Pandas高级教程之:处理缺失数据

分手后的思念是犯贱 2022-10-09 06:17 254阅读 0赞

### 文章目录 ###

*  简介
 *  NaN的例子
 *  整数类型的缺失值
 *  Datetimes 类型的缺失值
 *  None 和 np.nan 的转换
 *  缺失值的计算
 *  使用fillna填充NaN数据
 *  使用dropna删除包含NA的数据
 *  插值interpolation
 *  使用replace替换值

# 简介 #

在数据处理中，Pandas会将无法解析的数据或者缺失的数据使用NaN来表示。虽然所有的数据都有了相应的表示，但是NaN很明显是无法进行数学运算的。

本文将会讲解Pandas对于NaN数据的处理方法。

# NaN的例子 #

上面讲到了缺失的数据会被表现为NaN，我们来看一个具体的例子：

我们先来构建一个DF：

In [1]: df = pd.DataFrame(np.random.randn(5, 3), index=['a', 'c', 'e', 'f', 'h'],
       ...:                   columns=['one', 'two', 'three'])
       ...: 
    
    In [2]: df['four'] = 'bar'
    
    In [3]: df['five'] = df['one'] > 0
    
    In [4]: df
    Out[4]: 
            one       two     three four   five
    a  0.469112 -0.282863 -1.509059  bar   True
    c -1.135632  1.212112 -0.173215  bar  False
    e  0.119209 -1.044236 -0.861849  bar   True
    f -2.104569 -0.494929  1.071804  bar  False
    h  0.721555 -0.706771 -1.039575  bar   True

上面DF只有acefh这几个index，我们重新index一下数据：

In [5]: df2 = df.reindex(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'])
    
    In [6]: df2
    Out[6]: 
            one       two     three four   five
    a  0.469112 -0.282863 -1.509059  bar   True
    b       NaN       NaN       NaN  NaN    NaN
    c -1.135632  1.212112 -0.173215  bar  False
    d       NaN       NaN       NaN  NaN    NaN
    e  0.119209 -1.044236 -0.861849  bar   True
    f -2.104569 -0.494929  1.071804  bar  False
    g       NaN       NaN       NaN  NaN    NaN
    h  0.721555 -0.706771 -1.039575  bar   True

数据缺失，就会产生很多NaN。

为了检测是否NaN，可以使用isna()或者notna() 方法。

In [7]: df2['one']
    Out[7]: 
    a    0.469112
    b         NaN
    c   -1.135632
    d         NaN
    e    0.119209
    f   -2.104569
    g         NaN
    h    0.721555
    Name: one, dtype: float64
    
    In [8]: pd.isna(df2['one'])
    Out[8]: 
    a    False
    b     True
    c    False
    d     True
    e    False
    f    False
    g     True
    h    False
    Name: one, dtype: bool
    
    In [9]: df2['four'].notna()
    Out[9]: 
    a     True
    b    False
    c     True
    d    False
    e     True
    f     True
    g    False
    h     True
    Name: four, dtype: bool

注意在Python中None是相等的：

In [11]: None == None                                                 # noqa: E711
    Out[11]: True

但是np.nan是不等的：

In [12]: np.nan == np.nan
    Out[12]: False

# 整数类型的缺失值 #

NaN默认是float类型的，如果是整数类型，我们可以强制进行转换：

In [14]: pd.Series([1, 2, np.nan, 4], dtype=pd.Int64Dtype())
    Out[14]: 
    0       1
    1       2
    2    <NA>
    3       4
    dtype: Int64

# Datetimes 类型的缺失值 #

时间类型的缺失值使用NaT来表示：

In [15]: df2 = df.copy()
    
    In [16]: df2['timestamp'] = pd.Timestamp('20120101')
    
    In [17]: df2
    Out[17]: 
            one       two     three four   five  timestamp
    a  0.469112 -0.282863 -1.509059  bar   True 2012-01-01
    c -1.135632  1.212112 -0.173215  bar  False 2012-01-01
    e  0.119209 -1.044236 -0.861849  bar   True 2012-01-01
    f -2.104569 -0.494929  1.071804  bar  False 2012-01-01
    h  0.721555 -0.706771 -1.039575  bar   True 2012-01-01
    
    In [18]: df2.loc[['a', 'c', 'h'], ['one', 'timestamp']] = np.nan
    
    In [19]: df2
    Out[19]: 
            one       two     three four   five  timestamp
    a       NaN -0.282863 -1.509059  bar   True        NaT
    c       NaN  1.212112 -0.173215  bar  False        NaT
    e  0.119209 -1.044236 -0.861849  bar   True 2012-01-01
    f -2.104569 -0.494929  1.071804  bar  False 2012-01-01
    h       NaN -0.706771 -1.039575  bar   True        NaT
    
    In [20]: df2.dtypes.value_counts()
    Out[20]: 
    float64           3
    datetime64[ns]    1
    bool              1
    object            1
    dtype: int64

# None 和 np.nan 的转换 #

对于数字类型的，如果赋值为None，那么会转换为相应的NaN类型：

In [21]: s = pd.Series([1, 2, 3])
    
    In [22]: s.loc[0] = None
    
    In [23]: s
    Out[23]: 
    0    NaN
    1    2.0
    2    3.0
    dtype: float64

如果是对象类型，使用None赋值，会保持原样：

In [24]: s = pd.Series(["a", "b", "c"])
    
    In [25]: s.loc[0] = None
    
    In [26]: s.loc[1] = np.nan
    
    In [27]: s
    Out[27]: 
    0    None
    1     NaN
    2       c
    dtype: object

# 缺失值的计算 #

缺失值的数学计算还是缺失值：

In [28]: a
    Out[28]: 
            one       two
    a       NaN -0.282863
    c       NaN  1.212112
    e  0.119209 -1.044236
    f -2.104569 -0.494929
    h -2.104569 -0.706771
    
    In [29]: b
    Out[29]: 
            one       two     three
    a       NaN -0.282863 -1.509059
    c       NaN  1.212112 -0.173215
    e  0.119209 -1.044236 -0.861849
    f -2.104569 -0.494929  1.071804
    h       NaN -0.706771 -1.039575
    
    In [30]: a + b
    Out[30]: 
            one  three       two
    a       NaN    NaN -0.565727
    c       NaN    NaN  2.424224
    e  0.238417    NaN -2.088472
    f -4.209138    NaN -0.989859
    h       NaN    NaN -1.413542

但是在统计中会将NaN当成0来对待。

In [31]: df
    Out[31]: 
            one       two     three
    a       NaN -0.282863 -1.509059
    c       NaN  1.212112 -0.173215
    e  0.119209 -1.044236 -0.861849
    f -2.104569 -0.494929  1.071804
    h       NaN -0.706771 -1.039575
    
    In [32]: df['one'].sum()
    Out[32]: -1.9853605075978744
    
    In [33]: df.mean(1)
    Out[33]: 
    a   -0.895961
    c    0.519449
    e   -0.595625
    f   -0.509232
    h   -0.873173
    dtype: float64

如果是在cumsum或者cumprod中，默认是会跳过NaN，如果不想统计NaN，可以加上参数skipna=False

In [34]: df.cumsum()
    Out[34]: 
            one       two     three
    a       NaN -0.282863 -1.509059
    c       NaN  0.929249 -1.682273
    e  0.119209 -0.114987 -2.544122
    f -1.985361 -0.609917 -1.472318
    h       NaN -1.316688 -2.511893
    
    In [35]: df.cumsum(skipna=False)
    Out[35]: 
       one       two     three
    a  NaN -0.282863 -1.509059
    c  NaN  0.929249 -1.682273
    e  NaN -0.114987 -2.544122
    f  NaN -0.609917 -1.472318
    h  NaN -1.316688 -2.511893

# 使用fillna填充NaN数据 #

数据分析中，如果有NaN数据，那么需要对其进行处理，一种处理方法就是使用fillna来进行填充。

下面填充常量：

In [42]: df2
    Out[42]: 
            one       two     three four   five  timestamp
    a       NaN -0.282863 -1.509059  bar   True        NaT
    c       NaN  1.212112 -0.173215  bar  False        NaT
    e  0.119209 -1.044236 -0.861849  bar   True 2012-01-01
    f -2.104569 -0.494929  1.071804  bar  False 2012-01-01
    h       NaN -0.706771 -1.039575  bar   True        NaT
    
    In [43]: df2.fillna(0)
    Out[43]: 
            one       two     three four   five            timestamp
    a  0.000000 -0.282863 -1.509059  bar   True                    0
    c  0.000000  1.212112 -0.173215  bar  False                    0
    e  0.119209 -1.044236 -0.861849  bar   True  2012-01-01 00:00:00
    f -2.104569 -0.494929  1.071804  bar  False  2012-01-01 00:00:00
    h  0.000000 -0.706771 -1.039575  bar   True                    0

还可以指定填充方法，比如pad：

In [45]: df
    Out[45]: 
            one       two     three
    a       NaN -0.282863 -1.509059
    c       NaN  1.212112 -0.173215
    e  0.119209 -1.044236 -0.861849
    f -2.104569 -0.494929  1.071804
    h       NaN -0.706771 -1.039575
    
    In [46]: df.fillna(method='pad')
    Out[46]: 
            one       two     three
    a       NaN -0.282863 -1.509059
    c       NaN  1.212112 -0.173215
    e  0.119209 -1.044236 -0.861849
    f -2.104569 -0.494929  1.071804
    h -2.104569 -0.706771 -1.039575

可以指定填充的行数：

In [48]: df.fillna(method='pad', limit=1)

fill方法统计：

<table> 
 <thead> 
  <tr> 
   <th align="left">方法名</th> 
   <th align="left">描述</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <td align="left">pad / ffill</td> 
   <td align="left">向前填充</td> 
  </tr> 
  <tr> 
   <td align="left">bfill / backfill</td> 
   <td align="left">向后填充</td> 
  </tr> 
 </tbody> 
</table>

可以使用PandasObject来填充：

In [53]: dff
    Out[53]: 
              A         B         C
    0  0.271860 -0.424972  0.567020
    1  0.276232 -1.087401 -0.673690
    2  0.113648 -1.478427  0.524988
    3       NaN  0.577046 -1.715002
    4       NaN       NaN -1.157892
    5 -1.344312       NaN       NaN
    6 -0.109050  1.643563       NaN
    7  0.357021 -0.674600       NaN
    8 -0.968914 -1.294524  0.413738
    9  0.276662 -0.472035 -0.013960
    
    In [54]: dff.fillna(dff.mean())
    Out[54]: 
              A         B         C
    0  0.271860 -0.424972  0.567020
    1  0.276232 -1.087401 -0.673690
    2  0.113648 -1.478427  0.524988
    3 -0.140857  0.577046 -1.715002
    4 -0.140857 -0.401419 -1.157892
    5 -1.344312 -0.401419 -0.293543
    6 -0.109050  1.643563 -0.293543
    7  0.357021 -0.674600 -0.293543
    8 -0.968914 -1.294524  0.413738
    9  0.276662 -0.472035 -0.013960
    
    In [55]: dff.fillna(dff.mean()['B':'C'])
    Out[55]: 
              A         B         C
    0  0.271860 -0.424972  0.567020
    1  0.276232 -1.087401 -0.673690
    2  0.113648 -1.478427  0.524988
    3       NaN  0.577046 -1.715002
    4       NaN -0.401419 -1.157892
    5 -1.344312 -0.401419 -0.293543
    6 -0.109050  1.643563 -0.293543
    7  0.357021 -0.674600 -0.293543
    8 -0.968914 -1.294524  0.413738
    9  0.276662 -0.472035 -0.013960

上面操作等同于：

In [56]: dff.where(pd.notna(dff), dff.mean(), axis='columns')

# 使用dropna删除包含NA的数据 #

除了fillna来填充数据之外，还可以使用dropna删除包含na的数据。

In [57]: df
    Out[57]: 
       one       two     three
    a  NaN -0.282863 -1.509059
    c  NaN  1.212112 -0.173215
    e  NaN  0.000000  0.000000
    f  NaN  0.000000  0.000000
    h  NaN -0.706771 -1.039575
    
    In [58]: df.dropna(axis=0)
    Out[58]: 
    Empty DataFrame
    Columns: [one, two, three]
    Index: []
    
    In [59]: df.dropna(axis=1)
    Out[59]: 
            two     three
    a -0.282863 -1.509059
    c  1.212112 -0.173215
    e  0.000000  0.000000
    f  0.000000  0.000000
    h -0.706771 -1.039575
    
    In [60]: df['one'].dropna()
    Out[60]: Series([], Name: one, dtype: float64)

# 插值interpolation #

数据分析时候，为了数据的平稳，我们需要一些插值运算interpolate() ，使用起来很简单：

In [61]: ts
    Out[61]: 
    2000-01-31    0.469112
    2000-02-29         NaN
    2000-03-31         NaN
    2000-04-28         NaN
    2000-05-31         NaN
                    ...   
    2007-12-31   -6.950267
    2008-01-31   -7.904475
    2008-02-29   -6.441779
    2008-03-31   -8.184940
    2008-04-30   -9.011531
    Freq: BM, Length: 100, dtype: float64

In [64]: ts.interpolate()
    Out[64]: 
    2000-01-31    0.469112
    2000-02-29    0.434469
    2000-03-31    0.399826
    2000-04-28    0.365184
    2000-05-31    0.330541
                    ...   
    2007-12-31   -6.950267
    2008-01-31   -7.904475
    2008-02-29   -6.441779
    2008-03-31   -8.184940
    2008-04-30   -9.011531
    Freq: BM, Length: 100, dtype: float64

插值函数还可以添加参数，指定插值的方法，比如按时间插值：

In [67]: ts2
    Out[67]: 
    2000-01-31    0.469112
    2000-02-29         NaN
    2002-07-31   -5.785037
    2005-01-31         NaN
    2008-04-30   -9.011531
    dtype: float64
    
    In [68]: ts2.interpolate()
    Out[68]: 
    2000-01-31    0.469112
    2000-02-29   -2.657962
    2002-07-31   -5.785037
    2005-01-31   -7.398284
    2008-04-30   -9.011531
    dtype: float64
    
    In [69]: ts2.interpolate(method='time')
    Out[69]: 
    2000-01-31    0.469112
    2000-02-29    0.270241
    2002-07-31   -5.785037
    2005-01-31   -7.190866
    2008-04-30   -9.011531
    dtype: float64

按index的float value进行插值：

In [70]: ser
    Out[70]: 
    0.0      0.0
    1.0      NaN
    10.0    10.0
    dtype: float64
    
    In [71]: ser.interpolate()
    Out[71]: 
    0.0      0.0
    1.0      5.0
    10.0    10.0
    dtype: float64
    
    In [72]: ser.interpolate(method='values')
    Out[72]: 
    0.0      0.0
    1.0      1.0
    10.0    10.0
    dtype: float64

除了插值Series，还可以插值DF：

In [73]: df = pd.DataFrame({'A': [1, 2.1, np.nan, 4.7, 5.6, 6.8],
       ....:                    'B': [.25, np.nan, np.nan, 4, 12.2, 14.4]})
       ....: 
    
    In [74]: df
    Out[74]: 
         A      B
    0  1.0   0.25
    1  2.1    NaN
    2  NaN    NaN
    3  4.7   4.00
    4  5.6  12.20
    5  6.8  14.40
    
    In [75]: df.interpolate()
    Out[75]: 
         A      B
    0  1.0   0.25
    1  2.1   1.50
    2  3.4   2.75
    3  4.7   4.00
    4  5.6  12.20
    5  6.8  14.40

interpolate还接收limit参数，可以指定插值的个数。

In [95]: ser.interpolate(limit=1)
    Out[95]: 
    0     NaN
    1     NaN
    2     5.0
    3     7.0
    4     NaN
    5     NaN
    6    13.0
    7    13.0
    8     NaN
    dtype: float64

# 使用replace替换值 #

replace可以替换常量，也可以替换list：

In [102]: ser = pd.Series([0., 1., 2., 3., 4.])
    
    In [103]: ser.replace(0, 5)
    Out[103]: 
    0    5.0
    1    1.0
    2    2.0
    3    3.0
    4    4.0
    dtype: float64

In [104]: ser.replace([0, 1, 2, 3, 4], [4, 3, 2, 1, 0])
    Out[104]: 
    0    4.0
    1    3.0
    2    2.0
    3    1.0
    4    0.0
    dtype: float64

可以替换DF中特定的数值：

In [106]: df = pd.DataFrame({'a': [0, 1, 2, 3, 4], 'b': [5, 6, 7, 8, 9]})
    
    In [107]: df.replace({'a': 0, 'b': 5}, 100)
    Out[107]: 
         a    b
    0  100  100
    1    1    6
    2    2    7
    3    3    8
    4    4    9

可以使用插值替换：

In [108]: ser.replace([1, 2, 3], method='pad')
    Out[108]: 
    0    0.0
    1    0.0
    2    0.0
    3    0.0
    4    4.0
    dtype: float64

> 本文已收录于 [http://www.flydean.com/07-python-pandas-missingdata/][http_www.flydean.com_07-python-pandas-missingdata]
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[http_www.flydean.com_07-python-pandas-missingdata]: http://www.flydean.com/07-python-pandas-missingdata/