numpy & pandas 筆記 (2)

*本篇資料來源為莫煩 python:
https://morvanzhou.github.io/tutorials/data-manipulation/np-pd/

pandas 跟 numpy 的使用情境：
numpy 用來替代 python 當中的 list (陣列，矩陣)
pandas 用來替代 python 當中的 dictionary (字典)

pandas 基本功能

Pandas Series
會自動加上字典的 index















#coding=utf-8
import numpy as np 
import pandas as pd 

s = pd.Series([1,3,6, np.nan, 44, 1])
print(s)
'''
0     1.0
1     3.0
2     6.0
3     NaN
4    44.0
5     1.0
dtype: float64
'''

Pandas date_range











#coding=utf-8
import numpy as np 
import pandas as pd 

dates = pd.date_range('20170101',periods=6)
print(dates)
'''
DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03', '2017-01-04',
               '2017-01-05', '2017-01-06'],
              dtype='datetime64[ns]', freq='D')
'''

Pandas DataFrame 基本性質


























#coding=utf-8
import numpy as np 
import pandas as pd 

# 宣告 data frame
df=pd.DataFrame(np.random.randn(6,4), index=dates, columns=['a','b','c','d'])
print(df)
'''
                   a         b         c         d
2017-01-01  1.159450 -1.578301 -0.652926  0.375754
2017-01-02  0.393195 -0.346388 -0.754170 -0.008133
2017-01-03  1.192782  0.804110 -0.842838  0.012096
2017-01-04 -1.058825 -0.323025  0.722646 -1.166700
2017-01-05 -1.180235 -0.450869  1.203138 -0.815103
2017-01-06 -1.722095  0.304247  0.175714 -1.654301
'''

# 預設的 index 為 0 1 2 3 ... 
df1=pd.DataFrame(np.arange(12).reshape((3,4)))
print(df1)
'''
   0  1   2   3
0  0  1   2   3
1  4  5   6   7
2  8  9  10  11
'''

Pandas DataFrame 用法：使用 dictionary 宣告



















#coding=utf-8
import numpy as np 
import pandas as pd 

# 使用 dictionary 定義 data frame
df2=pd.DataFrame({'A':1.,
                'B':pd.Timestamp('20170101'),
                '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)
'''
     A          B    C  D      E    F
0  1.0 2017-01-01  1.0  3   test  foo
1  1.0 2017-01-01  1.0  3  train  foo
2  1.0 2017-01-01  1.0  3   test  foo
3  1.0 2017-01-01  1.0  3  train  foo
'''

Pandas DataFrame 使用 dtype 查看資料型態











# 查看資料格式
print(df2.dtypes)
'''
A           float64
B    datetime64[ns]
C           float32
D             int32
E          category
F            object
dtype: object
'''

Pandas DataFrame 查看 index 與資料數值




















# 輸出所有 row index 
print(df2.index) 
'''
Int64Index([0, 1, 2, 3], dtype='int64')
'''

# 輸出所有 column 
print(df2.columns)
'''
Index([u'A', u'B', u'C', u'D', u'E', u'F'], dtype='object')
'''

# 輸出所有值 values 
print(df2.values)
'''
[[1.0 Timestamp('2017-01-01 00:00:00') 1.0 3 'test' 'foo']
 [1.0 Timestamp('2017-01-01 00:00:00') 1.0 3 'train' 'foo']
 [1.0 Timestamp('2017-01-01 00:00:00') 1.0 3 'test' 'foo']
 [1.0 Timestamp('2017-01-01 00:00:00') 1.0 3 'train' 'foo']]
'''

Pandas DataFrame 使用 describe 查看數字資料描述













# 輸出 describe 查看數字形式的特性
print(df2.describe())
'''
         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
'''

Pandas DataFrame 轉置資料











# transpose 轉置矩陣
print(df2.T)
'''
                     0                    1                    2                   3
A                    1                    1                    1                   1
B  2017-01-01 00:00:00  2017-01-01 00:00:00  2017-01-01 00:00:00 2017-01-01 00:00:00
C                    1                    1                    1                   1
D                    3                    3                    3                   3
E                 test                train                 test               train
F                  foo                  foo                  foo                 foo
'''

Pandas DataFrame 排序資料 sort_index 與 sort_values





























# 排序，對於 axis=1 以 row 方向排序 ascending=False 倒排序
print(df2.sort_index(axis=1, ascending=False))
''' 排序結果: F E D C B A
     F      E  D    C          B    A
0  foo   test  3  1.0 2017-01-01  1.0
1  foo  train  3  1.0 2017-01-01  1.0
2  foo   test  3  1.0 2017-01-01  1.0
3  foo  train  3  1.0 2017-01-01  1.0
'''

# 排序，對於 axis=0 以 col 方向反向排序 (ascending=Fals)
print(df2.sort_index(axis=0, ascending=False))
''' 排序結果: 3 2 1 0
     A          B    C  D      E    F
3  1.0 2017-01-01  1.0  3  train  foo
2  1.0 2017-01-01  1.0  3   test  foo
1  1.0 2017-01-01  1.0  3  train  foo
0  1.0 2017-01-01  1.0  3   test  foo
'''

# 排序 sort_values 針對單行的值進行排序
print(df2.sort_values(by='E'))
''' 排序結果: test train 兩倆排在一起
     A          B    C  D      E    F
0  1.0 2017-01-01  1.0  3   test  foo
2  1.0 2017-01-01  1.0  3   test  foo
1  1.0 2017-01-01  1.0  3  train  foo
3  1.0 2017-01-01  1.0  3  train  foo
'''

pandas indexing

data frame 基本 indexing 方法










































#coding=utf-8
import pandas as pd 
import numpy as np 

dates = pd.date_range('2017-01-01',periods=6)
df = pd.DataFrame(np.arange(24).reshape((6,4)), index=dates, columns=['A','B','C','D'])

print(df['A'])
'''
2017-01-01     0
2017-01-02     4
2017-01-03     8
2017-01-04    12
2017-01-05    16
2017-01-06    20
Freq: D, Name: A, dtype: int64
'''
print(df.A)
'''
2017-01-01     0
2017-01-02     4
2017-01-03     8
2017-01-04    12
2017-01-05    16
2017-01-06    20
Freq: D, Name: A, dtype: int64
'''

print(df[0:3])
'''
            A  B   C   D
2017-01-01  0  1   2   3
2017-01-02  4  5   6   7
2017-01-03  8  9  10  11
'''
print(df['2017-01-02':'2017-01-04'])
'''
             A   B   C   D
2017-01-02   4   5   6   7
2017-01-03   8   9  10  11
2017-01-04  12  13  14  15
'''

data frame location 用法 loc
































# 使用 data frame loc 來選擇查看數據
# 查看指定的 row 的內容
print(df.loc['2017-01-02'])
'''
A    4
B    5
C    6
D    7
Name: 2017-01-02 00:00:00, dtype: int64
'''

# 篩選 col 的指令欄位 row 部分全部印出 
print(df.loc[:,['A','B']])
'''
             A   B
2017-01-01   0   1
2017-01-02   4   5
2017-01-03   8   9
2017-01-04  12  13
2017-01-05  16  17
2017-01-06  20  21
'''

# 指定 row 指定 col
print(df.loc['2017-01-03',['A','C']])
'''
A     8
C    10
Name: 2017-01-03 00:00:00, dtype: int64
'''

select py position 使用 iloc



















# 使用 index 數字來指定 select by position: iloc
print(df.iloc[3])
'''
A    12
B    13
C    14
D    15
Name: 2017-01-04 00:00:00, dtype: int64
'''
# 第 3 row, 第 1 col
print(df.iloc[3,1]) # 13

# 第 3~5 row, 第 1~3 col 
print(df.iloc[3:5,1:3])
'''
             B   C
2017-01-04  13  14
2017-01-05  17  18
'''

混合篩選使用 ix








# 混合篩選 使用 ix
print(df.ix[:3, ['A','C']])
'''
            A   C
2017-01-01  0   2
2017-01-02  4   6
2017-01-03  8  10
'''

Booling indexing



















# Booling indexing
print(df)
'''
             A   B   C   D
2017-01-01   0   1   2   3
2017-01-02   4   5   6   7
2017-01-03   8   9  10  11
2017-01-04  12  13  14  15
2017-01-05  16  17  18  19
2017-01-06  20  21  22  23
'''
# 針對 A 欄位資料 > 8 的篩選出來
print(df[df.A > 8])
'''
             A   B   C   D
2017-01-04  12  13  14  15
2017-01-05  16  17  18  19
2017-01-06  20  21  22  23
'''

pandas 設值

在資料表格中根據 index 設定值









































#coding=utf-8
import pandas as pd 
import numpy as np 

dates = pd.date_range('2017-01-01',periods=6)
df = pd.DataFrame(np.arange(24).reshape((6,4)), index=dates, columns=['A','B','C','D'])

print(df)
'''
             A   B   C   D
2017-01-01   0   1   2   3
2017-01-02   4   5   6   7
2017-01-03   8   9  10  11
2017-01-04  12  13  14  15
2017-01-05  16  17  18  19
2017-01-06  20  21  22  23
'''

df.iloc[2,2] = 1111
print(df)
'''
             A   B     C   D
2017-01-01   0   1     2   3
2017-01-02   4   5     6   7
2017-01-03   8   9  1111  11
2017-01-04  12  13    14  15
2017-01-05  16  17    18  19
2017-01-06  20  21    22  23
'''

df.loc['2017-01-02','C'] = 222
print(df)
'''
             A   B     C   D
2017-01-01   0   1     2   3
2017-01-02   4   5   222   7
2017-01-03   8   9  1111  11
2017-01-04  12  13    14  15
2017-01-05  16  17    18  19
2017-01-06  20  21    22  23
'''

設定符合條件，整欄位修改



















#coding=utf-8
import pandas as pd 
import numpy as np 

dates = pd.date_range('2017-01-01',periods=6)
df = pd.DataFrame(np.arange(24).reshape((6,4)), index=dates, columns=['A','B','C','D'])

# 全部都改到
df[df.A>8] = 0
print(df)
'''
            A  B     C   D
2017-01-01  0  1     2   3
2017-01-02  4  5   222   7
2017-01-03  8  9  1111  11
2017-01-04  0  0     0   0
2017-01-05  0  0     0   0
2017-01-06  0  0     0   0
'''

設定符合條件，指定特定欄位才修改




































#coding=utf-8
import pandas as pd 
import numpy as np 

dates = pd.date_range('2017-01-01',periods=6)
df = pd.DataFrame(np.arange(24).reshape((6,4)), index=dates, columns=['A','B','C','D'])

# 指定特定欄位修改
df.A[df.A>8] = 0
print(df)
'''
            A   B     C   D
2017-01-01  0   1     2   3
2017-01-02  4   5   222   7
2017-01-03  8   9  1111  11
2017-01-04  0  13    14  15
2017-01-05  0  17    18  19
2017-01-06  0  21    22  23
'''


dates = pd.date_range('2017-01-01',periods=6)
df = pd.DataFrame(np.arange(24).reshape((6,4)), index=dates, columns=['A','B','C','D'])

# 指定特定欄位修改
df.B[df.A>8] = 0
print(df)
'''
             A  B   C   D
2017-01-01   0  1   2   3
2017-01-02   4  5   6   7
2017-01-03   8  9  10  11
2017-01-04  12  0  14  15
2017-01-05  16  0  18  19
2017-01-06  20  0  22  23
'''

新增欄位並設定值




















#coding=utf-8
import pandas as pd 
import numpy as np 

dates = pd.date_range('2017-01-01',periods=6)
df = pd.DataFrame(np.arange(24).reshape((6,4)), index=dates, columns=['A','B','C','D'])

# 新增欄位
df['E'] = pd.Series([1,2,3,4,5,6],index=pd.date_range('2017-01-01',periods=6))
df['F'] = np.nan
print(df)
'''
             A   B   C   D  E   F
2017-01-01   0   1   2   3  1 NaN
2017-01-02   4   5   6   7  2 NaN
2017-01-03   8   9  10  11  3 NaN
2017-01-04  12  13  14  15  4 NaN
2017-01-05  16  17  18  19  5 NaN
2017-01-06  20  21  22  23  6 NaN
'''

pandas 處理資料表中的 "空值"

dropna 丟掉整欄/整列資料
























































#coding=utf-8
import pandas as pd 
import numpy as np 

dates = pd.date_range('2017-01-01',periods=6)
df = pd.DataFrame(np.arange(24).reshape((6,4)), index=dates, columns=['A','B','C','D'])

print(df)
'''
             A   B   C   D
2017-01-01   0   1   2   3
2017-01-02   4   5   6   7
2017-01-03   8   9  10  11
2017-01-04  12  13  14  15
2017-01-05  16  17  18  19
2017-01-06  20  21  22  23
'''

# 設定兩個空值
df.iloc[0,1] = np.nan
df.iloc[1,2] = np.nan

print(df)
'''
             A     B     C   D
2017-01-01   0   NaN   2.0   3
2017-01-02   4   5.0   NaN   7
2017-01-03   8   9.0  10.0  11
2017-01-04  12  13.0  14.0  15
2017-01-05  16  17.0  18.0  19
2017-01-06  20  21.0  22.0  23
'''

# dropna: how = any 偵測 row (axis=0) 當中有 nan 的就整個丟掉
print(df.dropna(axis=0, how='any'))  # how = 'any'  'all'
'''
             A     B     C   D
2017-01-03   8   9.0  10.0  11
2017-01-04  12  13.0  14.0  15
2017-01-05  16  17.0  18.0  19
2017-01-06  20  21.0  22.0  23
'''

# dropna: how = any 偵測 col (axis=1) 當中有 nan 的就整個丟掉
print(df.dropna(axis=1, how='any'))  # how = 'any'  'all'
'''
             A   D
2017-01-01   0   3
2017-01-02   4   7
2017-01-03   8  11
2017-01-04  12  15
2017-01-05  16  19
2017-01-06  20  23
'''

# p.s. 如果 how = all 的話，要整排 NaN 才會丟掉

fillna 在 NaN 空格中填入指定的值 value











# fillna: 遇到 NaN 填入指定的數字
print(df.fillna(value=0))
'''
             A     B     C   D
2017-01-01   0   0.0   2.0   3
2017-01-02   4   5.0   0.0   7
2017-01-03   8   9.0  10.0  11
2017-01-04  12  13.0  14.0  15
2017-01-05  16  17.0  18.0  19
2017-01-06  20  21.0  22.0  23
'''

isnull 查看資料是否為空值 NaN

















# isnull 查看資料格是否是 NaN
print(df.isnull())
'''
                A      B      C      D
2017-01-01  False   True  False  False
2017-01-02  False  False   True  False
2017-01-03  False  False  False  False
2017-01-04  False  False  False  False
2017-01-05  False  False  False  False
2017-01-06  False  False  False  False
'''

# isnull 的應用：查看整個資料表是否有空值 NaN
print(np.any(df.isnull())==True) # output: True
'''
True  代表 df.isnull 當中有 True --> 意思是資料表中有 NaN (丟失的數據)
'''

pandas 讀寫資料檔案

首先準備一個 student.csv 檔
裡面資料長這樣

Student ID,name ,age,gender
1100,Kelly,22,Female
1101,Clo,21,Female
1102,Tilly,22,Female
1103,Tony,24,Male
1104,David,20,Male
1105,Catty,22,Female
1106,M,3,Female
1107,N,43,Male
1108,A,13,Male
1109,S,12,Male
1110,David,33,Male
1111,Dw,3,Female
1112,Q,23,Male
1113,W,21,Female

pandas 可以讀取的檔案格式詳情：
https://pandas.pydata.org/pandas-docs/stable/api.html#input-output



























#coding=utf-8
import pandas as pd 
import numpy as np 

# 讀取
data = pd.read_csv('student.csv')
print(data)
'''
    Student ID  name   age  gender
0         1100  Kelly   22  Female
1         1101    Clo   21  Female
2         1102  Tilly   22  Female
3         1103   Tony   24    Male
4         1104  David   20    Male
5         1105  Catty   22  Female
6         1106      M    3  Female
7         1107      N   43    Male
8         1108      A   13    Male
9         1109      S   12    Male
10        1110  David   33    Male
11        1111     Dw    3  Female
12        1112      Q   23    Male
13        1113      W   21  Female
'''

# 存檔
data.to_pickle('student.pickle')

pandas concatenating 合併資料

concat 設定 axis=0 為直向合併













































#coding=utf-8
import pandas as pd 
import numpy as np 

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)
'''
     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
'''
print(df2)
'''
     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
'''
print(df3)
'''
     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
'''

# 使用 concat 合併 axis=0 為直向合併
res = pd.concat([df1,df2,df3],axis=0)
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
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
'''

ignore_index = True 可以忽略合併時舊的 index 欄位，改採用自動產生的 index















# 目前上面結果的問題是，左側的 index 在合併時不連續，我們必須要設定 ignore index 才能解掉 
res = pd.concat([df1,df2,df3],axis=0, 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
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
'''

concat 的 join 屬性有兩種模式 inner, outer(預設)


















































#coding=utf-8
import pandas as pd 
import numpy as np 

# concat 使用 join 設定
# join 有兩種模式，分別為 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))*0, columns=['b','c','d','e'],index=[2,3,4])

print(df1)
'''
     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
'''
print(df2)
'''
     b    c    d    e
2  0.0  0.0  0.0  0.0
3  0.0  0.0  0.0  0.0
4  0.0  0.0  0.0  0.0
'''

# 使用 concat 合併時，他預設的 join 模式是 'outer'，會直接把沒有的資料用 NaN 代替
res = pd.concat([df1,df2])               # 這兩行程式是全等的
res = pd.concat([df1,df2], join='outer') # 這兩行程式是全等的
print(res)
'''
     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  0.0  0.0  0.0  0.0
3  NaN  0.0  0.0  0.0  0.0
4  NaN  0.0  0.0  0.0  0.0
'''

# 使用 concat 的 join 模式為 'inner'，會直接把沒有完整資料的刪除掉
res = pd.concat([df1,df2], join='inner', ignore_index=True)
print(res)
'''
     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  0.0  0.0  0.0
4  0.0  0.0  0.0
5  0.0  0.0  0.0
'''

concat 的 join_axes 功能，用於水瓶合併時指定 index 參考者

















#coding=utf-8
import pandas as pd 
import numpy as np 

# concat 的 join_axes 功能
df1 = pd.DataFrame(np.ones((3,4))*0, columns=['a','b','c','d'],index=[1,2,3])
df2 = pd.DataFrame(np.ones((3,4))*0, columns=['b','c','d','e'],index=[2,3,4])

# 設定左右合併 axis=1, join_axes 設定成按照 df1 的 index 來進行合併
res = pd.concat([df1,df2],axis=1, join_axes=[df1.index])
print(res)
'''
     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  0.0  0.0  0.0  0.0
3  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0
'''

使用 DataFrame append 來合併資料，新增資料

















































#coding=utf-8
import pandas as pd 
import numpy as np 

# concat 的 append 功能
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'])

# append 預設是往下加
res = df1.append(df2, 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
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
'''

# append 多個
df3 = pd.DataFrame(np.ones((3,4))*3, columns=['a','b','c','d'])
res = df1.append([df2,df3], 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
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  3.0  3.0  3.0  3.0
7  3.0  3.0  3.0  3.0
8  3.0  3.0  3.0  3.0
'''

# 直接 append 一組資料
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
3  1.0  2.0  3.0  4.0
'''

pandas merge 合併資料

merge by 一個 key










































#coding=utf-8
import pandas as pd 
import numpy as np 

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)
'''
    A   B key
0  A0  B0  K0
1  A1  B1  K1
2  A2  B2  K2
3  A3  B3  K3
'''
print(right)
'''
    C   D key
0  C0  D0  K0
1  C1  D1  K1
2  C2  D2  K2
3  C3  D3  K3
'''

# 目標，基於 key 把 left 與 right 合併
# 使用 merge
res = pd.merge(left,right, on='key')
print(res)
'''
    A   B key   C   D
0  A0  B0  K0  C0  D0
1  A1  B1  K1  C1  D1
2  A2  B2  K2  C2  D2
3  A3  B3  K3  C3  D3
'''

merge by 多個 key

































#coding=utf-8
import pandas as pd 
import numpy as np 

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)
'''
    A   B key1 key2
0  A0  B0   K0   K0
1  A1  B1   K0   K1
2  A2  B2   K1   K0
3  A3  B3   K2   K1
'''
print(right)
'''
    C   D key1 key2
0  C0  D0   K0   K0
1  C1  D1   K1   K0
2  C2  D2   K1   K0
3  C3  D3   K2   K0
'''

inner 模式













# 目標，基於 key1, key2 把 left 與 right 合併
# 使用 merge 同時合併 by 多個 key 預設為 how='inner' 模式
res = pd.merge(left,right, on=['key1','key2'])           # 這兩行效果一樣
res = pd.merge(left,right, on=['key1','key2'],how='inner')  # 這兩行效果一樣
print(res)
''' 合併後，他預設只把相同的部分留下來 (inner 模式)
    A   B key1 key2   C   D
0  A0  B0   K0   K0  C0  D0
1  A2  B2   K1   K0  C1  D1
2  A2  B2   K1   K0  C2  D2
'''

outer 模式












# 使用 merge 同時合併 by 多個 key, how='outer' 模式
res = pd.merge(left,right, on=['key1','key2'],how='outer')
print(res)
'''
     A    B key1 key2    C    D
0   A0   B0   K0   K0   C0   D0
1   A1   B1   K0   K1  NaN  NaN
2   A2   B2   K1   K0   C1   D1
3   A2   B2   K1   K0   C2   D2
4   A3   B3   K2   K1  NaN  NaN
5  NaN  NaN   K2   K0   C3   D3
'''

right 模式










# 使用 merge 同時合併 by 多個 key, how='right' 模式
res = pd.merge(left,right, on=['key1','key2'],how='right')
print(res)
'''
     A    B key1 key2   C   D
0   A0   B0   K0   K0  C0  D0
1   A2   B2   K1   K0  C1  D1
2   A2   B2   K1   K0  C2  D2
3  NaN  NaN   K2   K0  C3  D3
'''

left 模式











# 使用 merge 同時合併 by 多個 key, how='left' 模式
res = pd.merge(left,right, on=['key1','key2'],how='left')
print(res)
'''
    A   B key1 key2    C    D
0  A0  B0   K0   K0   C0   D0
1  A1  B1   K0   K1  NaN  NaN
2  A2  B2   K1   K0   C1   D1
3  A2  B2   K1   K0   C2   D2
4  A3  B3   K2   K1  NaN  NaN
'''

使用 indicator 顯示 merge 的 mode

































#coding=utf-8
import pandas as pd 
import numpy as np 

df1 = pd.DataFrame({'col1':[0,1], 'col_left':['a','b']})
df2 = pd.DataFrame({'col1':[1,2,3], 'col_right':[2,2,2]})

print(df1)
'''
   col1 col_left
0     0        a
1     1        b
'''

print(df2)
'''
   col1  col_right
0     1          2
1     2          2
2     3          2
'''

# 使用 indicator 可以顯示 merge 的方式
res = pd.merge(df1,df2, on='col1', how='outer',indicator=True)

print(res)
'''
   col1 col_left  col_right      _merge
0     0        a        NaN   left_only
1     1        b        2.0        both
2     2      NaN        2.0  right_only
3     3      NaN        2.0  right_only
'''

設定 indicator 欄位的名字













# 設定 indicator 欄位的名字
res = pd.merge(df1,df2, on='col1', how='outer',indicator='indicator_column')

print(res)
'''
   col1 col_left  col_right indicator_column
0     0        a        NaN        left_only
1     1        b        2.0             both
2     2      NaN        2.0       right_only
3     3      NaN        2.0       right_only
'''

merge by index








































#coding=utf-8
import pandas as pd 
import numpy as np 

# merge by index 
left = pd.DataFrame({
    'A':['A0','A1','A2'],
    'B':['B0','B1','B2']},
    index=['K0','K1','K2'])

right = pd.DataFrame({
    'C':['C0','C2','C3'],
    'D':['D0','D2','D3']},
    index=['K0','K2','K3'])

print(left)
'''
     A   B
K0  A0  B0
K1  A1  B1
K2  A2  B2
'''

print(right)
'''
     C   D
K0  C0  D0
K2  C2  D2
K3  C3  D3
'''

res = pd.merge(left,right, left_index=True, right_index=True, how='outer')
print(res)
'''
      A    B    C    D
K0   A0   B0   C0   D0
K1   A1   B1  NaN  NaN
K2   A2   B2   C2   D2
K3  NaN  NaN   C3   D3
'''

merge 合併時，處理欄位名稱相同衝突，以 suffixes 區別



































#coding=utf-8
import pandas as pd 
import numpy as np 

# 處理 overlapping
boys = pd.DataFrame({'k':['K0','K1','K2'],'age':[1,2,3]})
girls = pd.DataFrame({'k':['K0','K0','K3'],'age':[4,5,6]})

print(boys)
'''
   age   k
0    1  K0
1    2  K1
2    3  K2
'''
print(girls)
'''
   age   k
0    4  K0
1    5  K0
2    6  K3
'''

# 目前 age 欄位是重複的，我們為了要區別 boy 與 girl，必須要在新的合併表格中，為 age 欄位取新的名字
# 使用 suffixes 屬性即可辦到
res = pd.merge(boys,girls, on='k', suffixes=['_boy','_girl'], how='outer')
print(res)
'''
   age_boy   k  age_girl
0      1.0  K0       4.0
1      1.0  K0       5.0
2      2.0  K1       NaN
3      3.0  K2       NaN
4      NaN  K3       6.0
'''

pandas plot

plot 基本用法畫出 Series 數據












#coding=utf-8
import pandas as pd 
import numpy as np 
import matplotlib.pyplot as plt 

# plot data

# Series 線性數據
data = pd.Series(np.random.randn(1000), index=np.arange(1000))
data = data.cumsum() # 做累加
data.plot()
plt.show()

plot 畫出 DataFrame 四個數據





















#coding=utf-8
import pandas as pd 
import numpy as np 
import matplotlib.pyplot as plt 

# DataFrame
data = pd.DataFrame(np.random.randn(1000,4),
                    index=np.arange(1000),
                    columns=list("ABCD"))
data = data.cumsum()
print(data.head(5))  # 印出前五個數據
'''
          A         B         C         D
0 -0.056981 -0.167990 -0.103564 -0.807399
1  1.008049 -1.633926  0.959755  0.405345
2  0.821038 -3.090023  2.821623 -0.880397
3  0.448243 -4.889474  4.477471 -1.378809
4 -1.453623 -3.347546  5.371346 -2.983690
'''
data.plot()
plt.show()

scatter 用法




























#coding=utf-8
import pandas as pd 
import numpy as np 
import matplotlib.pyplot as plt 

# plot 的方法種類:
# 'bar', 'hist', 'box', 'kde', 'area', 'scatter', 'hexbin', 'pie'

# DataFrame
data = pd.DataFrame(np.random.randn(1000,4),
                    index=np.arange(1000),
                    columns=list("ABCD"))
data = data.cumsum()
print(data.head(5))  # 印出前五個數據
'''
          A         B         C         D
0  1.149112 -1.189742 -1.108183 -1.276239
1  0.889289 -0.979980 -0.821403  0.726542
2  1.219525 -0.753984  0.279848  1.686624
3  1.006253 -0.191323  0.595033  0.578449
4  0.710900 -0.820767  0.064716  1.539593
'''

# 使用 scatter
ax = data.plot.scatter(x='A',y='B', color='Red',label='Class 1')  # 取 A 欄位 B 欄位
data.plot.scatter(x='A', y='C', color='Green', label='Class 2', ax=ax) # 取 A 欄位 C 欄位

plt.show()

Syntax	Example	Reference
# Header	Header	基本排版
- Unordered List	Unordered List
1. Ordered List	Ordered List
- [ ] Todo List	Todo List
> Blockquote	Blockquote
Bold font	Bold font
Italics font	Italics font
~~Strikethrough~~	~~Strikethrough~~
19^th^	19^th
H~2~O	H₂O
++Inserted text++	Inserted text
==Marked text==	Marked text
[link text](https:// "title")	Link
![image alt](https:// "title")	Image
`Code`	`Code`	在筆記中貼入程式碼
```javascript var i = 0; ```	`var i = 0;`	在筆記中貼入程式碼
:smile:		Emoji list
{%youtube youtube_id %}	Externals
$L^aT_eX$	L^aT_eX
:::info This is a alert area. :::	This is a alert area.