In [26]: dfmi = df.copy() In [27]: dfmi.index = pd.MultiIndex.from_tuples( ...: [(1, "a"), (1, "b"), (1, "c"), (2, "a")], names=["first", "second"] ...: ) ...: In [28]: dfmi.sub(column, axis=0, level="second") Out[28]: one two three first s...
The merging of a multi-index data frame with a single index data frame is almost similar to a join operation except for the fact that the first data frame is multi-indexed.To merge multi-indexed with single-indexed, we will set the index of both data frames to some specific columns we ...
pandas作者Wes McKinney 在【PYTHON FOR DATA ANALYSIS】中对pandas的方方面面都有了一个权威简明的入门级的介绍,但在实际使用过程中,我发现书中的内容还只是冰山一角。谈到pandas数据的行更新、表合并等操作,一般用到的方法有concat、join、merge。但这三种方法对于很多新手来说,都不太好分清使用的场合与用途。 构...
Hierarchical indexing is an important featuer of pandas that enables you to have multiple(two or more) indexlevels on an axis. Somewhat abstractly, it provides a way for you to to work with higher dimensional data in a lower dimensional form.(通过多层索引的方式去从低维看待高维数据). Let's...
Construct hierarchical index using the passed keys as the outermost level. If multiple levels passed, should contain tuples. levels : list of sequences, default None. Specific levels (unique values) to use for constructing a MultiIndex. Otherwise they will be inferred from the keys. names : ...
combine_first(other) #Combine two DataFrame objects and default to non-null values in frame calling the method. DataFrame函数应用 代码语言:javascript 代码运行次数:0 运行 AI代码解释 DataFrame.apply(func[, axis, broadcast,…]) #应用函数 DataFrame.applymap(func) #Apply a function to a DataFrame ...
What you're seeing is a prettified view of a Series with a MultiIndex as its index. The 'gaps' in the index display mean "use the lable directly above": data.index 1. MultiIndex(levels=[['a', 'b', 'c', 'd'], [1, 2, 3]], ...
现在支持在 MultiIndex 中超出词典排序深度的索引,尽管词典排序的索引性能更好。 (GH 2646) In [1]: df = pd.DataFrame({'jim':[0, 0, 1, 1], ...: 'joe':['x', 'x', 'z', 'y'], ...: 'jolie':np.random.rand(4)}).set_index(['jim', 'joe']) ...: In [2]: df Out[2...
将DataFrame.set_index与DataFrame.unstack一起使用,展平MultiIndex,最后通过DataFrame.join添加cat列: df1 = df.set_index('cat', append=True).unstack(fill_value=0)df1.columns = df1.columns.map(lambda x: f'{x[0]}_{x[1]}')df1 = df[['cat']].join(df1)print (df1) cat v1_1 v1_2 ...
The above tells you that your DataFrame df now has a MultiIndex with two levels, the first given by the date, the second by the the language. Recall that above you were able to slice the DataFrame using the index and the .loc accessor: df.loc['2017-01-02']. To be able to slice ...