当传递元组列表给Index构造函数时,它将尝试返回MultiIndex。以下示例演示了初始化 MultiIndexes 的不同方法。 代码语言:javascript 代码运行次数:0 运行 复制 In [1]: arrays = [ ...: ["bar", "bar", "baz", "baz", "foo", "foo", "qux", "qux"], ...: ["one", "two", "one", "two",...
side) 643 self._data._assert_tzawareness_compat(label) 644 return Timestamp(label) File ~/work/pandas/pandas/pandas/core/indexes/datetimelike.py:378, in DatetimeIndexOpsMixin._maybe_cast_slice_bound(self, label, side
方法get_level_values()将返回特定级别上每个位置的标签向量: In [23]: index.get_level_values(0)Out[23]: Index(['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'], dtype='object', name='first')In [24]: index.get_level_values("second")Out[24]: Index(['one', 'tw...
如果访问的标签不在索引中,则会引发异常 In[26]:s["f"]---KeyErrorTraceback(mostrecentcalllast)File~/work/pandas/pandas/pandas/core/indexes/base.py:3805,inIndex.get_loc(self,key)3804try:->3805returnself._engine.get_loc(casted_key)3806exceptKeyErroraserr:Fileindex.pyx:167,inpandas._libs.index...
pandas 提供了用于内存分析的数据结构,这使得使用 pandas 分析大于内存数据集的数据集有些棘手。即使是占用相当大内存的数据集也变得难以处理,因为一些 pandas 操作需要进行中间复制。 本文提供了一些建议,以便将您的分析扩展到更大的数据集。这是对提高性能的补充,后者侧重于加快适���内存的数据集的分析。
#<class 'pandas.core.indexes.range.RangeIndex'> From the above, we got the default indices in the form of a range from 0 to 6. Get the Custom Index in Series We can also create the Series with customized index labels for, that we need to pass the index as a list of values intopd...
indexes/base.py:3805, in Index.get_loc(self, key) 3804 try: -> 3805 return self._engine.get_loc(casted_key) 3806 except KeyError as err: File index.pyx:167, in pandas._libs.index.IndexEngine.get_loc() File index.pyx:196, in pandas._libs.index.IndexEngine.get_loc() File pandas/...
File ~/work/pandas/pandas/pandas/core/indexes/base.py:3812,inIndex.get_loc(self, key)3807ifisinstance(casted_key,slice)or(3808isinstance(casted_key, abc.Iterable)3809andany(isinstance(x,slice)forxincasted_key)3810):3811raiseInvalidIndexError(key) ...
# Get the index of DataFrame print("Get the index of DataFrame:\n", df.index) Yields below output. By default it returns the type of Index, since we have a range index it returnedRangeIndex(). Using any looping (Python for loop) technique we can access individual indexes of a given ...
import pandas as pd def nth_highest_salary(employee: pd.DataFrame, N: int) -> pd.DataFrame: df = employee[["salary"]].drop_duplicates() if len(df) < N: return pd.DataFrame({'getNthHighestSalary('+ str(N) +')': [None]}) df = df.sort_values(by = 'salary', ascending=False)...