|NO.Z.00042|——————————|BigDataEnd|——|Hadoop&Python.v06|——|Arithmetic.v06|Pandas数据分析库：Pandas数据集成|

一、数据集成：pandas 提供了多种将 Series、DataFrame 对象组合在⼀起的功能

### --- concat数据串联

~~~     # concat数据串联
import pandas as pd
import numpy as np
df1 = pd.DataFrame(data = np.random.randint(0,150,size = [10,3]),       # 计算机科⽬的考试成绩
                   index = list('ABCDEFGHIJ'),                          # ⾏标签，⽤户
                   columns=['Python','Tensorflow','Keras'])             # 考试科⽬
df2 = pd.DataFrame(data = np.random.randint(0,150,size = [10,3]),       # 计算机科⽬的考试成绩
                   index = list('KLMNOPQRST'),                          # ⾏标签，⽤户
                   columns=['Python','Tensorflow','Keras'])             # 考试科⽬
df3 = pd.DataFrame(data = np.random.randint(0,150,size = (10,2)),
                   index = list('ABCDEFGHIJ'),
                   columns=['PyTorch','Paddle'])
pd.concat([df1,df2],axis = 0)                                           # df1和df2⾏串联，df2的⾏追加df2⾏后⾯
df1.append(df2)                                                         # 在df1后⾯追加df2
pd.concat([df1,df3],axis = 1)                                           # df1和df2列串联，df2的列追加到df1列后⾯

### --- 插入

~~~     # 插⼊
import numpy as np
import pandas as pd
df = pd.DataFrame(data = np.random.randint(0,151,size = (10,3)),
                  index = list('ABCDEFGHIJ'),
                  columns = ['Python','Keras','Tensorflow'])
df.insert(loc = 1,column='Pytorch',value=1024)                          # 插⼊列
df
# 对⾏的操作，使⽤追加append，默认在最后⾯，⽆法指定位置
# 如果想要在指定位置插⼊⾏：切割-添加-合并

### --- oin SQL⻛格合并

~~~     # oin SQL⻛格合并
~~~     # 数据集的合并（merge）或连接（join）运算是通过⼀个或者多个键将数据链接起来的。这些运算是关
~~~     # 系型数据库的核⼼操作。pandas的merge函数是数据集进⾏join运算的主要切⼊点。

import pandas as pd
import numpy as np
# 表⼀中记录的是name和体重信息
df1 = pd.DataFrame(data = {'name':
['softpo','Daniel','Brandon','Ella'],'weight':[70,55,75,65]})
# 表⼆中记录的是name和身⾼信息
df2 = pd.DataFrame(data = {'name':
['softpo','Daniel','Brandon','Cindy'],'height':[172,170,170,166]})
df3 = pd.DataFrame(data = {'名字':
['softpo','Daniel','Brandon','Cindy'],'height':[172,170,170,166]})
# 根据共同的name将俩表的数据，进⾏合并
pd.merge(df1,df2,
         how = 'inner',                                                 # 内合并代表两对象交集
         on = 'name')
pd.merge(df1,df3,
         how = 'outer',                                                 # 全外连接，两对象并集
         left_on = 'name',                                              # 左边DataFrame使⽤列标签 name进⾏合并
         right_on = '名字')                                             # 右边DataFrame使⽤列标签 名字进⾏合并
# 创建10名学⽣的考试成绩
df4 = pd.DataFrame(data = np.random.randint(0,151,size = (10,3)),
                   index = list('ABCDEFHIJK'),
                   columns=['Python','Keras','Tensorflow'])
# 计算每位学⽣各科平均分，转换成DataFrame
score_mean = pd.DataFrame(df4.mean(axis = 1).round(1),columns=['平均分'])
# 将平均分和df3使⽤merge进⾏合并，它俩有共同的⾏索引
pd.merge(left = df4,right = score_mean,
         left_index=True,                                               # 左边DataFrame使⽤⾏索引进⾏合并
         right_index=True)                                              # 右边的DataFrame使⽤⾏索引进⾏合并

---

 

 

 

 

 

 

 

 

 

---

Walter Savage Landor:strove with none,for none was worth my strife.Nature I loved and, next to Nature, Art:I warm'd both hands before the fire of life.It sinks, and I am ready to depart

                                                                                                                                                   ——W.S.Landor

---