|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?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?的考试成绩
                   index = list('ABCDEFGHIJ'),                          # ?标签，?户
                   columns=['Python','Tensorflow','Keras'])             # 考试科?
df2 = pd.DataFrame(data = np.random.randint(0,150,size = [10,3]),       # 计算机科?的考试成绩
                   index = list('KLMNOPQRST'),                          # ?标签，?户
                   columns=['Python','Tensorflow','Keras'])             # 考试科?
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?串联，df2的?追加df2?后?
df1.append(df2)                                                         # 在df1后?追加df2
pd.concat([df1,df3],axis = 1)                                           # df1和df2列串联，df2的列追加到df1列后?

### --- 插入

~~~     # 插?
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?列
df
# 对?的操作，使?追加append，默认在最后?，?法指定位置
# 如果想要在指定位置插??：切割-添加-合并

### --- oin SQL?格合并

~~~     # oin SQL?格合并
~~~     # 数据集的合并（merge）或连接（join）运算是通过?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?操作。pandas的merge函数是数据集进?join运算的主要切?点。

import pandas as pd
import numpy as np
# 表?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?中记录的是name和身?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?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?列标签 name进?合并
         right_on = '名字')                                             # 右边DataFrame使?列标签 名字进?合并
# 创建10名学?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?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?merge进?合并，它俩有共同的?索引
pd.merge(left = df4,right = score_mean,
         left_index=True,                                               # 左边DataFrame使??索引进?合并
         right_index=True)                                              # 右边的DataFrame使??索引进?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

bdv027-python

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

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