Python数据分析（二）pandas缺失值处理
2024-08-26 22:42:58
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('################缺失值判断######################')
print('--------Series的缺失值判断---------')
print (df['one'].isnull())
'''
--------Series的缺失值判断---------
a False
b True
c False
d True
e False
f False
g True
h False
Name: one, dtype: bool
'''
print('---------输出Series缺失值和索引--------')
print(df['one'][df['one'].isnull()])
'''
---------输出Series缺失值和索引--------
b NaN
d NaN
g NaN
Name: one, dtype: float64
'''
print('--------dataframe的缺失值判断---------')
print(df.isnull())
'''
--------dataframe的缺失值判断---------
one two three
a False False False
b True True True
c False False False
d True True True
e False False False
f False False False
g True True True
h False False False
'''
print('--------输出dataframe的缺失值和索引---------')
data = df[df.isnull().values==True]
print(data[~data.index.duplicated()])
'''
--------输出dataframe的缺失值和索引---------
one two three
b NaN NaN NaN
d NaN NaN NaN
g NaN NaN NaN
'''
print('--------输出dataframe的有缺失值的列---------')
print(df.isnull().any())
'''
--------输出dataframe的有缺失值的列---------
one True
two True
three True
dtype: bool
'''
print('################缺失值过滤######################')
print('--------Series的缺失值过滤---------')
print(df['one'].isnull())
'''
################缺失值过滤######################
--------Series的缺失值过滤---------
a False
b True
c False
d True
e False
f False
g True
h False
Name: one, dtype: bool
'''
print('--------使用dropna方法删除缺失数据,返回一个删除后的Series--------')
print(df['one'].dropna())
'''
--------使用dropna方法删除缺失数据,返回一个删除后的Series--------
a -0.211055
c -0.870090
e -0.203259
f 0.490568
h 1.437819
Name: one, dtype: float64
'''
print('--------dataframe的缺失值过滤---------')
print(df.dropna())
'''
--------dataframe的缺失值过滤---------
one two three
a -0.211055 -2.869212 0.022179
c -0.870090 -0.878423 1.071588
e -0.203259 0.315897 0.495306
f 0.490568 -0.968058 -0.999899
h 1.437819 -0.370934 -0.482307
'''
print('-------当行全为NaN的时候,才删除,参数how默认是any,含有缺失值就删除--------')
print(df.dropna(how="all"))
'''
-------当行全为NaN的时候,才删除,参数how默认是any,含有缺失值就删除--------
one two three
a -0.211055 -2.869212 0.022179
c -0.870090 -0.878423 1.071588
e -0.203259 0.315897 0.495306
f 0.490568 -0.968058 -0.999899
h 1.437819 -0.370934 -0.482307
'''
print('################缺失值填充######################')
print('------指定特殊值填充缺失值-------')
print(df.fillna(0))
'''
################缺失值填充######################
------指定特殊值填充缺失值-------
one two three
a -0.211055 -2.869212 0.022179
b 0.000000 0.000000 0.000000
c -0.870090 -0.878423 1.071588
d 0.000000 0.000000 0.000000
e -0.203259 0.315897 0.495306
f 0.490568 -0.968058 -0.999899
g 0.000000 0.000000 0.000000
h 1.437819 -0.370934 -0.482307
'''
print('------不同的列用不同的值填充------')
print(df.fillna({'one':1,'two':2,'three':3}))
'''
------不同的列用不同的值填充------
one two three
a -0.211055 -2.869212 0.022179
b 1.000000 2.000000 3.000000
c -0.870090 -0.878423 1.071588
d 1.000000 2.000000 3.000000
e -0.203259 0.315897 0.495306
f 0.490568 -0.968058 -0.999899
g 1.000000 2.000000 3.000000
h 1.437819 -0.370934 -0.482307
'''
print('------前向填充------')
print(df.fillna(method="ffill"))
'''
------前向填充------
one two three
a -0.211055 -2.869212 0.022179
b -0.211055 -2.869212 0.022179
c -0.870090 -0.878423 1.071588
d -0.870090 -0.878423 1.071588
e -0.203259 0.315897 0.495306
f 0.490568 -0.968058 -0.999899
g 0.490568 -0.968058 -0.999899
h 1.437819 -0.370934 -0.482307
'''
print('------后向填充------')
print(df.fillna(method="bfill"))
'''
------后向填充------
one two three
a -0.211055 -2.869212 0.022179
b -0.870090 -0.878423 1.071588
c -0.870090 -0.878423 1.071588
d -0.203259 0.315897 0.495306
e -0.203259 0.315897 0.495306
f 0.490568 -0.968058 -0.999899
g 1.437819 -0.370934 -0.482307
h 1.437819 -0.370934 -0.482307
'''
print('------平均值填充------')
print(df.fillna(df.mean()))
'''
------平均值填充------
one two three
a -0.211055 -2.869212 0.022179
b 0.128797 -0.954146 0.021373
c -0.870090 -0.878423 1.071588
d 0.128797 -0.954146 0.021373
e -0.203259 0.315897 0.495306
f 0.490568 -0.968058 -0.999899
g 0.128797 -0.954146 0.021373
h 1.437819 -0.370934 -0.482307
'''
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