官方文档里的例子
Examples -------- >>> df = pd.DataFrame([('bird', 2, 2), ... ('mammal', 4, np.nan), ... ('arthropod', 8, 0), ... ('bird', 2, np.nan)], ... index=('falcon', 'horse', 'spider', 'ostrich'), ... columns=('species', 'legs', 'wings')) >>> df species legs wings falcon bird 2 2.0 horse mammal 4 NaN spider arthropod 8 0.0 ostrich bird 2 NaN By default, missing values are not considered, and the mode of wings are both 0 and 2. The second row of species and legs contains ``NaN``, because they have only one mode, but the DataFrame has two rows.
不负责任的翻译:默认不考虑缺失值，wings的众数 0 和 2。第二行中species和legs含有“NaN”，
因为它们都仅有一个众数，但DataFrame 有两行,所以凑数补个NaN。
>>> df.mode() species legs wings 0 bird 2.0 0.0 1 NaN NaN 2.0 Setting ``dropna=False`` ``NaN`` values are considered and they can be the mode (like for wings).
不负责任的翻译:设置dropna='False',即考虑计算缺失值Nan的数量
>>> df.mode(dropna=False) species legs wings 0 bird 2 NaN Setting ``numeric_only=True``, only the mode of numeric columns is computed, and columns of other types are ignored.
不负责任的翻译:设置参数numeric_only=True,即仅统计数字的众数
>>> df.mode(numeric_only=True) legs wings 0 2.0 0.0 1 NaN 2.0 To compute the mode over columns and not rows, use the axis parameter:
不负责任的翻译:通过设置axis轴参数,可以选择统计行或列
axis='columns'或axis='index'
发现axis=0或axis=1也可以
>>> df.mode(axis='columns', numeric_only=True) 0 1 falcon 2.0 NaN horse 4.0 NaN spider 0.0 8.0 ostrich 2.0 NaN
发现个有趣的规律: 随机设置不重复randint, mode后各列(或行)升序排序
有什么用?当数据无缺失值且唯一,可以一键查看各维度的最小值,或sort_index降序排查看各维度最大值?
但是行标变化感觉没什么用(摊手)