Pandas学习

素颜马尾好姑娘i 2022-05-31 03:44 178阅读 0赞

> 本文首发于我的博客：[gongyanli.com][]  
> 参考视频：[莫烦的Pandas视频][Pandas]  
> 我的简书：[https://www.jianshu.com/p/29839f285484][https_www.jianshu.com_p_29839f285484]

前言:其实我之前看过pandas的书，但是本着看过就忘记的原则，下次再看见他的时候，反正看着很熟悉，怎么用早就抛到九霄云外了。所以这次网上看了几个简单的小视频，然后总结了以下，虽然没有以前看得那么繁琐，但我一直认为够用就好，不够用的等到用的时候再去查找。

## 一、安装 ##

windows：pip install pandas（anaconda下）
    mac:pip install pandas
    linux:pip install pandas

## 二、导入 ##

import pandas as pd
    import numpy as np

## 三、Pandas–基本介绍 ##

Numpy和Pandas区别：

Numpy：列表形式，没有数值标签，类似于python的列表
    Pandas：字典形式，类似于python的字典

Series：

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
    """

DataFrame:

dates = pd.date_range('20160101',periods=6)
    df = pd.DataFrame(np.random.randn(6,4),index=dates,columns=['a','b','c','d'])
    print(df)
    
    """
                       a         b         c         d
    2016-01-01 -0.253065 -2.071051 -0.640515  0.613663
    2016-01-02 -1.147178  1.532470  0.989255 -0.499761
    2016-01-03  1.221656 -2.390171  1.862914  0.778070
    2016-01-04  1.473877 -0.046419  0.610046  0.204672
    2016-01-05 -1.584752 -0.700592  1.487264 -1.778293
    2016-01-06  0.633675 -1.414157 -0.277066 -0.442545
    """
    
    print(df['b'])
    
    """
    2016-01-01   -2.071051
    2016-01-02    1.532470
    2016-01-03   -2.390171
    2016-01-04   -0.046419
    2016-01-05   -0.700592
    2016-01-06   -1.414157
    Freq: D, Name: b, dtype: float64
    """
    
    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
    """
    
    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)
    
    """
         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
    """
    
    print(df2.dtypes)
    
    """
    df2.dtypes
    A           float64
    B    datetime64[ns]
    C           float32
    D             int32
    E          category
    F            object
    dtype: object
    """
    
    print(df2.index)
    
    # Int64Index([0, 1, 2, 3], dtype='int64')
    
    print(df2.columns)
    
    # Index(['A', 'B', 'C', 'D', 'E', 'F'], dtype='object')
    
    print(df2.values)
    
    """
    array([[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']], dtype=object)
    """
    
    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
    """
    
    print(df2.T)
    
    """                   
    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  
    
    """
    
    print(df2.sort_index(axis=1, ascending=False))
    
    """
         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
    """
    
    print(df2.sort_values(by='B'))
    
    """
         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
    """

## 四、Pandas–选择数据 ##

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

简单筛选

print(df['A'])
    print(df.A)
    
    """
    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: int64
    """
    
    print(df[0:3]) # 跨越多行
    
    """
                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
    """
    
    print(df['20130102':'20130104']) # 跨越多列
    
    """
    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
    """

标签loc筛选

print(df.loc['20130102'])
    """
    A    4
    B    5
    C    6
    D    7
    Name: 2013-01-02 00:00:00, dtype: int64
    """
    
    print(df.loc[:,['A','B']]) 
    """
                 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
    """
    
    print(df.loc['20130102',['A','B']])
    """
    A    4
    B    5
    Name: 2013-01-02 00:00:00, dtype: int64
    """

序列iloc筛选

print(df.iloc[3,1])
    # 13
    
    print(df.iloc[3:5,1:3])
    """
                 B   C
    2013-01-04  13  14
    2013-01-05  17  18
    """
    
    print(df.iloc[[1,3,5],1:3])
    """
                 B   C
    2013-01-02   5   6
    2013-01-04  13  14
    2013-01-06  21  22
    
    """

混合ix筛选

print(df.ix[:3,['A','C']])
    """
                A   C
    2013-01-01  0   2
    2013-01-02  4   6
    2013-01-03  8  10
    """

判断筛选

print(df[df.A>8])
    """
                 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
    """

## 五、Pandas–设置值 ##

根据位置loc和iloc设置值

df.iloc[2,2] = 1111
    df.loc['20130101','B'] = 2222
    
    """
                 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
    """

根据条件设置值

df.B[df.A>4] = 0
    """
                    A     B     C   D
    2013-01-01   0  2222     2   3
    2013-01-02   4     5     6   7
    2013-01-03   8     0  1111  11
    2013-01-04  12     0    14  15
    2013-01-05  16     0    18  19
    2013-01-06  20     0    22  23 
    """

按行或列设置值

df['F'] = np.nan
    """
                 A     B     C   D   F
    2013-01-01   0  2222     2   3 NaN
    2013-01-02   4     5     6   7 NaN
    2013-01-03   8     0  1111  11 NaN
    2013-01-04  12     0    14  15 NaN
    2013-01-05  16     0    18  19 NaN
    2013-01-06  20     0    22  23 NaN
    """

添加数据–长度必须对齐

df['E'] = pd.Series([1,2,3,4,5,6], index=pd.date_range('20130101',periods=6)) 
    """
                 A     B     C   D   F  E
    2013-01-01   0  2222     2   3 NaN  1
    2013-01-02   4     5     6   7 NaN  2
    2013-01-03   8     0  1111  11 NaN  3
    2013-01-04  12     0    14  15 NaN  4
    2013-01-05  16     0    18  19 NaN  5
    2013-01-06  20     0    22  23 NaN  6
    """

## 六、Pandas–处理丢失值 ##

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
    """
                 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
    """

pd.dropna()–删除缺失值所在的行或列

df.dropna(
    axis=0,     # 0: 对行进行操作; 1: 对列进行操作
    how='any'   # 'any': 只要存在 NaN 就 drop 掉; 'all': 必须全部是 NaN 才 drop 
    ) 
    """
                 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
    """

pd.fillna()–填充缺失值

df.fillna(value=0)
    """
                 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
    """

pd.isnull()–判断缺失值，True为缺失值

df.isnull() 
    """
                    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
    """

检查数据中是否存在NaN,如果存在返回True

np.any(df.isnull()) == True  
    # True

## 六、Pandas–导入导出数据 ##

pandas可读取格式：csv、excel、json、html、pickle等，详见[官方文档][Link 1]

读取csv

#读取csv
    data = pd.read_csv('student.csv')
    
    #打印出data
    print(data)

将资料存取成pickle

data.to_pickle('student.pickle')

## 七、Pandas–合并–concat ##

axis–合并方向（axis=0是预设值）

#定义资料集
    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'])
    
    #concat纵向合并
    res = pd.concat([df1, df2, df3], axis=0)
    
    #打印结果
    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 (重置 index)

#承上一个例子，并将index_ignore设定为True
    res = pd.concat([df1, df2, df3], axis=0, ignore_index=True)
    
    #打印结果
    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

join（合并方式）–join=’outer’是预设值

#定义资料集
    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])
    
    #纵向"外"合并df1与df2
    res = pd.concat([df1, df2], axis=0, join='outer')
    
    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  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
    
    
    #承上一个例子
    #纵向"内"合并df1与df2
    res = pd.concat([df1, df2], axis=0, join='inner')
    
    #打印结果
    print(res)
    #     b    c    d
    # 1  0.0  0.0  0.0
    # 2  0.0  0.0  0.0
    # 3  0.0  0.0  0.0
    # 2  1.0  1.0  1.0
    # 3  1.0  1.0  1.0
    # 4  1.0  1.0  1.0
    
    #重置index并打印结果
    res = pd.concat([df1, df2], axis=0, 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  1.0  1.0  1.0
    # 4  1.0  1.0  1.0
    # 5  1.0  1.0  1.0

join\_axes (依照 axes 合并)

#定义资料集
    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])
    
    #依照`df1.index`进行横向合并
    res = pd.concat([df1, df2], axis=1, join_axes=[df1.index])
    
    #打印结果
    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  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
    
    #移除join_axes，并打印结果
    res = pd.concat([df1, df2], axis=1)
    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  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

append (添加数据)–只有纵向合并，没有横向合并

#定义资料集
    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))*1, columns=['a','b','c','d'])
    s1 = pd.Series([1,2,3,4], index=['a','b','c','d'])
    
    #将df2合并到df1的下面，以及重置index，并打印出结果
    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
    
    #合并多个df，将df2与df3合并至df1的下面，以及重置index，并打印出结果
    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  1.0  1.0  1.0  1.0
    # 7  1.0  1.0  1.0  1.0
    # 8  1.0  1.0  1.0  1.0
    
    #合并series，将s1合并至df1，以及重置index，并打印出结果
    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

## 八、Pandas–合并– merge ##

用法：主要用于两组有key column的数据，统一索引的数据，通常也被用在Database的处理中。

依据一组key合并

#定义资料集并打印出
    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
    
    #依据key column合并，并打印出
    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

依据两组key合并–合并时有4种方法how = \[‘left’, ‘right’, ‘outer’, ‘inner’\]，预设值how=’inner’

#定义资料集并打印出
    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
    
    #依据key1与key2 columns进行合并，并打印出四种结果['left', 'right', 'outer', 'inner']
    res = pd.merge(left, right, on=['key1', 'key2'], how='inner')
    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
    
    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
    
    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
    
    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

Indicator–indicator=True会将合并的记录放在新的一列。

#定义资料集并打印出
    df1 = pd.DataFrame({'col1':[0,1], 'col_left':['a','b']})
    df2 = pd.DataFrame({'col1':[1,2,2],'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     2          2
    
    # 依据col1进行合并，并启用indicator=True，最后打印出
    res = pd.merge(df1, df2, on='col1', how='outer', indicator=True)
    print(res)
    #   col1 col_left  col_right      _merge
    # 0   0.0        a        NaN   left_only
    # 1   1.0        b        2.0        both
    # 2   2.0      NaN        2.0  right_only
    # 3   2.0      NaN        2.0  right_only
    
    # 自定indicator column的名称，并打印出
    res = pd.merge(df1, df2, on='col1', how='outer', indicator='indicator_column')
    print(res)
    #   col1 col_left  col_right indicator_column
    # 0   0.0        a        NaN        left_only
    # 1   1.0        b        2.0             both
    # 2   2.0      NaN        2.0       right_only
    # 3   2.0      NaN        2.0       right_only

依据index合并

#定义资料集并打印出
    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
    
    #依据左右资料集的index进行合并，how='outer',并打印出
    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
    
    #依据左右资料集的index进行合并，how='inner',并打印出
    res = pd.merge(left, right, left_index=True, right_index=True, how='inner')
    print(res)
    #     A   B   C   D
    # K0  A0  B0  C0  D0
    # K2  A2  B2  C2  D2

解决overlapping的问题

#定义资料集
    boys = pd.DataFrame({'k': ['K0', 'K1', 'K2'], 'age': [1, 2, 3]})
    girls = pd.DataFrame({'k': ['K0', 'K0', 'K3'], 'age': [4, 5, 6]})
    
    #使用suffixes解决overlapping的问题
    res = pd.merge(boys, girls, on='k', suffixes=['_boy', '_girl'], how='inner')
    print(res)
    #    age_boy   k  age_girl
    # 0        1  K0         4
    # 1        1  K0         5

## 九、Pandas–plot出图 ##

导入

import pandas as pd
    import numpy as np
    import matplotlib.pyplot as plt

Series可视化

# 随机生成1000个数据
    data = pd.Series(np.random.randn(1000),index=np.arange(1000))
    
    # 为了方便观看效果, 我们累加这个数据
    data.cumsum()
    
    # pandas 数据可以直接观看其可视化形式
    data.plot()
    
    plt.show()
    
    可以使用 plt.plot(x=, y=)，把x,y的数据作为参数存进去，但是data本来就是一个数据，所以我们可以直接plot。

DataFrame可视化

data = pd.DataFrame(
    np.random.randn(1000,4),
    index=np.arange(1000),
    columns=list("ABCD")
    )
    data.cumsum()
    data.plot()
    plt.show()
    
    常用方法:
        plot
        bar
        hist
        box
        kde
        area
        scatter
        hexbin
    
    ax = data.plot.scatter(x='A',y='B',color='DarkBlue',label='Class1')
    # 将之下这个 data 画在上一个 ax 上面
    data.plot.scatter(x='A',y='C',color='LightGreen',label='Class2',ax=ax)
    plt.show()

[gongyanli.com]: http://gongyanli.com/Pandas%E5%AD%A6%E4%B9%A0/
[Pandas]: https://morvanzhou.github.io/tutorials/data-manipulation/np-pd/
[https_www.jianshu.com_p_29839f285484]: https://www.jianshu.com/p/29839f285484
[Link 1]: http://pandas.pydata.org/pandas-docs/stable/io.html