基本介紹

import pandas as pd
import numpy as np

s = pd.Series([1,3,6,np.nan,44,1])
print(s)
dates = pd.date_range('20160101',periods = 6)
print(dates)
df = pd.DataFrame(np.random.randn(6,4),index = dates,columns = ['a','b','c','d'])
print(df)
df1 = pd.DataFrame(np.arange(12).reshape(3,4))
print(df1)
df2 =
 
 pd.DataFrame({
    'A':1.,
    'B':pd.Timestamp('20130102'),
    'C':pd.Series(1,index = list(range(4)),dtype = 'float32'),
    'D':np.array([3]*4,dtype = 'int32'),
    'E':pd.Categorical(['test','train','test','train']),
    'F':'foo'
})
print(df2)
print(df2.dtypes)#每一列的資料形式
print(df2.index)
print(df2.
 
columns)
print(df2.values)
print(df2.describe())
print(df2.T)
print(df2.sort_index(axis=1,ascending = False))#ascending = 1排序倒著排序
print(df2.sort_index(axis=0,ascending = False))
df2.sort_values(by='E')#對E列進行排序

0     1.0
1     3.0
2     6.0
3     NaN
4    44.0
5     1.0
dtype: float64
DatetimeIndex(['2016-01-01', '2016-01-02', '2016-01-03', '2016-01-04',
               '2016-01-05', '2016-01-06'],
              dtype='datetime64[ns]', freq='D')
                   a         b         c         d
2016-01-01 -1.487623  0.888221 -1.506509 -0.776984
2016-01-02 -0.040059  0.262338  1.658207 -0.713937
2016-01-03 -1.316003  1.942315  1.483371  2.473241
2016-01-04 -0.014973  0.465519  2.120004  1.569695
2016-01-05 -2.075723 -1.336707 -0.409277 -0.431981
2016-01-06 -0.776415 -0.293674  0.240641 -0.402298
   0  1   2   3
0  0  1   2   3
1  4  5   6   7
2  8  9  10  11
     A          B    C  D      E    F
0  1.0 2013-01-02  1.0  3   test  foo
1  1.0 2013-01-02  1.0  3  train  foo
2  1.0 2013-01-02  1.0  3   test  foo
3  1.0 2013-01-02  1.0  3  train  foo
A           float64
B    datetime64[ns]
C           float32
D             int32
E          category
F            object
dtype: object
Int64Index([0, 1, 2, 3], dtype='int64')
Index(['A', 'B', 'C', 'D', 'E', 'F'], dtype='object')
[[1.0 Timestamp('2013-01-02 00:00:00') 1.0 3 'test' 'foo']
 [1.0 Timestamp('2013-01-02 00:00:00') 1.0 3 'train' 'foo']
 [1.0 Timestamp('2013-01-02 00:00:00') 1.0 3 'test' 'foo']
 [1.0 Timestamp('2013-01-02 00:00:00') 1.0 3 'train' 'foo']]
         A    C    D
count  4.0  4.0  4.0
mean   1.0  1.0  3.0
std    0.0  0.0  0.0
min    1.0  1.0  3.0
25%    1.0  1.0  3.0
50%    1.0  1.0  3.0
75%    1.0  1.0  3.0
max    1.0  1.0  3.0
                     0                    1                    2  \
A                    1                    1                    1   
B  2013-01-02 00:00:00  2013-01-02 00:00:00  2013-01-02 00:00:00   
C                    1                    1                    1   
D                    3                    3                    3   
E                 test                train                 test   
F                  foo                  foo                  foo   

                     3  
A                    1  
B  2013-01-02 00:00:00  
C                    1  
D                    3  
E                train  
F                  foo  
     F      E  D    C          B    A
0  foo   test  3  1.0 2013-01-02  1.0
1  foo  train  3  1.0 2013-01-02  1.0
2  foo   test  3  1.0 2013-01-02  1.0
3  foo  train  3  1.0 2013-01-02  1.0
     A          B    C  D      E    F
3  1.0 2013-01-02  1.0  3  train  foo
2  1.0 2013-01-02  1.0  3   test  foo
1  1.0 2013-01-02  1.0  3  train  foo
0  1.0 2013-01-02  1.0  3   test  foo
 

選擇資料

import pandas as pd
import numpy as np

dates = pd.date_range('20130101',periods = 6)
df = pd.DataFrame(np.arange(24).reshape(6,4),index = dates,columns = ['A','B','C','D'])
print(df)
print(df.A,'\n',df['A'])
print(df[0:3],'\n',df['20130102':'20130104'])
print(df.loc['20130102'])#select by label:loc
print(df.loc[:,['A','B']])#列印所有行，A、B兩列元素
print(df.loc['20130102',['A','B']])
print(df.iloc[3])#select by position:iloc
print(df.iloc[3][2])
print(df.iloc[3:5,1:3])
print(df.iloc[[1,3,5],1:3])
print(df.ix[:3,['A','C']])#mixed selection:ix
print(df[df.A>8])

             A   B   C   D
2013-01-01   0   1   2   3
2013-01-02   4   5   6   7
2013-01-03   8   9  10  11
2013-01-04  12  13  14  15
2013-01-05  16  17  18  19
2013-01-06  20  21  22  23
2013-01-01     0
2013-01-02     4
2013-01-03     8
2013-01-04    12
2013-01-05    16
2013-01-06    20
Freq: D, Name: A, dtype: int32 
 2013-01-01     0
2013-01-02     4
2013-01-03     8
2013-01-04    12
2013-01-05    16
2013-01-06    20
Freq: D, Name: A, dtype: int32
            A  B   C   D
2013-01-01  0  1   2   3
2013-01-02  4  5   6   7
2013-01-03  8  9  10  11 
              A   B   C   D
2013-01-02   4   5   6   7
2013-01-03   8   9  10  11
2013-01-04  12  13  14  15
A    4
B    5
C    6
D    7
Name: 2013-01-02 00:00:00, dtype: int32
             A   B
2013-01-01   0   1
2013-01-02   4   5
2013-01-03   8   9
2013-01-04  12  13
2013-01-05  16  17
2013-01-06  20  21
A    4
B    5
Name: 2013-01-02 00:00:00, dtype: int32
A    12
B    13
C    14
D    15
Name: 2013-01-04 00:00:00, dtype: int32
14
             B   C
2013-01-04  13  14
2013-01-05  17  18
             B   C
2013-01-02   5   6
2013-01-04  13  14
2013-01-06  21  22
            A   C
2013-01-01  0   2
2013-01-02  4   6
2013-01-03  8  10
             A   B   C   D
2013-01-04  12  13  14  15
2013-01-05  16  17  18  19
2013-01-06  20  21  22  23


I:\Anaconda3\lib\site-packages\ipykernel_launcher.py:16: DeprecationWarning: 
.ix is deprecated. Please use
.loc for label based indexing or
.iloc for positional indexing

See the documentation here:
http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated
  app.launch_new_instance()

設定值

import pandas as pd
import numpy as np

dates = pd.date_range('20130101',periods = 6)
df = pd.DataFrame(np.arange(24).reshape(6,4),index = dates,columns = ['A','B','C','D'])
df.iloc[2,2] = 1111
print(df)
df.loc['20130101','B'] = 2222
print(df)
df.A[df.A>4] = 0
print(df)
df[df.B>9]= 0
print(df)
df['F'] = np.nan
print(df)
df['E'] = pd.Series([1,2,3,4,5,6],index = pd.date_range('20130101',periods = 6))
print(df)

             A   B     C   D
2013-01-01   0   1     2   3
2013-01-02   4   5     6   7
2013-01-03   8   9  1111  11
2013-01-04  12  13    14  15
2013-01-05  16  17    18  19
2013-01-06  20  21    22  23
             A     B     C   D
2013-01-01   0  2222     2   3
2013-01-02   4     5     6   7
2013-01-03   8     9  1111  11
2013-01-04  12    13    14  15
2013-01-05  16    17    18  19
2013-01-06  20    21    22  23
            A     B     C   D
2013-01-01  0  2222     2   3
2013-01-02  4     5     6   7
2013-01-03  0     9  1111  11
2013-01-04  0    13    14  15
2013-01-05  0    17    18  19
2013-01-06  0    21    22  23
            A  B     C   D
2013-01-01  0  0     0   0
2013-01-02  4  5     6   7
2013-01-03  0  9  1111  11
2013-01-04  0  0     0   0
2013-01-05  0  0     0   0
2013-01-06  0  0     0   0
            A  B     C   D   F
2013-01-01  0  0     0   0 NaN
2013-01-02  4  5     6   7 NaN
2013-01-03  0  9  1111  11 NaN
2013-01-04  0  0     0   0 NaN
2013-01-05  0  0     0   0 NaN
2013-01-06  0  0     0   0 NaN
            A  B     C   D   F  E
2013-01-01  0  0     0   0 NaN  1
2013-01-02  4  5     6   7 NaN  2
2013-01-03  0  9  1111  11 NaN  3
2013-01-04  0  0     0   0 NaN  4
2013-01-05  0  0     0   0 NaN  5
2013-01-06  0  0     0   0 NaN  6

處理丟失資料

import pandas as pd
import numpy as np

dates = pd.date_range('20130101',periods = 6)
df = pd.DataFrame(np.arange(24).reshape(6,4),index = dates,columns = ['A','B','C','D'])
df.iloc[0,1] = np.nan
df.iloc[1,2] = np.nan

print(df)
print(df.dropna(axis = 0,how = 'any'))#how = {'any','all'} 丟失元素中有NAN的行
print(df.dropna(axis = 1,how = 'any'))#丟失元素中有NAN的列
print(df.fillna(value = 0))
print(df.isnull())
print(np.any(df.isnull()) == True)#是否至少有一個元素是NAN

             A     B     C   D
2013-01-01   0   NaN   2.0   3
2013-01-02   4   5.0   NaN   7
2013-01-03   8   9.0  10.0  11
2013-01-04  12  13.0  14.0  15
2013-01-05  16  17.0  18.0  19
2013-01-06  20  21.0  22.0  23
             A     B     C   D
2013-01-03   8   9.0  10.0  11
2013-01-04  12  13.0  14.0  15
2013-01-05  16  17.0  18.0  19
2013-01-06  20  21.0  22.0  23
             A   D
2013-01-01   0   3
2013-01-02   4   7
2013-01-03   8  11
2013-01-04  12  15
2013-01-05  16  19
2013-01-06  20  23
             A     B     C   D
2013-01-01   0   0.0   2.0   3
2013-01-02   4   5.0   0.0   7
2013-01-03   8   9.0  10.0  11
2013-01-04  12  13.0  14.0  15
2013-01-05  16  17.0  18.0  19
2013-01-06  20  21.0  22.0  23
                A      B      C      D
2013-01-01  False   True  False  False
2013-01-02  False  False   True  False
2013-01-03  False  False  False  False
2013-01-04  False  False  False  False
2013-01-05  False  False  False  False
2013-01-06  False  False  False  False
True

匯入匯出資料

import pandas as pd

data = pd.read_csv (‘D:\list.csv’)

data.to_pickle(‘student.pickle’)

合併concat

import pandas as pd
import numpy as np

#concatenating
df1 = pd.DataFrame(np.ones((3,4))*0,columns = ['a','b','c','d'])
df2 = pd.DataFrame(np.ones((3,4))*1,columns = ['a','b','c','d'])
df3 = pd.DataFrame(np.ones((3,4))*2,columns = ['a','b','c','d'])

print(df1)
print(df2)
print(df3)
res = pd.concat([df1,df2,df3],axis = 0)
print(res)#索引發生重複，可能存在問題
res = pd.concat([df1,df2,df3],axis = 1)
print(res)
res = pd.concat([df1,df2,df3],axis = 0,ignore_index = True)
print(res)#合併矩陣索引，產生新的索引

#join,['inner','outer']
df1 = pd.DataFrame(np.ones((3,4))*0,columns = ['a','b','c','d'],index = [1,2,3])
df2 = pd.DataFrame(np.ones((3,4))*1,columns = ['b','c','d','e'],index = [2,3,4])
print(df1)
print(df2)
res = pd.concat([df1,df2],join = 'outer')#合併兩個矩陣中沒有的部分填充NAN
print(res)
res = pd.concat([df1,df2],join = 'inner',ignore_index = True)#裁剪出兩個矩陣都有的部分
print(res)
res = pd.concat([df1,df2],axis = 1,join_axes=[df1.index])
#按照df1的索引進行填充，df2中沒有的部分用NAN進行填充
print(res)
res = pd.concat([df1,df2],axis = 1)
print(res)
#append
df1 = pd.DataFrame(np.ones((3,4))*0,columns = ['a','b','c','d'])
df2 = pd.DataFrame(np.ones((3,4))*1,columns = ['a','b','c','d'])
df3 = pd.DataFrame(np.ones((3,4))*2,columns = ['a','b','c','d'])
res = df1.append(df2,ignore_index = True)
print(res)
res = df1.append([df2,df3],ignore_index = True)
print(res)
s1 = pd.Series([1,2,3,4],index = ['a','b','c','d'])
res = df1.append(s1,ignore_index = True)
print(res)

     a    b    c    d
0  0.0  0.0  0.0  0.0
1  0.0  0.0  0.0  0.0
2  0.0  0.0  0.0  0.0
     a    b    c    d
0  1.0  1.0  1.0  1.0
1  1.0  1.0  1.0  1.0
2  1.0  1.0  1.0  1.0
     a    b    c    d
0  2.0  2.0  2.0  2.0
1  2.0  2.0  2.0  2.0
2  2.0  2.0  2.0  2.0
     a    b    c    d
0  0.0  0.0  0.0  0.0
1  0.0  0.0  0.0  0.0
2  0.0  0.0  0.0  0.0
0  1.0  1.0  1.0  1.0
1  1.0  1.0  1.0  1.0
2  1.0  1.0  1.0  1.0
0  2.0  2.0  2.0  2.0
1  2.0  2.0  2.0  2.0
2  2.0  2.0  2.0  2.0
     a    b    c    d    a    b    c    d    a    b    c    d
0  0.0  0.0  0.0  0.0  1.0  1.0  1.0  1.0  2.0  2.0  2.0  2.0
1  0.0  0.0  0.0  0.0  1.0  1.0  1.0  1.0  2.0  2.0  2.0  2.0
2  0.0  0.0  0.0  0.0  1.0  1.0  1.0  1.0  2.0  2.0  2.0  2.0
     a    b    c    d
0  0.0  0.0  0.0  0.0
1  0.0  0.0  0.0  0.0
2  0.0  0.0  0.0  0.0
3  1.0  1.0  1.0  1.0
4  1.0  1.0  1.0  1.0
5  1.0  1.0  1.0  1.0
6  2.0  2.0  2.0  2.0
7  2.0  2.0  2.0  2.0
8  2.0  2.0  2.0  2.0
     a    b    c    d
1  0.0  0.0  0.0  0.0
2  0.0  0.0  0.0  0.0
3  0.0  0.0  0.0  0.0
     b    c    d    e
2  1.0  1.0  1.0  1.0
3  1.0  1.0  1.0  1.0
4  1.0  1.0  1.0  1.0
     a    b    c    d    e
1  0.0  0.0  0.0  0.0  NaN
2  0.0  0.0  0.0  0.0  NaN
3  0.0  0.0  0.0  0.0  NaN
2  NaN  1.0  1.0  1.0  1.0
3  NaN  1.0  1.0  1.0  1.0
4  NaN  1.0  1.0  1.0  1.0
     b    c    d
0  0.0  0.0  0.0
1  0.0  0.0  0.0
2  0.0  0.0  0.0
3  1.0  1.0  1.0
4  1.0  1.0  1.0
5  1.0  1.0  1.0
     a    b    c    d    b    c    d    e
1  0.0  0.0  0.0  0.0  NaN  NaN  NaN  NaN
2  0.0  0.0  0.0  0.0  1.0  1.0  1.0  1.0
3  0.0  0.0  0.0  0.0  1.0  1.0  1.0  1.0
     a    b    c    d    b    c    d    e
1  0.0  0.0  0.0  0.0  NaN  NaN  NaN  NaN
2  0.0  0.0  0.0  0.0  1.0  1.0  1.0  1.0
3  0.0  0.0  0.0  0.0  1.0  1.0  1.0  1.0
4  NaN  NaN  NaN  NaN  1.0  1.0  1.0  1.0
     a    b    c    d
0  0.0  0.0  0.0  0.0
1  0.0  0.0  0.0  0.0
2  0.0  0.0  0.0  0.0
3  1.0  1.0  1.0  1.0
4  1.0  1.0  1.0  1.0
5  1.0  1.0  1.0  1.0
     a    b    c    d
0  0.0  0.0  0.0  0.0
1  0.0  0.0  0.0  0.0
2  0.0  0.0  0.0  0.0
3  1.0  1.0  1.0  1.0
4  1.0  1.0  1.0  1.0
5  1.0  1.0  1.0  1.0
6  2.0  2.0  2.0  2.0
7  2.0  2.0  2.0  2.0
8  2.0  2.0  2.0  2.0
     a    b    c    d
0  0.0  0.0  0.0  0.0
1  0.0  0.0  0.0  0.0
2  0.0  0.0  0.0  0.0
3  1.0  2.0  3.0  4.0

合併merge

import pandas as pd

#merging two df by key/keys.(may be used in database)
left = pd.DataFrame({'key':['K0','K1','K2','K3'],
                        'A':['A0','A1','A2','A3'],
                        'B':['B0','B1','B2','B3']})
right = pd.DataFrame({'key':['K0','K1','K2','K3'],
                         'C':['C0','C1','C2','C3'],
                         'D':['D0','D1','D2','D3']})
print(left)
print(right)
res = pd.merge(left,right,on='key')
print(res)

#consider two keys
left = pd.DataFrame({'key1':['K0','K0','K1','K2'],
                     'key2':['K0','K1','K0','K1'],
                        'A':['A0','A1','A2','A3'],
                        'B':['B0','B1','B2','B3']})
right = pd.DataFrame({'key1':['K0','K1','K1','K2'],
                      'key2':['K0','K0','K0','K0'],
                         'C':['C0','C1','C2','C3'],
                         'D':['D0','D1','D2','D3']})
print(left)
print(right)
#how = ['inner','outer','left','right']
res = pd.merge(left,right,on=['key1','key2'],how='inner')
print(res)
res = pd.merge(left,right,on=['key1','key2'],how='outer')
print(res)
res = pd.merge(left,right,on=['key1','key2'],how='left')
print(res)

#indicator
df1 = pd.DataFrame({'col1':[0,1],'col_left':['a','b']})
df2 = pd.DataFrame({'col1':[1,2,2],'col_left':[2,2,2] 
 

            
  
           

              
              
            
            
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							apply 是 pandas 庫的一個很重要的函式，多和 groupby 函式一起用，也可以直接用於 DataFrame 和 Series 物件。主要用於資料聚合運算，可以很方便的對分組進行現有的運算和 

  
 

    

    
    
【翻譯】Pandas 十分鐘入門
     
 
							
							
							這是一個簡短的介紹pandas用法，主要面向新使用者。 在Cookbook你可以看到更復雜的方法。

通常，我們匯入以下模組：

In [1]: import pandas as pd

In [2]: import numpy as np

In [3]: i 

  
 

    

    
    
【python】pandas庫pd.read_pickle操作讀取pickle資料與.to_pickle()永久儲存資料
     
 
							
							
							之前總結了python的pickle庫的操作，儲存資料使用pickle.dump(obj, file, [,protocol]),將物件obj儲存到檔案file中去。使用pickle.load(file)從file中讀取一個字串，並將它重構為原來的python物 

  
 

    

    
    
【Python】Pandas——apply函式
     
 apply 是 pandas 庫的一個很重要的函式，多和 groupby 函式一起用，也可以直接用於 DataFrame 和 Series 物件。主要用於資料聚合運算，可以很方便的對分組進行現有的運算和自定義的運算。





資料集

使用的資料集是美國人口普查的資料，可以從這裡下載，裡面包含了CSV資料檔 

  
 

    

    
    
【Python】“pandas”庫“to_sql”報錯“Invalid MySQL identifier”處理記錄
     
 
                

     最近又要幫客戶寫指令碼拉一份統計日報，就先把一堆一堆的小SQL寫好測好後，準備用Python封裝一下。但是用到第三方“pandas”模組的“to_sql”方法往MySQL資料庫寫資料時，卻報了“Invalid MySQL identifier”無效識別符號的錯。 

  
 

    

    
    
【python】pandas庫pd.read_excel操作讀取excel檔案引數整理與例項
     
 
							
							
							除了使用xlrd庫或者xlwt庫進行對excel表格的操作讀與寫，而且pandas庫同樣支援excel的操作；且pandas操作更加簡介方便。

首先是pd.read_excel的引數：函式為：

pd.read_excel(io, sheetname=0,he 

  
 

    

    
    
【Python】Pandas的建立、查詢、修改
     
 
                DataFrame建立方法有很多，常用基本格式是：DataFrame 構造器引數：DataFrame(data=[],index=[],coloumns=[])

In [272]: df2=DataFrame(np.arange(16).reshape((4,4)),ind 

  
 

    

    
    
【Python】pandas遍歷讀取excel檔案，修改並儲存（一）
     
 
                code ide and lib version：

python            2.7（64）

pycharm            2018.1.4

xlrd            1.1.0

pandas            0.23.1

play：
 

  
 

    

    
    
【python】pandas的簡單用法
     
 
								
								            
							
							
							
pandas安裝

pandas依賴處理Excel的xlrd模組，所以我們需要先安裝xlrd
安裝pandas模組還需要一定的編碼環境，所以我們自己在安裝的時候，確保你的電腦有這些環境：Net.4 、 

  
 

    

    
    
【轉】pandas DataFrame 逐行操作（可修改資料）
     
 
                
df.iterrows() 是不能修改的，只能檢視。
下面這篇文章裡提到的 df.column_name 則可以助行操作，包括修改資料
例子也挺好的
https://www.reddit.com/r/learnpython/comments/665aer/how_to_cr 

  
 

    

    
    
【轉載】python安裝numpy和pandas
     
 nump   列數   tro   pac   nio   libs   hub   linux環境   github   轉載：原文地址 http://www.cnblogs.com/lxmhhy/p/6029465.html
 
最近要對一系列數據做同比比較，需要用到numpy和pandas來計算，不過使 

  
 

    

    
    
【pandas】pandas.Series.str.split()---字符串分割
     
 span   code   utf   color   display   adding   play   split   strong   原創博文，轉載請註明出處！ 本文代碼的github地址  例子：   1 # -*- coding: utf-8 -*-
  2 # 創建dataframe
  3 i 

  
 

    

    
    
【pandas】pandas.to_datatime()---時間格式轉換
     
 ID   top   --   AS   style   port   adding   int   2-2   標準時間格式：2012-12-21 時間轉換函數：pandas.to_datatime() # -*- coding: utf-8 -*-

# 生成數據
import pandas as pd  

  
 

    

    
    
【譯】使用 Pandas 在 Python 中建立一個簡單的推薦系統
     
  
  
  原文地址：Creating a Simple Recommender System in Python using Pandas 
  原文作者：Usman Malik 
  譯文出自：掘金翻譯計劃 
  本文永久連結：github.com/xitu/gold-m… 
  譯者：xilihuas 

  
 

    

    
    
【python】詳解pandas.DataFrame.plot( ) 中引數secondary_y實現雙座標軸使用
     
  
 
  
  
 首先看官網的DataFrame.plot( )函式 
  
 secondary_y : boolean or sequence, default False  # 可以是布林值或者是數列
			  Whether to plot on the secondary y-axis