百度做网站免费,买卖域名哪个网站好,企业宣传网站,跨境电商seo本篇详解pandas中缺失值#xff08;Missing data handling#xff09;处理常用操作。缺失值处理常用于数据分析数据清洗阶段#xff1b;Pandas中将如下类型定义为缺失值#xff1a;NaN: ‘’, ‘#N/A’, ‘#N/A N/A’, ‘#NA’, ‘-1.#IND’, ‘-1.#QNAN’,‘-NaN’, ‘-na…本篇详解pandas中缺失值Missing data handling处理常用操作。缺失值处理常用于数据分析数据清洗阶段Pandas中将如下类型定义为缺失值NaN: ‘’, ‘#N/A’, ‘#N/A N/A’, ‘#NA’, ‘-1.#IND’, ‘-1.#QNAN’,‘-NaN’, ‘-nan’, ‘1.#IND’, ‘1.#QNAN’, ‘’, ‘N/A’, ‘NA’,‘NULL’, ‘NaN’, ‘n/a’, ‘nan’, ‘null’None欢迎微信随缘关注pythonic生物人本文将学到什么1、pandas中缺失值注意事项
pandas和numpy中任意两个缺失值不相等np.nan ! np.nan
pandas读取文件时那些值被视为缺失值2、pandas缺失值操作
pandas.DataFrame中判断那些值是缺失值isna方法
pandas.DataFrame中删除包含缺失值的行dropna(axis0)
pandas.DataFrame中删除包含缺失值的列dropna(axis1)
pandas.DataFrame中删除包含缺失值的列和行dropna(howany)
pandas.DataFrame中删除全是缺失值的行dropna(axis0,howall)
pandas.DataFrame中删除全是缺失值的列dropna(axis1,howall)
pandas.DataFrame中使用某个值填充缺失值fillna(某个值)
pandas.DataFrame中使用前一列的值填充缺失值fillna(axis1,methodffill)
pandas.DataFrame中使用前一行的值填充缺失值fillna(axis0,methodffill)
pandas.DataFrame中使用字典传值填充指定列的缺失值
更好的阅读体验请戳Pandas处理缺失值NA1、pandas中缺失值注意事项pandas和numpy中任意两个缺失值不相等np.nan ! np.nan 下图中两个NaN不相等 In [224]: df1.iloc[3:,0].values#取出one列中的NaN
Out[224]: array([nan])In [225]: df1.iloc[2:3,1].values#取出two列中的NaN
Out[225]: array([nan])In [226]: df1.iloc[3:,0].values df1.iloc[2:3,1].values#两个NaN值不相等
Out[226]: array([False])pandas读取文件时那些值被视为缺失值 NaN: ‘’, ‘#N/A’, ‘#N/A N/A’, ‘#NA’, ‘-1.#IND’, ‘-1.#QNAN’,‘-NaN’, ‘-nan’, ‘1.#IND’, ‘1.#QNAN’, ‘NA’, ‘N/A’, ‘NA’,‘NULL’, ‘NaN’, ‘n/a’, ‘nan’, ‘null’None2、pandas缺失值操作pandas.DataFrame中判断那些值是缺失值isna方法 #定义一个实验DataFrame
In [47]: d {one: pd.Series([1., 2., 3.], index[a, b, c]),two: pd.Series([1., 2., 3., 4.], index[a, b, c, d])}In [48]: df pd.DataFrame(d)
In [49]: df
Out[49]:one two
a 1.0 1.0
b 2.0 2.0
c 3.0 3.0
d NaN 4.0In [120]: df.isna()#返回形状一样的bool值填充DataFrame
Out[120]:one two
a False False
b False False
c False False
d True Falsepandas.DataFrame中删除包含缺失值的行dropna(axis0) In [67]: df
Out[67]:one two
a 1.0 1.0
b 2.0 2.0
c 3.0 3.0
d NaN 4.0In [68]: df.dropna()#默认axis0
Out[68]:one two
a 1.0 1.0
b 2.0 2.0
c 3.0 3.0pandas.DataFrame中删除包含缺失值的列dropna(axis1) In [72]: df.dropna(axis1)
Out[72]:two
a 1.0
b 2.0
c 3.0
d 4.0pandas.DataFrame中删除包含缺失值的列和行dropna(howany) In [97]: df[three]np.nan#新增一列全为NaN
In [98]: df
Out[98]:one two three
a 1.0 1.0 NaN
b 2.0 2.0 NaN
c 3.0 3.0 NaN
d NaN 4.0 NaNIn [99]: df.dropna(howany)
Out[99]:
Empty DataFrame#全删除了
Columns: [one, two, three]
Index: []pandas.DataFrame中删除全是缺失值的行dropna(axis0,howall) In [101]: df.dropna(axis0,howall)
Out[101]:one two three
a 1.0 1.0 NaN
b 2.0 2.0 NaN
c 3.0 3.0 NaN
d NaN 4.0 NaNpandas.DataFrame中删除全是缺失值的列dropna(axis1,howall) In [102]: df.dropna(axis1,howall)
Out[102]:one two
a 1.0 1.0
b 2.0 2.0
c 3.0 3.0
d NaN 4.0pandas.DataFrame中使用某个值填充缺失值fillna(某个值) In [103]: df.fillna(666)#使用666填充
Out[103]:one two three
a 1.0 1.0 666.0
b 2.0 2.0 666.0
c 3.0 3.0 666.0
d 666.0 4.0 666.0pandas.DataFrame中使用前一列的值填充缺失值fillna(axis1,methodffill) #后一列填充为fillna(axis1,methodbfill)
In [109]: df.fillna(axis1,methodffill)
Out[109]:one two three
a 1.0 1.0 1.0
b 2.0 2.0 2.0
c 3.0 3.0 3.0
d NaN 4.0 4.0pandas.DataFrame中使用前一行的值填充缺失值fillna(axis0,methodffill) #后一行填充为fillna(axis1,methodbfill)
In [110]: df.fillna(methodffill)
Out[110]:one two three
a 1.0 1.0 NaN
b 2.0 2.0 NaN
c 3.0 3.0 NaN
d 3.0 4.0 NaNpandas.DataFrame中使用字典传值填充指定列的缺失值 In [112]: df.fillna({one:666})#填充one列的NaN值
Out[112]:one two three
a 1.0 1.0 NaN
b 2.0 2.0 NaN
c 3.0 3.0 NaN
d 666.0 4.0 NaNIn [113]: df.fillna({three:666})
Out[113]:one two three
a 1.0 1.0 666.0
b 2.0 2.0 666.0
c 3.0 3.0 666.0
d NaN 4.0 666.03、参考资料https://pandas.pydata.org/pandas-docs/stable/reference/frame.html?highlightmissing欢迎微信随缘关注pythonic生物人
