in Flags.allows_duplicate_labels(self, value) 94 if not value: 95 for ax in obj.axes: ---> 96 ax._maybe_check_unique() 98 self._allows_duplicate_labels = value File ~/work/pandas/pandas/pandas/core/indexes/base.py:715, in Index._maybe_check_unique(...
或者您可以在读取数据后使用to_numeric()函数强制转换 dtypes, 代码语言:javascript 代码运行次数:0 运行 复制 In [21]: df2 = pd.read_csv(StringIO(data)) In [22]: df2["col_1"] = pd.to_numeric(df2["col_1"], errors="coerce") In [23]: df2 Out[23]: col_1 0 1.00 1 2.00 2 NaN ...
原文:pandas.pydata.org/docs/reference/api/pandas.io.formats.style.Styler.to_latex.html Styler.to_latex(buf=None, *, column_format=None, position=None, position_float=None, hrules=None, clines=None, label=None, caption=None, sparse_index=None, sparse_columns=None, multirow_align=None, mu...
_(self, other, method, **kwargs)6255 if other.attrs:6256 # We want attrs propagation to have minimal performance6257 # impact if attrs are not used; i.e. attrs is an empty dict.6258 # One could make the deepcopy unconditionally, but a deepcopy6259 # of an empty dict is 50x more ...
修复了DataFrame.to_numpy()中的回归,当转换为str时会引发混合类型时的RuntimeError(GH 35455) 修复了read_csv()中的回归,当设置pandas.options.mode.use_inf_as_na为True时会引发ValueError(GH 35493) 修复了pandas.testing.assert_series_equal()中的回归,当使用check_exact=True时传递非数字类型时会引发错误(...
(1654 "Merge keys are not unique in left dataset; not a one-to-one merge"1655 )1656 if not right_unique:-> 1657 raise MergeError(1658 "Merge keys are not unique in right dataset; not a one-to-one merge"1659 )1661 elif validate in ["one_to_many", "1:m"]:1662 if not left_...
Pandas在这些基本操作方面非常缓慢,因为它正确地处理了缺失值。Pandas需要NaNs (not-a-number)来实现所有这些类似数据库的机制,比如分组和旋转,而且这在现实世界中是很常见的。在Pandas中,我们做了大量工作来统一所有支持的数据类型对NaN的使用。根据定义(在CPU级别上强制执行),nan+anything会得到nan。所以...
ne() Returns True for values that are not equal to the specified value(s), otherwise False nlargest() Sort the DataFrame by the specified columns, descending, and return the specified number of rows notna() Finds values that are not not-a-number notnull() Finds values that are not NULL...
Do not include columns whose entries are all NaN. If True, rows with a NaN value in any column will be omitted before computing margins. Given that dropna=True by default, I'd expect all columns and rows to be present as each has at least one non-NaN value. Expected output col 0.0 ...
# copy If True, always copy underlying data even if new index is equivalent to old index; if False, do not copy # the data when the indexes are equivalent """ 轴向上删除条目 """ obj = pd.Series(np.arange(5.), index=['a', 'b'...