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.
则访问 DataFrame 的底层 NumPy 数组将返回只读数组: 如果初始 DataFrame 由多个数组组成,则该数组是一个拷贝: ```py In [49]: df = pd.DataFrame({"a": [1, 2], "b": [1.5, 2.5]}) In [50]: df.to_numpy() Out[50]: array([[1\. , 1.5], [2\. , ...
原文:pandas.pydata.org/docs/whatsnew/v1.3.5.html 这些是 pandas 1.3.5 的变化。查看发布说明获取包括其他版本在内的完整更改日志。 修复的回归问题 修复了在比较浮点数与对象数据类型的Series.equals()中的回归问题,与 None 比较时 (GH 44190) 修复了merge_asof()中的回归问题,当数组作为连接键提供时引发错...
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(...
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...
Pandas DataFrame - ne() function: The ne() function is used to get not equal to of dataframe and other, element-wise.
(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 ...
(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_...
df.isnull()、df.notnull():两个函数互为取反 df.isna():等同于df.isnull() df.dropna():删除缺失值 df.fillna():填充缺失值 相关概念 首先介绍下Pandas或者Python涉及到的几个可能模糊的概念: 空值在pandas中的空值是""(直接一对双引号);空字符串:" ",中间多了一个空格 缺失值在DataFrame指的是NaN...
==: test whether two variables are equal to each other !=: test whether two variables are not equal to each other other types of logic: >, >=, <, <= is_huge=brics["area"]>8brics[is_huge] importnumpyasnpbrics[np.logical_and(brics["area"]>8,brics["area"]<10)]...