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  • pandas nan值处理

    创建DataFrame样例数据

    >>> import pandas as pd
    >>> import numpy as np
    >>> data = pd.DataFrame({'a': [1, 2, 4, np.nan,7, 9], 'b': ['a', 'b', np.nan, np.nan, 'd', 'e'], 'c': [np.nan, 0, 4, np.nan, np.nan, 5], 'd': [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan]})
    >>> data
         a    b    c   d
    0  1.0    a  NaN NaN
    1  2.0    b  0.0 NaN
    2  4.0  NaN  4.0 NaN
    3  NaN  NaN  NaN NaN
    4  7.0    d  NaN NaN
    5  9.0    e  5.0 NaN
    
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    判断值value是否为NaN

    >>> np.isnan(value)    # return Ture or False #
    >>> value is np.nan    # return Ture or False #
    
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    删除NaN所在行

    '''use dropna(axis=0,how='all')'''
    >>> data.dropna(axis=0,how='all')
         a    b    c   d
    0  1.0    a  NaN NaN
    1  2.0    b  0.0 NaN
    2  4.0  NaN  4.0 NaN
    4  7.0    d  NaN NaN
    5  9.0    e  5.0 NaN
    
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    删除表中含有任何NaN的行

    '''use dropna(axis=0,how='any')'''
    >>> data.dropna(axis=0,how='any')
    Empty DataFrame
    Columns: [a, b, c, d]
    Index: []
    
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    删除表中全部为NaN的列

    '''use dropna(axis=1, how='all')'''
    >>> data.dropna(axis=1, how='all')
         a    b    c
    0  1.0    a  NaN
    1  2.0    b  0.0
    2  4.0  NaN  4.0
    3  NaN  NaN  NaN
    4  7.0    d  NaN
    5  9.0    e  5.0
    
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    删除表中含有任何NaN的列

    '''use dropna(axis=1, how='any')'''
    >>> data.dropna(axis=1, how='any')
    Empty DataFrame
    Columns: []
    Index: [0, 1, 2, 3, 4, 5]
    
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  • 原文地址:https://www.cnblogs.com/echoboy/p/10331731.html
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