python-数据分析-pandas

4.1 pandas及其数据结构

4.1.2Series数据结构及其创建

第一种：通过标量创建Series

import pandas as pd 
s1=pd.Series(62)
s1

0    62
dtype: int64

import pandas as pd 
s1=pd.Series(62,index=["x","y","z"])
s1

x    62
y    62
z    62
dtype: int64

第二种：通过列表创建Series

import pandas as pd 
s2=pd.Series([30,10,60],index=["x","y","z"])
s2

x    30
y    10
z    60
dtype: int64

第三种：通过字典创建Series

import pandas as pd
s3=pd.Series({"匪警":110,"火警":119,"急救中心":120,"交通事故":122})
s3

匪警        110
火警        119
急救中心    120
交通事故    122
dtype: int64

第四种：通过ndarray创建Series

import pandas as pd
import numpy as np
s4=pd.Series(np.arange(6),index=["a","b","c","d","e","f"])
s4

a    0
b    1
c    2
d    3
e    4
f    5
dtype: int32

values和index

import pandas as pd
s3=pd.Series({"匪警":110,"火警":119,"急救中心":120,"交通事故":122})
s3.index
s3.values

array([110, 119, 120, 122], dtype=int64)

索引和切片

import pandas as pd 
s2=pd.Series([30,10,60],index=["x","y","z"])
s2["x"]

30

s2[0]

30

s2[:2]

x    30
y    10
dtype: int64

4.1.3DataFrame数据结构及其创建

第一种：通过一维列表构成的字典创建DataFrame

import pandas as pd
d1={"姓名":["张三","李四","王五","赵六"],"数学":[87,45,34,98],"语文":[54,76,55,90],"计算机":[34,56,77,87]}
df1=pd.DataFrame(d1)
df1

import pandas as pd
d1={"姓名":["张三","李四","王五","赵六"],"数学":[87,45,34,98],"语文":[54,76,55,90],"计算机":[34,56,77,87]}
df1=pd.DataFrame(d1,index=[101,102,103,104])
df1

通过二维ndarray创建DataFrame

import pandas as pd
import numpy as np
nd1=np.arange(12).reshape(3,4)
nd1

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

df2=pd.DataFrame(nd1)
df2

df2=pd.DataFrame(nd1,index=["a","b","c"])
df2

values,index和columns三部分

import pandas as pd
d1={"姓名":["张三","李四","王五","赵六"],"数学":[87,45,34,98],"语文":[54,76,55,90],"计算机":[34,56,77,87]}
df1=pd.DataFrame(d1,index=[202201,202202,202203,202204])
df1

df1.columns

Index(['姓名', '数学', '语文', '计算机'], dtype='object')

df1.index

Int64Index([202201, 202202, 202203, 202204], dtype='int64')

df1.values

array([['张三', 87, 54, 34],
       ['李四', 45, 76, 56],
       ['王五', 34, 55, 77],
       ['赵六', 98, 90, 87]], dtype=object)

4.2利用pandas导入导出数据

导入外部数据

import pandas as pd
f1=pd.read_csv("C:\\Users\\wsy\\Desktop\\a.csv")
f1

import pandas as pd
f1=pd.read_csv("C:\\Users\\wsy\\Desktop\\b.csv",encoding="gbk")
f1

导出外部数据

4.3数据概览及预处理

import pandas as pd
pd.set_option("display.unicode.east_asian_width",True)#解决数据输出时列名不对齐的问题
df=pd.read_excel("C:\\Users\\wsy\\Desktop\\cj.xlsx")
df

4.3.1数据概览分析

利用基础属性查看数据基本信息

print("索引：",df.index)

索引： RangeIndex(start=0, stop=57, step=1)

print("列名：",df.columns)

列名： Index(['学号', '姓名', '性别', '专业', '英语', '数学', 'Python', '选修',
       '管理学'], dtype='object')

print("数据元素：",df.values[:10])

数据元素： [[2020802045 '魏天' '男' '信息管理与信息系统' 67.11666666666667 90.80000000000001
  93.0 95.0 106.0]
 [2020844001 '郭夏' '男' '国际贸易' 91.05 83.4 86.0 100.0 99.0]
 [2020844002 '王晓加' '男' nan 54.2 83.4 74.0 nan 90.0]
 [2020844003 '黄婷婷' '女' '国际贸易' 87.8 91.4 79.66 95.0 92.66]
 [2020844004 '赵小瑜' nan '国际贸易' 61.15 82.2 84.66 100.0 97.66]
 [2020844005 '辛禧' '男' '国际贸易' 65.125 88.6 68.0 80.0 81.0]
 [2020844007 '王晨' '男' '国际贸易' 62.4 80.0 65.0 90.0 78.0]
 [2020844008 '韩天' '男' '国际贸易' 96.25 91.0 85.0 97.0 98.0]
 [2020844009 '刘玉' '女' '国际贸易' 89.05 91.4 80.32 100.0 93.32]
 [2020844010 '谢亚鹏' '男' '市场营销' 70.5 85.2 60.0 90.0 73.0]]

print("数据类型：\n",df.dtypes)

数据类型：
 学号        int64
姓名       object
性别       object
专业       object
英语      float64
数学      float64
Python    float64
选修      float64
管理学    float64
dtype: object

利用基础属性查看数据规模

print("元素个数：",df.size)

元素个数： 513

print("维度数：",df.ndim)

维度数： 2

print("形状：",df.shape)

形状： (57, 9)

print("行数：",df.index.size)

行数： 57

print("列数",df.columns.size)

列数 9

利用常用方法查看样本数据

df.head()

df.head(2)

df.tail()

df.tail(3)

利用常用方法查看数据质量

print(df.notnull())#查看数据的缺失值情况

    学号  姓名   性别   专业  英语  数学  Python   选修  管理学
0   True  True   True   True  True  True    True   True    True
1   True  True   True   True  True  True    True   True    True
2   True  True   True  False  True  True    True  False    True
3   True  True   True   True  True  True    True   True    True
4   True  True  False   True  True  True    True   True    True
5   True  True   True   True  True  True    True   True    True
6   True  True   True   True  True  True    True   True    True
7   True  True   True   True  True  True    True   True    True
8   True  True   True   True  True  True    True   True    True
9   True  True   True   True  True  True    True   True    True
10  True  True   True   True  True  True    True  False    True
11  True  True   True   True  True  True    True   True    True
12  True  True   True   True  True  True    True   True    True
13  True  True   True   True  True  True    True   True    True
14  True  True   True   True  True  True    True   True    True
15  True  True   True   True  True  True    True   True    True
16  True  True   True   True  True  True    True   True    True
17  True  True   True   True  True  True    True   True    True
18  True  True   True   True  True  True    True   True    True
19  True  True   True   True  True  True    True   True    True
20  True  True  False   True  True  True    True   True    True
21  True  True   True   True  True  True    True   True    True
22  True  True   True   True  True  True    True   True    True
23  True  True   True  False  True  True    True   True    True
24  True  True   True   True  True  True    True   True    True
25  True  True   True   True  True  True    True   True    True
26  True  True   True   True  True  True    True  False    True
27  True  True   True   True  True  True    True   True    True
28  True  True   True   True  True  True    True   True    True
29  True  True   True   True  True  True    True   True    True
30  True  True   True   True  True  True    True   True    True
31  True  True   True   True  True  True    True   True    True
32  True  True   True   True  True  True    True   True    True
33  True  True   True   True  True  True    True   True    True
34  True  True   True   True  True  True    True   True    True
35  True  True   True   True  True  True    True   True    True
36  True  True   True   True  True  True    True   True    True
37  True  True   True   True  True  True    True   True    True
38  True  True   True   True  True  True    True   True    True
39  True  True   True   True  True  True    True   True    True
40  True  True   True   True  True  True    True   True    True
41  True  True   True   True  True  True    True   True    True
42  True  True   True  False  True  True    True   True    True
43  True  True   True   True  True  True    True   True    True
44  True  True   True   True  True  True    True  False    True
45  True  True  False   True  True  True    True   True    True
46  True  True   True   True  True  True    True   True    True
47  True  True   True   True  True  True    True   True    True
48  True  True   True   True  True  True    True   True    True
49  True  True   True   True  True  True    True   True    True
50  True  True   True   True  True  True    True   True    True
51  True  True   True   True  True  True    True   True    True
52  True  True   True   True  True  True    True   True    True
53  True  True   True   True  True  True    True   True    True
54  True  True   True   True  True  True    True   True    True
55  True  True   True   True  True  True    True   True    True
56  True  True   True   True  True  True    True   True    True

print(df.isnull())#isna()是isnull()的别名

     学号   姓名   性别   专业   英语   数学  Python   选修  管理学
0   False  False  False  False  False  False   False  False   False
1   False  False  False  False  False  False   False  False   False
2   False  False  False   True  False  False   False   True   False
3   False  False  False  False  False  False   False  False   False
4   False  False   True  False  False  False   False  False   False
5   False  False  False  False  False  False   False  False   False
6   False  False  False  False  False  False   False  False   False
7   False  False  False  False  False  False   False  False   False
8   False  False  False  False  False  False   False  False   False
9   False  False  False  False  False  False   False  False   False
10  False  False  False  False  False  False   False   True   False
11  False  False  False  False  False  False   False  False   False
12  False  False  False  False  False  False   False  False   False
13  False  False  False  False  False  False   False  False   False
14  False  False  False  False  False  False   False  False   False
15  False  False  False  False  False  False   False  False   False
16  False  False  False  False  False  False   False  False   False
17  False  False  False  False  False  False   False  False   False
18  False  False  False  False  False  False   False  False   False
19  False  False  False  False  False  False   False  False   False
20  False  False   True  False  False  False   False  False   False
21  False  False  False  False  False  False   False  False   False
22  False  False  False  False  False  False   False  False   False
23  False  False  False   True  False  False   False  False   False
24  False  False  False  False  False  False   False  False   False
25  False  False  False  False  False  False   False  False   False
26  False  False  False  False  False  False   False   True   False
27  False  False  False  False  False  False   False  False   False
28  False  False  False  False  False  False   False  False   False
29  False  False  False  False  False  False   False  False   False
30  False  False  False  False  False  False   False  False   False
31  False  False  False  False  False  False   False  False   False
32  False  False  False  False  False  False   False  False   False
33  False  False  False  False  False  False   False  False   False
34  False  False  False  False  False  False   False  False   False
35  False  False  False  False  False  False   False  False   False
36  False  False  False  False  False  False   False  False   False
37  False  False  False  False  False  False   False  False   False
38  False  False  False  False  False  False   False  False   False
39  False  False  False  False  False  False   False  False   False
40  False  False  False  False  False  False   False  False   False
41  False  False  False  False  False  False   False  False   False
42  False  False  False   True  False  False   False  False   False
43  False  False  False  False  False  False   False  False   False
44  False  False  False  False  False  False   False   True   False
45  False  False   True  False  False  False   False  False   False
46  False  False  False  False  False  False   False  False   False
47  False  False  False  False  False  False   False  False   False
48  False  False  False  False  False  False   False  False   False
49  False  False  False  False  False  False   False  False   False
50  False  False  False  False  False  False   False  False   False
51  False  False  False  False  False  False   False  False   False
52  False  False  False  False  False  False   False  False   False
53  False  False  False  False  False  False   False  False   False
54  False  False  False  False  False  False   False  False   False
55  False  False  False  False  False  False   False  False   False
56  False  False  False  False  False  False   False  False   False

print("df中每个特征的缺失情况：\n",df.isna().sum())

df中每个特征的缺失情况：
 学号      0
姓名      0
性别      3
专业      3
英语      0
数学      0
Python    0
选修      4
管理学    0
dtype: int64

#判断数据中是否有重复的
df.duplicated()

0     False
1     False
2     False
3     False
4     False
5     False
6     False
7     False
8     False
9     False
10    False
11    False
12    False
13    False
14    False
15    False
16    False
17    False
18    False
19    False
20    False
21    False
22    False
23    False
24    False
25    False
26    False
27    False
28    False
29    False
30    False
31     True
32     True
33    False
34    False
35    False
36    False
37    False
38    False
39    False
40    False
41    False
42    False
43    False
44    False
45    False
46    False
47     True
48    False
49    False
50    False
51    False
52    False
53    False
54    False
55    False
56    False
dtype: bool

#判断指定列中是否有重复的
df.duplicated("姓名")

0     False
1     False
2     False
3     False
4     False
5     False
6     False
7     False
8     False
9     False
10    False
11    False
12    False
13    False
14    False
15    False
16    False
17    False
18    False
19    False
20    False
21    False
22    False
23    False
24    False
25    False
26    False
27    False
28    False
29    False
30    False
31     True
32     True
33    False
34    False
35    False
36    False
37    False
38    False
39    False
40    False
41    False
42    False
43    False
44    False
45    False
46    False
47     True
48    False
49    False
50    False
51    False
52    False
53    False
54    False
55    False
56    False
dtype: bool

df.info()#给出样本数据的相关信息概览 ：行数，列数，列索引，列非空值个数，列类型，内存占用

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 57 entries, 0 to 56
Data columns (total 9 columns):
学号        57 non-null int64
姓名        57 non-null object
性别        54 non-null object
专业        54 non-null object
英语        57 non-null float64
数学        57 non-null float64
Python    57 non-null float64
选修        53 non-null float64
管理学       57 non-null float64
dtypes: float64(5), int64(1), object(3)
memory usage: 4.1+ KB

4.3.2数据清洗

import pandas as pd
pd.set_option("display.unicode.east_asian_width",True)#解决数据输出时列名不对齐的问题
df=pd.read_excel("C:\\Users\\wsy\\Desktop\\cj.xlsx")
df

缺失值处理

#存在任一缺失值即删除
df1=df.dropna()
print("删出前",df.shape)
print("删出后",df1.shape)

删出前 (57, 9)
删出后 (48, 9)

#所有列均为缺失值即删除
df1=df.dropna(how="all")
print("删出前",df.shape)
print("删出后",df1.shape)

删出前 (57, 9)
删出后 (57, 9)

#指定列均为缺失值即删除
df1=df.dropna(how="all",subset=["专业","选修"])
print("删出前",df.shape)
print("删出后",df1.shape)

删出前 (57, 9)
删出后 (56, 9)

#保留某些属性不存在缺失值的情况
df1=df[df["性别"].notnull()]
print("删出前",df.shape)
print("删出后",df1.shape)
df1

删出前 (57, 9)
删出后 (54, 9)

#将缺失值NaN填充为0
df["选修"].fillna(0)

0      95.0
1     100.0
2       0.0
3      95.0
4     100.0
5      80.0
6      90.0
7      97.0
8     100.0
9      90.0
10      0.0
11    100.0
12    100.0
13     80.0
14    100.0
15     95.0
16    100.0
17    100.0
18    100.0
19     87.0
20    100.0
21    100.0
22    100.0
23    100.0
24    100.0
25    100.0
26      0.0
27    100.0
28    100.0
29    100.0
30    100.0
31    100.0
32    100.0
33    100.0
34    100.0
35    100.0
36    100.0
37     68.0
38    100.0
39    100.0
40     77.0
41    100.0
42    100.0
43    100.0
44      0.0
45    100.0
46     90.0
47     90.0
48    100.0
49    100.0
50     90.0
51    100.0
52    100.0
53    100.0
54    100.0
55    100.0
56    100.0
Name: 选修, dtype: float64

#将缺失值NaN填充与后面的值相同
df["选修"].fillna(method="ffill")

0      95.0
1     100.0
2      95.0
3      95.0
4     100.0
5      80.0
6      90.0
7      97.0
8     100.0
9      90.0
10    100.0
11    100.0
12    100.0
13     80.0
14    100.0
15     95.0
16    100.0
17    100.0
18    100.0
19     87.0
20    100.0
21    100.0
22    100.0
23    100.0
24    100.0
25    100.0
26    100.0
27    100.0
28    100.0
29    100.0
30    100.0
31    100.0
32    100.0
33    100.0
34    100.0
35    100.0
36    100.0
37     68.0
38    100.0
39    100.0
40     77.0
41    100.0
42    100.0
43    100.0
44    100.0
45    100.0
46     90.0
47     90.0
48    100.0
49    100.0
50     90.0
51    100.0
52    100.0
53    100.0
54    100.0
55    100.0
56    100.0
Name: 选修, dtype: float64

import numpy as np
#将缺失值NaN填充选修课的平均分
df["选修"].fillna(np.mean(df["选修"]))

0      95.000000
1     100.000000
2      96.679245
3      95.000000
4     100.000000
5      80.000000
6      90.000000
7      97.000000
8     100.000000
9      90.000000
10     96.679245
11    100.000000
12    100.000000
13     80.000000
14    100.000000
15     95.000000
16    100.000000
17    100.000000
18    100.000000
19     87.000000
20    100.000000
21    100.000000
22    100.000000
23    100.000000
24    100.000000
25    100.000000
26     96.679245
27    100.000000
28    100.000000
29    100.000000
30    100.000000
31    100.000000
32    100.000000
33    100.000000
34    100.000000
35    100.000000
36    100.000000
37     68.000000
38    100.000000
39    100.000000
40     77.000000
41    100.000000
42    100.000000
43    100.000000
44     96.679245
45    100.000000
46     90.000000
47     90.000000
48    100.000000
49    100.000000
50     90.000000
51    100.000000
52    100.000000
53    100.000000
54    100.000000
55    100.000000
56    100.000000
Name: 选修, dtype: float64

重复值处理

#去除全部重复数据
df1=df.drop_duplicates()
print("去重前：",df.shape)
print("去重后：",df1.shape)

去重前： (57, 9)
去重后： (54, 9)

#去除指定列中重复数据
df1=df.drop_duplicates(["专业"])
print("去重前：",df.shape)
print("去重后：",df1.shape)
df1

去重前： (57, 9)
去重后： (6, 9)

#去除指定列中重复数据，设置keep参数
df1=df.drop_duplicates(["专业"],keep="last")
print("去重前：",df.shape)
print("去重后：",df1.shape)
df1

去重前： (57, 9)
去重后： (6, 9)

#去除指定若干列中重复数据
df1=df.drop_duplicates(["学号","姓名"])
print("去重前：",df.shape)
print("去重后：",df1.shape)
df1

去重前： (57, 9)
去重后： (54, 9)

4.3.3数据的抽取和合并

import pandas as pd
pd.set_option("display.unicode.east_asian_width",True)#解决数据输出时列名不对齐的问题
df=pd.read_excel("C:\\Users\\wsy\\Desktop\\cj.xlsx")

数据抽取

1、抽取列

df.学号

0     2020802045
1     2020844001
2     2020844002
3     2020844003
4     2020844004
5     2020844005
6     2020844007
7     2020844008
8     2020844009
9     2020844010
10    2020844011
11    2020844012
12    2020844013
13    2020844014
14    2020844015
15    2020844017
16    2020844018
17    2020844019
18    2020844020
19    2020844021
20    2020844022
21    2020844023
22    2020844024
23    2020844025
24    2020844026
25    2020844027
26    2020844028
27    2020844029
28    2020844030
29    2020848001
30    2020848002
31    2020848002
32    2020848002
33    2020848003
34    2020848004
35    2020848005
36    2020848006
37    2020848007
38    2020848008
39    2020848010
40    2020848011
41    2020848013
42    2020848014
43    2020848015
44    2020848016
45    2020848017
46    2020848018
47    2020848018
48    2020848019
49    2020848020
50    2020848021
51    2020848023
52    2020848024
53    2020848027
54    2020848028
55    2020848029
56    2020848031
Name: 学号, dtype: int64

df["学号"]
type(df["学号"])

pandas.core.series.Series

df[["学号"]]
type(df[["学号"]])

pandas.core.frame.DataFrame

df[["学号","姓名","专业"]]

df.loc[:,["学号"]]

df.loc[:,["学号","姓名","专业"]]

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

2、抽取行

df.loc[1:20,]

df.iloc[1:20,]

df.loc[[1,2,3,6,7],]

df.iloc[[1,2,3,16,7],]

df.loc[df.英语>90,]

4、抽取行列

df[["学号","姓名","专业"]][:10]

df[["学号","姓名","专业"]][df.数学>90]

df.loc[1:10,["学号","姓名","专业"]]

df.iloc[1:10,2:5]

数据合并

df1=df[["学号","姓名","专业"]][:10]
df2=df[["学号","Python"]][:10]
df3=df[["数学","选修"]][:10]
df4=df.loc[20:25,["学号","姓名","专业"]]

df1

df2

1、按列合并

df1.join(df2)#有同名列，无法区分，报错

---------------------------------------------------------------------------

ValueError                                Traceback (most recent call last)

<ipython-input-237-92ca22d0224c> in <module>()
----> 1 df1.join(df2)#有同名列，无法区分，报错


D:\anacoda\anzhuang\lib\site-packages\pandas\core\frame.py in join(self, other, on, how, lsuffix, rsuffix, sort)
   6334         # For SparseDataFrame's benefit
   6335         return self._join_compat(other, on=on, how=how, lsuffix=lsuffix,
-> 6336                                  rsuffix=rsuffix, sort=sort)
   6337 
   6338     def _join_compat(self, other, on=None, how='left', lsuffix='', rsuffix='',


D:\anacoda\anzhuang\lib\site-packages\pandas\core\frame.py in _join_compat(self, other, on, how, lsuffix, rsuffix, sort)
   6349             return merge(self, other, left_on=on, how=how,
   6350                          left_index=on is None, right_index=True,
-> 6351                          suffixes=(lsuffix, rsuffix), sort=sort)
   6352         else:
   6353             if on is not None:


D:\anacoda\anzhuang\lib\site-packages\pandas\core\reshape\merge.py in merge(left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator, validate)
     60                          copy=copy, indicator=indicator,
     61                          validate=validate)
---> 62     return op.get_result()
     63 
     64 


D:\anacoda\anzhuang\lib\site-packages\pandas\core\reshape\merge.py in get_result(self)
    572 
    573         llabels, rlabels = items_overlap_with_suffix(ldata.items, lsuf,
--> 574                                                      rdata.items, rsuf)
    575 
    576         lindexers = {1: left_indexer} if left_indexer is not None else {}


D:\anacoda\anzhuang\lib\site-packages\pandas\core\internals.py in items_overlap_with_suffix(left, lsuffix, right, rsuffix)
   5242         if not lsuffix and not rsuffix:
   5243             raise ValueError('columns overlap but no suffix specified: '
-> 5244                              '{rename}'.format(rename=to_rename))
   5245 
   5246         def lrenamer(x):


ValueError: columns overlap but no suffix specified: Index(['学号', 'Python'], dtype='object')

df1.join(df3)#默认以index为连接主键，可以不需要同名列

df1.join(df2,lsuffix="x")#给同名列起别名

df1.merge(df3)#必须有同名列

---------------------------------------------------------------------------

MergeError                                Traceback (most recent call last)

<ipython-input-242-036768b080a3> in <module>()
----> 1 df1.merge(df3)#必须有同名列


D:\anacoda\anzhuang\lib\site-packages\pandas\core\frame.py in merge(self, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator, validate)
   6387                      right_on=right_on, left_index=left_index,
   6388                      right_index=right_index, sort=sort, suffixes=suffixes,
-> 6389                      copy=copy, indicator=indicator, validate=validate)
   6390 
   6391     def round(self, decimals=0, *args, **kwargs):


D:\anacoda\anzhuang\lib\site-packages\pandas\core\reshape\merge.py in merge(left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator, validate)
     59                          right_index=right_index, sort=sort, suffixes=suffixes,
     60                          copy=copy, indicator=indicator,
---> 61                          validate=validate)
     62     return op.get_result()
     63 


D:\anacoda\anzhuang\lib\site-packages\pandas\core\reshape\merge.py in __init__(self, left, right, how, on, left_on, right_on, axis, left_index, right_index, sort, suffixes, copy, indicator, validate)
    544             warnings.warn(msg, UserWarning)
    545 
--> 546         self._validate_specification()
    547 
    548         # note this function has side effects


D:\anacoda\anzhuang\lib\site-packages\pandas\core\reshape\merge.py in _validate_specification(self)
   1033                         'left_index={lidx}, right_index={ridx}'
   1034                         .format(lon=self.left_on, ron=self.right_on,
-> 1035                                 lidx=self.left_index, ridx=self.right_index))
   1036                 if not common_cols.is_unique:
   1037                     raise MergeError("Data columns not unique: {common!r}"


MergeError: No common columns to perform merge on. Merge options: left_on=None, right_on=None, left_index=False, right_index=False

df1.merge(df2)#可以按照同名列进行连接，自动删除同名列

ddf=pd.merge(df1,df2)
ddf

ddf=pd.concat([df1,df2],axis=1)#按行拼接
ddf

2、按行合并

df1.append(df4)#有相同列

df1.append(df3) #列不相同，实现并集拼接

D:\anacoda\anzhuang\lib\site-packages\pandas\core\frame.py:6211: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version
of pandas will change to not sort by default.

To accept the future behavior, pass 'sort=False'.

To retain the current behavior and silence the warning, pass 'sort=True'.

  sort=sort)

pd.concat([df1,df2,df3],axis=0,join="outer")#按行拼接   #inner 

D:\anacoda\anzhuang\lib\site-packages\ipykernel_launcher.py:1: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version
of pandas will change to not sort by default.

To accept the future behavior, pass 'sort=False'.

To retain the current behavior and silence the warning, pass 'sort=True'.

  """Entry point for launching an IPython kernel.

4.3.4数据的增删改

import pandas as pd
pd.set_option("display.unicode.east_asian_width",True)#解决数据输出时列名不对齐的问题
df=pd.read_excel("C:\\Users\\wsy\\Desktop\\cj.xlsx")
df