Pandas 是基于NumPy 的一种工具，该工具是为了解决数据分析任务而创建的。Pandas 纳入了大量库和一些标准的数据模型，提供了高效地操作大型数据集所需的工具。Pandas提供了大量能使我们快速便捷地处理数据的函数和方法。它也是使Python成为强大而高效的数据分析环境的重要因素之一。
若对Numpy不够了解，可以参考我的另一篇博客：https://blog.youkuaiyun.com/strivequeen/article/details/112984264
1.Series
import pandas as pd
import numpy as np
s = pd.Series([1,3,6,np.nan,44,1])
print(s) # 默认index从0开始,如果想要按照自己的索引设置，则修改index参数,如:index=[3,4,3,7,8,9]
0 1.0
1 3.0
2 6.0
3 NaN
4 44.0
5 1.0
dtype: float64
2.DataFrame的简单运用
DataFrame 既有行索引也有列索引， 它可以被看做由 Series 组成的大字典。
# 指定行标签和列标签的数据
dates = pd.date_range('2021-01-31',periods=6)
# dates = pd.date_range('2021-01-31','2021-02-05') # 起始、结束 与上述等价
'''
numpy.random.randn(d0, d1, …, dn)是从标准正态分布中返回一个或多个样本值。
numpy.random.rand(d0, d1, …, dn)的随机样本位于[0, 1)中。
(6,4)表示6行4列数据
'''
df = pd.DataFrame(np.random.randn(6,4),index=dates,columns=['a','b','c','d'])
print(df)
a b c d
2021-01-31 -0.089777 -0.004411 1.032583 2.570713
2021-02-01 -0.918877 0.600092 -0.711374 -0.087149
2021-02-02 0.000248 0.956044 -0.256532 -2.224948
2021-02-03 1.537203 -1.313810 1.283764 -0.412839
2021-02-04 -0.010970 -0.789566 -0.307353 -0.498532
2021-02-05 0.324443 0.201022 0.956131 -1.140553
print(df['b'])
2021-01-31 -0.004411
2021-02-01 0.600092
2021-02-02 0.956044
2021-02-03 -1.313810
2021-02-04 -0.789566
2021-02-05 0.201022
Freq: D, Name: b, dtype: float64
# 未指定行标签和列标签的数据
df1 = pd.DataFrame(np.arange(12).reshape(3,4))
print(df1)
0 1 2 3
0 0 1 2 3
1 4 5 6 7
2 8 9 10 11
# 另一种方式
df2 = pd.DataFrame({
'A': [1,2,3,4],
'B': pd.Timestamp('20210131'),
'C': pd.Series([1,6,9,10],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 2021-01-31 1.0 3 test foo
1 2 2021-01-31 6.0 3 train foo
2 3 2021-01-31 9.0 3 test foo
3 4 2021-01-31 10.0 3 train foo
print(df2.index)
RangeIndex(start=0, stop=4, step=1)
print(df2.columns)
Index(['A', 'B', 'C', 'D', 'E', 'F'], dtype='object')
print(df2.values)
[[1 Timestamp('2021-01-31 00:00:00') 1.0 3 'test' 'foo']
[2 Timestamp('2021-01-31 00:00:00') 6.0 3 'train' 'foo']
[3 Timestamp('2021-01-31 00:00:00') 9.0 3 'test' 'foo']
[4 Timestamp('2021-01-31 00:00:00') 10.0 3 'train' 'foo']]
# 数据总结
print(df2.describe()) # 只对数字数据进行描述
A C D
count 4.000000 4.000000 4.0
mean 2.500000 6.500000 3.0
std 1.290994 4.041452 0.0
min 1.000000 1.000000 3.0
25% 1.750000 4.750000 3.0
50% 2.500000 7.500000 3.0
75% 3.250000 9.250000 3.0
max 4.000000 10.000000 3.0
# 翻转数据
print(df2.T) # print(np.transpose(df2))为等价操作
0 1 2 \
A 1 2 3
B 2021-01-31 00:00:00 2021-01-31 00:00:00 2021-01-31 00:00:00
C 1 6 9
D 3 3 3
E test train test
F foo foo foo
3
A 4
B 2021-01-31 00:00:00
C 10
D 3
E train
F foo
'''
axis=1表示行
axis=0表示列
默认ascending(升序)为True。ascending=True表示升序,ascending=False表示降序
'''
print(df2.sort_index(axis=1,ascending=True))
A B C D E F
0 1 2021-01-31 1.0 3 test foo
1 2 2021-01-31 6.0 3 train foo
2 3 2021-01-31 9.0 3 test foo
3 4 2021-01-31 10.0 3 train foo
print(df2.sort_index(axis=1,ascending=False))
F E D C B A
0 foo test 3 1.0 2021-01-31 1
1 foo train 3 6.0 2021-01-31 2
2 foo test 3 9.0 2021-01-31 3
3 foo train 3 10.0 2021-01-31 4
print(df2.sort_index(axis=0,ascending=False)) # 表示按列降序与按列升序
A B C D E F
3 4 2021-01-31 10.0 3 train foo
2 3 2021-01-31 9.0 3 test foo
1 2 2021-01-31 6.0 3 train foo
0 1 2021-01-31 1.0 3 test foo
print(df2.sort_index(axis=0,ascending=True))
A B C D E F
0 1 2021-01-31 1.0 3 test foo
1 2 2021-01-31 6.0 3 train foo
2 3 2021-01-31 9.0 3 test foo
3 4 2021-01-31 10.0 3 train foo
# 对特定列数值排列
print(df2.sort_values(by='C',ascending=False)) # 表示对C列降序排列
A B C D E F
3 4 2021-01-31 10.0 3 train foo
2 3 2021-01-31 9.0 3 test foo
1 2 2021-01-31 6.0 3 train foo
0 1 2021-01-31 1.0 3 test foo
3.pandas选择数据
3.1 实战筛选
dates = pd.date_range('20210131', periods=6)
df = pd.DataFrame(np.arange(24).reshape((6,4)),index=dates, columns=['A','B','C','D'])
print(df)
A B C D
2021-01-31 0 1 2 3
2021-02-01 4 5 6 7
2021-02-02 8 9 10 11
2021-02-03 12 13 14 15
2021-02-04 16 17 18 19
2021-02-05 20 21 22 23
print(df.A) # 与 print(df['A']) 等价
2021-01-31 0
2021-02-01 4
2021-02-02 8
2021-02-03 12
2021-02-04 16
2021-02-05 20
Freq: D, Name: A, dtype: int32
# 选择跨越多行或多列
print(df[0:3]) # 选取前3行，与 print(df['2021-01-31':'2021-02-02']) 等价
A B C D
2021-01-31 0 1 2 3
2021-02-01 4 5 6 7
2021-02-02 8 9 10 11
# 根据标签选择数据，获取特定行或列
print(df.loc['20210202']) # 指定行数据
A 8
B 9
C 10
D 11
Name: 2021-02-02 00:00:00, dtype: int32
# 指定列
print(df.loc[:,'A':'B']) # 与 print(df.loc[:,['A','B']]) 等价
A B
2021-01-31 0 1
2021-02-01 4 5
2021-02-02 8 9
2021-02-03 12 13
2021-02-04 16 17
2021-02-05 20 21
# 行与列同时检索
print(df.loc['20210131',['A','B']])
A 0
B 1
Name: 2021-01-31 00:00:00, dtype: int32
# 根据序列iloc，获取特定位置的值
print(df.iloc[3,1])
13
print(df.iloc[3:5,1:3]) # 不包含末尾5或3，同列表切片
B C
2021-02-03 13 14
2021-02-04 17 18
# 跨行操作
print(df.iloc[[1,3,5],1:3])
B C
2021-02-01 5 6
2021-02-03 13 14
2021-02-05 21 22
print(df.iloc[:3,[0,2]]) # 混合选择
A C
2021-01-31 0 2
2021-02-01 4 6
2021-02-02 8 10
# 通过判断的筛选
print(df[df.A>8]) # 与 print(df.loc[df.A>8]) 等价
A B C D
2021-02-03 12 13 14 15
2021-02-04 16 17 18 19
2021-02-05 20 21 22 23
3.2 筛选总结
-
iloc与ix
相同点：iloc可以取相应的值，操作方便，与ix操作类似。
不同点：ix可以混合选择，可以填入column对应的字符选择，而iloc只能采用index索引，对于列数较多情况下，ix要方便操作许多。 -
loc与iloc
相同点：都可以索引处块数据
不同点：iloc可以检索对应值,两者操作不同。
3.ix与loc、iloc
ix是混合loc与iloc操作
print(df.loc['20210131','A':'B'])
print(df.iloc[0,0:2])
print(df.ix[0,'A':'B'])
# 以上三种结果均为：
A 0
B 1
Name: 2021-01-31 00:00:00, dtype: int32
4.Pandas设置值
4.1 创建数据
# 创建数据
dates = pd.date_range('20210131',periods=6)
df = pd.DataFrame(np.arange(24).reshape(6,4), index=dates, columns=['A','B','C','D'])
print(df)
A B C D
2021-01-31 0 1 2 3
2021-02-01 4 5 6 7
2021-02-02 8 9 10 11
2021-02-03 12 13 14 15
2021-02-04 16 17 18 19
2021-02-05 20 21 22 23
4.2 根据位置设置loc和iloc
# 根据位置设置loc和iloc
df.iloc[2,2] = 111
df.loc['20210131','B'] = 2222
print(df)
A B C D
2021-01-31 0 2222 2 3
2021-02-01 4 5 6 7
2021-02-02 8 9 111 11
2021-02-03 12 13 14 15
2021-02-04 16 17 18 19
2021-02-05 20 21 22 23
4.3 根据条件设置
# 更改B中的数，而更改的位置取决于4的位置，并设相应位置的数为0
df.B[df.A>4] = 0 # 与 df.B.loc[df.A>4] = 0 等价
print(df)
A B C D
2021-01-31 0 2222 2 3
2021-02-01 4 5 6 7
2021-02-02 8 0 111 11
2021-02-03 12 0 14 15
2021-02-04 16 0 18 19
2021-02-05 20 0 22 23
4.4 按行或列设置
df['F'] = np.nan # 列批处理，F列全改为NaN
print(df)
A B C D F
2021-01-31 0 2222 2 3 NaN
2021-02-01 4 5 6 7 NaN
2021-02-02 8 0 111 11 NaN
2021-02-03 12 0 14 15 NaN
2021-02-04 16 0 18 19 NaN
2021-02-05 20 0 22 23 NaN
4.5 添加Series序列(长度必须对齐)
df['E'] = pd.Series([1,2,3,4,5,6], index=pd.date_range('20210131',periods=6))
print(df)
A B C D F E
2021-01-31 0 2222 2 3 NaN 1
2021-02-01 4 5 6 7 NaN 2
2021-02-02 8 0 111 11 NaN 3
2021-02-03 12 0 14 15 NaN 4
2021-02-04 16 0 18 19 NaN 5
2021-02-05 20 0 22 23 NaN 6
4.6 设定某行某列为特定值
#ix 以后要剥离了，尽量不要用了
df.loc['20210131','A'] = 67 # 与 df.iloc[0,0] = 67 等价
print(df)
A B C D F E
2021-01-31 67 2222 2 3 NaN 1
2021-02-01 4 5 6 7 NaN 2
2021-02-02 8 0 111 11 NaN 3
2021-02-03 12 0 14 15 NaN 4
2021-02-04 16 0 18 19 NaN 5
2021-02-05 20 0 22 23 NaN 6
4.7 修改一整行数据
df.iloc[1] = np.nan # df.iloc[1,:]=np.nan
print(df)
A B C D F E
2021-01-31 67.0 2222.0 2.0 3.0 NaN 1.0
2021-02-01 NaN NaN NaN NaN NaN NaN
2021-02-02 8.0 0.0 111.0 11.0 NaN 3.0
2021-02-03 12.0 0.0 14.0 15.0 NaN 4.0
2021-02-04 16.0 0.0 18.0 19.0 NaN 5.0
2021-02-05 20.0 0.0 22.0 23.0 NaN 6.0
df.loc['20210131'] = np.nan # df.loc['20210131,:']=np.nan
print(df)
A B C D F E
2021-01-31 NaN NaN NaN NaN NaN NaN
2021-02-01 NaN NaN NaN NaN NaN NaN
2021-02-02 8.0 0.0 111.0 11.0 NaN 3.0
2021-02-03 12.0 0.0 14.0 15.0 NaN 4.0
2021-02-04 16.0 0.0 18.0 19.0 NaN 5.0
2021-02-05 20.0 0.0 22.0 23.0 NaN 6.0
5.Pandas处理丢失数据
5.1 创建含NaN的矩阵
# 创建含NaN的矩阵
dates = pd.date_range('20210131',periods=6)
df = pd.DataFrame(np.arange(24).reshape((6,4)),index=dates,columns=['A','B','C','D'])
print(df)
A B C D
2021-01-31 0 1 2 3
2021-02-01 4 5 6 7
2021-02-02 8 9 10 11
2021-02-03 12 13 14 15
2021-02-04 16 17 18 19
2021-02-05 20 21 22 23
# a.reshape(6,4)等价于a.reshape((6,4))
df.iloc[0,1] = np.nan
df.iloc[1,2] = np.nan
print(df)
A B C D
2021-01-31 0 NaN 2.0 3
2021-02-01 4 5.0 NaN 7
2021-02-02 8 9.0 10.0 11
2021-02-03 12 13.0 14.0 15
2021-02-04 16 17.0 18.0 19
2021-02-05 20 21.0 22.0 23
5.2 删除有NaN的行或列
print(df.dropna()) # 默认是删除掉含有NaN的行
A B C D
2021-02-02 8 9.0 10.0 11
2021-02-03 12 13.0 14.0 15
2021-02-04 16 17.0 18.0 19
2021-02-05 20 21.0 22.0 23
print(df.dropna(
axis=0, # 0对行进行操作;1对列进行操作
how='any' # 'any':只要存在NaN就drop掉；'all':必须全部是NaN才drop
))
# 结果同上
# 删除掉所有含有NaN的列
print(df.dropna(
axis=1,
how='any'
))
A D
2021-01-31 0 3
2021-02-01 4 7
2021-02-02 8 11
2021-02-03 12 15
2021-02-04 16 19
2021-02-05 20 23
5.3 替换NaN值为0或者其他
print(df.fillna(value=0))
A B C D
2021-01-31 0 0.0 2.0 3
2021-02-01 4 5.0 0.0 7
2021-02-02 8 9.0 10.0 11
2021-02-03 12 13.0 14.0 15
2021-02-04 16 17.0 18.0 19
2021-02-05 20 21.0 22.0 23
5.4 是否有缺失数据NaN
print(df.isnull()) # 与 print(df.isna()) 等价
A B C D
2021-01-31 False True False False
2021-02-01 False False True False
2021-02-02 False False False False
2021-02-03 False False False False
2021-02-04 False False False False
2021-02-05 False False False False
# 检测某列是否有缺失数据NaN
print(df.isnull().any())
A False
B True
C True
D False
dtype: bool
# 检测数据中是否存在NaN,如果存在就返回True
print(np.any(df.isnull())==True)
True
6.Pandas导入导出
6.1 导入数据
import pandas as pd # 加载模块
data = pd.read_csv('student.csv') # 读取csv
print(data) # 打印出data
Student ID name age gender
0 1100 Kelly 22 Female
1 1101 Clo 21 Female
2 1102 Tilly 22 Female
3 1103 Tony 24 Male
4 1104 David 20 Male
5 1105 Catty 22 Female
6 1106 M 3 Female
7 1107 N 43 Male
8 1108 A 13 Male
9 1109 S 12 Male
10 1110 David 33 Male
11 1111 Dw 3 Female
12 1112 Q 23 Male
13 1113 W 21 Female
print(data.head(3)) # 前三行
Student ID name age gender
0 1100 Kelly 22 Female
1 1101 Clo 21 Female
2 1102 Tilly 22 Female
print(data.tail(3)) # 后三行
Student ID name age gender
11 1111 Dw 3 Female
12 1112 Q 23 Male
13 1113 W 21 Female
6.2 导出数据
# 将资料存取成pickle
data.to_pickle('student.pickle')
# 读取pickle文件并打印
print(pd.read_pickle('student.pickle'))
7.Pandas合并操作
7.1 Pandas合并
7.1.1 concat
# 定义资料集
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)
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
print(df2)
a 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 1.0
print(df3)
a 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
# concat纵向合并
res = pd.concat([df1,df2,df3],axis=0)
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
# 上述合并过程中，index重复，重置index方法：只需要将index_ignore设定为True即可
res = pd.concat([df1,df2,df3],axis=0,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 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 合并方式
#定义资料集
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])
print(df1)
a b c d
1 0.0 0.0 0.0 0.0
2 0.0 0.0 0.0 0.0
3 0.0 0.0 0.0 0.0
print(df2)
b c d e
2 1.0 1.0 1.0 1.0
3 1.0 1.0 1.0 1.0
4 1.0 1.0 1.0 1.0
'''
join='outer',函数默认为join='outer'。此方法是依照column来做纵向合并，有相同的column上下合并在一起，
其他独自的column各自成列，原来没有值的位置皆为NaN填充。
'''
# 纵向"外"合并df1与df2
res = pd.concat([df1,df2],axis=0,join='outer')
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
# 修改index
res = pd.concat([df1,df2],axis=0,join='outer',ignore_index=True)
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
# join='inner'合并相同的字段，纵向"内"合并df1与df2
res = pd.concat([df1,df2],axis=0,join='inner')
print(res)
b c d
1 0.0 0.0 0.0
2 0.0 0.0 0.0
3 0.0 0.0 0.0
2 1.0 1.0 1.0
3 1.0 1.0 1.0
4 1.0 1.0 1.0
# join_axes(依照axes合并)
#定义资料集
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])
print(df1)
a b c d
1 0.0 0.0 0.0 0.0
2 0.0 0.0 0.0 0.0
3 0.0 0.0 0.0 0.0
print(df2)
b c d e
2 1.0 1.0 1.0 1.0
3 1.0 1.0 1.0 1.0
4 1.0 1.0 1.0 1.0
# 横向合并
res = pd.concat([df1,df2],axis=1)
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
4 NaN NaN NaN NaN 1.0 1.0 1.0 1.0
7.1.2 append添加数据
# append只有纵向合并，没有横向合并
#定义资料集
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'])
s1 = pd.Series([1,2,3,4], index=['a','b','c','d'])
# 将df2合并到df1下面,以及重置index,并打印出结果
res = df1.append(df2,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 1.0 1.0 1.0
4 1.0 1.0 1.0 1.0
5 1.0 1.0 1.0 1.0
# 合并多个df,将df2与df3合并至df1的下面,以及重置index,并打印出结果
res = df1.append([df2,df3], 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 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
# 合并series,将s1合并至df1，以及重置index，并打印结果
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
7.1.3 两种常用合并方式总结
res = pd.concat([df1, df2, df3], axis=0, ignore_index=True)
res1 = df1.append([df2, df3], ignore_index=True)
# 上述两种结果一样：
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
7.2.Pandas 合并 merge
7.2.1 定义资料集并打印
# 依据一组key合并
# 定义资料集并打印出
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'],
'C' : ['C0', 'C1', 'C2', 'C3'],
'D' : ['D0', 'D1', 'D2', 'D3']})
print(left)
key A B
0 K0 A0 B0
1 K1 A1 B1
2 K2 A2 B2
3 K3 A3 B3
print(right)
key C D
0 K0 C0 D0
1 K1 C1 D1
2 K2 C2 D2
3 K3 C3 D3
7.2.2 依据key column合并
res = pd.merge(left,right,on='key')
print(res)
key A B C D
0 K0 A0 B0 C0 D0
1 K1 A1 B1 C1 D1
2 K2 A2 B2 C2 D2
3 K3 A3 B3 C3 D3
7.2.3 两列合并
# 依据两组key合并
#定义资料集并打印出
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'],
'C': ['C0', 'C1', 'C2', 'C3'],
'D': ['D0', 'D1', 'D2', 'D3']})
print(left)
key1 key2 A B
0 K0 K0 A0 B0
1 K0 K1 A1 B1
2 K1 K0 A2 B2
3 K2 K1 A3 B3
print(right)
key1 key2 C D
0 K0 K0 C0 D0
1 K1 K0 C1 D1
2 K1 K0 C2 D2
3 K2 K0 C3 D3
# 依据key1与key2 columns进行合并，并打印出四种结果['left', 'right', 'outer', 'inner']
res = pd.merge(left, right, on=['key1', 'key2'], how='inner')
print(res)
key1 key2 A B C D
0 K0 K0 A0 B0 C0 D0
1 K1 K0 A2 B2 C1 D1
2 K1 K0 A2 B2 C2 D2
res = pd.merge(left, right, on=['key1', 'key2'], how='outer')
print(res)
key1 key2 A B C D
0 K0 K0 A0 B0 C0 D0
1 K0 K1 A1 B1 NaN NaN
2 K1 K0 A2 B2 C1 D1
3 K1 K0 A2 B2 C2 D2
4 K2 K1 A3 B3 NaN NaN
5 K2 K0 NaN NaN C3 D3
res = pd.merge(left, right, on=['key1', 'key2'], how='left')
print(res)
key1 key2 A B C D
0 K0 K0 A0 B0 C0 D0
1 K0 K1 A1 B1 NaN NaN
2 K1 K0 A2 B2 C1 D1
3 K1 K0 A2 B2 C2 D2
4 K2 K1 A3 B3 NaN NaN
res = pd.merge(left, right, on=['key1', 'key2'], how='right')
print(res)
key1 key2 A B C D
0 K0 K0 A0 B0 C0 D0
1 K1 K0 A2 B2 C1 D1
2 K1 K0 A2 B2 C2 D2
3 K2 K0 NaN NaN C3 D3
7.2.4 Indicator设置合并列名称
df1 = pd.DataFrame({'col1':[0,1],'col_left':['a','b']})
df2 = pd.DataFrame({'col1':[1,2,2],'col_right':[2,2,2]})
print(df1)
col1 col_left
0 0 a
1 1 b
print(df2)
col1 col_right
0 1 2
1 2 2
2 2 2
# 依据col1进行合并,并启用indicator=True,最后打印
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
# 自定义indicator column的名称,并打印出
res = pd.merge(df1,df2,on='col1',how='outer',indicator='indicator_column')
print(res)
col1 col_left col_right indicator_column
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
7.2.5 依据index合并
left = pd.DataFrame({'A': ['A0', 'A1', 'A2'],
'B': ['B0', 'B1', 'B2']},
index=['K0', 'K1', 'K2'])
right = pd.DataFrame({'C': ['C0', 'C2', 'C3'],
'D': ['D0', 'D2', 'D3']},
index=['K0', 'K2', 'K3'])
print(left)
A B
K0 A0 B0
K1 A1 B1
K2 A2 B2
print(right)
C D
K0 C0 D0
K2 C2 D2
K3 C3 D3
# 依据左右资料集的index进行合并,how='outer',并打印
res = pd.merge(left,right,left_index=True,right_index=True,how='outer')
print(res)
A B C D
K0 A0 B0 C0 D0
K1 A1 B1 NaN NaN
K2 A2 B2 C2 D2
K3 NaN NaN C3 D3
# 依据左右资料集的index进行合并,how='inner',并打印
res = pd.merge(left,right,left_index=True,right_index=True,how='inner')
print(res)
A B C D
K0 A0 B0 C0 D0
K2 A2 B2 C2 D2
7.2.6 解决overlapping的问题
boys = pd.DataFrame({'k': ['K0', 'K1', 'K2'], 'age': [1, 2, 3]})
girls = pd.DataFrame({'k': ['K0', 'K0', 'K3'], 'age': [4, 5, 6]})
print(boys)
k age
0 K0 1
1 K1 2
2 K2 3
print(girls)
k age
0 K0 4
1 K0 5
2 K3 6
# 使用suffixes解决overlapping的问题
# 比如将上面两个合并时,age重复了,则可通过suffixes设置,以此保证不重复,不同名
res = pd.merge(boys,girls,on='k',suffixes=['_boy','_girl'],how='inner')
print(res)
k age_boy age_girl
0 K0 1 4
1 K0 1 5
8.Pandas plot出图
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data = pd.Series(np.random.randn(1000), index=np.arange(1000))
print(data)
0 -0.090306
1 0.537896
2 0.577394
3 0.298975
4 -0.506510
...
995 0.243735
996 0.462577
997 0.054838
998 -0.514172
999 -0.592451
Length: 1000, dtype: float64
print(data.cumsum())
0 -0.090306
1 0.447590
2 1.024985
3 1.323960
4 0.817450
...
995 20.989576
996 21.452154
997 21.506992
998 20.992820
999 20.400369
Length: 1000, dtype: float64
# data本来就是一个数据，所以我们可以直接plot
data.plot()
plt.show()
# np.random.randn(1000,4) 随机生成1000行4列数据
# list("ABCD")会变为['A','B','C','D']
data = pd.DataFrame(
np.random.randn(1000,4),
index=np.arange(1000),
columns=list("ABCD")
)
data.cumsum()
data.plot()
plt.show()
ax = data.plot.scatter(x='A',y='B',color='DarkBlue',label='Class1')
# 将之下这个 data 画在上一个 ax 上面
data.plot.scatter(x='A',y='C',color='LightGreen',label='Class2',ax=ax)
plt.show()
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