You can also use the DataFrame.loc label-based indexer to reorder the rows of a DataFrame based on an index list. main.py import pandas as pd df = pd.DataFrame({ 'name': ['Alice', 'Bobby', 'Carl', 'Dan', 'Ethan'], 'experience': [1, 1, 5, 7, 7], 'salary': [175.1, 18...
In [29]: df.columns.levels # original MultiIndex Out[29]: FrozenList([['bar', 'baz', 'foo', 'qux'], ['one', 'two']]) In [30]: df[["foo","qux"]].columns.levels # sliced Out[30]: FrozenList([['bar', 'baz', 'foo', 'qux'], ['one', 'two']]) 这样做是为了避免...
You need to pass columns=[$list_of_columns] to reindex() method to reorder columns of Pandas DataFrame. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 # import pandas package as pd in this code import pandas as pd # make a dictionary containing ...
Sometimes you may want to reorder rows based on their row labels (i.e., the DataFrame’sindex) rather than by specific columns. If that is the case, you can use thesort_index()method instead ofsort_values(). Remember that, by default,sort_index()will sort your rows inascendingorder by...
Out[14]:FalseIn [15]: df2.columns.is_unique Out[15]:True 注意 检查索引是否唯一对于大型数据集来说有点昂贵。pandas 会缓存此结果,因此在相同的索引上重新检查非常快。 Index.duplicated()将返回一个布尔数组,指示标签是否重复。 In [16]: df2.index.duplicated() ...
通过Categorical.reorder_categories()和Categorical.set_categories()方法可以重新排序类别。对于Categorical.reorder_categories(),所有旧类别必须包含在新类别中,不允许有新类别。这将必然使排序顺序与类别顺序相同。 In [102]: s = pd.Series([ 1, 2, 3, 1], dtype="category")In [103]: s = s.cat.reor...
I wouldn't expectcombine_firstto reorder the columns alphabetically, but it does. Bug might be a stretch, but it's certainly unexpected and awkward. Expected Behavior Preserve the column order, as show in# Workaround. Installed Versions
reorder_levels(order) 使用输入顺序重新排列索引级别。 repeat(repeats[, axis]) 重复Series的元素。 replace([to_replace, value, inplace, limit, ...]) 用给定值替换to_replace中的值。 resample(rule[, axis, closed, label, ...]) 重新采样时间序列数据。 reset_index([level, drop, name, inplace...
pivot([index, columns, values]) #Reshape data (produce a “pivot” table) based on column values. DataFrame.reorder_levels(order[, axis]) #Rearrange index levels using input order. DataFrame.sort_values(by[, axis, ascending]) #Sort by the values along either axis DataFrame.sort_index([...
You can see that both levels of the MultiIndex are converted into common DataFrame columns while the index is reset to the default integer-based one. You can also use the level parameter to remove selected levels from the DataFrame index. It converts the selected levels into common DataFrame ...