二、pandas入门

import numpy as np
import pandas as pd

Series：

#创建Series方法1
s1=pd.Series([1,2,3,4])
s1

# 0    1
# 1    2
# 2    3
# 3    4
# dtype: int64

s1.values#array([1, 2, 3, 4], dtype=int64)
s1.index#RangeIndex(start=0, stop=4, step=1)

　　

#创建Series方法2
s2=pd.Series(np.arange(5,10))
print(s2)

# 0    5
# 1    6
# 2    7
# 3    8
# 4    9
# dtype: int32

#创建Series方法3
s3=pd.Series({'5':1,'6':3,'7':9})
print(s3)

# 5    1
# 6    3
# 7    9
# dtype: int64

print(s3.index)#Index(['5', '6', '7'], dtype='object')

#创建Series方法4
s4=pd.Series([1,2,3,4],index=['A','B','C','D'])
print(s4)
# A    1
# B    2
# C    3
# D    4
# dtype: int64

#取值
print(s4['A'])#1
print(s4[s4>2])
# C    3
# D    4
# dtype: int64

#将Series转换成字典
dict=s4.to_dict()
print(dict)#{'A': 1, 'B': 2, 'C': 3, 'D': 4}

#将字典转换为Series
seri=pd.Series(dict)
print(seri)
# A    1
# B    2
# C    3
# D    4
# dtype: int64

#改变Series的index
index_1=['z','A','B','v','C']
s5=pd.Series(s4,index=index_1)
print(s5)
# z    NaN
# A    1.0
# B    2.0
# v    NaN
# C    3.0
# dtype: float64

#判断是不是null
print(pd.isnull(s5))
# z     True
# A    False
# B    False
# v     True
# C    False
# dtype: bool
print(pd.notnull(s5))
# z    False
# A     True
# B     True
# v    False
# C     True
# dtype: bool

#给Series起名字
s5.name='demo'
print(s5)
# z    NaN
# A    1.0
# B    2.0
# v    NaN
# C    3.0
# Name: demo, dtype: float64

s5.index.name='demo index'
print(s5.index)#Index(['z', 'A', 'B', 'v', 'C'], dtype='object', name='demo index')

DataFrame：

from pandas import Series,DataFrame
import webbrowser
link='https://www.tiobe.com/tiobe-index/'
webbrowser.open(link)#打开该网站
#复制网站中一下内容内容
'''
   Jan 2019  Jan 2018  Change Programming Language  Ratings Change.1
0         1         1     NaN                 Java  16.904%   +2.69%
1         2         2     NaN                    C  13.337%   +2.30%
2         3         4  change               Python   8.294%   +3.62%
3         4         3  change                  C++   8.158%   +2.55%
4         5         7  change    Visual Basic .NET   6.459%   +3.20%
'''
df=pd.read_clipboard()#从剪切板里创建DataFrame
type(df)#pandas.core.frame.DataFrame
print(df)#打印出和上述内容一样的DataFrame
#    Jan 2019  Jan 2018  Change Programming Language  Ratings Change.1
# 0         1         1     NaN                 Java  16.904%   +2.69%
# 1         2         2     NaN                    C  13.337%   +2.30%
# 2         3         4  change               Python   8.294%   +3.62%
# 3         4         3  change                  C++   8.158%   +2.55%
# 4         5         7  change    Visual Basic .NET   6.459%   +3.20%

　　

#获取列名
print(df.columns)#Index(['Jan 2019', 'Jan 2018', 'Change', 'Programming Language', 'Ratings','Change.1'],dtype='object')

　　

#获取某一列的value
print(df.Ratings)#获取Ratings列
# 0    16.904%
# 1    13.337%
# 2     8.294%
# 3     8.158%
# 4     6.459%
# Name: Ratings, dtype: object
print(df['Jan 2019'])#获取'Jan 2019'列，因为两个单词，所以不能用上式   获取两列则用print(df[['Jan 2019',Ratings]]),得到的类型为DataFrame
# 0    1
# 1    2
# 2    3
# 3    4
# 4    5
# Name: Jan 2019, dtype: int64
print(type(df.Ratings),type(df['Jan 2019']))#<class 'pandas.core.series.Series'> <class 'pandas.core.series.Series'>

　　

# 提取旧的DataFrame某些列生成新的DataFrame
df_new=DataFrame(df,columns=['Programming Language','Jan 2019'])
print(df_new)
#   Programming Language  Jan 2019
# 0                 Java         1
# 1                    C         2
# 2               Python         3
# 3                  C++         4
# 4    Visual Basic .NET         5

　　

#提取旧的DataFrame某些列生成新的DataFrame,但新的DataFrame中有的列在旧的没有，会生成新的列
df_new2=DataFrame(df,columns=['new lie','Jan 2019'])
print(df_new2)
#    new lie  Jan 2019
# 0      NaN         1
# 1      NaN         2
# 2      NaN         3
# 3      NaN         4
# 4      NaN         5

#可以给new lie赋值
df_new2['new lie']=range(5,10)
df_new2['new lie']=np.arange(5,10)#也可以通过numpy赋值
df_new2['new lie']=pd.Series(np.arange(5,10))#也可以通过Series赋值
print(df_new2)
#    new lie  Jan 2019
# 0        5         1
# 1        6         2
# 2        7         3
# 3        8         4
# 4        9         5

df_new2['new lie']=pd.Series([200,200],index=[2,3])#指定某一列某一两个元素值的更改
print(df_new2)
#    new lie  Jan 2019
# 0      NaN         1
# 1      NaN         2
# 2    200.0         3
# 3    200.0         4
# 4      NaN         5

Series与DataFrame：

import numpy as np
import pandas as pd
from pandas import Series,DataFrame
data={'country':['Belgium','India','Brazil'],
      'Capital':['Brussels','New Delhi','Brasillia'],
      'Population':[11190846,1303171035,207847528]}

#Seiries
s1=pd.Series(data['country'])
# 0    Belgium
# 1      India
# 2     Brazil
# dtype: object
s1.values#array(['Belgium', 'India', 'Brazil'], dtype=object)
s1.index#RangeIndex(start=0, stop=3, step=1)

#DataFrame
df1=pd.DataFrame(data)#通过字典创建DataFrame
# 	country	Capital	Population
# 0	Belgium	Brussels	11190846
# 1	India	New Delhi	1303171035
# 2	Brazil	Brasillia	207847528
df1['country']#访问某一列
df1.country##访问某一列的另一种方式，效果同上
# 0    Belgium
# 1      India
# 2     Brazil
# Name: country, dtype: object
type(df1['country'])#pandas.core.series.Series

#访问DataFrame的行
df1.iterrows()#<generator object DataFrame.iterrows at 0x0000000004E8F888>
for row in  df1.iterrows():
    print(row)
    print('类型：',type(row))
    print('长度：',len(row),'
')
'''
(0, country        Belgium
Capital       Brussels
Population    11190846
Name: 0, dtype: object)
类型： <class 'tuple'>
长度： 2 

(1, country            India
Capital        New Delhi
Population    1303171035
Name: 1, dtype: object)
类型： <class 'tuple'>
长度： 2 

(2, country          Brazil
Capital       Brasillia
Population    207847528
Name: 2, dtype: object)
类型： <class 'tuple'>
长度： 2 
'''
for row in  df1.iterrows():
    print('第一个：',row[0])
    print('第二个：', row[1],'
')
    print('类型：',type(row[0]),type(row[1]))
    break
'''
第一个： 0
第二个： country        Belgium
Capital       Brussels
Population    11190846
Name: 0, dtype: object 

类型： <class 'int'> <class 'pandas.core.series.Series'>
'''

#通过Series创建DataFrame
s1=pd.Series(data['Capital'])
s2=pd.Series(data['country'])
s3=pd.Series(data['Population'])
df_new1=pd.DataFrame([s1,s2,s3])
print(df_new1)
'''
          0           1          2
0  Brussels   New Delhi  Brasillia
1   Belgium       India     Brazil
2  11190846  1303171035  207847528
'''
print(df_new1.T)# 转置
'''
           0        1           2
0   Brussels  Belgium    11190846
1  New Delhi    India  1303171035
2  Brasillia   Brazil   207847528
'''
df_new2=pd.DataFrame([s1,s2,s3],index=['Capital','country','Population']).T
print(df_new2)
'''
     Capital  country  Population
0   Brussels  Belgium    11190846
1  New Delhi    India  1303171035
2  Brasillia   Brazil   207847528
'''

 pandas中的DateFrame的IO操作：

import numpy as np
import pandas as pd
from pandas import Series,DataFrame
import webbrowser
link='http://pandas.pydata.org/pandas-docs/version/0.20/io.html'
webbrowser.open(link)#打开该网站
#复制网站中一下内容内容
'''
Format Type Data Description    Reader  Writer
text    CSV read_csv    to_csv
text    JSON    read_json   to_json
text    HTML    read_html   to_html
text    Local clipboard read_clipboard  to_clipboard
binary  MS Excel    read_excel  to_excel
binary  HDF5 Format read_hdf    to_hdf
binary  Feather Format  read_feather    to_feather
binary  Msgpack read_msgpack    to_msgpack
binary  Stata   read_stata  to_stata
binary  SAS read_sas    
binary  Python Pickle Format    read_pickle to_pickle
SQL SQL read_sql    to_sql
SQL Google Big Query    read_gbq    to_gbq
'''
df1=pd.read_clipboard()
print(df1)
'''
   Format Type      Data Description          Reader        Writer
0         text                   CSV        read_csv        to_csv
1         text                  JSON       read_json       to_json
2         text                  HTML       read_html       to_html
3         text       Local clipboard  read_clipboard  to_clipboard
4       binary              MS Excel      read_excel      to_excel
5       binary           HDF5 Format        read_hdf        to_hdf
6       binary        Feather Format    read_feather    to_feather
7       binary               Msgpack    read_msgpack    to_msgpack
8       binary                 Stata      read_stata      to_stata
9       binary                   SAS        read_sas              
10      binary  Python Pickle Format     read_pickle     to_pickle
11         SQL                   SQL        read_sql        to_sql
12         SQL      Google Big Query        read_gbq        to_gbq
'''
df1.to_clipboard()#将df1的内容复制到粘贴板
df1.to_csv('df1.csv')#将df1的内容输出到df1.csv文件中，包括index
df1.to_csv('df11.csv',index=False)#将df1的内容输出到df2.csv文件中,但不包括index
df2=pd.read_csv('df11.csv')#读取csv文件
print(df2)
'''
   Format Type      Data Description          Reader        Writer
0         text                   CSV        read_csv        to_csv
1         text                  JSON       read_json       to_json
2         text                  HTML       read_html       to_html
3         text       Local clipboard  read_clipboard  to_clipboard
4       binary              MS Excel      read_excel      to_excel
5       binary           HDF5 Format        read_hdf        to_hdf
6       binary        Feather Format    read_feather    to_feather
7       binary               Msgpack    read_msgpack    to_msgpack
8       binary                 Stata      read_stata      to_stata
9       binary                   SAS        read_sas              
10      binary  Python Pickle Format     read_pickle     to_pickle
11         SQL                   SQL        read_sql        to_sql
12         SQL      Google Big Query        read_gbq        to_gbq
'''
df3=df1.to_json()#输出为json格式
print(df3)
'''
{"Format Type":{"0":"text","1":"text","2":"text","3":"text","4":"binary","5":"binary","6":"binary","7":"binary","8":"binary","9":"binary","10":"binary","11":"SQL","12":"SQL"},
"Data Description":{"0":"CSV","1":"JSON","2":"HTML","3":"Local clipboard","4":"MS Excel","5":"HDF5 Format","6":"Feather Format","7":"Msgpack","8":"Stata","9":"SAS","10":"Python Pickle Format","11":"SQL","12":"Google Big Query"},
"Reader":{"0":"read_csv","1":"read_json","2":"read_html","3":"read_clipboard","4":"read_excel","5":"read_hdf","6":"read_feather","7":"read_msgpack","8":"read_stata","9":"read_sas","10":"read_pickle","11":"read_sql","12":"read_gbq"},
"Writer":{"0":"to_csv","1":"to_json","2":"to_html","3":"to_clipboard","4":"to_excel","5":"to_hdf","6":"to_feather","7":"to_msgpack","8":"to_stata","9":" ","10":"to_pickle","11":"to_sql","12":"to_gbq"}}
'''
print(pd.read_json(df3))#读json格式
'''
   Format Type      Data Description          Reader        Writer
0         text                   CSV        read_csv        to_csv
1         text                  JSON       read_json       to_json
10      binary  Python Pickle Format     read_pickle     to_pickle
11         SQL                   SQL        read_sql        to_sql
12         SQL      Google Big Query        read_gbq        to_gbq
2         text                  HTML       read_html       to_html
3         text       Local clipboard  read_clipboard  to_clipboard
4       binary              MS Excel      read_excel      to_excel
5       binary           HDF5 Format        read_hdf        to_hdf
6       binary        Feather Format    read_feather    to_feather
7       binary               Msgpack    read_msgpack    to_msgpack
8       binary                 Stata      read_stata      to_stata
9       binary                   SAS        read_sas              
'''
df1.to_json('df1.json')#生成json文件
print(pd.read_json('df1.json'))#读取json文件
'''
   Format Type      Data Description          Reader        Writer
0         text                   CSV        read_csv        to_csv
1         text                  JSON       read_json       to_json
10      binary  Python Pickle Format     read_pickle     to_pickle
11         SQL                   SQL        read_sql        to_sql
12         SQL      Google Big Query        read_gbq        to_gbq
2         text                  HTML       read_html       to_html
3         text       Local clipboard  read_clipboard  to_clipboard
4       binary              MS Excel      read_excel      to_excel
5       binary           HDF5 Format        read_hdf        to_hdf
6       binary        Feather Format    read_feather    to_feather
7       binary               Msgpack    read_msgpack    to_msgpack
8       binary                 Stata      read_stata      to_stata
9       binary                   SAS        read_sas              
'''
df1.to_html('df1.html')#生成html文件
print(pd.read_html('df1.html'))#读取html文件
'''
[    Unnamed: 0 Format Type      Data Description          Reader        Writer
0            0        text                   CSV        read_csv        to_csv
1            1        text                  JSON       read_json       to_json
2            2        text                  HTML       read_html       to_html
3            3        text       Local clipboard  read_clipboard  to_clipboard
4            4      binary              MS Excel      read_excel      to_excel
5            5      binary           HDF5 Format        read_hdf        to_hdf
6            6      binary        Feather Format    read_feather    to_feather
7            7      binary               Msgpack    read_msgpack    to_msgpack
8            8      binary                 Stata      read_stata      to_stata
9            9      binary                   SAS        read_sas           NaN
10          10      binary  Python Pickle Format     read_pickle     to_pickle
11          11         SQL                   SQL        read_sql        to_sql
12          12         SQL      Google Big Query        read_gbq        to_gbq]
'''
df1.to_excel('df1.xlsx')#生成excell文件
print(pd.read_excel('df1.xlsx'))#读取excell文件
'''
   Format Type      Data Description          Reader        Writer
0         text                   CSV        read_csv        to_csv
1         text                  JSON       read_json       to_json
2         text                  HTML       read_html       to_html
3         text       Local clipboard  read_clipboard  to_clipboard
4       binary              MS Excel      read_excel      to_excel
5       binary           HDF5 Format        read_hdf        to_hdf
6       binary        Feather Format    read_feather    to_feather
7       binary               Msgpack    read_msgpack    to_msgpack
8       binary                 Stata      read_stata      to_stata
9       binary                   SAS        read_sas              
10      binary  Python Pickle Format     read_pickle     to_pickle
11         SQL                   SQL        read_sql        to_sql
12         SQL      Google Big Query        read_gbq        to_gbq
'''

Series和DataFrame的indexing

import numpy as np
import pandas as pd
imdb=pd.read_csv(r'C:UsersAdministratorDesktoppy_workcodespresidential_polls.csv')
print(imdb.shape)#查看行列数   (10236, 27)
print(imdb.head())#默认打印前五行
'''
   cycle     branch        type                        matchup forecastdate  
0   2016  President  polls-plus  Clinton vs. Trump vs. Johnson      11/1/16   
1   2016  President  polls-plus  Clinton vs. Trump vs. Johnson      11/1/16   
2   2016  President  polls-plus  Clinton vs. Trump vs. Johnson      11/1/16   
3   2016  President  polls-plus  Clinton vs. Trump vs. Johnson      11/1/16   
4   2016  President  polls-plus  Clinton vs. Trump vs. Johnson      11/1/16   

      state   startdate     enddate                  pollster grade  
0      U.S.  10/25/2016  10/31/2016   Google Consumer Surveys     B   
1      U.S.  10/27/2016  10/30/2016  ABC News/Washington Post    A+   
2  Virginia  10/27/2016  10/30/2016  ABC News/Washington Post    A+   
3   Florida  10/20/2016  10/24/2016                 SurveyUSA     A   
4      U.S.  10/20/2016  10/25/2016       Pew Research Center    B+   

           ...           adjpoll_clinton adjpoll_trump  adjpoll_johnson  
0          ...                  42.64140      40.86509         5.675099   
1          ...                  43.29659      44.72984         3.401513   
2          ...                  46.29779      40.72604         6.401513   
3          ...                  46.35931      45.30585         1.777730   
4          ...                  45.32744      42.20888         3.618320   

   adjpoll_mcmullin  multiversions  
0               NaN            NaN   
1               NaN            NaN   
2               NaN            NaN   
3               NaN            NaN   
4               NaN            NaN   

                                                 url  poll_id  question_id  
0  https://datastudio.google.com/u/0/#/org//repor...    47940        74999   
1  http://www.langerresearch.com/wp-content/uploa...    47881        74936   
2  https://www.washingtonpost.com/local/virginia-...    47880        74934   
3  http://www.baynews9.com/content/news/baynews9/...    47465        74252   
4  http://www.people-press.org/2016/10/27/as-elec...    47616        74519   

   createddate             timestamp  
0      11/1/16  15:09:38  1 Nov 2016  
1      11/1/16  15:09:38  1 Nov 2016  
2      11/1/16  15:09:38  1 Nov 2016  
3     10/25/16  15:09:38  1 Nov 2016  
4     10/27/16  15:09:38  1 Nov 2016  

[5 rows x 27 columns]
'''
print(imdb.tail())#默认打印后5行，与head用法相同
'''
       cycle     branch        type                        matchup  
10231   2016  President  polls-only  Clinton vs. Trump vs. Johnson   
10232   2016  President  polls-only  Clinton vs. Trump vs. Johnson   
10233   2016  President  polls-only  Clinton vs. Trump vs. Johnson   
10234   2016  President  polls-only  Clinton vs. Trump vs. Johnson   
10235   2016  President  polls-only  Clinton vs. Trump vs. Johnson   

      forecastdate           state  startdate     enddate  
10231      11/1/16         Alabama  9/30/2016  10/13/2016   
10232      11/1/16        Virginia  9/30/2016   10/6/2016   
10233      11/1/16        Virginia  9/16/2016   9/22/2016   
10234      11/1/16  North Carolina  6/20/2016   6/21/2016   
10235      11/1/16            Utah  7/29/2016   8/18/2016   

                    pollster grade          ...           adjpoll_clinton  
10231                  Ipsos    A-          ...                  37.30964   
10232                  Ipsos    A-          ...                  49.13094   
10233                  Ipsos    A-          ...                  45.97130   
10234  Public Policy Polling    B+          ...                  45.29390   
10235                  Ipsos    A-          ...                  31.62721   

      adjpoll_trump  adjpoll_johnson  adjpoll_mcmullin  multiversions  
10231      54.76821              NaN               NaN            NaN   
10232      39.41588              NaN               NaN            NaN   
10233      39.97518              NaN               NaN            NaN   
10234      46.66175         1.596946               NaN            NaN   
10235      44.65947              NaN               NaN            NaN   

                                                     url  poll_id  
10231              http://reuters.com/statesofthenation/    46817   
10232          http://www.reuters.com/statesofthenation/    46675   
10233          http://www.reuters.com/statesofthenation/    46096   
10234  http://www.publicpolicypolling.com/pdf/2015/PP...    44400   
10235           http://www.reuters.com/statesofthenation    44978   

       question_id  createddate             timestamp  
10231        73263     10/15/16  14:57:58  1 Nov 2016  
10232        72969     10/10/16  14:57:58  1 Nov 2016  
10233        72088      9/26/16  14:57:58  1 Nov 2016  
10234        67363      6/23/16  14:57:58  1 Nov 2016  
10235        69011      8/24/16  14:57:58  1 Nov 2016  

[5 rows x 27 columns]
'''

　　

print(imdb.iloc[10:13,0:5])#查看第10到12行，0到4列(iloc通过index搜索的，基于位置信息，类似切片，不包含末尾位置)
'''
    cycle     branch        type                        matchup forecastdate
10   2016  President  polls-plus  Clinton vs. Trump vs. Johnson      11/1/16
11   2016  President  polls-plus  Clinton vs. Trump vs. Johnson      11/1/16
12   2016  President  polls-plus  Clinton vs. Trump vs. Johnson      11/1/16
'''
df=imdb.iloc[10:13,0:5]
print(df.iloc[1:3,1:3])
'''
       branch        type
11  President  polls-plus
12  President  polls-plus
'''
print(df.loc[10:12,:'type'])#loc是通过lable查询的，基于lable信息查询，包含末尾位置
'''
    cycle     branch        type
10   2016  President  polls-plus
11   2016  President  polls-plus
12   2016  President  polls-plus
'''
print(imdb['adjpoll_clinton'].head())#查看某列
'''
0    42.64140
1    43.29659
2    46.29779
3    46.35931
4    45.32744
Name: adjpoll_clinton, dtype: float64
'''
print(imdb['adjpoll_clinton'][10])#查看某个元素  44.53217
print(imdb[['adjpoll_trump','adjpoll_johnson']])#通过某（些）列生成新的DataFrame
'''
       adjpoll_trump  adjpoll_johnson
0           40.86509         5.675099
1           44.72984         3.401513
2           40.72604         6.401513
3           45.30585         1.777730
4           42.20888         3.618320
5           42.26663         6.114222
6           43.56017         3.153590
7           43.50333         3.466432
8           37.24948         6.420006
9           41.69540         4.220173
10          43.84845              NaN
11          47.92262         2.676897
12          29.50605         3.170510
13          40.34972         5.823322
14          42.01937         6.499082
15          45.07725         3.499082
16          39.33826         5.044833
17          46.11255         3.054228
18          39.80679         6.359501
19          41.34735         4.421316
20          39.99571         6.272840
21          50.75720              NaN
22          38.87231         8.359501
23          41.55637         4.964521
24          43.84806         5.359501
25          45.03370         2.193952
26          44.78595         4.359501
27          44.18040         5.160502
28          40.41809         3.333669
29          49.47709         4.308866
...              ...              ...
10206       36.75014         9.152230
10207       40.08237              NaN
10208       43.67710              NaN
10209       43.40106              NaN
10210       35.52956              NaN
10211       35.03328              NaN
10212       44.77681              NaN
10213       38.24798              NaN
10214       41.25978              NaN
10215       41.59738              NaN
10216       41.64499         1.974752
10217       36.15054              NaN
10218       38.65057              NaN
10219       29.49314         9.007062
10220       37.87221              NaN
10221       39.42957              NaN
10222       53.95455              NaN
10223       33.07150         3.328916
10224       41.88533         1.974752
10225       36.82408         9.741756
10226       47.80848              NaN
10227       42.01089         3.671217
10228       45.06726              NaN
10229       40.16534        12.889780
10230       41.56030         2.872088
10231       54.76821              NaN
10232       39.41588              NaN
10233       39.97518              NaN
10234       46.66175         1.596946
10235       44.65947              NaN

[10236 rows x 2 columns]
'''　　

Series和DataFrame的Reindexing

import numpy as np
import pandas as pd
s1=pd.Series([1,2,3,4],index=['A','B','C','D'])
print(s1)

A    1
B    2
C    3
D    4
dtype: int64

print(s1.reindex(['A','C','E']))

A    1.0
C    3.0
E    NaN
dtype: float64

print(s1.reindex(['A','C','m'],fill_value=11))#通过fille_value填充数值

A     1
C     3
m    11
dtype: int64

s2=pd.Series(['a','b','c'],index=[1,5,10])
print(s2)

1     a
5     b
10    c
dtype: object

print(s2.reindex(index=range(15)))

0     NaN
1       a
2     NaN
3     NaN
4     NaN
5       b
6     NaN
7     NaN
8     NaN
9     NaN
10      c
11    NaN
12    NaN
13    NaN
14    NaN
dtype: object

print(s2.reindex(index=range(15),method='ffill'))#自动填充，第0个是NaN,第1到4用a填充（<=4）,第5到9用b填充（大于等于5小于10），大于等于10用c填充

0     NaN
1       a
2       a
3       a
4       a
5       b
6       b
7       b
8       b
9       b
10      c
11      c
12      c
13      c
14      c
dtype: object

print(s2)

1     a
5     b
10    c
dtype: object

df1=pd.DataFrame(np.random.rand(25).reshape([5,5]))
print(df1)

          0         1         2         3         4
0  0.499115  0.244375  0.849224  0.348352  0.472657
1  0.676503  0.769790  0.479774  0.468003  0.703029
2  0.153982  0.699009  0.379184  0.151905  0.921860
3  0.904037  0.196925  0.421180  0.384442  0.642122
4  0.641124  0.748790  0.824351  0.101550  0.412564

df2=pd.DataFrame(np.random.rand(25).reshape([5,5]),index=['A','B','D','E','F'],columns=['c1','c2','c3','c4','c5'])
print(df2)

         c1        c2        c3        c4        c5
A  0.279563  0.267224  0.077868  0.080046  0.528182
B  0.660053  0.088954  0.512298  0.259552  0.108562
D  0.734865  0.776419  0.581695  0.578712  0.157753
E  0.926365  0.729410  0.328161  0.531319  0.550878
F  0.849754  0.770988  0.537104  0.833631  0.062303

print(df2.reindex(index=['A','B','C','D','E','F']))

         c1        c2        c3        c4        c5
A  0.279563  0.267224  0.077868  0.080046  0.528182
B  0.660053  0.088954  0.512298  0.259552  0.108562
C       NaN       NaN       NaN       NaN       NaN
D  0.734865  0.776419  0.581695  0.578712  0.157753
E  0.926365  0.729410  0.328161  0.531319  0.550878
F  0.849754  0.770988  0.537104  0.833631  0.062303

print(df2.reindex(columns=['c1','c2','c3','c4','c5','c6']))

         c1        c2        c3        c4        c5  c6
A  0.279563  0.267224  0.077868  0.080046  0.528182 NaN
B  0.660053  0.088954  0.512298  0.259552  0.108562 NaN
D  0.734865  0.776419  0.581695  0.578712  0.157753 NaN
E  0.926365  0.729410  0.328161  0.531319  0.550878 NaN
F  0.849754  0.770988  0.537104  0.833631  0.062303 NaN

print(df2.reindex(index=['A','B','C','D','E','F'],columns=['c1','c2','c3','c4','c5','c6']))

         c1        c2        c3        c4        c5  c6
A  0.279563  0.267224  0.077868  0.080046  0.528182 NaN
B  0.660053  0.088954  0.512298  0.259552  0.108562 NaN
C       NaN       NaN       NaN       NaN       NaN NaN
D  0.734865  0.776419  0.581695  0.578712  0.157753 NaN
E  0.926365  0.729410  0.328161  0.531319  0.550878 NaN
F  0.849754  0.770988  0.537104  0.833631  0.062303 NaN

s1=pd.Series([1,2,3,4],index=['A','B','C','D'])
print(s1)

A    1
B    2
C    3
D    4
dtype: int64

print(s1.reindex(['A','C']))#也可写成print(s1.reindex(index=['A','C']))

A    1
C    3
dtype: int64

print(df2.reindex(index=['A','C']))

         c1        c2        c3        c4        c5
A  0.279563  0.267224  0.077868  0.080046  0.528182
C       NaN       NaN       NaN       NaN       NaN

print(s1.drop(['B','C']))

A    1
D    4
dtype: int64

print(s1.drop('A'))

B    2
C    3
D    4
dtype: int64

print(df2.drop(['A'],axis=0))

         c1        c2        c3        c4        c5
B  0.660053  0.088954  0.512298  0.259552  0.108562
D  0.734865  0.776419  0.581695  0.578712  0.157753
E  0.926365  0.729410  0.328161  0.531319  0.550878
F  0.849754  0.770988  0.537104  0.833631  0.062303

print(df2.drop(['c1'],axis=1))

         c2        c3        c4        c5
A  0.267224  0.077868  0.080046  0.528182
B  0.088954  0.512298  0.259552  0.108562
D  0.776419  0.581695  0.578712  0.157753
E  0.729410  0.328161  0.531319  0.550878
F  0.770988  0.537104  0.833631  0.062303

谈一谈NaN-means Not a Number

n=np.nan
print(type(n))#<class 'float'>
print(1+n)#结果：nan   任何一个numuber与nan做运算结果永远都是not a nunmber

　　

s1=pd.Series([1,2,np.nan,3,4],index=['A','B','C','D','E'])
print(s1)

A    1.0
B    2.0
C    NaN
D    3.0
E    4.0
dtype: float64

print(s1.isnull())

A    False
B    False
C     True
D    False
E    False
dtype: bool

print(s1.notnull())

A     True
B     True
C    False
D     True
E     True
dtype: bool

print(s1.dropna())#drop掉value为nan的

A    1.0
B    2.0
D    3.0
E    4.0
dtype: float64

NaN in DataFrame

dframe=pd.DataFrame([[1,2,3],[np.nan,5,6],[7,np.nan,9],[np.nan,np.nan,np.nan]])
print(dframe)

     0    1    2
0  1.0  2.0  3.0
1  NaN  5.0  6.0
2  7.0  NaN  9.0
3  NaN  NaN  NaN

print(dframe.isnull())

       0      1      2
0  False  False  False
1   True  False  False
2  False   True  False
3   True   True   True

print(dframe.notnull())

       0      1      2
0   True   True   True
1  False   True   True
2   True  False   True
3  False  False  False

print(dframe.dropna())#默认axis=0，相当于print(dframe.dropna(axis=0))    默认how='any'

     0    1    2
0  1.0  2.0  3.0

print(dframe.dropna(how='any'))#any指的是凡是含有nan的都会drop掉

     0    1    2
0  1.0  2.0  3.0

print(dframe.dropna(how='all'))#all指的是所有都是all的都会drop掉

     0    1    2
0  1.0  2.0  3.0
1  NaN  5.0  6.0
2  7.0  NaN  9.0

print(dframe.dropna(axis=1))#只剩下index了

Empty DataFrame
Columns: []
Index: [0, 1, 2, 3]

dframe2=pd.DataFrame([[1,2,3,np.nan],[2,np.nan,5,6],[np.nan,7,np.nan,9],[1,np.nan,np.nan,np.nan]])
print(dframe2)

     0    1    2    3
0  1.0  2.0  3.0  NaN
1  2.0  NaN  5.0  6.0
2  NaN  7.0  NaN  9.0
3  1.0  NaN  NaN  NaN

df2=dframe2.dropna()#默认thresh=None，相当于df2=dframe2.dropna(thresh=None)
print(df2)

Empty DataFrame
Columns: [0, 1, 2, 3]
Index: []

df3=dframe2.dropna(thresh=2)#只要一行中NaN个数大于2，就删除该行
print(df3)

     0    1    2    3
0  1.0  2.0  3.0  NaN
1  2.0  NaN  5.0  6.0
2  NaN  7.0  NaN  9.0

print(dframe2.fillna(value=10))#将NaN填充为10

      0     1     2     3
0   1.0   2.0   3.0  10.0
1   2.0  10.0   5.0   6.0
2  10.0   7.0  10.0   9.0
3   1.0  10.0  10.0  10.0

print(dframe2.fillna(value={0:'A',1:'16',2:'中国',3:'k'}))#将每列各自的NaN赋值,即：第0列用A填充，第1列用16填充。。。。。。
#注意：fillna和dropna不会改变原本的Series和DataFrame

   0   1   2  3
0  1   2   3  k
1  2  16   5  6
2  A   7  中国  9
3  1  16  中国  k

多级index

s1=pd.Series(np.random.rand(6),index=[['1','1','1','2','2','2'],['a','b','c','a','b','c']])
print(s1)

1  a    0.973831
   b    0.762415
   c    0.135763
2  a    0.974687
   b    0.471638
   c    0.573157
dtype: float64

print(type(s1))#<class 'pandas.core.series.Series'>

print(s1['1'])

a    0.973831
b    0.762415
c    0.135763
dtype: float64

print(type(s1['1']))#<class 'pandas.core.series.Series'>

print(s1['1']['a'])#0.9738309965219155

print(s1[:,'a'])

1    0.973831
2    0.974687
dtype: float64

#二级的series转换成dataframe(两种方法)
df1=s1.unstack()
print(df1)

df2=pd.DataFrame([s1['1'],s1['2']])
print(df2)

          a         b         c
1  0.973831  0.762415  0.135763
2  0.974687  0.471638  0.573157
          a         b         c
0  0.973831  0.762415  0.135763
1  0.974687  0.471638  0.573157

#dataframe转换成二级series
s2=df1.unstack()
print(s2)

a  1    0.973831
   2    0.974687
b  1    0.762415
   2    0.471638
c  1    0.135763
   2    0.573157
dtype: float64

print(df1.T.unstack())

1  a    0.973831
   b    0.762415
   c    0.135763
2  a    0.974687
   b    0.471638
   c    0.573157
dtype: float64

df=pd.DataFrame(np.arange(16).reshape(4,4),index=[['a','a','b','b'],[1,2,1,2]],columns=[['BJ','BJ','上海','广州'],[111,222,111,222]])
print(df)

     BJ      上海  广州
    111 222 111 222
a 1   0   1   2   3
  2   4   5   6   7
b 1   8   9  10  11
  2  12  13  14  15

print(df['BJ'])

     111  222
a 1    0    1
  2    4    5
b 1    8    9
  2   12   13

print(type(df['BJ']))#<class 'pandas.core.frame.DataFrame'>

print(df['BJ',111])#print(df['BJ'][111])效果相同

a  1     0
   2     4
b  1     8
   2    12
Name: (BJ, 111), dtype: int32

Mapping

df1=pd.DataFrame({'城市':['北京','上海','广州'],'人口':[1000,2000,1500]})
print(df1)

   城市    人口
0  北京  1000
1  上海  2000
2  广州  1500

#增加一列
df1['GDP']=pd.Series([9999,8888,7777])#方法一
print(df1)

   城市    人口   GDP
0  北京  1000  9999
1  上海  2000  8888
2  广州  1500  7777

salary={'北京':10,'上海':20,'广州':30}#方法二，尽量用此方法，原因看df2
df1['工资']=df1['城市'].map(salary)
print(df1)

   城市    人口   GDP  工资
0  北京  1000  9999  10
1  上海  2000  8888  20
2  广州  1500  7777  30

df2=pd.DataFrame({'城市':['北京','上海','广州'],'人口':[1000,2000,1500]},index=['A','B','C'])
print(df2)

   城市    人口
A  北京  1000
B  上海  2000
C  广州  1500

df2['GDP']=pd.Series([9999,8888,7777])
print(df2)

   城市    人口  GDP
A  北京  1000  NaN
B  上海  2000  NaN
C  广州  1500  NaN

df2['GGDDPP']=pd.Series([9999,8888,7777],index=['A','B','C'])
print(df2)

   城市    人口  GDP  GGDDPP
A  北京  1000  NaN    9999
B  上海  2000  NaN    8888
C  广州  1500  NaN    7777

Replace

s1=pd.Series(np.arange(100,105))
print(s1)

0    100
1    101
2    102
3    103
4    104
dtype: int32

print(s1.replace(101,np.nan))

0    100.0
1      NaN
2    102.0
3    103.0
4    104.0
dtype: float64

print(s1.replace({101:np.nan}))

0    100.0
1      NaN
2    102.0
3    103.0
4    104.0
dtype: float64

print(s1.replace([100,103,104],['中','eng','s']))

0      中
1    101
2    102
3    eng
4      s
dtype: object

print(s1)#s1并没有发生变化

0    100
1    101
2    102
3    103
4    104
dtype: int32