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 3 4.22 In [24]: df2["col_1"].apply(type).value_counts() Out[24]: col_1 <class 'float'> 4 ...
5155 method=method, 5156 copy=copy, 5157 level=level, 5158 fill_value=fill_value, 5159 limit=limit, 5160 tolerance=tolerance, 5161 ) File ~/work/pandas/pandas/pandas/core/generic.py:5610, in NDFrame.reindex(self, labels, index, columns, axis, method, copy, level, fill_value, limit...
为了正确地比较nan,需要用数组中一定没有的元素替换nan。例如,使用-1或∞: >>> np.all(s1.fillna(np.inf) == s2.fillna(np.inf)) # works for all dtypes True 或者,更好的做法是使用NumPy或Pandas的标准比较函数: >>> s = pd.Series([1., None, 3.]) >>> np.array_equal(s.values, s.va...
downcast:A dict of item->dtype of what to downcast if possible, or the string ‘infer’ which will try to downcast to an appropriate equal type (e.g. float64 to int64 if possible). 模拟数据 再模拟一份数据: df2 = pd.DataFrame([[np.nan, 2, np.nan, 0], [3, 4, np.nan, 1],...
a == b or (isnan(a) and isnan(b)) 因此,要么a等于b,要么a和b都是NaN。 如果您有小的数据框,则使用assert_frame_equal将是可以的。然而,对于大型数据框(10M行),assert_frame_equal几乎没有用处。我不得不中断它,因为它花费了太长时间。 In [1]: df = DataFrame(rand(1e7, 15)) In [2]:...
或者用平均值替换NaN。 # Replace all null values with the mean (mean can be replaced with almost any function from the statistics module)df = round(df.fillna(df.mean()),2) 方法可用于替换DataFrame中的值 one = df.replace(100,'A') # Replace all values equal to 1 with 'one' ...
File ~/work/pandas/pandas/pandas/core/flags.py:96,inFlags.allows_duplicate_labels(self, value)94ifnotvalue:95foraxinobj.axes: --->96ax._maybe_check_unique()98self._allows_duplicate_labels = value File ~/work/pandas/pandas/pandas/core/indexes/base.py:715,inIndex._maybe_check_unique(self...
_(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...
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...
为了正确地比较nan,需要用数组中一定没有的元素替换nan。例如,使用-1或∞: >>> np.all(s1.fillna(np.inf) == s2.fillna(np.inf)) # works for all dtypesTrue 或者,更好的做法是使用NumPy或Pandas的标准比较函数: >>> s = pd.Series([1., None, 3.])>>> np.array_equal(s.values, s.values...