4、data为标量的情况

pd.Series(5, index=[100, 200, 300])

100    5
200    5
300    5
dtype: int64

12.1.2 Pandas DataFrame对象

DataFrame 是带标签数据的多维数组

DataFrame对象的创建

通用结构: pd.DataFrame(data, index=index, columns=columns)

data：数据，可以是列表，字典或Numpy数组

index：索引，为可选参数

columns: 列标签，为可选参数

1、通过Series对象创建

population_dict = {"BeiJing": 2154,
                   "ShangHai": 2424,
                   "ShenZhen": 1303,
                   "HangZhou": 981 }

population = pd.Series(population_dict)    
pd.DataFrame(population)

	0
BeiJing	2154
ShangHai	2424
ShenZhen	1303
HangZhou	981

pd.DataFrame(population, columns=["population"])

	population
BeiJing	2154
ShangHai	2424
ShenZhen	1303
HangZhou	981

2、通过Series对象字典创建

GDP_dict = {"BeiJing": 30320,
            "ShangHai": 32680,
            "ShenZhen": 24222,
            "HangZhou": 13468 }

GDP = pd.Series(GDP_dict)
GDP

BeiJing     30320
ShangHai    32680
ShenZhen    24222
HangZhou    13468
dtype: int64

pd.DataFrame({"population": population,
              "GDP": GDP})

	population	GDP
BeiJing	2154	30320
ShangHai	2424	32680
ShenZhen	1303	24222
HangZhou	981	13468

注意：数量不够的会自动补齐

pd.DataFrame({"population": population,
              "GDP": GDP,
              "country": "China"})

	population	GDP	country
BeiJing	2154	30320	China
ShangHai	2424	32680	China
ShenZhen	1303	24222	China
HangZhou	981	13468	China

3、通过字典列表对象创建

• 字典索引作为index，字典键作为columns

import numpy as np
import pandas as pd

data = [{"a": i, "b": 2*i} for i in range(3)]
data

[{'a': 0, 'b': 0}, {'a': 1, 'b': 2}, {'a': 2, 'b': 4}]

data = pd.DataFrame(data)
data

	a	b
0	0	0
1	1	2
2	2	4

data1 = data["a"].copy()
data1

0    0
1    1
2    2
Name: a, dtype: int64

data1[0] = 10
data1

0    10
1     1
2     2
Name: a, dtype: int64

data

	a	b
0	0	0
1	1	2
2	2	4

• 不存在的键，会默认值为NaN

data = [{"a": 1, "b":1},{"b": 3, "c":4}]
data

[{'a': 1, 'b': 1}, {'b': 3, 'c': 4}]

pd.DataFrame(data)

	a	b	c
0	1.0	1	NaN
1	NaN	3	4.0

4、通过Numpy二维数组创建

data = np.random.randint(10, size=(3, 2))
data

array([[1, 6],
       [2, 9],
       [4, 0]])

pd.DataFrame(data, columns=["foo", "bar"], index=["a", "b", "c"])

	foo	bar
a	1	6
b	2	9
c	4	0

12.2 DataFrame性质

1、属性

data = pd.DataFrame({"pop": population, "GDP": GDP})
data

	pop	GDP
BeiJing	2154	30320
ShangHai	2424	32680
ShenZhen	1303	24222
HangZhou	981	13468

（1）df.values 返回numpy数组表示的数据

data.values

array([[ 2154, 30320],
       [ 2424, 32680],
       [ 1303, 24222],
       [  981, 13468]], dtype=int64)

（2）df.index 返回行索引

data.index

Index(['BeiJing', 'ShangHai', 'ShenZhen', 'HangZhou'], dtype='object')

（3）df.columns 返回列索引

data.columns

Index(['pop', 'GDP'], dtype='object')

（4）df.shape 形状

data.shape

(4, 2)

（5） pd.size 大小

data.size

8

（6）pd.dtypes 返回每列数据类型

data.dtypes

pop    int64
GDP    int64
dtype: object

2、索引

data

	pop	GDP
BeiJing	2154	30320
ShangHai	2424	32680
ShenZhen	1303	24222
HangZhou	981	13468

（1）获取列

• 字典式

data["pop"]

BeiJing     2154
ShangHai    2424
ShenZhen    1303
HangZhou     981
Name: pop, dtype: int64

data[["GDP", "pop"]]

	GDP	pop
BeiJing	30320	2154
ShangHai	32680	2424
ShenZhen	24222	1303
HangZhou	13468	981

• 对象属性式

data.GDP

BeiJing     30320
ShangHai    32680
ShenZhen    24222
HangZhou    13468
Name: GDP, dtype: int64

（2）获取行

• 绝对索引 df.loc

data.loc["BeiJing"]

pop     2154
GDP    30320
Name: BeiJing, dtype: int64

data.loc[["BeiJing", "HangZhou"]]

	pop	GDP
BeiJing	2154	30320
HangZhou	981	13468

• 相对索引 df.iloc

data

	pop	GDP
BeiJing	2154	30320
ShangHai	2424	32680
ShenZhen	1303	24222
HangZhou	981	13468

data.iloc[0]

pop     2154
GDP    30320
Name: BeiJing, dtype: int64

data.iloc[[1, 3]]

	pop	GDP
ShangHai	2424	32680
HangZhou	981	13468

（3）获取标量

data

	pop	GDP
BeiJing	2154	30320
ShangHai	2424	32680
ShenZhen	1303	24222
HangZhou	981	13468

data.loc["BeiJing", "GDP"]

30320

data.iloc[0, 1]

30320

data.values[0][1]

30320

（4）Series对象的索引

type(data.GDP)

pandas.core.series.Series

GDP

BeiJing     30320
ShangHai    32680
ShenZhen    24222
HangZhou    13468
dtype: int64

GDP["BeiJing"]

30320

3、切片

dates = pd.date_range(start='2019-01-01', periods=6)
dates

DatetimeIndex(['2019-01-01', '2019-01-02', '2019-01-03', '2019-01-04',
               '2019-01-05', '2019-01-06'],
              dtype='datetime64[ns]', freq='D')

df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=["A", "B", "C", "D"])
df

	A	B	C	D
2019-01-01	-0.935378	-0.190742	0.925984	-0.818969
2019-01-02	-0.234414	-1.194674	1.080779	-2.294395
2019-01-03	-0.141572	0.058118	1.102248	1.207726
2019-01-04	0.305088	0.535920	-0.978434	0.177251
2019-01-05	0.313383	0.234041	0.163155	-0.296649
2019-01-06	0.250613	-0.904400	-0.858240	-1.573342

（1）行切片

df["2019-01-01": "2019-01-03"]

	A	B	C	D
2019-01-01	-0.935378	-0.190742	0.925984	-0.818969
2019-01-02	-0.234414	-1.194674	1.080779	-2.294395
2019-01-03	-0.141572	0.058118	1.102248	1.207726

df.loc["2019-01-01": "2019-01-03"]

	A	B	C	D
2019-01-01	-0.935378	-0.190742	0.925984	-0.818969
2019-01-02	-0.234414	-1.194674	1.080779	-2.294395
2019-01-03	-0.141572	0.058118	1.102248	1.207726

df.iloc[0: 3]

	A	B	C	D
2019-01-01	-0.935378	-0.190742	0.925984	-0.818969
2019-01-02	-0.234414	-1.194674	1.080779	-2.294395
2019-01-03	-0.141572	0.058118	1.102248	1.207726

（2）列切片

df

	A	B	C	D
2019-01-01	-0.935378	-0.190742	0.925984	-0.818969
2019-01-02	-0.234414	-1.194674	1.080779	-2.294395
2019-01-03	-0.141572	0.058118	1.102248	1.207726
2019-01-04	0.305088	0.535920	-0.978434	0.177251
2019-01-05	0.313383	0.234041	0.163155	-0.296649
2019-01-06	0.250613	-0.904400	-0.858240	-1.573342

df.loc[:, "A": "C"]

	A	B	C
2019-01-01	-0.935378	-0.190742	0.925984
2019-01-02	-0.234414	-1.194674	1.080779
2019-01-03	-0.141572	0.058118	1.102248
2019-01-04	0.305088	0.535920	-0.978434
2019-01-05	0.313383	0.234041	0.163155
2019-01-06	0.250613	-0.904400	-0.858240

df.iloc[:, 0: 3]

	A	B	C
2019-01-01	-0.935378	-0.190742	0.925984
2019-01-02	-0.234414	-1.194674	1.080779
2019-01-03	-0.141572	0.058118	1.102248
2019-01-04	0.305088	0.535920	-0.978434
2019-01-05	0.313383	0.234041	0.163155
2019-01-06	0.250613	-0.904400	-0.858240

（3）多种多样的取值

df

	A	B	C	D
2019-01-01	-0.935378	-0.190742	0.925984	-0.818969
2019-01-02	-0.234414	-1.194674	1.080779	-2.294395
2019-01-03	-0.141572	0.058118	1.102248	1.207726
2019-01-04	0.305088	0.535920	-0.978434	0.177251
2019-01-05	0.313383	0.234041	0.163155	-0.296649
2019-01-06	0.250613	-0.904400	-0.858240	-1.573342

• 行、列同时切片

df.loc["2019-01-02": "2019-01-03", "C":"D"]

	C	D
2019-01-02	1.080779	-2.294395
2019-01-03	1.102248	1.207726

df.iloc[1: 3, 2:]

	C	D
2019-01-02	1.080779	-2.294395
2019-01-03	1.102248	1.207726

• 行切片，列分散取值

df.loc["2019-01-04": "2019-01-06", ["A", "C"]]

	A	C
2019-01-04	0.305088	-0.978434
2019-01-05	0.313383	0.163155
2019-01-06	0.250613	-0.858240

df.iloc[3:, [0, 2]]

	A	C
2019-01-04	0.305088	-0.978434
2019-01-05	0.313383	0.163155
2019-01-06	0.250613	-0.858240

• 行分散取值，列切片

df.loc[["2019-01-02", "2019-01-06"], "C": "D"]

df.iloc[[1, 5], 0: 3]

	A	B	C
2019-01-02	-0.234414	-1.194674	1.080779
2019-01-06	0.250613	-0.904400	-0.858240

• 行、列均分散取值

df.loc[["2019-01-04", "2019-01-06"], ["A", "D"]]

df.iloc[[1, 5], [0, 3]]

	A	D
2019-01-02	-0.234414	-2.294395
2019-01-06	0.250613	-1.573342

4、布尔索引

df

	A	B	C	D
2019-01-01	-0.935378	-0.190742	0.925984	-0.818969
2019-01-02	-0.234414	-1.194674	1.080779	-2.294395
2019-01-03	-0.141572	0.058118	1.102248	1.207726
2019-01-04	0.305088	0.535920	-0.978434	0.177251
2019-01-05	0.313383	0.234041	0.163155	-0.296649
2019-01-06	0.250613	-0.904400	-0.858240	-1.573342

df > 0

	A	B	C	D
2019-01-01	False	False	True	False
2019-01-02	False	False	True	False
2019-01-03	False	True	True	True
2019-01-04	True	True	False	True
2019-01-05	True	True	True	False
2019-01-06	True	False	False	False

df[df > 0]

	A	B	C	D
2019-01-01	NaN	NaN	0.925984	NaN
2019-01-02	NaN	NaN	1.080779	NaN
2019-01-03	NaN	0.058118	1.102248	1.207726
2019-01-04	0.305088	0.535920	NaN	0.177251
2019-01-05	0.313383	0.234041	0.163155	NaN
2019-01-06	0.250613	NaN	NaN	NaN

df.A > 0

2019-01-01    False
2019-01-02    False
2019-01-03    False
2019-01-04     True
2019-01-05     True
2019-01-06     True
Freq: D, Name: A, dtype: bool

df[df.A > 0]

	A	B	C	D
2019-01-04	0.305088	0.535920	-0.978434	0.177251
2019-01-05	0.313383	0.234041	0.163155	-0.296649
2019-01-06	0.250613	-0.904400	-0.858240	-1.573342

• isin（）方法

df2 = df.copy()
df2['E'] = ['one', 'one', 'two', 'three', 'four', 'three']
df2

	A	B	C	D	E
2019-01-01	-0.935378	-0.190742	0.925984	-0.818969	one
2019-01-02	-0.234414	-1.194674	1.080779	-2.294395	one
2019-01-03	-0.141572	0.058118	1.102248	1.207726	two
2019-01-04	0.305088	0.535920	-0.978434	0.177251	three
2019-01-05	0.313383	0.234041	0.163155	-0.296649	four
2019-01-06	0.250613	-0.904400	-0.858240	-1.573342	three

ind = df2["E"].isin(["two", "four"])
ind     

2019-01-01    False
2019-01-02    False
2019-01-03     True
2019-01-04    False
2019-01-05     True
2019-01-06    False
Freq: D, Name: E, dtype: bool

df2[ind]

	A	B	C	D	E
2019-01-03	-0.141572	0.058118	1.102248	1.207726	two
2019-01-05	0.313383	0.234041	0.163155	-0.296649	four

（5）赋值

df

• DataFrame 增加新列

s1 = pd.Series([1, 2, 3, 4, 5, 6], index=pd.date_range('20190101', periods=6))
s1

2019-01-01    1
2019-01-02    2
2019-01-03    3
2019-01-04    4
2019-01-05    5
2019-01-06    6
Freq: D, dtype: int64

df["E"] = s1
df

	A	B	C	D	E
2019-01-01	-0.935378	-0.190742	0.925984	-0.818969	1
2019-01-02	-0.234414	-1.194674	1.080779	-2.294395	2
2019-01-03	-0.141572	0.058118	1.102248	1.207726	3
2019-01-04	0.305088	0.535920	-0.978434	0.177251	4
2019-01-05	0.313383	0.234041	0.163155	-0.296649	5
2019-01-06	0.250613	-0.904400	-0.858240	-1.573342	6

• 修改赋值

df.loc["2019-01-01", "A"] = 0
df

	A	B	C	D	E
2019-01-01	0.000000	-0.190742	0.925984	-0.818969	1
2019-01-02	-0.234414	-1.194674	1.080779	-2.294395	2
2019-01-03	-0.141572	0.058118	1.102248	1.207726	3
2019-01-04	0.305088	0.535920	-0.978434	0.177251	4
2019-01-05	0.313383	0.234041	0.163155	-0.296649	5
2019-01-06	0.250613	-0.904400	-0.858240	-1.573342	6

df.iloc[0, 1] = 0
df

	A	B	C	D	E
2019-01-01	0.000000	0.000000	0.925984	-0.818969	1
2019-01-02	-0.234414	-1.194674	1.080779	-2.294395	2
2019-01-03	-0.141572	0.058118	1.102248	1.207726	3
2019-01-04	0.305088	0.535920	-0.978434	0.177251	4
2019-01-05	0.313383	0.234041	0.163155	-0.296649	5
2019-01-06	0.250613	-0.904400	-0.858240	-1.573342	6

df["D"] = np.array([5]*len(df))   # 可简化成df["D"] = 5
df

	A	B	C	D	E
2019-01-01	0.000000	0.000000	0.925984	5	1
2019-01-02	-0.234414	-1.194674	1.080779	5	2
2019-01-03	-0.141572	0.058118	1.102248	5	3
2019-01-04	0.305088	0.535920	-0.978434	5	4
2019-01-05	0.313383	0.234041	0.163155	5	5
2019-01-06	0.250613	-0.904400	-0.858240	5	6

• 修改index和columns

df.index = [i for i in range(len(df))]
df

	A	B	C	D	E
0	0.000000	0.000000	0.925984	5	1
1	-0.234414	-1.194674	1.080779	5	2
2	-0.141572	0.058118	1.102248	5	3
3	0.305088	0.535920	-0.978434	5	4
4	0.313383	0.234041	0.163155	5	5
5	0.250613	-0.904400	-0.858240	5	6

df.columns = [i for i in range(df.shape[1])]
df

	0	1	2	3	4
0	0.000000	0.000000	0.925984	5	1
1	-0.234414	-1.194674	1.080779	5	2
2	-0.141572	0.058118	1.102248	5	3
3	0.305088	0.535920	-0.978434	5	4
4	0.313383	0.234041	0.163155	5	5
5	0.250613	-0.904400	-0.858240	5	6

12.3 数值运算及统计分析

1、数据的查看

import pandas as pd
import numpy as np

dates = pd.date_range(start='2019-01-01', periods=6)
df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=["A", "B", "C", "D"])
df

	A	B	C	D
2019-01-01	-0.854043	0.412345	-2.296051	-0.048964
2019-01-02	1.371364	-0.121454	-0.299653	1.095375
2019-01-03	-0.714591	-1.103224	0.979250	0.319455
2019-01-04	-1.397557	0.426008	0.233861	-1.651887
2019-01-05	0.434026	0.459830	-0.095444	1.220302
2019-01-06	-0.133876	0.074500	-1.028147	0.605402

（1）查看前面的行

df.head()    # 默认5行

	A	B	C	D
2019-01-01	-0.854043	0.412345	-2.296051	-0.048964
2019-01-02	1.371364	-0.121454	-0.299653	1.095375
2019-01-03	-0.714591	-1.103224	0.979250	0.319455
2019-01-04	-1.397557	0.426008	0.233861	-1.651887
2019-01-05	0.434026	0.459830	-0.095444	1.220302

df.head(2)

	A	B	C	D
2019-01-01	-0.854043	0.412345	-2.296051	-0.048964
2019-01-02	1.371364	-0.121454	-0.299653	1.095375

（2）查看后面的行

df.tail()    # 默认5行

	A	B	C	D
2019-01-02	1.371364	-0.121454	-0.299653	1.095375
2019-01-03	-0.714591	-1.103224	0.979250	0.319455
2019-01-04	-1.397557	0.426008	0.233861	-1.651887
2019-01-05	0.434026	0.459830	-0.095444	1.220302
2019-01-06	-0.133876	0.074500	-1.028147	0.605402

df.tail(3) 

	A	B	C	D
2019-01-04	-1.397557	0.426008	0.233861	-1.651887
2019-01-05	0.434026	0.459830	-0.095444	1.220302
2019-01-06	-0.133876	0.074500	-1.028147	0.605402

（3）查看总体信息

df.iloc[0, 3] = np.nan
df

	A	B	C	D
2019-01-01	-0.854043	0.412345	-2.296051	NaN
2019-01-02	1.371364	-0.121454	-0.299653	1.095375
2019-01-03	-0.714591	-1.103224	0.979250	0.319455
2019-01-04	-1.397557	0.426008	0.233861	-1.651887
2019-01-05	0.434026	0.459830	-0.095444	1.220302
2019-01-06	-0.133876	0.074500	-1.028147	0.605402

df.info()

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 6 entries, 2019-01-01 to 2019-01-06
Freq: D
Data columns (total 4 columns):
A    6 non-null float64
B    6 non-null float64
C    6 non-null float64
D    5 non-null float64
dtypes: float64(4)
memory usage: 240.0 bytes

2、Numpy通用函数同样适用于Pandas

（1）向量化运算

x = pd.DataFrame(np.arange(4).reshape(1, 4))
x

	0	1	2	3
0	0	1	2	3

x+5

	0	1	2	3
0	5	6	7	8

np.exp(x)

	0	1	2	3
0	1.0	2.718282	7.389056	20.085537

y = pd.DataFrame(np.arange(4,8).reshape(1, 4))
y

	0	1	2	3
0	4	5	6	7

x*y

	0	1	2	3
0	0	5	12	21

（2）矩阵化运算

np.random.seed(42)
x = pd.DataFrame(np.random.randint(10, size=(30, 30)))
x

	0	1	2	3	4	5	6	7	8	9	...	20	21	22	23	24	25	26	27	28	29
0	6	3	7	4	6	9	2	6	7	4	...	4	0	9	5	8	0	9	2	6	3
1	8	2	4	2	6	4	8	6	1	3	...	2	0	3	1	7	3	1	5	5	9
2	3	5	1	9	1	9	3	7	6	8	...	6	8	7	0	7	7	2	0	7	2
3	2	0	4	9	6	9	8	6	8	7	...	0	2	4	2	0	4	9	6	6	8
4	9	9	2	6	0	3	3	4	6	6	...	9	6	8	6	0	0	8	8	3	8
5	2	6	5	7	8	4	0	2	9	7	...	2	0	4	0	7	0	0	1	1	5
6	6	4	0	0	2	1	4	9	5	6	...	5	0	8	5	2	3	3	2	9	2
7	2	3	6	3	8	0	7	6	1	7	...	3	0	1	0	4	4	6	8	8	2
8	2	2	3	7	5	7	0	7	3	0	...	1	1	5	2	8	3	0	3	0	4
9	3	7	7	6	2	0	0	2	5	6	...	4	2	3	2	0	0	4	5	2	8
10	4	7	0	4	2	0	3	4	6	0	...	5	6	1	9	1	9	0	7	0	8
11	5	6	9	6	9	2	1	8	7	9	...	6	5	2	8	9	5	9	9	5	0
12	3	9	5	5	4	0	7	4	4	6	...	0	7	2	9	6	9	4	9	4	6
13	8	4	0	9	9	0	1	5	8	7	...	5	8	4	0	3	4	9	9	4	6
14	3	0	4	6	9	9	5	4	3	1	...	6	1	0	3	7	1	2	0	0	2
15	4	2	0	0	7	9	1	2	1	2	...	6	3	9	4	1	7	3	8	4	8
16	3	9	4	8	7	2	0	2	3	1	...	8	0	0	3	8	5	2	0	3	8
17	2	8	6	3	2	9	4	4	2	8	...	6	9	4	2	6	1	8	9	9	0
18	5	6	7	9	8	1	9	1	4	4	...	3	5	2	5	6	9	9	2	6	2
19	1	9	3	7	8	6	0	2	8	0	...	4	3	2	2	3	8	1	8	0	0
20	4	5	5	2	6	8	9	7	5	7	...	3	5	0	8	0	4	3	2	5	1
21	2	4	8	1	9	7	1	4	6	7	...	0	1	8	2	0	4	6	5	0	4
22	4	5	2	4	6	4	4	4	9	9	...	1	7	6	9	9	1	5	5	2	1
23	0	5	4	8	0	6	4	4	1	2	...	8	5	0	7	6	9	2	0	4	3
24	9	7	0	9	0	3	7	4	1	5	...	3	7	8	2	2	1	9	2	2	4
25	4	1	9	5	4	5	0	4	8	9	...	9	3	0	7	0	2	3	7	5	9
26	6	7	1	9	7	2	6	2	6	1	...	0	6	5	9	8	0	3	8	3	9
27	2	8	1	3	5	1	7	7	0	2	...	8	0	4	5	4	5	5	6	3	7
28	6	8	6	2	2	7	4	3	7	5	...	1	7	9	2	4	5	9	5	3	2
29	3	0	3	0	0	9	5	4	3	2	...	1	3	0	4	8	0	8	7	5	6

30 rows × 30 columns

• 转置

z = x.T
z

	0	1	2	3	4	5	6	7	8	9	...	20	21	22	23	24	25	26	27	28	29
0	6	8	3	2	9	2	6	2	2	3	...	4	2	4	0	9	4	6	2	6	3
1	3	2	5	0	9	6	4	3	2	7	...	5	4	5	5	7	1	7	8	8	0
2	7	4	1	4	2	5	0	6	3	7	...	5	8	2	4	0	9	1	1	6	3
3	4	2	9	9	6	7	0	3	7	6	...	2	1	4	8	9	5	9	3	2	0
4	6	6	1	6	0	8	2	8	5	2	...	6	9	6	0	0	4	7	5	2	0
5	9	4	9	9	3	4	1	0	7	0	...	8	7	4	6	3	5	2	1	7	9
6	2	8	3	8	3	0	4	7	0	0	...	9	1	4	4	7	0	6	7	4	5
7	6	6	7	6	4	2	9	6	7	2	...	7	4	4	4	4	4	2	7	3	4
8	7	1	6	8	6	9	5	1	3	5	...	5	6	9	1	1	8	6	0	7	3
9	4	3	8	7	6	7	6	7	0	6	...	7	7	9	2	5	9	1	2	5	2
10	3	8	7	1	3	5	3	0	7	5	...	4	0	2	6	4	1	9	9	1	0
11	7	1	4	0	6	7	6	8	3	5	...	7	5	0	5	1	0	5	8	3	5
12	7	9	1	6	2	8	7	8	5	5	...	9	0	4	1	2	9	2	4	3	1
13	2	8	4	6	5	3	0	1	7	2	...	3	1	8	5	8	8	2	5	5	7
14	5	9	7	7	1	0	5	6	3	5	...	9	0	0	1	6	9	8	3	5	9
15	4	4	9	4	9	0	7	9	2	7	...	7	4	2	1	6	8	6	9	0	4
16	1	1	8	2	8	9	4	2	8	1	...	9	9	3	1	5	8	4	1	7	6
17	7	3	8	7	4	3	3	6	2	4	...	1	8	0	2	7	5	9	7	5	9
18	5	6	0	5	5	6	1	9	8	0	...	4	5	0	1	3	7	6	5	2	1
19	1	7	8	2	3	1	5	8	1	0	...	8	0	7	3	7	0	8	4	8	7
20	4	2	6	0	9	2	5	3	1	4	...	3	0	1	8	3	9	0	8	1	1
21	0	0	8	2	6	0	0	0	1	2	...	5	1	7	5	7	3	6	0	7	3
22	9	3	7	4	8	4	8	1	5	3	...	0	8	6	0	8	0	5	4	9	0
23	5	1	0	2	6	0	5	0	2	2	...	8	2	9	7	2	7	9	5	2	4
24	8	7	7	0	0	7	2	4	8	0	...	0	0	9	6	2	0	8	4	4	8
25	0	3	7	4	0	0	3	4	3	0	...	4	4	1	9	1	2	0	5	5	0
26	9	1	2	9	8	0	3	6	0	4	...	3	6	5	2	9	3	3	5	9	8
27	2	5	0	6	8	1	2	8	3	5	...	2	5	5	0	2	7	8	6	5	7
28	6	5	7	6	3	1	9	8	0	2	...	5	0	2	4	2	5	3	3	3	5
29	3	9	2	8	8	5	2	2	4	8	...	1	4	1	3	4	9	9	7	2	6

30 rows × 30 columns

np.random.seed(1)
y = pd.DataFrame(np.random.randint(10, size=(30, 30)))
y

	0	1	2	3	4	5	6	7	8	9	...	20	21	22	23	24	25	26	27	28	29
0	5	8	9	5	0	0	1	7	6	9	...	1	7	0	6	9	9	7	6	9	1
1	0	1	8	8	3	9	8	7	3	6	...	9	2	0	4	9	2	7	7	9	8
2	6	9	3	7	7	4	5	9	3	6	...	7	7	1	1	3	0	8	6	4	5
3	6	2	5	7	8	4	4	7	7	4	...	0	1	9	8	2	3	1	2	7	2
4	6	0	9	2	6	6	2	7	7	0	...	1	5	4	0	7	8	9	5	7	0
5	9	3	9	1	4	4	6	8	8	9	...	1	8	7	0	3	4	2	0	3	5
6	1	2	4	3	0	6	0	7	2	8	...	4	3	3	6	7	3	5	3	2	4
7	4	0	3	3	8	3	5	6	7	5	...	1	7	3	1	6	6	9	6	9	6
8	0	0	2	9	6	0	6	7	0	3	...	6	7	9	5	4	9	5	2	5	6
9	6	8	7	7	7	2	6	0	5	2	...	7	0	6	2	4	3	6	7	6	3
10	0	6	4	7	6	2	9	5	9	9	...	4	9	3	9	1	2	5	4	0	8
11	2	3	9	9	4	4	8	2	1	6	...	0	5	9	8	6	6	0	4	7	3
12	0	1	6	0	6	1	6	4	2	5	...	8	8	0	7	2	0	7	1	1	9
13	5	1	5	9	6	4	9	8	7	5	...	2	4	3	2	0	0	4	2	5	0
14	0	3	8	5	3	1	4	7	3	2	...	8	5	5	7	5	9	1	3	9	3
15	3	3	6	1	3	0	5	0	5	2	...	7	1	7	7	3	8	3	0	6	3
16	0	6	5	9	6	4	6	6	2	2	...	3	6	8	6	5	1	3	2	6	3
17	6	7	2	8	0	1	8	6	0	0	...	5	6	2	5	4	3	0	6	2	1
18	9	4	4	0	9	8	7	7	6	1	...	7	9	9	7	1	1	4	6	5	6
19	4	1	1	5	1	2	6	2	3	3	...	0	0	0	9	8	5	9	3	4	0
20	9	8	6	3	9	9	0	8	1	6	...	2	9	0	1	3	9	4	8	8	8
21	2	8	6	4	9	0	5	5	6	1	...	6	7	5	6	8	7	4	2	4	0
22	0	3	5	9	0	3	6	5	1	1	...	6	2	5	3	9	3	9	5	1	9
23	7	7	0	8	6	1	2	0	4	4	...	1	9	6	0	2	8	3	7	2	5
24	6	0	4	2	3	1	0	5	7	0	...	1	1	2	7	5	2	9	4	7	3
25	5	0	2	1	4	9	4	6	9	3	...	5	5	3	5	9	2	7	4	1	6
26	9	8	1	8	1	6	2	6	1	8	...	2	5	1	2	5	3	3	6	1	8
27	1	8	6	4	6	9	5	4	7	2	...	9	3	1	5	1	1	7	1	2	6
28	0	7	7	4	3	2	7	8	5	2	...	0	2	8	3	7	3	9	2	3	8
29	8	0	2	6	8	3	6	4	9	7	...	6	7	8	5	7	2	5	3	4	5

30 rows × 30 columns

x.dot(y)

	0	1	2	3	4	5	6	7	8	9	...	20	21	22	23	24	25	26	27	28	29
0	616	560	723	739	612	457	681	799	575	590	...	523	739	613	580	668	602	733	585	657	700
1	520	438	691	600	612	455	666	764	707	592	...	555	681	503	679	641	506	779	494	633	590
2	557	570	786	807	690	469	804	828	704	573	...	563	675	712	758	793	672	754	550	756	638
3	605	507	664	701	660	496	698	806	651	575	...	582	685	668	586	629	534	678	484	591	626
4	599	681	753	873	721	563	754	770	620	654	...	633	747	661	677	726	649	716	610	735	706
5	422	354	602	627	613	396	617	627	489	423	...	456	572	559	537	499	384	589	436	574	507
6	359	446	599	599	481	357	577	572	451	464	...	449	550	495	532	633	554	663	476	565	602
7	531	520	698	590	607	537	665	696	571	472	...	576	588	551	665	652	527	742	528	650	599
8	449	322	547	533	593	399	584	638	587	424	...	402	596	523	523	447	362	561	386	529	484
9	373	433	525	601	522	345	551	521	434	447	...	508	498	438	478	459	418	488	407	503	496
10	500	427	574	607	667	477	652	656	615	477	...	622	702	531	610	558	532	598	471	582	561
11	664	694	772	841	779	574	730	810	711	608	...	591	760	616	638	721	676	846	678	754	708
12	545	547	687	701	721	576	689	724	710	532	...	674	684	648	694	710	564	757	571	671	656
13	574	586	723	750	691	494	696	787	667	523	...	618	681	568	682	715	644	756	557	690	604
14	502	382	645	557	570	403	538	677	500	501	...	369	650	507	576	546	531	554	437	616	463
15	510	505	736	651	649	510	719	733	694	557	...	605	717	574	642	678	576	755	455	598	654
16	567	376	614	612	643	514	598	724	547	464	...	456	639	520	560	569	442	596	517	659	532
17	626	716	828	765	740	603	809	852	692	591	...	664	716	655	721	742	612	819	593	744	712
18	600	559	667	664	641	556	624	815	638	564	...	581	701	559	677	710	554	748	597	614	657
19	445	431	661	681	641	552	690	719	602	474	...	515	637	576	620	572	512	599	455	622	538
20	523	569	784	725	713	501	740	772	638	640	...	589	775	664	686	726	672	747	548	723	645
21	487	465	553	639	517	449	592	609	454	398	...	492	567	534	404	554	417	561	466	498	492
22	479	449	574	686	583	377	566	614	563	455	...	453	539	491	501	596	520	722	478	565	501
23	483	386	476	526	550	426	492	585	536	482	...	322	541	438	456	487	408	502	426	474	481
24	523	551	658	767	537	444	663	731	576	577	...	522	590	525	664	691	548	635	526	641	538
25	652	656	738	753	853	508	752	815	669	576	...	694	833	693	606	575	616	704	559	728	672
26	578	577	744	856	699	497	779	800	733	587	...	630	754	704	834	760	680	765	592	731	629
27	554	494	665	689	630	574	695	703	636	599	...	554	685	532	658	649	554	693	577	634	668
28	498	552	659	784	552	492	690	775	544	551	...	567	636	518	599	742	521	733	533	605	604
29	513	491	563	642	477	367	589	647	516	484	...	428	574	504	548	553	483	540	407	547	455

30 rows × 30 columns

%timeit x.dot(y)

218 µs ± 18.7 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

%timeit np.dot(x, y)

81.1 µs ± 2.85 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)

• 执行相同运算，Numpy与Pandas的对比

x1 = np.array(x)
x1

y1 = np.array(y)
y1

%timeit x1.dot(y1)

22.1 µs ± 992 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)

%timeit np.dot(x1, y1)

22.6 µs ± 766 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)

%timeit np.dot(x.values, y.values)

42.9 µs ± 1.24 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)

x2 = list(x1)
y2 = list(y1)
x3 = []
y3 = []
for i in x2:
    res = []
    for j in i:
        res.append(int(j))
    x3.append(res)
for i in y2:
    res = []
    for j in i:
        res.append(int(j))
    y3.append(res)

def f(x, y):
    res = []
    for i in range(len(x)):
        row = []
        for j in range(len(y[0])):
            sum_row = 0
            for k in range(len(x[0])):
                sum_row += x[i][k]*y[k][j]
            row.append(sum_row)
        res.append(row)
    return res          

%timeit f(x3, y3)

4.29 ms ± 207 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

一般来说，纯粹的计算在Numpy里执行的更快

Numpy更侧重于计算，Pandas更侧重于数据处理

（3）广播运算

np.random.seed(42)
x = pd.DataFrame(np.random.randint(10, size=(3, 3)), columns=list("ABC"))
x

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

• 按行广播

x.iloc[0]

A    6
B    3
C    7
Name: 0, dtype: int32

x/x.iloc[0]

	A	B	C
0	1.000000	1.0	1.000000
1	0.666667	2.0	1.285714
2	0.333333	2.0	1.000000

• 按列广播

x.A

0    6
1    4
2    2
Name: A, dtype: int32

x.div(x.A, axis=0)             # add sub div mul

	A	B	C
0	1.0	0.5	1.166667
1	1.0	1.5	2.250000
2	1.0	3.0	3.500000

x.div(x.iloc[0], axis=1)

	A	B	C
0	1.000000	1.0	1.000000
1	0.666667	2.0	1.285714
2	0.333333	2.0	1.000000

3、新的用法

（1）索引对齐

A = pd.DataFrame(np.random.randint(0, 20, size=(2, 2)), columns=list("AB"))
A

	A	B
0	3	7
1	2	1

B = pd.DataFrame(np.random.randint(0, 10, size=(3, 3)), columns=list("ABC"))
B

	A	B	C
0	7	5	1
1	4	0	9
2	5	8	0

• pandas会自动对齐两个对象的索引，没有的值用np.nan表示

A+B

	A	B	C
0	10.0	12.0	NaN
1	6.0	1.0	NaN
2	NaN	NaN	NaN

• 缺省值也可用fill_value来填充

A.add(B, fill_value=0)

	A	B	C
0	10.0	12.0	1.0
1	6.0	1.0	9.0
2	5.0	8.0	0.0

A*B

	A	B	C
0	21.0	35.0	NaN
1	8.0	0.0	NaN
2	NaN	NaN	NaN

（2）统计相关

• 数据种类统计

y = np.random.randint(3, size=20)
y

array([2, 2, 2, 1, 2, 1, 1, 2, 1, 2, 2, 0, 2, 0, 2, 2, 0, 0, 2, 1])

np.unique(y)

array([0, 1, 2])

from collections import Counter
Counter(y)

Counter({2: 11, 1: 5, 0: 4})

y1 = pd.DataFrame(y, columns=["A"])
y1

	A
0	2
1	2
2	2
3	1
4	2
5	1
6	1
7	2
8	1
9	2
10	2
11	0
12	2
13	0
14	2
15	2
16	0
17	0
18	2
19	1

np.unique(y1)

array([0, 1, 2])

y1["A"].value_counts()

2    11
1     5
0     4
Name: A, dtype: int64

• 产生新的结果，并进行排序

population_dict = {"BeiJing": 2154,
                   "ShangHai": 2424,
                   "ShenZhen": 1303,
                   "HangZhou": 981 }
population = pd.Series(population_dict) 

GDP_dict = {"BeiJing": 30320,
            "ShangHai": 32680,
            "ShenZhen": 24222,
            "HangZhou": 13468 }
GDP = pd.Series(GDP_dict)

city_info = pd.DataFrame({"population": population,"GDP": GDP})
city_info

	population	GDP
BeiJing	2154	30320
ShangHai	2424	32680
ShenZhen	1303	24222
HangZhou	981	13468

city_info["per_GDP"] = city_info["GDP"]/city_info["population"]
city_info

	population	GDP	per_GDP
BeiJing	2154	30320	14.076137
ShangHai	2424	32680	13.481848
ShenZhen	1303	24222	18.589409
HangZhou	981	13468	13.728848

递增排序

city_info.sort_values(by="per_GDP")

	population	GDP	per_GDP
ShangHai	2424	32680	13.481848
HangZhou	981	13468	13.728848
BeiJing	2154	30320	14.076137
ShenZhen	1303	24222	18.589409

递减排序

city_info.sort_values(by="per_GDP", ascending=False)

	population	GDP	per_GDP
ShenZhen	1303	24222	18.589409
BeiJing	2154	30320	14.076137
HangZhou	981	13468	13.728848
ShangHai	2424	32680	13.481848

按轴进行排序

data = pd.DataFrame(np.random.randint(20, size=(3, 4)), index=[2, 1, 0], columns=list("CBAD"))
data

	C	B	A	D
2	3	13	17	8
1	1	19	14	6
0	11	7	14	2

行排序

data.sort_index()

	C	B	A	D
0	11	7	14	2
1	1	19	14	6
2	3	13	17	8

列排序

data.sort_index(axis=1)

	A	B	C	D
2	17	13	3	8
1	14	19	1	6
0	14	7	11	2

data.sort_index(axis=1, ascending=False)

	D	C	B	A
2	8	3	13	17
1	6	1	19	14
0	2	11	7	14

• 统计方法

df = pd.DataFrame(np.random.normal(2, 4, size=(6, 4)),columns=list("ABCD"))
df

	A	B	C	D
0	1.082198	3.557396	-3.060476	6.367969
1	13.113252	6.774559	2.874553	5.527044
2	-2.036341	-4.333177	5.094802	-0.152567
3	-3.386712	-1.522365	-2.522209	2.537716
4	4.328491	5.550994	5.577329	5.019991
5	1.171336	-0.493910	-4.032613	6.398588

非空个数

df.count()

A    6
B    6
C    6
D    6
dtype: int64

求和

df.sum()

A    14.272224
B     9.533497
C     3.931385
D    25.698741
dtype: float64

df.sum(axis=1)

0     7.947086
1    28.289408
2    -1.427283
3    -4.893571
4    20.476806
5     3.043402
dtype: float64

最大值 最小值

df.min()

A   -3.386712
B   -4.333177
C   -4.032613
D   -0.152567
dtype: float64

df.max(axis=1)

0     6.367969
1    13.113252
2     5.094802
3     2.537716
4     5.577329
5     6.398588
dtype: float64

df

	A	B	C	D
0	1.082198	3.557396	-3.060476	6.367969
1	13.113252	6.774559	2.874553	5.527044
2	-2.036341	-4.333177	5.094802	-0.152567
3	-3.386712	-1.522365	-2.522209	2.537716
4	4.328491	5.550994	5.577329	5.019991
5	1.171336	-0.493910	-4.032613	6.398588

df.idxmax()

A    1
B    1
C    4
D    5
dtype: int64

均值

df.mean()

A    2.378704
B    1.588916
C    0.655231
D    4.283124
dtype: float64

方差

df.var()

A    34.980702
B    19.110656
C    18.948144
D     6.726776
dtype: float64

标准差

df.std()

A    5.914449
B    4.371574
C    4.352947
D    2.593603
dtype: float64

中位数

df.median()

A    1.126767
B    1.531743
C    0.176172
D    5.273518
dtype: float64

众数

data = pd.DataFrame(np.random.randint(5, size=(10, 2)), columns=list("AB"))
data

	A	B
0	4	2
1	3	2
2	2	0
3	2	4
4	2	0
5	4	1
6	2	0
7	1	1
8	3	4
9	2	0

data.mode()

	A	B
0	2	0

75%分位数

df.quantile(0.75)

A    3.539202
B    5.052594
C    4.539740
D    6.157738
Name: 0.75, dtype: float64

一网打尽

df.describe()

	A	B	C	D
count	6.000000	6.000000	6.000000	6.000000
mean	2.378704	1.588916	0.655231	4.283124
std	5.914449	4.371574	4.352947	2.593603
min	-3.386712	-4.333177	-4.032613	-0.152567
25%	-1.256706	-1.265251	-2.925910	3.158284
50%	1.126767	1.531743	0.176172	5.273518
75%	3.539202	5.052594	4.539740	6.157738
max	13.113252	6.774559	5.577329	6.398588

data_2 = pd.DataFrame([["a", "a", "c", "d"],
                       ["c", "a", "c", "b"],
                       ["a", "a", "d", "c"]], columns=list("ABCD"))
data_2

	A	B	C	D
0	a	a	c	d
1	c	a	c	b
2	a	a	d	c

data_2.describe()

	A	B	C	D
count	3	3	3	3
unique	2	1	2	3
top	a	a	c	d
freq	2	3	2	1

相关性系数和协方差

df.corr()

	A	B	C	D
A	1.000000	0.831063	0.331060	0.510821
B	0.831063	1.000000	0.179244	0.719112
C	0.331060	0.179244	1.000000	-0.450365
D	0.510821	0.719112	-0.450365	1.000000

df.corrwith(df["A"])

A    1.000000
B    0.831063
C    0.331060
D    0.510821
dtype: float64

自定义输出

apply（method）的用法：使用method方法默认对每一列进行相应的操作

df

	A	B	C	D
0	1.082198	3.557396	-3.060476	6.367969
1	13.113252	6.774559	2.874553	5.527044
2	-2.036341	-4.333177	5.094802	-0.152567
3	-3.386712	-1.522365	-2.522209	2.537716
4	4.328491	5.550994	5.577329	5.019991
5	1.171336	-0.493910	-4.032613	6.398588

df.apply(np.cumsum)

	A	B	C	D
0	1.082198	3.557396	-3.060476	6.367969
1	14.195450	10.331955	-0.185923	11.895013
2	12.159109	5.998778	4.908878	11.742447
3	8.772397	4.476413	2.386669	14.280162
4	13.100888	10.027406	7.963999	19.300153
5	14.272224	9.533497	3.931385	25.698741

df.apply(np.cumsum, axis=1)

	A	B	C	D
0	1.082198	4.639594	1.579117	7.947086
1	13.113252	19.887811	22.762364	28.289408
2	-2.036341	-6.369518	-1.274717	-1.427283
3	-3.386712	-4.909077	-7.431287	-4.893571
4	4.328491	9.879485	15.456814	20.476806
5	1.171336	0.677427	-3.355186	3.043402

df.apply(sum)

A    14.272224
B     9.533497
C     3.931385
D    25.698741
dtype: float64

df.sum()

A    14.272224
B     9.533497
C     3.931385
D    25.698741
dtype: float64

df.apply(lambda x: x.max()-x.min())

A    16.499965
B    11.107736
C     9.609942
D     6.551155
dtype: float64

def my_describe(x):
    return pd.Series([x.count(), x.mean(), x.max(), x.idxmin(), x.std()], \
                     index=["Count", "mean", "max", "idxmin", "std"])
df.apply(my_describe)

	A	B	C	D
Count	6.000000	6.000000	6.000000	6.000000
mean	2.378704	1.588916	0.655231	4.283124
max	13.113252	6.774559	5.577329	6.398588
idxmin	3.000000	2.000000	5.000000	2.000000
std	5.914449	4.371574	4.352947	2.593603

12.4 缺失值处理

1、发现缺失值

import pandas as pd
import numpy as np

data = pd.DataFrame(np.array([[1, np.nan, 2],
                              [np.nan, 3, 4],
                              [5, 6, None]]), columns=["A", "B", "C"])
data

	A	B	C
0	1	NaN	2
1	NaN	3	4
2	5	6	None

注意：有None、字符串等，数据类型全部变为object，它比int和float更消耗资源

data.dtypes

A    object
B    object
C    object
dtype: object

data.isnull()

	A	B	C
0	False	True	False
1	True	False	False
2	False	False	True

data.notnull()

	A	B	C
0	True	False	True
1	False	True	True
2	True	True	False

2、删除缺失值

data = pd.DataFrame(np.array([[1, np.nan, 2, 3],
                              [np.nan, 4, 5, 6],
                              [7, 8, np.nan, 9],
                              [10, 11 , 12, 13]]), columns=["A", "B", "C", "D"])
data

	A	B	C	D
0	1.0	NaN	2.0	3.0
1	NaN	4.0	5.0	6.0
2	7.0	8.0	NaN	9.0
3	10.0	11.0	12.0	13.0

注意：np.nan是一种特殊的浮点数

data.dtypes

A    float64
B    float64
C    float64
D    float64
dtype: object

（1）删除整行

data.dropna()

	A	B	C	D
3	10.0	11.0	12.0	13.0

（2）删除整列

data.dropna(axis="columns")

	D
0	3.0
1	6.0
2	9.0
3	13.0

data["D"] = np.nan
data

	A	B	C	D
0	1.0	NaN	2.0	NaN
1	NaN	4.0	5.0	NaN
2	7.0	8.0	NaN	NaN
3	10.0	11.0	12.0	NaN

data.dropna(axis="columns", how="all")

	A	B	C
0	1.0	NaN	2.0
1	NaN	4.0	5.0
2	7.0	8.0	NaN
3	10.0	11.0	12.0

data.dropna(axis="columns", how="any")

0
1
2
3

data.loc[3] = np.nan
data

	A	B	C	D
0	1.0	NaN	2.0	NaN
1	NaN	4.0	5.0	NaN
2	7.0	8.0	NaN	NaN
3	NaN	NaN	NaN	NaN

data.dropna(how="all")

	A	B	C	D
0	1.0	NaN	2.0	NaN
1	NaN	4.0	5.0	NaN
2	7.0	8.0	NaN	NaN

3、填充缺失值

data = pd.DataFrame(np.array([[1, np.nan, 2, 3],
                              [np.nan, 4, 5, 6],
                              [7, 8, np.nan, 9],
                              [10, 11 , 12, 13]]), columns=["A", "B", "C", "D"])
data

	A	B	C	D
0	1.0	NaN	2.0	3.0
1	NaN	4.0	5.0	6.0
2	7.0	8.0	NaN	9.0
3	10.0	11.0	12.0	13.0

data.fillna(value=5)

	A	B	C	D
0	1.0	5.0	2.0	3.0
1	5.0	4.0	5.0	6.0
2	7.0	8.0	5.0	9.0
3	10.0	11.0	12.0	13.0

• 用均值进行替换

fill = data.mean()
fill

A    6.000000
B    7.666667
C    6.333333
D    7.750000
dtype: float64

data.fillna(value=fill)

	A	B	C	D
0	1.0	7.666667	2.000000	3.0
1	6.0	4.000000	5.000000	6.0
2	7.0	8.000000	6.333333	9.0
3	10.0	11.000000	12.000000	13.0

fill = data.stack().mean()
fill

7.0

data.fillna(value=fill)

	A	B	C	D
0	1.0	7.0	2.0	3.0
1	7.0	4.0	5.0	6.0
2	7.0	8.0	7.0	9.0
3	10.0	11.0	12.0	13.0

12.5 合并数据

• 构造一个生产DataFrame的函数

import pandas as pd
import numpy as np

def make_df(cols, ind):
    "一个简单的DataFrame"
    data = {c: [str(c)+str(i) for i in ind]  for c in cols}
    return pd.DataFrame(data, ind)

make_df("ABC", range(3))

	A	B	C
0	A0	B0	C0
1	A1	B1	C1
2	A2	B2	C2

• 垂直合并

df_1 = make_df("AB", [1, 2])
df_2 = make_df("AB", [3, 4])
print(df_1)
print(df_2)

    A   B
1  A1  B1
2  A2  B2
    A   B
3  A3  B3
4  A4  B4

pd.concat([df_1, df_2])

	A	B
1	A1	B1
2	A2	B2
3	A3	B3
4	A4	B4

• 水平合并

df_3 = make_df("AB", [0, 1])
df_4 = make_df("CD", [0, 1])
print(df_3)
print(df_4)

    A   B
0  A0  B0
1  A1  B1
    C   D
0  C0  D0
1  C1  D1

pd.concat([df_3, df_4], axis=1)

	A	B	C	D
0	A0	B0	C0	D0
1	A1	B1	C1	D1

• 索引重叠

行重叠

df_5 = make_df("AB", [1, 2])
df_6 = make_df("AB", [1, 2])
print(df_5)
print(df_6)

    A   B
1  A1  B1
2  A2  B2
    A   B
1  A1  B1
2  A2  B2

pd.concat([df_5, df_6])

	A	B
1	A1	B1
2	A2	B2
1	A1	B1
2	A2	B2

pd.concat([df_5, df_6],ignore_index=True)

	A	B
0	A1	B1
1	A2	B2
2	A1	B1
3	A2	B2

列重叠

df_7 = make_df("ABC", [1, 2])
df_8 = make_df("BCD", [1, 2])
print(df_7)
print(df_8)

    A   B   C
1  A1  B1  C1
2  A2  B2  C2
    B   C   D
1  B1  C1  D1
2  B2  C2  D2

pd.concat([df_7, df_8], axis=1)

	A	B	C	B	C	D
1	A1	B1	C1	B1	C1	D1
2	A2	B2	C2	B2	C2	D2

pd.concat([df_7, df_8],axis=1, ignore_index=True)

	0	1	2	3	4	5
1	A1	B1	C1	B1	C1	D1
2	A2	B2	C2	B2	C2	D2

• 对齐合并merge()

df_9 = make_df("AB", [1, 2])
df_10 = make_df("BC", [1, 2])
print(df_9)
print(df_10)

    A   B
1  A1  B1
2  A2  B2
    B   C
1  B1  C1
2  B2  C2

pd.merge(df_9, df_10)

	A	B	C
0	A1	B1	C1
1	A2	B2	C2

df_9 = make_df("AB", [1, 2])
df_10 = make_df("CB", [2, 1])
print(df_9)
print(df_10)

    A   B
1  A1  B1
2  A2  B2
    C   B
2  C2  B2
1  C1  B1

pd.merge(df_9, df_10)

	A	B	C
0	A1	B1	C1
1	A2	B2	C2

【例】 合并城市信息

population_dict = {"city": ("BeiJing", "HangZhou", "ShenZhen"),
                   "pop": (2154, 981, 1303)}
population = pd.DataFrame(population_dict)
population

	city	pop
0	BeiJing	2154
1	HangZhou	981
2	ShenZhen	1303

GDP_dict = {"city": ("BeiJing", "ShangHai", "HangZhou"),
            "GDP": (30320, 32680, 13468)}
GDP = pd.DataFrame(GDP_dict)
GDP

	city	GDP
0	BeiJing	30320
1	ShangHai	32680
2	HangZhou	13468

city_info = pd.merge(population, GDP)
city_info

	city	pop	GDP
0	BeiJing	2154	30320
1	HangZhou	981	13468

city_info = pd.merge(population, GDP, how="outer")
city_info

	city	pop	GDP
0	BeiJing	2154.0	30320.0
1	HangZhou	981.0	13468.0
2	ShenZhen	1303.0	NaN
3	ShangHai	NaN	32680.0

12.6 分组和数据透视表

df = pd.DataFrame({"key":["A", "B", "C", "C", "B", "A"],
                  "data1": range(6),
                  "data2": np.random.randint(0, 10, size=6)})
df

	key	data1	data2
0	A	0	1
1	B	1	4
2	C	2	9
3	C	3	9
4	B	4	1
5	A	5	9

（1）分组

• 延迟计算

df.groupby("key")

<pandas.core.groupby.generic.DataFrameGroupBy object at 0x000002276795A240>

df.groupby("key").sum()

	data1	data2
key
A	5	10
B	5	6
C	5	11

df.groupby("key").mean()

	data1	data2
key
A	2.5	5.0
B	2.5	3.0
C	2.5	5.5

for i in df.groupby("key"):
    print(str(i))

('A',   key  data1  data2
0   A      0      2
5   A      5      8)
('B',   key  data1  data2
1   B      1      2
4   B      4      4)
('C',   key  data1  data2
2   C      2      8
3   C      3      3)

• 按列取值

df.groupby("key")["data2"].sum()

key
A    10
B     6
C    11
Name: data2, dtype: int32

• 按组迭代

for data, group in df.groupby("key"):
    print("{0:5} shape={1}".format(data, group.shape))

A     shape=(2, 3)
B     shape=(2, 3)
C     shape=(2, 3)

• 调用方法

df.groupby("key")["data1"].describe()

	count	mean	std	min	25%	50%	75%	max
key
A	2.0	2.5	3.535534	0.0	1.25	2.5	3.75	5.0
B	2.0	2.5	2.121320	1.0	1.75	2.5	3.25	4.0
C	2.0	2.5	0.707107	2.0	2.25	2.5	2.75	3.0

• 支持更复杂的操作

df.groupby("key").aggregate(["min", "median", "max"])

	data1	data2
	min	median	max	min	median	max
key
A	0	2.5	5	2	5.0	8
B	1	2.5	4	2	3.0	4
C	2	2.5	3	3	5.5	8

• 过滤

def filter_func(x):
    return x["data2"].std() > 3
df.groupby("key")["data2"].std()

key
A    4.242641
B    1.414214
C    3.535534
Name: data2, dtype: float64

df.groupby("key").filter(filter_func)

	key	data1	data2
0	A	0	2
2	C	2	8
3	C	3	3
5	A	5	8

• 转换

df

	key	data1	data2
0	A	0	2
1	B	1	2
2	C	2	8
3	C	3	3
4	B	4	4
5	A	5	8

df.groupby("key").transform(lambda x: x-x.mean())

	data1	data2
0	-2.5	-3.0
1	-1.5	-1.0
2	-0.5	2.5
3	0.5	-2.5
4	1.5	1.0
5	2.5	3.0

df

	key	data1	data2
0	A	0	1
1	B	1	4
2	C	2	9
3	C	3	9
4	B	4	1
5	A	5	9

df.groupby("key").apply(lambda x: x-x.mean())

	data1	data2
0	-2.5	-4.0
1	-1.5	1.5
2	-0.5	0.0
3	0.5	0.0
4	1.5	-1.5
5	2.5	4.0

• apply（）方法

df

	key	data1	data2
0	A	0	2
1	B	1	2
2	C	2	8
3	C	3	3
4	B	4	4
5	A	5	8

def norm_by_data2(x):
    x["data1"] /= x["data2"].sum()
    return x

df.groupby("key").apply(norm_by_data2)

	key	data1	data2
0	A	0.000000	2
1	B	0.166667	2
2	C	0.181818	8
3	C	0.272727	3
4	B	0.666667	4
5	A	0.500000	8

• 将列表、数组设为分组键

L = [0, 1, 0, 1, 2, 0]
df

	key	data1	data2
0	A	0	2
1	B	1	2
2	C	2	8
3	C	3	3
4	B	4	4
5	A	5	8

df.groupby(L).sum()

	data1	data2
0	7	18
1	4	5
2	4	4

• 用字典将索引映射到分组

df2 = df.set_index("key")
df2

	data1	data2
key
A	0	2
B	1	2
C	2	8
C	3	3
B	4	4
A	5	8

mapping = {"A": "first", "B": "constant", "C": "constant"}
df2.groupby(mapping).sum()

	data1	data2
constant	10	17
first	5	10

• 任意Python函数

df2.groupby(str.lower).mean()

	data1	data2
a	2.5	5.0
b	2.5	3.0
c	2.5	5.5

• 多个有效值组成的列表

df2.groupby([str.lower, mapping]).mean()

		data1	data2
a	first	2.5	5.0
b	constant	2.5	3.0
c	constant	2.5	5.5

【例1】 行星观测数据处理

import seaborn as sns

planets = sns.load_dataset("planets")

planets.shape

(1035, 6)

planets.head()

	method	number	orbital_period	mass	distance	year
0	Radial Velocity	1	269.300	7.10	77.40	2006
1	Radial Velocity	1	874.774	2.21	56.95	2008
2	Radial Velocity	1	763.000	2.60	19.84	2011
3	Radial Velocity	1	326.030	19.40	110.62	2007
4	Radial Velocity	1	516.220	10.50	119.47	2009

planets.describe()

	number	orbital_period	mass	distance	year
count	1035.000000	992.000000	513.000000	808.000000	1035.000000
mean	1.785507	2002.917596	2.638161	264.069282	2009.070531
std	1.240976	26014.728304	3.818617	733.116493	3.972567
min	1.000000	0.090706	0.003600	1.350000	1989.000000
25%	1.000000	5.442540	0.229000	32.560000	2007.000000
50%	1.000000	39.979500	1.260000	55.250000	2010.000000
75%	2.000000	526.005000	3.040000	178.500000	2012.000000
max	7.000000	730000.000000	25.000000	8500.000000	2014.000000

planets.head()

	method	number	orbital_period	mass	distance	year
0	Radial Velocity	1	269.300	7.10	77.40	2006
1	Radial Velocity	1	874.774	2.21	56.95	2008
2	Radial Velocity	1	763.000	2.60	19.84	2011
3	Radial Velocity	1	326.030	19.40	110.62	2007
4	Radial Velocity	1	516.220	10.50	119.47	2009

decade = 10 * (planets["year"] // 10)
decade.head()

0    2000
1    2000
2    2010
3    2000
4    2000
Name: year, dtype: int64

decade = decade.astype(str) + "s"
decade.name = "decade"
decade.head()

0    2000s
1    2000s
2    2010s
3    2000s
4    2000s
Name: decade, dtype: object

planets.head()

	method	number	orbital_period	mass	distance	year
0	Radial Velocity	1	269.300	7.10	77.40	2006
1	Radial Velocity	1	874.774	2.21	56.95	2008
2	Radial Velocity	1	763.000	2.60	19.84	2011
3	Radial Velocity	1	326.030	19.40	110.62	2007
4	Radial Velocity	1	516.220	10.50	119.47	2009

planets.groupby(["method", decade]).sum()

		number	orbital_period	mass	distance	year
method	decade
Astrometry	2010s	2	1.262360e+03	0.00000	35.75	4023
Eclipse Timing Variations	2000s	5	1.930800e+04	6.05000	261.44	6025
2010s	10	2.345680e+04	4.20000	1000.00	12065
Imaging	2000s	29	1.350935e+06	0.00000	956.83	40139
2010s	21	6.803750e+04	0.00000	1210.08	36208
Microlensing	2000s	12	1.732500e+04	0.00000	0.00	20070
2010s	15	4.750000e+03	0.00000	41440.00	26155
Orbital Brightness Modulation	2010s	5	2.127920e+00	0.00000	2360.00	6035
Pulsar Timing	1990s	9	1.900153e+02	0.00000	0.00	5978
2000s	1	3.652500e+04	0.00000	0.00	2003
2010s	1	9.070629e-02	0.00000	1200.00	2011
Pulsation Timing Variations	2000s	1	1.170000e+03	0.00000	0.00	2007
Radial Velocity	1980s	1	8.388800e+01	11.68000	40.57	1989
1990s	52	1.091561e+04	68.17820	723.71	55943
2000s	475	2.633526e+05	945.31928	15201.16	619775
2010s	424	1.809630e+05	316.47890	11382.67	432451
Transit	2000s	64	2.897102e+02	0.00000	31823.31	124462
2010s	712	8.087813e+03	1.47000	102419.46	673999
Transit Timing Variations	2010s	9	2.393505e+02	0.00000	3313.00	8050

planets.groupby(["method", decade])[["number"]].sum().unstack().fillna(0)

	number
decade	1980s	1990s	2000s	2010s
method
Astrometry	0.0	0.0	0.0	2.0
Eclipse Timing Variations	0.0	0.0	5.0	10.0
Imaging	0.0	0.0	29.0	21.0
Microlensing	0.0	0.0	12.0	15.0
Orbital Brightness Modulation	0.0	0.0	0.0	5.0
Pulsar Timing	0.0	9.0	1.0	1.0
Pulsation Timing Variations	0.0	0.0	1.0	0.0
Radial Velocity	1.0	52.0	475.0	424.0
Transit	0.0	0.0	64.0	712.0
Transit Timing Variations	0.0	0.0	0.0	9.0

（2）数据透视表

【例2】泰坦尼克号乘客数据分析

import seaborn as sns

titanic = sns.load_dataset("titanic")

titanic.head()

	survived	pclass	sex	age	sibsp	parch	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
0	0	3	male	22.0	1	0	7.2500	S	Third	man	True	NaN	Southampton	no	False
1	1	1	female	38.0	1	0	71.2833	C	First	woman	False	C	Cherbourg	yes	False
2	1	3	female	26.0	0	0	7.9250	S	Third	woman	False	NaN	Southampton	yes	True
3	1	1	female	35.0	1	0	53.1000	S	First	woman	False	C	Southampton	yes	False
4	0	3	male	35.0	0	0	8.0500	S	Third	man	True	NaN	Southampton	no	True

T = titanic[titanic.age.notnull()].copy()

T.age.apply(lambda x: 60 if x>=60 else x)
T.age.value_counts()

24.00    30
22.00    27
60.00    26
18.00    26
28.00    25
30.00    25
19.00    25
21.00    24
25.00    23
36.00    22
29.00    20
35.00    18
32.00    18
27.00    18
26.00    18
31.00    17
16.00    17
34.00    15
20.00    15
33.00    15
23.00    15
39.00    14
40.00    13
17.00    13
42.00    13
45.00    12
38.00    11
4.00     10
50.00    10
2.00     10
         ..
8.00      4
5.00      4
11.00     4
6.00      3
7.00      3
46.00     3
30.50     2
57.00     2
0.83      2
55.00     2
10.00     2
59.00     2
13.00     2
28.50     2
40.50     2
45.50     2
0.75      2
32.50     2
34.50     1
55.50     1
0.92      1
36.50     1
12.00     1
53.00     1
14.50     1
0.67      1
20.50     1
23.50     1
24.50     1
0.42      1
Name: age, Length: 77, dtype: int64

Age = 10*(T["age"]//10)
Age = Age.astype(int)
Age.head()
Age.value_counts()

20    220
30    167
10    102
40     89
0      62
50     48
60     26
Name: age, dtype: int64

Age.astype(str)+"s"

0      20s
1      30s
2      20s
3      30s
4      30s
6      50s
7       0s
8      20s
9      10s
10      0s
11     50s
12     20s
13     30s
14     10s
15     50s
16      0s
18     30s
20     30s
21     30s
22     10s
23     20s
24      0s
25     30s
27     10s
30     40s
33     60s
34     20s
35     40s
37     20s
38     10s
      ... 
856    40s
857    50s
858    20s
860    40s
861    20s
862    40s
864    20s
865    40s
866    20s
867    30s
869     0s
870    20s
871    40s
872    30s
873    40s
874    20s
875    10s
876    20s
877    10s
879    50s
880    20s
881    30s
882    20s
883    20s
884    20s
885    30s
886    20s
887    10s
889    20s
890    30s
Name: age, Length: 714, dtype: object

T.groupby(["sex", Age])["survived"].mean().unstack()

age	0	10	20	30	40	50	60
sex
female	0.633333	0.755556	0.722222	0.833333	0.687500	0.888889	1.000000
male	0.593750	0.122807	0.168919	0.214953	0.210526	0.133333	0.136364

T.age = Age
T.pivot_table("survived", index="sex", columns="age")

age	0	10	20	30	40	50	60
sex
female	0.633333	0.755556	0.722222	0.833333	0.687500	0.888889	1.000000
male	0.593750	0.122807	0.168919	0.214953	0.210526	0.133333	0.136364

titanic.describe()

	survived	pclass	age	sibsp	parch	fare
count	891.000000	891.000000	714.000000	891.000000	891.000000	891.000000
mean	0.383838	2.308642	29.699118	0.523008	0.381594	32.204208
std	0.486592	0.836071	14.526497	1.102743	0.806057	49.693429
min	0.000000	1.000000	0.420000	0.000000	0.000000	0.000000
25%	0.000000	2.000000	20.125000	0.000000	0.000000	7.910400
50%	0.000000	3.000000	28.000000	0.000000	0.000000	14.454200
75%	1.000000	3.000000	38.000000	1.000000	0.000000	31.000000
max	1.000000	3.000000	80.000000	8.000000	6.000000	512.329200

titanic.groupby("sex")[["survived"]].mean()

	survived
sex
female	0.742038
male	0.188908

titanic.groupby("sex")["survived"].mean()

sex
female    0.742038
male      0.188908
Name: survived, dtype: float64

titanic.groupby(["sex", "class"])["survived"].aggregate("mean").unstack()

class	First	Second	Third
sex
female	0.968085	0.921053	0.500000
male	0.368852	0.157407	0.135447

• 数据透视表

titanic.pivot_table("survived", index="sex", columns="class")

class	First	Second	Third
sex
female	0.968085	0.921053	0.500000
male	0.368852	0.157407	0.135447

titanic.pivot_table("survived", index="sex", columns="class", aggfunc="mean", margins=True)

class	First	Second	Third	All
sex
female	0.968085	0.921053	0.500000	0.742038
male	0.368852	0.157407	0.135447	0.188908
All	0.629630	0.472826	0.242363	0.383838

titanic.pivot_table(index="sex", columns="class", aggfunc={"survived": "sum", "fare": "mean"})

	fare	survived
class	First	Second	Third	First	Second	Third
sex
female	106.125798	21.970121	16.118810	91	70	72
male	67.226127	19.741782	12.661633	45	17	47

12.7 其他

（1）向量化字符串操作

（2） 处理时间序列

（3） 多级索引：用于多维数据

base_data = np.array([[1771, 11115 ],
                      [2154, 30320],
                      [2141, 14070],
                      [2424, 32680],
                      [1077, 7806],
                      [1303, 24222],
                      [798, 4789],
                      [981, 13468]]) 
data = pd.DataFrame(base_data, index=[["BeiJing","BeiJing","ShangHai","ShangHai","ShenZhen","ShenZhen","HangZhou","HangZhou"]\
                                     , [2008, 2018]*4], columns=["population", "GDP"])
data

		population	GDP
BeiJing	2008	1771	11115
2018	2154	30320
ShangHai	2008	2141	14070
2018	2424	32680
ShenZhen	2008	1077	7806
2018	1303	24222
HangZhou	2008	798	4789
2018	981	13468

data.index.names = ["city", "year"]
data

		population	GDP
city	year
BeiJing	2008	1771	11115
2018	2154	30320
ShangHai	2008	2141	14070
2018	2424	32680
ShenZhen	2008	1077	7806
2018	1303	24222
HangZhou	2008	798	4789
2018	981	13468

data["GDP"]

city      year
BeiJing   2008    11115
          2018    30320
ShangHai  2008    14070
          2018    32680
ShenZhen  2008     7806
          2018    24222
HangZhou  2008     4789
          2018    13468
Name: GDP, dtype: int32

data.loc["ShangHai", "GDP"]

year
2008    14070
2018    32680
Name: GDP, dtype: int32

data.loc["ShangHai", 2018]["GDP"]

32680

（4） 高性能的Pandas：eval（）

df1, df2, df3, df4 = (pd.DataFrame(np.random.random((10000,100))) for i in range(4))

%timeit (df1+df2)/(df3+df4)

17.6 ms ± 120 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

• 减少了复合代数式计算中间过程的内存分配

%timeit pd.eval("(df1+df2)/(df3+df4)")

10.5 ms ± 153 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

np.allclose((df1+df2)/(df3+df4), pd.eval("(df1+df2)/(df3+df4)"))

True

• 实现列间运算

df = pd.DataFrame(np.random.random((1000, 3)), columns=list("ABC"))
df.head()

	A	B	C
0	0.418071	0.381836	0.500556
1	0.059432	0.749066	0.302429
2	0.489147	0.739153	0.777161
3	0.175441	0.016556	0.348979
4	0.766534	0.559252	0.310635

res_1 = pd.eval("(df.A+df.B)/(df.C-1)")

res_2 = df.eval("(A+B)/(C-1)")

np.allclose(res_1, res_2)

True

df["D"] = pd.eval("(df.A+df.B)/(df.C-1)")
df.head()

	A	B	C	D
0	0.418071	0.381836	0.500556	-1.601593
1	0.059432	0.749066	0.302429	-1.159019
2	0.489147	0.739153	0.777161	-5.512052
3	0.175441	0.016556	0.348979	-0.294917
4	0.766534	0.559252	0.310635	-1.923199

df.eval("D=(A+B)/(C-1)", inplace=True)
df.head()

	A	B	C	D
0	0.418071	0.381836	0.500556	-1.601593
1	0.059432	0.749066	0.302429	-1.159019
2	0.489147	0.739153	0.777161	-5.512052
3	0.175441	0.016556	0.348979	-0.294917
4	0.766534	0.559252	0.310635	-1.923199

• 使用局部变量

column_mean = df.mean(axis=1)
res = df.eval("A+@column_mean")
res.head()

0    0.342788
1    0.047409
2   -0.387501
3    0.236956
4    0.694839
dtype: float64

（4） 高性能的Pandas：query（）

df.head()

	A	B	C	D
0	0.418071	0.381836	0.500556	-1.601593
1	0.059432	0.749066	0.302429	-1.159019
2	0.489147	0.739153	0.777161	-5.512052
3	0.175441	0.016556	0.348979	-0.294917
4	0.766534	0.559252	0.310635	-1.923199

%timeit df[(df.A < 0.5) & (df.B > 0.5)]

1.11 ms ± 9.38 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

%timeit df.query("(A < 0.5)&(B > 0.5)")

2.55 ms ± 199 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

df.query("(A < 0.5)&(B > 0.5)").head()

	A	B	C	D
1	0.059432	0.749066	0.302429	-1.159019
2	0.489147	0.739153	0.777161	-5.512052
7	0.073950	0.730144	0.646190	-2.272672
10	0.393200	0.610467	0.697096	-3.313485
11	0.065734	0.764699	0.179380	-1.011958

np.allclose(df[(df.A < 0.5) & (df.B > 0.5)], df.query("(A < 0.5)&(B > 0.5)"))

True

（5）eval（）和query（）的使用时机

小数组时，普通方法反而更快

df.values.nbytes

32000

df1.values.nbytes

8000000