In [1]: dates = pd.date_range('1/1/2000', periods=8) In [2]: df = pd.DataFrame(np.random.randn(8, 4), ...: index=dates, columns=['A', 'B', 'C', 'D']) ...: In [3]: df Out[3]: A B C D 2000-01-01 0.469112 -0.282863 -1.509059 -1.135632 2000-01-02 1.212112...
6. 多层索引(MultiIndex) 多层索引(也称为层次化索引)是Pandas中处理高维数据的强大工具。 6.1 创建多层索引 # 从元组列表创建多层索引index=pd.MultiIndex.from_tuples([('Group1','A'),('Group1','B'),('Group2','A'),('Group2','B')],names=['Group','Type'])data={'Value':[10,20,30,4...
参数: axis : {index (0), columns (1)} skipna : 布尔值,默认为True.表示跳过NaN值.如果整行/列都是NaN,那么结果也就是NaN level : int or level name, default None If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series numeric_only : boolean...
A MultiIndex can be created by itself and then reused; the columns in the preceding DataFrame with level names could be created like this. tmp = pd.MultiIndex.from_arrays([['Ohio','Ohio','Colorado'], ['Green','Red','Green']], names=['state','color']) tmp MultiIndex(levels=[['Col...
DataFrame( [[None, 10], [11, 7.0]], index=['London', 'Oxford'], columns=multi_col_2 ) df_multi_level_3.stack() df_multi_level_3.stack(dropna=False) 6. unstack: 简单案例 同样,Pandas unstack() 也支持参数级别,默认为 -1,它将对最内层索引应用操作。 index = pd.MultiIndex.from_...
axis:{0 or ‘index’, 1 or ‘columns’},默认0 如果是 0 或“索引”,为每列生成计数。如果是 1 或“列”,为每一行生成计数。 level:int 或 str,可选 如果轴是 MultiIndex(分层),则沿特定级别计数,折叠成 DataFrame。 str 指定级别名称。
See the indexing documentationIndexing and Selecting DataandMultiIndex / Advanced Indexing Getting Selecting a single column, which yields aSeries, equivalent todf.A In [23]:df['A']Out[23]:2013-01-01 0.4691122013-01-02 1.2121122013-01-03 -0.8618492013-01-04 0.7215552013-01-05 -0.4249722013-01-...
Reset single level of MultiIndex df.reset_index(level=1) Copy Typical Errors on Pandas Drop MultiIndex When the droplevel is invoked on wrong axis: columns or rows like: cols=pd.MultiIndex.from_tuples([(0,1),(0,1)])df=pd.DataFrame([[1,2],[3,4]],index=cols)df.columns.droplevel()...
to_csv(self, path_or_buf: 'FilePathOrBuffer[AnyStr] | None' = None, sep: 'str' = ',', na_rep: 'str' = '', float_format: 'str | None' = None, columns: 'Sequence[Hashable] | None' = None, header: 'bool_t | list[str]' = True, index: 'bool_t' = True, index_label...
注释:explode('Products') 将 Products 列中的每个列表元素拆分成新行,OrderID 的值会相应复制。reset_index(drop=True) 用于重置索引,避免因展开产生重复索引。 2. stack() 与 unstack():重塑多级索引 stack() 和 unstack() 主要用于处理具有多级索引 (MultiIndex) 的 DataFrame,在宽格式 (wide format) 和长...