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编程语言Python 基础教程 —— Pandas 库常用方法实例说明

admin python 2021-05-25 09:24:42 基础教程 实例 常用 方法 python 
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目录

1. 常用方法 pandas.Series

2.pandas.DataFrame ([data],[index]) 根据行建立数据

3. pandas.DataFrame ({dic})根据列建立数据

4. pandas.DataFrame([list])根据数据建立列数据

5. loc / iloc 数据筛选

6. 多级行索引

7. 使用 pandas.MultiIndex 显式创建多级行索引

8. 多级行索引的升维及降维

9.在DataFrame 中添加列 insert

10. 排序 sort

11. 根据多级索引进行数据统计

12. 简易合并 pandas.concat

13. pandas.merge 合并与链接

14. 列统计函数 describe

15. groupby 分组运算

16. pivot_table 数据透视表

17. 高性能列间运算 eval 与 query

1. pandas.Series(data=None, index=None, dtype=None, name=None, copy=False, fastpath=False )

data:支持多种数据类型

index:可选参数,数据索引,如为空则是由0开始的整数排序,索引确定后只能查看不能修改

dtype: 数据类型,可为空

name: 列名,可为空

 1 # index 为空时,默认由0开始顺序排列
 2 list=pd.Series(['a','b','c'])
 3 print(list)
 4 --------------------------------------------------------
 5 out:
 6 1 a
 7 2 b
 8 3 c
 9 =======================================================
10 
11 #使用 index 输入
12 list=pd.Series(['Leslie','Jack','Mike'],[2,1,3])
13 print(list)
14 --------------------------------------------------------
15 out:
16 2 Leslie
17 1 Jack
18 3 Mike
19 ========================================================
20 
21 # 以dic字典输入数据
22 list=pd.Series({2:'Leslie',1:'Jack',3:'Mike'})
23 print(list)
24 --------------------------------------------------------
25 out:
26 2 Leslie
27 1 Jack
28 3 Mike
29 ========================================================
30 
31 #显示筛选结果
32 list=pd.Series({2:'Leslie',1:'Jack',3:'Mike'},[2,3])
33 print(list)
34 --------------------------------------------------------
35 out:
36 2 Leslie
37 3 Mike
38 =========================================================
39 
40 #指定列名name
41 price=pd.Series(['68','90'],name='price',index=['JAVA IN ACTION','Python Data Science Handbook'])
42 print(price)
43 --------------------------------------------------------
44 out:
45 JAVA IN ACTION                  68
46 Python Data Science Handbook    90
47 Name: price, dtype: object

注意:列名默认以0开始的整数

回到目录

2. pandas.DataFrame ([data],[index]) 根据行建立数据

   DataFrame可看作panads的行索引,最基础是通过单个已有的series对象创建DataFrame

   data: 被panads序列化的行数据集

index:行索引集合,为空时将由0开始按整数排列

1 java=pd.Series({'price':68,'count':1})
2 python=pd.Series({'price':90,'count':1})
3 frame=pd.DataFrame(data=[java,python],index=['JAVA IN ACTION','Python Data Science Handbook'])
4 print(frame)

输出

注意:data, index 参数必须是集合,否则会报错

回到目录

3. pandas.DataFrame ({dic}) 根据列建立数据

可通过此方法利用字典建立列数据

1 #每本书的价格列
2 price=pd.Series({'JAVA IN ACTION':68,'Python Data Science Handbook':90})
3 #每本书的数据列
4 count=pd.Series({'JAVA IN ACTION':1,'Python Data Science Handbook':1})
5 #使用字典建立DataFrame
6 frame=pd.DataFrame({'price':price,'count':count})
7 print(frame)

结果与上面一样,系统会根据行索引绑定数据

回到目录

4. pandas.DataFrame([list])根据数据建立列数据

注意:使用 list 与 dic 最大不同在 dic 在调用于生成列时先通过 index 指定行索引

1 price1=pd.Series(['68','90'],name='price1',index=['JAVA IN ACTION','Python Data Science Handbook'])
2 count1=pd.Series(['1','1'],name='count1',index=['JAVA IN ACTION','Python Data Science Handbook'])
3 frame1=pd.DataFrame([price1,count1])
4 print(frame1)

对比上面例子,当以数组建立 DataFrame 时,数组内的数据默认为行数据

回到目录

5. loc 、iloc数据筛选

data=pandas.Series(['Leslie',‘Rose','Jack','Mike'])

显式索引即 data[ 'Leslie' : 'Jack'] 作切片时,结果包含最后一个索引即 Jack

隐式索引即 data[ 0 : 2 ]作切片时,结果不包含最后一个

为了避免混淆,建议使用 loc(显式)、iloc(隐式)

data[ 'Leslie' : 'Jack'] 等效于data.loc[ 'Leslie' : 'Jack']

data[ 0 : 2 ]等效于data.iloc[ 0 : 2 ]

同时,loc 也可作为数据的筛选条件

1 age=pd.Series({'Leslie':28,'Jack':32,'Rose':18})
2 address=pd.Series({'Jack':'Beijing','Rose':'Shanghai','Leslie':'Guangzhou'})
3 person=pd.DataFrame({'address':address,'age':age})
4 print(person.loc[person['age']<30])

显示结果

多条件筛选

1 age=pd.Series({'Leslie':28,'Jack':32,'Rose':18})
2 address=pd.Series({'Jack':'Beijing','Rose':'Shanghai','Leslie':'Guangzhou'})
3 person=pd.DataFrame({'address':address,'age':age})
4 print(person.loc[(person['age']<30) & (person['age']>20)])

回到目录

6. 多级行索引

将 index 行索引分成多维级别

1 test=pd.DataFrame(data=np.random.rand(4,2),
2                    index=[['index0','index0','index1','index1'],[0,1,0,1]],
3                    columns=['column0','column1'])
4 print(test)

结果

可为多级行索引建立名称,容易管理

1 test1=pd.DataFrame(data=np.random.rand(4,2),
2                    index=[['index0','index0','index1','index1'],[0,1,0,1]],
3                    columns=['column0','column1'])
4 test1.index.names=['indexName0','indexName1']
5 print(test1)

结果

回到目录

7. 使用 pandas.MultiIndex 显式创建多级行索引

使用数组方法MultiIndex.from_arrays ()

1 data=[['Python Learning from Scratch','1','68'],['Pro Apahe Hadoop','1','105'],['Python Crash Course','2','89']
2     ,['Beginning Python From Novice','1','76'],['Python Appclications','2','120'],['Deep Learning with TensorFlow','1','58']]
3 index=pd.MultiIndex.from_arrays([['Leslie','Leslie','Jack','Jack','Mike','Mike'],[2020,2021,2020,2021,2020,2021]])
4 column=['Book','Count','Price']
5 book=pd.DataFrame(data=data,index=index,columns=column)

使用索引值的元组方法MultiIndex.from_tuples()

1 data=[['Python Learning from Scratch','1','68'],['Pro Apahe Hadoop','1','105'],['Python Crash Course','2','89']
2     ,['Beginning Python From Novice','1','76'],['Python Appclications','2','120'],['Deep Learning with TensorFlow','1','58']]
3 index=pd.MultiIndex.from_tuples([('Leslie',2020),('Leslie',2021),('Jack',2020),('Jack',2021),('Mike',2020),('Mike',2021)])
4 column=['Book','Count','Price']
5 book=pd.DataFrame(data=data,index=index,columns=column)

使用笛卡乐积方法 MultiIndex.from_product ()

1 data=[['Python Learning from Scratch','1','68'],['Pro Apahe Hadoop','1','105'],['Python Crash Course','2','89']
2     ,['Beginning Python From Novice','1','76'],['Python Appclications','2','120'],['Deep Learning with TensorFlow','1','58']]
3 index=pd.MultiIndex.from_product([['Leslie','Jack','Mike'],[2020,2021]])
4 column=['Book','Count','Price']
5 book=pd.DataFrame(data=data,index=index,columns=column)

上面3种方法可获取相同结果,3种方法有不同的使用场景

回到目录

8. 多级行索引的升维及降维

继续以上面例子为例,使用 stack(level) 可以把 DataFrame 升维,使用 unstack(level) 可能把 DataFrame 降维

注意:数据升维降维后都将返回一个数据集的副本,修改其值不会影响原数据

 1 data=[['Python Learning from Scratch',1,68],['Pro Apahe Hadoop',1,105],['Python Crash Course',2,89]
 2     ,['Beginning Python From Novice',1,76],['Python Appclications',2,120],['Deep Learning with TensorFlow',1,58]]
 3 index=pd.MultiIndex.from_tuples([('Leslie',2020),('Leslie',2021),('Jack',2020),('Jack',2021),('Mike',2020),('Mike',2021)])
 4 column=['Book','Count','Price']
 5 book=pd.DataFrame(data=data,index=index,columns=column)
 6 //计算总体价格
 7 total=book['Price']*book['Count']
 8 print(str(total)+'\n')
 9 //降维显示,把二维的行索引转化为列
10 print(total.unstack())

结果

使用 level 参数可以设置降维的层级,level 为 0 即把多维行的第一维度进行转换 ( 即name参数 ),level 为 1 即把多维行的第二维度进行转换 ( 即 year 参数 )

1 data=[['Python Learning from Scratch',1,68],['Pro Apahe Hadoop',1,105],['Python Crash Course',2,89]
2     ,['Beginning Python From Novice',1,76],['Python Appclications',2,120],['Deep Learning with TensorFlow',1,58]]
3 index=pd.MultiIndex.from_tuples([('Leslie',2020),('Leslie',2021),('Jack',2020),('Jack',2021),('Mike',2020),('Mike',2021)])
4 column=['Book','Count','Price']
5 book=pd.DataFrame(data=data,index=index,columns=column)
6 //计算总价
7 total=book['Price']*book['Count']
8 //把第一维name进行降维
9 print(total.unstack(level=0))

可见结果刚好与上面的例子相反,若把level设置为1,则结果跟上面相同

使用 stack 把数据进行升维,level 使用与 unstack 类似

1 data=[['Python Learning from Scratch',1,68],['Pro Apahe Hadoop',1,105],['Python Crash Course',2,89]
2     ,['Beginning Python From Novice',1,76],['Python Appclications',2,120],['Deep Learning with TensorFlow',1,58]]
3 index=pd.MultiIndex.from_tuples([('Leslie',2020),('Leslie',2021),('Jack',2020),('Jack',2021),('Mike',2020),('Mike',2021)])
4 column=['Book','Count','Price']
5 book=pd.DataFrame(data=data,index=index,columns=column)
6 print(book.stack())

结果

索引重置的另外两个常用方法 reset_index() 与 set_index()

reset_index(self,level,drop: bool = False,inplace: bool = False,col_level: Hashable = 0,col_fill: Label = "")把行标签转换成列

  level:默认为 None,从索引中删除给定的级别,默认情况下删除所有级别。

  drop:默认为 False不要尝试将索引插入 DataFrame 列,这会将索引重置为默认的整数索引。

  inplace:bool, 默认为 False,修改DataFrame到位(不要创建新对象)。

  col_level:int 或 str, 默认为 0,如果列有多个级别,请确定将标签插入到哪个级别。默认情况下,它被插入到第一级。

  col_fill:object, 默认为空,如果列具有多个级别,请确定如何命名其他级别。如果为None,则重复索引名称。

1 age=pd.Series({'Leslie':28,'Jack':32,'Rose':18})
2 address=pd.Series({'Jack':'Beijing','Rose':'Shanghai','Leslie':'Guangzhou'})
3 person=pd.DataFrame({'address':address,'age':age})
4 print(str(person)+"\n")
5 #把name转换成列,转换后列名默认为index
6 person=person.reset_index()
7 #把列名改为name
8 person.rename(columns={'index':'name'},inplace=True)
9 print(person)

显示结果

set_index(keys, drop=True, append=False, inplace=False, verify_integrity=False)

  keys:label or array-like or list of labels/arrays,这个是需要设置为索引的列名,可以是单个列名,或者是多个列名
  drop:bool, default True,删除要用作新索引的列
  append:bool, default False,添加新索引
  inplace:bool, default False,是否要覆盖数据集
  verify_integrity:bool, default False,检查新索引是否重复

 1 age=pd.Series({'Leslie':28,'Jack':32,'Rose':18})
 2 address=pd.Series({'Jack':'Beijing','Rose':'Shanghai','Leslie':'Guangzhou'})
 3 person=pd.DataFrame({'address':address,'age':age})
 4 print(str(person)+"\n")
 5 #把行索引name转换成列,默认列名为index
 6 person=person.reset_index()
 7 #把列名改为name
 8 person.rename(columns={'index':'name'},inplace=True)
 9 print(str(person)+"\n")
10 #重新把列name转换成行索引
11 person=person.set_index(['name'],append=True)
12 print(person)

运行结果

回到目录

9.在DataFrame 中添加列 insert

def insert(loc, column, value, allow_duplicates=False) 可以直接组DataFrame添加列

  • loc: 所添加的位置索引,添加到哪一列
  • column:列名称
  • value: 添加的数据集
1 age=pd.Series({'Leslie':28,'Jack':32,'Rose':18})
2 address=pd.Series({'Jack':'Beijing','Rose':'Shanghai','Leslie':'Guangzhou'})
3 person=pd.DataFrame({'address':address,'age':age})
4 person.insert(2,'sex',[’male','male','female'])

运行结果

回到目录

10. 排序 sort

如果在使用 MultiIndex 不是有序索引,那在切片时候系统经常会报以下错误(注意:数据排序后返回的将是原数据的一个副本,副本值修改不会改变原数据值)

此时可使用 sort_index() 或 sortlevel() 先对数据进行排序再进行切片

1 data=[['Python Learning from Scratch',1,68],['Pro Apahe Hadoop',1,105],['Python Crash Course',2,89]
2     ,['Beginning Python From Novice',1,76],['Python Appclications',2,120],['Deep Learning with TensorFlow',1,58]]
3 index=pd.MultiIndex.from_tuples([('Leslie',2020),('Leslie',2021),('Jack',2020),('Jack',2021),('Mike',2020),('Mike',2021)])
4 column=['Book','Count','Price']
5 book=pd.DataFrame(data=data,index=index,columns=column)
6 #先按 index 进行排序
7 book=book.sort_index()
8 print(str(book.loc['Leslie':,:])+'\n')
9 print(book.loc[('Leslie',2021):,:'Count'])

运行结果

回到目录

11. 根据多级索引进行数据统计

用户还可以使用 mean()、sum()、max() 等方法对多级索引进行数据统计,也可使用 level 参数设置所统计的维度

 1 data=[['Python Learning from Scratch',1,68],['Pro Apahe Hadoop',1,105],['Python Crash Course',2,89]
 2     ,['Beginning Python From Novice',1,76],['Python Appclications',2,120],['Deep Learning with TensorFlow',1,58]]
 3 index=pd.MultiIndex.from_tuples([('Leslie',2020),('Leslie',2021),('Jack',2020),('Jack',2021),('Mike',2020),('Mike',2021)])
 4 column=['Book','Count','Price']
 5 book=pd.DataFrame(data=data,index=index,columns=column)
 6 book=book.sort_index()
 7 #原始数据
 8 print(str(book)+'\n')
 9 #以name为纬度计算每年总价
10 print(str(book.sum(level=0))+'\n')
11 #以year为纬度设计平均数
12 print(str(book.mean(level=1))+'\n')
13 #以year为纬度计算最大值
14 print(book.max(level=1))

运行结果,可见在计算平均值和总值时关于Book等不匹配的字段系统全自动忽略

回到目录

12. 简易合并 pandas.concat

pd.concat(objs: Union[Iterable["NDFrame"], Mapping[Label, "NDFrame"]],axis=0,join="outer",

ignore_index: bool = False,keys=None,levels=None,names=None,

verify_integrity: bool = False,sort: bool = False,copy: bool = True,)

  • objs: series,dataframe或者是panel构成的序列lsit
  • axis: 需要合并链接的轴,0是行,1是列
  • join:连接的方式 inner,或者outer
  • ignore_index: 是否把索引重置
  • verify_intergrity: 捕捉重复索引的错误

concat 默认会将所在列进行合并,确失列默认为 NaN 表示,index 默认允许重复(若不想要重复索引,可以把 ignore_index 设置为 True)
若把 verify_intergrity 设置为 True,一旦出现重复索引,系统就抛出异常

 1 data2=[['Python Learning from Scratch',68,'Eric Matthes'],['Pro Apahe Hadoop',72,'Magnus Lie'],['Python Crash Course',98,'Wes Mckinney']]
 2 data3=[['Beginning Python From Novice','Brandon Rhodes'],['Python Appclications','John Goerzen'],['Deep Learning with TensorFlow','Md Rezaul']]
 3 
 4 column2=['Book','Price','Author']
 5 column3=['Book','Author']
 6 
 7 book2=pd.DataFrame(data=data2,columns=column2).sort_index()
 8 book3=pd.DataFrame(data=data3,columns=column3).sort_index()
 9 
10 print(pd.concat([book2,book3]))

运行结果

若想要去掉缺失列,可以把参数 join 设置为 ' inner '

 1 data2=[['Python Learning from Scratch',68,'Eric Matthes'],['Pro Apahe Hadoop',72,'Magnus Lie'],['Python Crash Course',98,'Wes Mckinney']]
 2 data3=[['Beginning Python From Novice','Brandon Rhodes'],['Python Appclications','John Goerzen'],['Deep Learning with TensorFlow','Md Rezaul']]
 3 
 4 column2=['Book','Price','Author']
 5 column3=['Book','Author']
 6 
 7 book2=pd.DataFrame(data=data2,columns=column2).sort_index()
 8 book3=pd.DataFrame(data=data3,columns=column3).sort_index()
 9 
10 print(pd.concat([book2,book3],join='inner'))

运行结果

回到目录

13. merge 合并与连接

pandas.merge (left, right, how: str = "inner", on=None, left_on=None, right_on=None,
left_index: bool = False, right_index: bool = False, sort: bool = False,
      suffixes=("_x", "_y"), copy: bool = True, indicator: bool = False,validate=None)

  • left: 集合数据
  • right: 集合数据
  • how: 连接方式,默认为 inner 内链接,还可以是 outer 外链接, left 左链接, right 右链接
  • on:链接条件,若为空时,默认为left/right 的交集作为链接条件
  • left_on: 指定链接条件的列名
  • right_on: 指定链接条件的列名
  • left_index: 是否用索引为链接条件
  • right_index:是否用索引为链接条件
  • sort: 是否排序
  • suffixes: 当出现重复列名时可加上后缀
  • copy:默认是True, 合并数据为复制数据
  • indicator:
  • validate: 对应方式 (一对一为 1:1) ( 一对多为1:m )(多对一为m:1) (多对多为m:m )

merge 是最常用的合并连接,用法与SQL数据库中的使用方法极为相似,支持一对一,一对多,多对多方式
在缺失值时,merge也会用 NaN 代替,与 concat 不一样的是 merge 默认会自动生成新的索引
方法可通过on参数与配置关联列,若为空时,则默认为 left / right 的交集作为链接条件,此例中即为 Book 列

 1 _book=[['Python Learning from Scratch','Eric Matthes'],['Pro Apahe Hadoop','Magnus Lie'],['Python Crash Course','Wes Mckinney'],
 2        ['Beginning Python From Novice','Brandon Rhodes'],['Python Appclications','John Goerzen'],['Deep Learning with TensorFlow','Md Rezaul']]
 3 column1=['Book','Author']
 4 book=pd.DataFrame(data=_book,columns=column1)
 5 
 6 _price=[['Python Learning from Scratch',68,2],['Pro Apahe Hadoop',105,3],['Python Crash Course',89,1]
 7     ,['Beginning Python From Novice',76,2],['Python Appclications',120],['Deep Learning with TensorFlow',58,3]]
 8 
 9 column2=['Book','Price','Count']
10 price=pd.DataFrame(data=_price,columns=column2)
11 
12 print(pd.merge(book,price,on='Book'))

运行结果,index=4 的书本没有设定 Count 时,系统默认为 NaN

当关联列的名称不同时,可通过 left_on 和 right_on 分开指定列名

 1 _book=[['Python Learning from Scratch','Eric Matthes'],['Pro Apahe Hadoop','Magnus Lie'],['Python Crash Course','Wes Mckinney'],
 2        ['Beginning Python From Novice','Brandon Rhodes'],['Python Appclications','John Goerzen'],['Deep Learning with TensorFlow','Md Rezaul']]
 3 column1=['Name','Author']
 4 book=pd.DataFrame(data=_book,columns=column1)
 5 
 6 _price=[['Python Learning from Scratch',68,2],['Pro Apahe Hadoop',105,3],['Python Crash Course',89,1]
 7     ,['Beginning Python From Novice',76,2],['Python Appclications',120],['Deep Learning with TensorFlow',58,3]]
 8 price=pd.DataFrame(data=_price,columns=column2)
 9 
10 pd.set_option('display.max_columns',None)
11 print(pd.merge(book,price,left_on='Name',right_on='Book'))

运行结果

为了避免关系列Name与Book同时显示,可以通过 drop()方法把重复列去掉

 1 _book=[['Python Learning from Scratch','Eric Matthes'],['Pro Apahe Hadoop','Magnus Lie'],['Python Crash Course','Wes Mckinney'],
 2        ['Beginning Python From Novice','Brandon Rhodes'],['Python Appclications','John Goerzen'],['Deep Learning with TensorFlow','Md Rezaul']]
 3 column1=['Name','Author']
 4 book=pd.DataFrame(data=_book,columns=column1)
 5 
 6 _price=[['Python Learning from Scratch',68,2],['Pro Apahe Hadoop',105,3],['Python Crash Course',89,1]
 7     ,['Beginning Python From Novice',76,2],['Python Appclications',120],['Deep Learning with TensorFlow',58,3]]
 8 column2=['Book','Price','Count']
 9 price=pd.DataFrame(data=_price,columns=column2)
10 
11 pd.set_option('display.max_columns',None)
12 print(pd.merge(book,price,left_on='Name',right_on='Book').drop('Name',axis=1))

运行结果

也可能通过 left_index 和 right_index 来通过索引进行合并

 1 _book=[['Python Learning from Scratch','Eric Matthes'],['Pro Apahe Hadoop','Magnus Lie'],['Python Crash Course','Wes Mckinney'],
 2        ['Beginning Python From Novice','Brandon Rhodes'],['Python Appclications','John Goerzen'],['Deep Learning with TensorFlow','Md Rezaul']]
 3 column1=['Name','Author']
 4 book=pd.DataFrame(data=_book,columns=column1)
 5 
 6 _price=[['Python Learning from Scratch',68,2],['Pro Apahe Hadoop',105,3],['Python Crash Course',89,1]
 7     ,['Beginning Python From Novice',76,2],['Python Appclications',120,1],['Deep Learning with TensorFlow',58,3]]
 8 column2=['Book','Price','Count']
 9 price=pd.DataFrame(data=_price,columns=column2)
10 
11 pd.set_option('display.max_columns',None)
12 print(pd.merge(book,price,left_index=True,right_index=True).drop('Name',axis=1))

运行结果

以上例子中都是默认使用内链接 how='inner' 返回数据的交集, 也可通过设置 how=’outer' 返回并集

book 集合中不存在书本Deep Learning with TensorFlow 的信息,所以默认情况下,合并数据后应该只剩下5行数据

 1 _book=[['Python Learning from Scratch','Eric Matthes'],['Pro Apahe Hadoop','Magnus Lie'],['Python Crash Course','Wes Mckinney'],
 2        ['Beginning Python From Novice','Brandon Rhodes'],['Python Appclications','John Goerzen']]   
 3 column1=['Name','Author']
 4 book=pd.DataFrame(data=_book,columns=column1)
 5 
 6 _price=[['Python Learning from Scratch',68,2],['Pro Apahe Hadoop',105,3],['Python Crash Course',89,1]
 7     ,['Beginning Python From Novice',76,2],['Python Appclications',120,1],['Deep Learning with TensorFlow',58,3]]
 8 column2=['Book','Price','Count']
 9 price=pd.DataFrame(data=_price,columns=column2)
10 
11 pd.set_option('display.max_columns',None)
12 print(pd.merge(book,price,left_index=True,right_index=True,how='inner').drop('Name',axis=1))

运行结果

把 how设置为 outer后,运行结果

同理,通过把 how 设置为 left / right,可以使用左右链接

 1 _book=[['Python Learning from Scratch','Eric Matthes'],['Pro Apahe Hadoop','Magnus Lie'],['Python Crash Course','Wes Mckinney'],
 2        ['Beginning Python From Novice','Brandon Rhodes'],['Python Appclications','John Goerzen']]   
 3 column1=['Name','Author']
 4 book=pd.DataFrame(data=_book,columns=column1)
 5 
 6 _price=[['Python Learning from Scratch',68,2],['Pro Apahe Hadoop',105,3],['Python Crash Course',89,1]
 7     ,['Beginning Python From Novice',76,2],['Deep Learning with TensorFlow',58,3]] 
 8 column2=['Book','Price','Count']
 9 price=pd.DataFrame(data=_price,columns=column2)
10 
11 pd.set_option('display.max_columns',None)
12 print(pd.merge(book,price,left_on='Name',right_on='Book',how='left').drop('Name',axis=1))

运行结果

回到目录

14. 列统计函数 describe

panads 中还有一个非常方便统计的 describe 函数,它作用是对每一列的若干个常用统计函数(count、mean、std、min 等)进行计算

1 _book=[['Python Learning from Scratch','Python',68,2],['Pro Apahe Hadoop','Hadoop',105,3],['Python Crash Course','Python',89,1]
2     ,['Beginning Python From Novice','Python',76,4],['Deep Learning with TensorFlow','TensorFlow',58,3],['Hadoop:The Definitive Guide','Hadoop',99,3]]
3 column=['Book','Type','Price','Count']
4 book=pd.DataFrame(data=_book,columns=column)
5 print(book.describe())

运行结果

回到目录

15. groupby 分组运算

groupby可以使数据进行分组后再计算,常用的累计方式有 count 计算行数量、mean 平均值 、median中位数 、min 最小值 、max 最大值、std 标准差 、var 方差 、mad 均值绝对偏差 、prod 所有项乘积 、sum 所有项求和等方法

1 _book=[['Python Learning from Scratch','Python',68,2],['Pro Apahe Hadoop','Hadoop',105,3],['Python Crash Course','Python',89,1]
2     ,['Beginning Python From Novice','Python',76,2],['Deep Learning with TensorFlow','TensorFlow',58,3],['Hadoop:The Definitive Guide','Hadoop',99,3]]
3 column=['Book','Type','Price','Count']
4 book=pd.DataFrame(data=_book,columns=column)
5 
6 print(book.groupby('Type').sum())

运行结果

也可专门针对某一列进分组运算

1 _book=[['Python Learning from Scratch','Python',68,2],['Pro Apahe Hadoop','Hadoop',105,3],['Python Crash Course','Python',89,1]
2     ,['Beginning Python From Novice','Python',76,4],['Deep Learning with TensorFlow','TensorFlow',58,3],['Hadoop:The Definitive Guide','Hadoop',99,3]]
3 column=['Book','Type','Price','Count']
4 book=pd.DataFrame(data=_book,columns=column)
5 print(str(book)+'\n')
6 print(book.groupby('Type')['Count'].describe())

运行结果

除了普通计算,在分组后还可以进行 aggregate 累计、filter 过滤、transform 转换、apply 应用等操作

通过 aggregate 可针对不同列进行不同的累计操作,例子中就是计算各类书本的平均价格与销售总数

1 _book=[['Python Learning from Scratch','Python',68,2],['Pro Apahe Hadoop','Hadoop',105,3],['Python Crash Course','Python',89,1]
2     ,['Beginning Python From Novice','Python',76,4],['Deep Learning with TensorFlow','TensorFlow',58,3],['Hadoop:The Definitive Guide','Hadoop',99,3]]
3 column=['Book','Type','Price','Count']
4 book=pd.DataFrame(data=_book,columns=column)
5 print(str(book)+'\n')
6 print(book.groupby('Type').aggregate({'Price':'mean','Count':'sum'}))

运行结果

使用 filter 就是常用的条件过滤,只有符合过滤条件的数据才会被算到分组计算当中
func传入的参数是 group 的分组的数据集,而返回是 bool,通过返回值判断此组数据是否符合筛选条件
下面的例子就是找出销量总数大于 6 的书本

1 def func(x):
2     return sum(x['Count'])>6
3 
4 _book=[['Python Learning from Scratch','Python',68,2],['Pro Apahe Hadoop','Hadoop',105,3],['Python Crash Course','Python',89,1]
5     ,['Beginning Python From Novice','Python',76,4],['Deep Learning with TensorFlow','TensorFlow',58,3],['Hadoop:The Definitive Guide','Hadoop',99,3]]
6 column=['Book','Type','Price','Count']
7 book=pd.DataFrame(data=_book,columns=column)
8 print(str(book)+'\n')
9 print(book.groupby('Type').filter(func))

运行结果

transform 可以对分组内全部数据进行运算后返回一个全新的数据组,最常见的就是计算数据与平均的差别

1 _book=[['Python Learning from Scratch','Python',68,2],['Pro Apahe Hadoop','Hadoop',105,3],['Python Crash Course','Python',89,1]
2     ,['Beginning Python From Novice','Python',76,4],['Deep Learning with TensorFlow','TensorFlow',58,3],['Hadoop:The Definitive Guide','Hadoop',99,3]]
3 column=['Book','Type','Price','Count']
4 book=pd.DataFrame(data=_book,columns=column)
5 print(str(book)+'\n')
6 print(book.groupby('Type')['Price'].transform(lambda x:x-x.mean()))

运行结果

apply 可以对每个分组里的数据进行任意方法操作,唯一不同的是它输入的参数是一个 DataFrame,返回的则是一个数据集
下面例子就是统计每组数据内不同书本所占的销售占比

 1 def data(x):
 2     x.insert(4,'Rate','')
 3     x['Rate'] = x['Count']/sum(x['Count'])*100
 4     return x
 5 
 6 _book=[['Python Learning from Scratch','Python',68,2],['Pro Apahe Hadoop','Hadoop',105,3],['Python Crash Course','Python',89,1]
 7     ,['Beginning Python From Novice','Python',76,4],['Deep Learning with TensorFlow','TensorFlow',58,3],['Hadoop:The Definitive Guide','Hadoop',99,3]]
 8 column=['Book','Type','Price','Count']
 9 book=pd.DataFrame(data=_book,columns=column)
10 11 print(book.groupby('Type').apply(data).sort_values('Type'))

运行结果

groupby 除了可以根据列等分组外,可以根据索引,数据,列表等多种方式进行分组,前提是数组长度必须与DataFrame的长度一致
下面的例子数据就是根据预先定义的数组进行分组的

1 _book=[['Python Learning from Scratch','Python',68,2],['Pro Apahe Hadoop','Hadoop',105,3],['Python Crash Course','Python',89,1]
2     ,['Beginning Python From Novice','Python',76,4],['Deep Learning with TensorFlow','TensorFlow',58,3],['Hadoop:The Definitive Guide','Hadoop',99,3]]
3 column=['Book','Type','Price','Count']
4 book=pd.DataFrame(data=_book,columns=column)
5 print(str(book)+'\n')
6 index=[0,1,0,2,1,3]
7 print(book.groupby(index).sum())

运行结果

除了使用数组以外,还可以使用字典对数据进行分组
下面的例子把Type为 Python、TensorFlow的书本归入AI类,把Type为Hadoop归入BD类再进行统计

1 _book=[['Python Learning from Scratch','Python',68,2],['Pro Apahe Hadoop','Hadoop',105,3],['Python Crash Course','Python',89,1]
2     ,['Beginning Python From Novice','Python',76,4],['Deep Learning with TensorFlow','TensorFlow',58,3],['Hadoop:The Definitive Guide','Hadoop',99,3]]
3 column=['Book','Type','Price','Count']
4 book=pd.DataFrame(data=_book,columns=column).set_index('Type')
5 print(str(book)+'\n')
6 mapping={'Python':'AI','TensorFlow':'AI','Hadoop':'BD'}
7 print(book.groupby(mapping).sum())

运行结果

另外,数据还可以根据组合键进行分组,从而返回一个多级索引的结果
下面的例子把Type为 Python、TensorFlow的书本归入AI类,把Type为Hadoop归入BD类再进行统计,在AI中再分别统计 Python、TesnsorFlow数据

1 _book=[['Python Learning from Scratch','Python',68,2],['Pro Apahe Hadoop','Hadoop',105,3],['Python Crash Course','Python',89,1]
2     ,['Beginning Python From Novice','Python',76,4],['Deep Learning with TensorFlow','TensorFlow',58,3],['Hadoop:The Definitive Guide','Hadoop',99,3]]
3 column=['Book','Type','Price','Count']
4 book=pd.DataFrame(data=_book,columns=column).set_index('Type')
5 print(str(book)+'\n')
6 index=[0,2,0,0,1,2]
7 mapping={'Python':'AI','TensorFlow':'AI','Hadoop':'BD'}
8 print(book.groupby([mapping,index]).sum())

运行结果

回到目录

16. pivot_table 数据透视表

试想一下,如果有一组数据,它包含了书本的开发语言(Language)、类型(Tpye)、单价(Price)、销售数量(Count),现在想根据书本的的Language、Type去统计书本的平均价格 Price,如果用回上一节的例子,我们可以通过 groupby 来实现

1 _book=[['Python Learning from Scratch','Python','AI',68,2],['Pro Apahe Hadoop','Hadoop','BG',105,3],['Python Crash Course','Python','AI',89,1]
2     ,['Beginning Python From Novice','Python','AI',76,4],['Deep Learning with TensorFlow','TensorFlow','AI',58,3]
3     ,['Hadoop:The Definitive Guide','Hadoop','BG',99,3],['HBase: The Definitive Guide','HBase','BG',108,2],['HBase In Action','HBase','BG',79,2]]
4 column=['Book','Language','Type','Price','Count']
5 book=pd.DataFrame(data=_book,columns=column)
6 print(str(book)+'\n')
7 8 print(str(book.groupby(['Language','Type'])['Price'].mean().unstack())+'\n')

运行结果

然而这种操作看起来比较繁琐,而且可读性差,往往开发人员需要仔细看一段时间才能明白其中用意,有见及此系统为开发人员准备了一个方法去实现此功能

pivot_table(values=None,index=None, columns=None,aggfunc="mean",
fill_value=None,margins=False,dropna=True, margins_name="All",observed=False)

  • values:可选参数,用来做集合的值,其用法与pivot的values类似。默认是显示所有的值。
  • index:必选参数,用来指定行索引。如果用数组做行索引,数据必须等长。
  • columns:必选参数,用来指定列索引。
  • aggfunc:聚合函数, pivot_table后新dataframe的值都会通过aggfunc进行运算,默认使用mean算法求平均值,aggfunc有多种书写格式:
      aggfunc = [ np.mean ]
      aggfunc = [ np.sum,np.mean ]
      aggfunc = { 'Price':'mean' }
      aggfunc = { 'Price':[np.mean] }
      aggfunc = { 'Price':np.mean,'Count':np.sum }
    aggfunc = { 'Price':'mean','Count':'sum'}
  • fill_value:填充NA值。默认不填充
  • margins:添加行列的总计,默认不显示。
  • dropna:如果整行都为NA值,则进行丢弃,默认丢弃。
  • margins_name:在margins参数为ture时,用来修改margins的名称

使用以下方法,可以更简单得到相同的效果,而且可读性更强,因为 aggfunc 默认是计算平均值,所以如果统计的是单列,可以不用输入 aggfunc

1 _book=[['Python Learning from Scratch','Python','AI',68,2],['Pro Apahe Hadoop','Hadoop','BG',105,3],['Python Crash Course','Python','AI',89,1]
2     ,['Beginning Python From Novice','Python','AI',76,4],['Deep Learning with TensorFlow','TensorFlow','AI',58,3]
3     ,['Hadoop:The Definitive Guide','Hadoop','BG',99,3],['HBase: The Definitive Guide','HBase','BG',108,2],['HBase In Action','HBase','BG',79,2]]
4 column=['Book','Language','Type','Price','Count']
5 book=pd.DataFrame(data=_book,columns=column)
6 print(str(book)+'\n')
7 print(book.pivot_table(values='Price',index='Language',columns='Type'))

运行结果

如果需要进行多列计算,刚可以通过 aggfunc 参数为不同的列设置不同的算法,下面的例子就是统计平均价格 Price 和总体数量 Count

1 _book=[['Python Learning from Scratch','Python','AI',68,2],['Pro Apahe Hadoop','Hadoop','BG',105,3],['Python Crash Course','Python','AI',89,1]
2     ,['Beginning Python From Novice','Python','AI',76,4],['Deep Learning with TensorFlow','TensorFlow','AI',58,3]
3     ,['Hadoop:The Definitive Guide','Hadoop','BG',99,3],['HBase: The Definitive Guide','HBase','BG',108,2],['HBase In Action','HBase','BG',79,2]]
4 column=['Book','Language','Type','Price','Count']
5 book=pd.DataFrame(data=_book,columns=column)
6 print(str(book)+'\n')
7 print(book.pivot_table(index='Language',columns='Type',aggfunc={'Price':np.mean,'Count':'sum'}))

运行结果

回到目录

17. 高性能列间运算 eval 与 query

pandas 还提供了一个 eval 函数,可以对 DataFrame 进行快速运算,还可以快速生成列
下面例子是以 单价*数据 计算出总体价格,并插入 DataFrame

1 data=[['Python Learning from Scratch',1,68],['Pro Apahe Hadoop',1,105],['Python Crash Course',2,89]
2     ,['Beginning Python From Novice',1,76],['Python Appclications',2,120],['Deep Learning with TensorFlow',1,58]]
3 column=['Book','Count','Price']
4 book=pd.DataFrame(data=data,index=None,columns=column)
5 print(str(book)+'\n')
6 book.eval('Total=Price*Count',inplace=True)
7 print(book)

运行结果

除引以外,还可以与变量进行运算,下面例子就是计算 Price 与平均价格的差额
注意当 eval 方法用到外部变量时,需要加上@符号

1 data=[['Python Learning from Scratch',1,68],['Pro Apahe Hadoop',1,105],['Python Crash Course',2,89]
2     ,['Beginning Python From Novice',1,76],['Python Appclications',2,120],['Deep Learning with TensorFlow',1,58]]
3 column=['Book','Count','Price']
4 book=pd.DataFrame(data=data,index=None,columns=column)
5 print(str(book)+'\n')
6 avg=book['Price'].mean()
7 book.eval('Dif=Price-@avg',inplace=True)
8 print(book)

除了可以使用 eval 进行计算外,还提供了一个 query 进行条件运算
下面的例子就是打印出价格高于平均价格的数据

1 data=[['Python Learning from Scratch',1,68],['Pro Apahe Hadoop',1,105],['Python Crash Course',2,89]
2     ,['Beginning Python From Novice',1,76],['Python Appclications',2,120],['Deep Learning with TensorFlow',1,58]]
3 column=['Book','Count','Price']
4 book=pd.DataFrame(data=data,index=None,columns=column)
5 print(str(book)+'\n')
6 avg=book['Price'].mean()
7 reult=book.query('Price>@avg')
8 print(reult)

运行结果

由于 eval() 与 query()是基于 Numexpr 库实现的,它比 python 的重复运算更具高效性,而且耗费的内存更少,当需要使用大数据进行运算时,推荐使用。

回到目录

本章只是对 Pandas 常用方法的进行简单介绍,希望对各位的开发有所帮助,想要更深入地了解其用法,可以参考 pandas 的官网说明https://pandas.pydata.org/
由于时间仓促,文章难免有出现错漏的地方,敬请点评
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作者:风尘浪子

https://www.cnblogs.com/leslies2/p/14764130.html

原创作品,转载时请注明作者及出处

文章来源:https://www.cnblogs.com/leslies2/p/14764130.html

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