莫烦Python Numpy&Pandas 学习笔记

原文(视频)地址：https://www.bilibili.com/video/BV1Ex411L7oT

1. 安装

numpy官方网站：download numpy

#pip3 install numpy pandas

2. numpy基本属性

# 导入
import numpy as np

# 定义一个矩阵
array = np.array([[1, 2, 3], [2, 3, 4]])
print(array)

[[1 2 3][2 3 4]]

print("number of dim:", array.ndim)  # 维度
print("shape:", array.shape)  # 形状
print("size:", array.size)  # 元素个数

number of dim: 2
shape: (2, 3)
size: 6

3. 创建array

import numpy as np
a= np.array([2, 23, 4], dtype=np.int64)  # 对类型做定义
print(a)
print(a.dtype)

[ 2 23  4]
int64

# 矩阵
a = np.array([[2, 34, 4],[2, 32, 4]])
print(a)

[[ 2 34  4][ 2 32  4]]

# 0矩阵
a = np.zeros((3, 4))
print(a)

[[0. 0. 0. 0.][0. 0. 0. 0.][0. 0. 0. 0.]]

# 1矩阵
a = np.ones((3, 4), dtype=np.float)
print(a)

[[1. 1. 1. 1.][1. 1. 1. 1.][1. 1. 1. 1.]]

# empty矩阵
b = np.empty((3, 4))
print(b)

[[1. 1. 1. 1.][1. 1. 1. 1.][1. 1. 1. 1.]]

# 有序矩阵
b = np.arange(10, 20, 2)  # 从10到20，步长为2
print(b)

[10 12 14 16 18]

# reshape改变形状
b = np.arange(12).reshape((3, 4))
print(b)

[[ 0  1  2  3][ 4  5  6  7][ 8  9 10 11]]

# 线段
a = np.linspace(1, 10, 6).reshape((2, 3))  # 从1到10，要6个数，形状为2行3列
print(a)

[[ 1.   2.8  4.6][ 6.4  8.2 10. ]]

4. 基础运算

import numpy as np
a = np.array([10, 20, 30, 40])
b = np.arange(4)
c = a - b  # 同样支持 + * **
print(a, b, c)

[10 20 30 40] [0 1 2 3] [10 19 28 37]

c = 10*np.sin(a)  # 三角函数
print(c)

[-5.44021111  9.12945251 -9.88031624  7.4511316 ]

print(b)
print(b<3)  # 比较

[0 1 2 3]
[ True  True  True False]

a = np.array([[1, 1], [0, 1]])
b = np.arange(4).reshape((2,2))
print(a)
print(b)

[[1 1][0 1]]
[[0 1][2 3]]

print(a*b)  # 对应位置相乘
print(a.dot(b))  # 矩阵乘法
print(np.dot(a, b))  # 矩阵乘法另一种写法

[[0 1][0 3]]
[[2 4][2 3]]
[[2 4][2 3]]

a = np.random.random((2, 4))  # 随机[0,1),形状为2*4
print(a)

[[0.8410904  0.60731039 0.2572012  0.42907328][0.92080189 0.10937016 0.70279503 0.94849117]]

print(np.sum(a))
print(np.min(a))
print(np.max(a))

4.816133528743711
0.10937015671859662
0.9484911685139872

print(np.sum(a, axis=1)) # axis=1，对列作操作，相当于每一行求和
print(np.max(a, axis=0)) # axis=0，对行做操作，相当于每一列中求最大

[2.13467528 2.68145825]
[0.92080189 0.60731039 0.70279503 0.94849117]

A = np.arange(2, 14).reshape((3, 4))
print(np.argmin(A))  # 最小值索引
print(np.argmax(A))  # 最大值索引

0
11

print("A: ", A)
print("np.mean(A): ", np.mean(A))  # 平均值，可指定axis=0每列的平均，axis=1每行的平均
print("np.mean(A, axis=0): ", np.mean(A, axis=0))  # axis=0对行作操作，相当于每一列
print("np.mean(A, axis=1): ", np.mean(A, axis=1))  # axis=1对列作操作，相当于每一行
print("np.average(A): ", np.average(A))  # 平均值
print("np.median(A): ", np.median(A))  # 中位数
print("np.cumsum(A): ", np.cumsum(A))  # 累加
print("np.diff(A): ", np.diff(A)) # 累差
print("np.nonzero(A): ", np.nonzero(A))  # 非零元素的位置，第一个数组为行，第二个数组为列，一对一。
print("np.sort(A): ", np.sort(A))  # 排序.
print("np.transpose(A): ", np.transpose(A))  # 反向，转置
print("A.T: ", A.T)  # 转置
print("np.clip(A, 5, 9, ): ", np.clip(A, 5, 9, ))  # <5的成为5，>9的成为9

A:  [[ 2  3  4  5][ 6  7  8  9][10 11 12 13]]
np.mean(A):  7.5
np.mean(A, axis=0):  [6. 7. 8. 9.]
np.mean(A, axis=1):  [ 3.5  7.5 11.5]
np.average(A):  7.5
np.median(A):  7.5
np.cumsum(A):  [ 2  5  9 14 20 27 35 44 54 65 77 90]
np.diff(A):  [[1 1 1][1 1 1][1 1 1]]
np.nonzero(A):  (array([0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2], dtype=int64), array([0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3], dtype=int64))
np.sort(A):  [[ 2  3  4  5][ 6  7  8  9][10 11 12 13]]
np.transpose(A):  [[ 2  6 10][ 3  7 11][ 4  8 12][ 5  9 13]]
A.T:  [[ 2  6 10][ 3  7 11][ 4  8 12][ 5  9 13]]
np.clip(A, 5, 9, ):  [[5 5 5 5][6 7 8 9][9 9 9 9]]

5. numpy的索引

A = np.arange(3, 15)
print(A)

[ 3  4  5  6  7  8  9 10 11 12 13 14]

print(A[3]) # 打印索引为3的元素的值

6

A = A.reshape((3, 4))  # 改变形状3*4
print("A:", A)
print("A[2]:", A[2])  # 打印索引为2的行

A: [[ 3  4  5  6][ 7  8  9 10][11 12 13 14]]
A[2]: [11 12 13 14]

print(A[1][1]) # 第二行第二列，不推荐使用这种方式进行索引

8

print(A[1, 1])  # 建议使用这种方式

8

print(A[2, :])  # 第三行所有数

[11 12 13 14]

print(A[1, 1:3])  # 做切片

[8 9]

for row in A:  # 打印每一行print(row)

[3 4 5 6]
[ 7  8  9 10]
[11 12 13 14]

print(A.T)
for column in A.T:  # 打印每一列print(column)

[[ 3  7 11][ 4  8 12][ 5  9 13][ 6 10 14]]
[ 3  7 11]
[ 4  8 12]
[ 5  9 13]
[ 6 10 14]

print(A.flatten())
for item in A.flat:  # 打印每一项,A.flat为迭代器print(item)

[ 3  4  5  6  7  8  9 10 11 12 13 14]
3
4
5
6
7
8
9
10
11
12
13
14

6. 合并

print("out：")
A = np.array([1,1,1])
B = np.array([2,2,2])
print(np.vstack((A, B)))  # 垂直方向上合并

out：
[[1 1 1][2 2 2]]

print("out：")
C = np.vstack((A, B))
print(C.shape)  # 合并后的shape

out：
(2, 3)

print("out：")
D = np.hstack((A, B))  # 水平方向上合并
print(D)
print(D.shape)  # 水平方向合并后的shape

out：
[1 1 1 2 2 2]
(6,)

print("out：")
print(A.T)  # 一个维度，横向变竖向：失败

out：
[1 1 1]

print("out：")
print(A[np.newaxis, :])  # 多加一个维度
print(A[:, np.newaxis])

out：
[[1 1 1]]
[[1][1][1]]

print("out：")
print(np.hstack((A[:, np.newaxis], B[:, np.newaxis])))

out：
[[1 2][1 2][1 2]]

print("out：")
A = A[:, np.newaxis]
B = B[:, np.newaxis]
C = np.concatenate((A, B, B, A), axis=0)  # 多个array合并
print(C)

out：
[[1][1][1][2][2][2][2][2][2][1][1][1]]

7. 分割

A = np.arange(12).reshape((3, 4))
print(A)

[[ 0  1  2  3][ 4  5  6  7][ 8  9 10 11]]

print(np.split(A, 2, axis=1))

[array([[0, 1],[4, 5],[8, 9]]), array([[ 2,  3],[ 6,  7],[10, 11]])]

# print(np.split(A, 3, axis=1))  # 不能进行不等的分割

print(np.array_split(A, 3, axis=1))  # 进行不等量的分割

[array([[0, 1],[4, 5],[8, 9]]), array([[ 2],[ 6],[10]]), array([[ 3],[ 7],[11]])]

print(np.vsplit(A, 3))
print(np.hsplit(A, 2))

[array([[0, 1, 2, 3]]), array([[4, 5, 6, 7]]), array([[ 8,  9, 10, 11]])]
[array([[0, 1],[4, 5],[8, 9]]), array([[ 2,  3],[ 6,  7],[10, 11]])]

8. 浅拷贝&深拷贝

a = np.arange(4)
print(a)

[0 1 2 3]

b = a
c = a
d = b  # 全是浅拷贝，共用同一块存储空间，改变任一个，其他的也改变

a[0] = 11

print(b)
print(d)

[11  1  2  3]
[11  1  2  3]

print(b is a)  # b是完全的a
print(d is a)  # d是完全的a

True
True

b = a.copy()  # 深拷贝，b和a指向不同的内存地址
print(b is a)

False

a[2] = 22
print(b)  # b没有被改变

[11  1  2  3]

pandas

1. 基本介绍

import pandas as pd
import numpy as np

s = pd.Series([1, 3, 6, np.nan, 44, 1])  # 一个Series
print(s)

0     1.0
1     3.0
2     6.0
3     NaN
4    44.0
5     1.0
dtype: float64

dates = pd.date_range('20160101', periods=6)  # 时间日期range
print(dates)

DatetimeIndex(['2016-01-01', '2016-01-02', '2016-01-03', '2016-01-04','2016-01-05', '2016-01-06'],dtype='datetime64[ns]', freq='D')

df = pd.DataFrame(np.random.randn(6, 4), index=dates, columns=['a', 'b', 'c', 'd'])  # 指定数据、索引和列名
print(df)

                   a         b         c         d
2016-01-01 -1.742936  1.751451 -1.369113  0.626934
2016-01-02  0.550725 -0.640860 -0.309800  1.268160
2016-01-03 -0.194772  1.615315  1.015855 -0.176221
2016-01-04 -0.339469 -1.418400  0.146489  0.957804
2016-01-05 -0.844710 -0.292351 -0.286376 -0.658872
2016-01-06  1.736696  1.144769 -0.178112 -1.267666

df1 = pd.DataFrame(np.random.randn(12).reshape((3, 4)))  # 不指定索引和列名
print(df1)

          0         1         2         3
0  0.453689 -0.691620  0.743595 -1.090077
1 -0.347534  1.358612 -0.187446  0.322624
2  1.309691  0.007173 -1.440739 -1.164850

# 键值对形式定义
df2 = pd.DataFrame({'A':1.,'B':pd.Timestamp('20130102'), 'C':pd.Series(1, index=list(range(4)), dtype='float32'), 'D':np.array([3]*4,dtype='int32'),'E':pd.Categorical(["test","train","test", "train"]),'F':'foo'})
print(df2)

     A          B    C  D      E    F
0  1.0 2013-01-02  1.0  3   test  foo
1  1.0 2013-01-02  1.0  3  train  foo
2  1.0 2013-01-02  1.0  3   test  foo
3  1.0 2013-01-02  1.0  3  train  foo

print(df2.dtypes) # 列的类型

A           float64
B    datetime64[ns]
C           float32
D             int32
E          category
F            object
dtype: object

print(df2.index)  # 列的序号

Int64Index([0, 1, 2, 3], dtype='int64')

print(df2.columns)  # 列的名字

Index(['A', 'B', 'C', 'D', 'E', 'F'], dtype='object')

print(df2.values)  # 元素值

[[1.0 Timestamp('2013-01-02 00:00:00') 1.0 3 'test' 'foo'][1.0 Timestamp('2013-01-02 00:00:00') 1.0 3 'train' 'foo'][1.0 Timestamp('2013-01-02 00:00:00') 1.0 3 'test' 'foo'][1.0 Timestamp('2013-01-02 00:00:00') 1.0 3 'train' 'foo']]

df2.describe()  # 仅能描述数值形式的列
A C D
count 4.0 4.0 4.0
mean 1.0 1.0 3.0
std 0.0 0.0 0.0
min 1.0 1.0 3.0
25% 1.0 1.0 3.0
50% 1.0 1.0 3.0
75% 1.0 1.0 3.0
max 1.0 1.0 3.0 
print(df2.T)  # 转置

                     0                    1                    2  \
A                    1                    1                    1
B  2013-01-02 00:00:00  2013-01-02 00:00:00  2013-01-02 00:00:00
C                    1                    1                    1
D                    3                    3                    3
E                 test                train                 test
F                  foo                  foo                  foo   3
A                    1
B  2013-01-02 00:00:00
C                    1
D                    3
E                train
F                  foo

print(df2.sort_index(axis=1, ascending=False))  # 按列排序，逆序

     F      E  D    C          B    A
0  foo   test  3  1.0 2013-01-02  1.0
1  foo  train  3  1.0 2013-01-02  1.0
2  foo   test  3  1.0 2013-01-02  1.0
3  foo  train  3  1.0 2013-01-02  1.0

print(df2.sort_index(axis=0, ascending=False))  # 按行排序，逆序

     A          B    C  D      E    F
3  1.0 2013-01-02  1.0  3  train  foo
2  1.0 2013-01-02  1.0  3   test  foo
1  1.0 2013-01-02  1.0  3  train  foo
0  1.0 2013-01-02  1.0  3   test  foo

print(df2.sort_values(by='E'))  # 对值排序，选关键字

     A          B    C  D      E    F
0  1.0 2013-01-02  1.0  3   test  foo
2  1.0 2013-01-02  1.0  3   test  foo
1  1.0 2013-01-02  1.0  3  train  foo
3  1.0 2013-01-02  1.0  3  train  foo

2. 选择数据

dates = pd.date_range('20130101', periods=6)
df = pd.DataFrame(np.arange(24).reshape((6, 4)), index=dates, columns=['A', 'B', 'C', 'D'])
print(df)

             A   B   C   D
2013-01-01   0   1   2   3
2013-01-02   4   5   6   7
2013-01-03   8   9  10  11
2013-01-04  12  13  14  15
2013-01-05  16  17  18  19
2013-01-06  20  21  22  23

print(df['A'])  # 选择列
print(df.A)  # 同上

2013-01-01     0
2013-01-02     4
2013-01-03     8
2013-01-04    12
2013-01-05    16
2013-01-06    20
Freq: D, Name: A, dtype: int32
2013-01-01     0
2013-01-02     4
2013-01-03     8
2013-01-04    12
2013-01-05    16
2013-01-06    20
Freq: D, Name: A, dtype: int32

print(df[0:3])  # 按行切片
print(df['20130102':'20130104'])  # 闭区间

            A  B   C   D
2013-01-01  0  1   2   3
2013-01-02  4  5   6   7
2013-01-03  8  9  10  11A   B   C   D
2013-01-02   4   5   6   7
2013-01-03   8   9  10  11
2013-01-04  12  13  14  15

print(df.loc['20130102'])  # 通过标签选择

A    4
B    5
C    6
D    7
Name: 2013-01-02 00:00:00, dtype: int32

print(df.loc[:, ['A', 'B']])

             A   B
2013-01-01   0   1
2013-01-02   4   5
2013-01-03   8   9
2013-01-04  12  13
2013-01-05  16  17
2013-01-06  20  21

print(df.loc['20130102', ['A', 'B']])

A    4
B    5
Name: 2013-01-02 00:00:00, dtype: int32

print(df.iloc[3])  # 通过position来选择

A    12
B    13
C    14
D    15
Name: 2013-01-04 00:00:00, dtype: int32

print(df.iloc[3, 1])

13

print(df.iloc[3:5, 1:3])  # 切片形式

             B   C
2013-01-04  13  14
2013-01-05  17  18

print(df.iloc[[1,3,5], 1:3])  # 不连续的切片

             B   C
2013-01-02   5   6
2013-01-04  13  14
2013-01-06  21  22

print(df.ix[:3, ['A', 'C']])  # 混合筛选

            A   C
2013-01-01  0   2
2013-01-02  4   6
2013-01-03  8  10C:\ProgramData\Anaconda3\lib\site-packages\ipykernel_launcher.py:1: DeprecationWarning:
.ix is deprecated. Please use
.loc for label based indexing or
.iloc for positional indexingSee the documentation here:
http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated"""Entry point for launching an IPython kernel.

print(df)
print(df[df.A > 8])  # Boolean index打印在特征A上值大于8的所有行

             A   B   C   D
2013-01-01   0   1   2   3
2013-01-02   4   5   6   7
2013-01-03   8   9  10  11
2013-01-04  12  13  14  15
2013-01-05  16  17  18  19
2013-01-06  20  21  22  23A   B   C   D
2013-01-04  12  13  14  15
2013-01-05  16  17  18  19
2013-01-06  20  21  22  23

3. 设置值

dates = pd.date_range('20130101', periods=6)
df = pd.DataFrame(np.arange(24).reshape((6, 4)), index=dates,columns=['A', 'B', 'C', 'D'])
print(df)

             A   B   C   D
2013-01-01   0   1   2   3
2013-01-02   4   5   6   7
2013-01-03   8   9  10  11
2013-01-04  12  13  14  15
2013-01-05  16  17  18  19
2013-01-06  20  21  22  23

df.iloc[2, 2] = 1111
print(df)

             A   B     C   D
2013-01-01   0   1     2   3
2013-01-02   4   5     6   7
2013-01-03   8   9  1111  11
2013-01-04  12  13    14  15
2013-01-05  16  17    18  19
2013-01-06  20  21    22  23

df.loc['20130101', 'B'] = 2222
print(df)

             A     B     C   D
2013-01-01   0  2222     2   3
2013-01-02   4     5     6   7
2013-01-03   8     9  1111  11
2013-01-04  12    13    14  15
2013-01-05  16    17    18  19
2013-01-06  20    21    22  23

df[df.A>4] = 0
print(df)

            A     B  C  D
2013-01-01  0  2222  2  3
2013-01-02  4     5  6  7
2013-01-03  0     0  0  0
2013-01-04  0     0  0  0
2013-01-05  0     0  0  0
2013-01-06  0     0  0  0

dates = pd.date_range('20130101', periods=6)
df = pd.DataFrame(np.arange(24).reshape((6, 4)), index=dates,columns=['A', 'B', 'C', 'D'])
df.B[df.A > 4] = 0
print(df)

             A  B   C   D
2013-01-01   0  1   2   3
2013-01-02   4  5   6   7
2013-01-03   8  0  10  11
2013-01-04  12  0  14  15
2013-01-05  16  0  18  19
2013-01-06  20  0  22  23

df['F'] = np.nan
print(df)

             A  B   C   D   F
2013-01-01   0  1   2   3 NaN
2013-01-02   4  5   6   7 NaN
2013-01-03   8  0  10  11 NaN
2013-01-04  12  0  14  15 NaN
2013-01-05  16  0  18  19 NaN
2013-01-06  20  0  22  23 NaN

df['E'] = pd.Series([1, 2, 3, 4, 5,6], index=dates)
print(df)

             A  B   C   D   F  E
2013-01-01   0  1   2   3 NaN  1
2013-01-02   4  5   6   7 NaN  2
2013-01-03   8  0  10  11 NaN  3
2013-01-04  12  0  14  15 NaN  4
2013-01-05  16  0  18  19 NaN  5
2013-01-06  20  0  22  23 NaN  6

4. 处理丢失数据

dates = pd.date_range('20130101', periods=6)
df = pd.DataFrame(np.arange(24).reshape((6, 4)), index=dates,columns=['A', 'B', 'C', 'D'])
df.iloc[0, 1] = np.nan
df.iloc[1, 2] = np.nan
print(df)

             A     B     C   D
2013-01-01   0   NaN   2.0   3
2013-01-02   4   5.0   NaN   7
2013-01-03   8   9.0  10.0  11
2013-01-04  12  13.0  14.0  15
2013-01-05  16  17.0  18.0  19
2013-01-06  20  21.0  22.0  23

print(df.dropna(axis=0, how='any'))  # how={'any', 'all'}，丢弃那一行数据

             A     B     C   D
2013-01-03   8   9.0  10.0  11
2013-01-04  12  13.0  14.0  15
2013-01-05  16  17.0  18.0  19
2013-01-06  20  21.0  22.0  23

print(df.fillna(value=0))

             A     B     C   D
2013-01-01   0   0.0   2.0   3
2013-01-02   4   5.0   0.0   7
2013-01-03   8   9.0  10.0  11
2013-01-04  12  13.0  14.0  15
2013-01-05  16  17.0  18.0  19
2013-01-06  20  21.0  22.0  23

print(df.isnull())

                A      B      C      D
2013-01-01  False   True  False  False
2013-01-02  False  False   True  False
2013-01-03  False  False  False  False
2013-01-04  False  False  False  False
2013-01-05  False  False  False  False
2013-01-06  False  False  False  False

print(np.any(df.isnull()) == True)  # 判断是否有缺失的值

True

5. 导入导出数据

data = pd.read_csv('student.csv')  # 导入
print(data)  # 自动加了索引

   Student ID   name  age  gender
0        1100  Kelly   22  Female
1        1101    Tom   21  Female
2        1102  Tilly   23    Male
3        1103  David   20    Male
4        1104  Catty   22  Female

data.to_pickle('student.pickle')  # 导出为pandas的数据格式

6. 合并concatenating

df1 = pd.DataFrame(np.ones((3, 4))*0, columns=['a', 'b', 'c', 'd'])
df2 = pd.DataFrame(np.ones((3, 4))*1, columns=['a', 'b', 'c', 'd'])
df3 = pd.DataFrame(np.ones((3, 4))*2, columns=['a', 'b', 'c', 'd'])
print(df1)
print(df2)
print(df3)

     a    b    c    d
0  0.0  0.0  0.0  0.0
1  0.0  0.0  0.0  0.0
2  0.0  0.0  0.0  0.0a    b    c    d
0  1.0  1.0  1.0  1.0
1  1.0  1.0  1.0  1.0
2  1.0  1.0  1.0  1.0a    b    c    d
0  2.0  2.0  2.0  2.0
1  2.0  2.0  2.0  2.0
2  2.0  2.0  2.0  2.0

res = pd.concat([df1, df2, df3], axis=0)  # 竖向合并
print(res)

     a    b    c    d
0  0.0  0.0  0.0  0.0
1  0.0  0.0  0.0  0.0
2  0.0  0.0  0.0  0.0
0  1.0  1.0  1.0  1.0
1  1.0  1.0  1.0  1.0
2  1.0  1.0  1.0  1.0
0  2.0  2.0  2.0  2.0
1  2.0  2.0  2.0  2.0
2  2.0  2.0  2.0  2.0

res = pd.concat([df1, df2, df3], axis=0, ignore_index=True)  # index重新进行排序
print(res)

     a    b    c    d
0  0.0  0.0  0.0  0.0
1  0.0  0.0  0.0  0.0
2  0.0  0.0  0.0  0.0
3  1.0  1.0  1.0  1.0
4  1.0  1.0  1.0  1.0
5  1.0  1.0  1.0  1.0
6  2.0  2.0  2.0  2.0
7  2.0  2.0  2.0  2.0
8  2.0  2.0  2.0  2.0

# join, ['inner', 'outer']
df1 = pd.DataFrame(np.ones((3, 4))*0, columns=['a', 'b', 'c', 'd'], index=[1, 2, 3])
df2 = pd.DataFrame(np.ones((3, 4))*1, columns=['b', 'c', 'd', 'e'], index=[2, 3, 4])
res = pd.concat([df1, df2], join='outer', sort=False)  # 所有特征都保留
print(res)

     a    b    c    d    e
1  0.0  0.0  0.0  0.0  NaN
2  0.0  0.0  0.0  0.0  NaN
3  0.0  0.0  0.0  0.0  NaN
2  NaN  1.0  1.0  1.0  1.0
3  NaN  1.0  1.0  1.0  1.0
4  NaN  1.0  1.0  1.0  1.0

res = pd.concat([df1, df2], join='inner', ignore_index=True)  # 只保留公共特征
print(res)

     b    c    d
0  0.0  0.0  0.0
1  0.0  0.0  0.0
2  0.0  0.0  0.0
3  1.0  1.0  1.0
4  1.0  1.0  1.0
5  1.0  1.0  1.0

res = pd.concat([df1, df2], axis=1, join_axes=[df1.index])  # 左右合并
print(res)

     a    b    c    d    b    c    d    e
1  0.0  0.0  0.0  0.0  NaN  NaN  NaN  NaN
2  0.0  0.0  0.0  0.0  1.0  1.0  1.0  1.0
3  0.0  0.0  0.0  0.0  1.0  1.0  1.0  1.0

res = df1.append(df2, ignore_index=True, sort=False)
print(res)

     a    b    c    d    e
0  0.0  0.0  0.0  0.0  NaN
1  0.0  0.0  0.0  0.0  NaN
2  0.0  0.0  0.0  0.0  NaN
3  NaN  1.0  1.0  1.0  1.0
4  NaN  1.0  1.0  1.0  1.0
5  NaN  1.0  1.0  1.0  1.0

df3 = pd.DataFrame(np.ones((3, 4))*2, columns=['a', 'b', 'c', 'd'])
res = df1.append([df2, df3], ignore_index=True, sort=False)
print(res)

     a    b    c    d    e
0  0.0  0.0  0.0  0.0  NaN
1  0.0  0.0  0.0  0.0  NaN
2  0.0  0.0  0.0  0.0  NaN
3  NaN  1.0  1.0  1.0  1.0
4  NaN  1.0  1.0  1.0  1.0
5  NaN  1.0  1.0  1.0  1.0
6  2.0  2.0  2.0  2.0  NaN
7  2.0  2.0  2.0  2.0  NaN
8  2.0  2.0  2.0  2.0  NaN

s1 = pd.Series([1, 2, 3,4], index=['a', 'b', 'c', 'd'])  # 一行数据
res = df1.append(s1, ignore_index=True)
print(res)

     a    b    c    d
0  0.0  0.0  0.0  0.0
1  0.0  0.0  0.0  0.0
2  0.0  0.0  0.0  0.0
3  1.0  2.0  3.0  4.0

7. 合并merge

import pandas as pd
left = pd.DataFrame({'key':['K0', 'K1', 'K2', 'K3'],'A':['A0', 'A1', 'A2', 'A3'],'B':['B0', 'B1', 'B2', 'B3']})
right = pd.DataFrame({'key':['K0', 'K1', 'K2', 'K3'],'A':['C0', 'C1', 'C2', 'C3'],'B':['D0', 'D1', 'D2', 'D3']})
print(left)
print(right)

  key   A   B
0  K0  A0  B0
1  K1  A1  B1
2  K2  A2  B2
3  K3  A3  B3key   A   B
0  K0  C0  D0
1  K1  C1  D1
2  K2  C2  D2
3  K3  C3  D3

res = pd.merge(left, right, on='key')
print(res)

  key A_x B_x A_y B_y
0  K0  A0  B0  C0  D0
1  K1  A1  B1  C1  D1
2  K2  A2  B2  C2  D2
3  K3  A3  B3  C3  D3

left = pd.DataFrame({'key1':['K0', 'K0', 'K1', 'K2'],'key2':['K0', 'K1', 'K0', 'K1'],'A':['A0', 'A1', 'A2', 'A3'],'B':['B0', 'B1', 'B2', 'B3']})
right = pd.DataFrame({'key1':['K0', 'K1', 'K1', 'K2'],'key2':['K0', 'K0', 'K0', 'K0'],'A':['C0', 'C1', 'C2', 'C3'],'B':['D0', 'D1', 'D2', 'D3']})
print(left)
print(right)

  key1 key2   A   B
0   K0   K0  A0  B0
1   K0   K1  A1  B1
2   K1   K0  A2  B2
3   K2   K1  A3  B3key1 key2   A   B
0   K0   K0  C0  D0
1   K1   K0  C1  D1
2   K1   K0  C2  D2
3   K2   K0  C3  D3

res = pd.merge(left, right, on=['key1', 'key2'], how='inner')  # how: left, right, outer, inner
print(res)

  key1 key2 A_x B_x A_y B_y
0   K0   K0  A0  B0  C0  D0
1   K1   K0  A2  B2  C1  D1
2   K1   K0  A2  B2  C2  D2

df1 = pd.DataFrame({'col1':[0,1],'col_left':['a', 'b']})
df2 = pd.DataFrame({'col1':[1, 2, 2],'col_right':[2, 2, 2]})
print(df1)
print(df2)

   col1 col_left
0     0        a
1     1        bcol1  col_right
0     1          2
1     2          2
2     2          2

res = pd.merge(df1, df2, on='col1', how='outer', indicator=True)
print(res)

   col1 col_left  col_right      _merge
0     0        a        NaN   left_only
1     1        b        2.0        both
2     2      NaN        2.0  right_only
3     2      NaN        2.0  right_only

res = pd.merge(df1, df2, on='col1', how='outer', indicator='indicator_col')  # 添加指示说明
print(res)

   col1 col_left  col_right indicator_col
0     0        a        NaN     left_only
1     1        b        2.0          both
2     2      NaN        2.0    right_only
3     2      NaN        2.0    right_only

left = pd.DataFrame({'A':['A0', 'A1', 'A2'],'B':['B0', 'B1', 'B2']},index=['K0', 'K1', 'K2'])
right = pd.DataFrame({'C':['C0', 'C1', 'C2'],'D':['D0', 'D1', 'D2']},index=['K0', 'K2', 'K3'])
print(left)
print(right)

     A   B
K0  A0  B0
K1  A1  B1
K2  A2  B2C   D
K0  C0  D0
K2  C1  D1
K3  C2  D2

res = pd.merge(left, right, left_index=True, right_index=True, how='outer')
print(res)
res = pd.merge(left, right, left_index=True, right_index=True, how='inner')
print(res)

      A    B    C    D
K0   A0   B0   C0   D0
K1   A1   B1  NaN  NaN
K2   A2   B2   C1   D1
K3  NaN  NaN   C2   D2A   B   C   D
K0  A0  B0  C0  D0
K2  A2  B2  C1  D1

boys = pd.DataFrame({'k':['K0', 'K1', 'K2'], 'age':[1, 2, 3]})
girls = pd.DataFrame({'k':['K0', 'K0', 'K2'], 'age':[4, 5, 6]})
print(boys)
print(girls)

    k  age
0  K0    1
1  K1    2
2  K2    3k  age
0  K0    4
1  K0    5
2  K2    6

res = pd.merge(boys, girls, on='k', suffixes=['_boy', '_girls'], how='inner')
print(res)

    k  age_boy  age_girls
0  K0        1          4
1  K0        1          5
2  K2        3          6

res = pd.merge(boys, girls, on='k', suffixes=['_boy', '_girls'], how='outer')
print(res)

    k  age_boy  age_girls
0  K0        1        4.0
1  K0        1        5.0
2  K1        2        NaN
3  K2        3        6.0

9. plot画图

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

data = pd.Series(np.random.randn(1000), index=np.arange(1000))  # 正态分布
data = data.cumsum()  # 累加
data.plot()
plt.show()


data  = pd.DataFrame(np.random.randn(1000, 4), index=np.arange(1000), columns=list("ABCD"))
print(data.head(5))  # 输出data的前5行数据

          A         B         C         D
0 -1.473072 -0.751053  0.131861 -0.693762
1 -0.947544 -0.147147 -0.556534  0.424414
2  0.388600 -1.637560  0.161108  0.756645
3  1.372510 -0.675933 -0.441797 -1.028523
4 -1.941062  1.263854 -1.704671  1.076629

data = data.cumsum()
ax = data.plot.scatter(x="A", y="B", color="DarkBlue", label="class 1")
data.plot.scatter(x="A", y="C", color="DarkGreen", label="class 2", ax = ax)
# plt: bat hist box kde area scatter hexbin pie
plt.show()

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