Pandas | 17 缺失数据处理

时间 2019-11-10

原文原文链接

数据丢失(缺失)在现实生活中老是一个问题。机器学习和数据挖掘等领域因为数据缺失致使的数据质量差，在模型预测的准确性上面临着严重的问题。在这些领域，缺失值处理是使模型更加准确和有效的重点。python

使用重构索引(reindexing)，建立了一个缺乏值的DataFrame。在输出中，NaN表示不是数字的值。shell

1、检查缺失值

为了更容易地检测缺失值(以及跨越不一样的数组dtype)，Pandas提供了isnull()和notnull()函数，它们也是Series和DataFrame对象的方法 数组

示例1dom

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randn(5, 3),
                  index=['a', 'c', 'e', 'f','h'],
                  columns=['one', 'two', 'three'])

df = df.reindex(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'])

print(df)
print('\n')

print (df['one'].isnull())

输出结果：机器学习

        one       two     three
a  0.036297 -0.615260 -1.341327
b       NaN       NaN       NaN
c -1.908168 -0.779304  0.212467
d       NaN       NaN       NaN
e  0.527409 -2.432343  0.190436
f  1.428975 -0.364970  1.084148
g       NaN       NaN       NaN
h  0.763328 -0.818729  0.240498


a    False
b     True
c    False
d     True
e    False
f    False
g     True
h    False
Name: one, dtype: bool

示例2函数

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randn(5, 3), index=['a', 'c', 'e', 'f',
'h'],columns=['one', 'two', 'three'])

df = df.reindex(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'])

print (df['one'].notnull())

输出结果：

a     True
b    False
c     True
d    False
e     True
f     True
g    False
h     True
Name: one, dtype: bool

2、缺乏数据的计算

在求和数据时，NA将被视为0
若是数据所有是NA，那么结果将是NA

实例1学习

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randn(5, 3), index=['a', 'c', 'e', 'f',
'h'],columns=['one', 'two', 'three'])

df = df.reindex(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'])

print(df)
print('\n')

print (df['one'].sum())

输出结果：spa

        one       two     three
a -1.191036  0.945107 -0.806292
b       NaN       NaN       NaN
c  0.127794 -1.812588 -0.466076
d       NaN       NaN       NaN
e  2.358568  0.559081  1.486490
f -0.242589  0.574916 -0.831853
g       NaN       NaN       NaN
h -0.328030  1.815404 -1.706736


0.7247067964060545

示例2code

import pandas as pd

df = pd.DataFrame(index=[0,1,2,3,4,5],columns=['one','two'])

print(df)
print('\n')

print (df['one'].sum())

输出结果：对象

   one  two
0  NaN  NaN
1  NaN  NaN
2  NaN  NaN
3  NaN  NaN
4  NaN  NaN
5  NaN  NaN

0

3、填充缺乏数据

Pandas提供了各类方法来清除缺失的值。fillna()函数能够经过几种方法用非空数据“填充”NA值。

用标量值替换NaN

如下程序显示如何用0替换NaN。

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randn(3, 3), index=['a', 'c', 'e'],columns=['one','two', 'three'])

df = df.reindex(['a', 'b', 'c'])

print (df)
print('\n')

print ("NaN replaced with '0':")
print (df.fillna(0))

输出结果：

one two three a -0.479425 -1.711840 -1.453384 b  NaN   NaN NaN c -0.733606 -0.813315 0.476788
 NaN replaced with '0': one two three a -0.479425 -1.711840 -1.453384 b 0.000000 0.000000 0.000000 c -0.733606 -0.813315 0.476788

在这里填充零值; 固然，也能够填写任何其余的值。

替换丢失(或)通用值

不少时候，必须用一些具体的值取代一个通用的值。能够经过应用替换方法来实现这一点。用标量值替换NA是fillna()函数的等效行为。

示例

import pandas as pd

df = pd.DataFrame({'one':[10,20,30,40,50,2000],'two':[1000,0,30,40,50,60]})

print(df)
print('\n')

print (df.replace({1000:10,2000:60}))

输出结果：

    one   two
0    10  1000
1    20     0
2    30    30
3    40    40
4    50    50
5  2000    60

   one  two
0   10   10
1   20    0
2   30   30
3   40   40
4   50   50
5   60   60

填写NA前进和后退

使用重构索引章节讨论的填充概念，来填补缺失的值。

方法	动做
`pad/fill`	填充方法向前
`bfill/backfill`	填充方法向后

示例1

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randn(5, 3), index=['a', 'c', 'e', 'f',
'h'],columns=['one', 'two', 'three'])

df = df.reindex(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'])

print(df)
print('\n')

print (df.fillna(method='pad'))

输出结果：

        one       two     three
a -0.023243  1.671621 -1.687063
b       NaN       NaN       NaN
c -0.933355  0.609602 -0.620189
d       NaN       NaN       NaN
e  0.151455 -1.324563 -0.598897
f  0.605670 -0.924828 -1.050643
g       NaN       NaN       NaN
h  0.892414 -0.137194 -1.101791


        one       two     three
a -0.023243  1.671621 -1.687063
b -0.023243  1.671621 -1.687063
c -0.933355  0.609602 -0.620189
d -0.933355  0.609602 -0.620189
e  0.151455 -1.324563 -0.598897
f  0.605670 -0.924828 -1.050643
g  0.605670 -0.924828 -1.050643
h  0.892414 -0.137194 -1.101791

示例2

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randn(5, 3), index=['a', 'c', 'e', 'f',
'h'],columns=['one', 'two', 'three'])

df = df.reindex(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'])

print (df.fillna(method='backfill'))

输出结果：

one       two     three
a  2.278454  1.550483 -2.103731
b -0.779530  0.408493  1.247796
c -0.779530  0.408493  1.247796
d  0.262713 -1.073215  0.129808
e  0.262713 -1.073215  0.129808
f -0.600729  1.310515 -0.877586
g  0.395212  0.219146 -0.175024
h  0.395212  0.219146 -0.175024

4、丢失缺乏的值

使用dropna函数和axis参数。 默认状况下，axis = 0，即在行上应用，这意味着若是行内的任何值是NA，那么整个行被排除。

实例1

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randn(5, 3), index=['a', 'c', 'e', 'f','h'],columns=['one', 'two', 'three'])

df = df.reindex(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'])

print (df.dropna())

输出结果 ：

one       two     three
a -0.719623  0.028103 -1.093178
c  0.040312  1.729596  0.451805
e -1.029418  1.920933  1.289485
f  1.217967  1.368064  0.527406
h  0.667855  0.147989 -1.035978

示例2

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randn(5, 3), index=['a', 'c', 'e', 'f',
'h'],columns=['one', 'two', 'three'])

df = df.reindex(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'])

print (df.dropna(axis=1))

输出结果：

Empty DataFrame
Columns: []
Index: [a, b, c, d, e, f, g, h]