更新：**在较新的Pandas版本中使用pd.isna()：
validate)809 # If argument passed to validate,810 # check if columns specified as unique811 # are in fact unique.812 if validate is not None:--> 813 self._validate_validate_kwd(validate)File ~/work/pandas/pandas/pandas/core/
In [19]: pd.Series([0, 1, 2], index=["a", "b", "b"]).set_flags(allows_duplicate_labels=False) --- DuplicateLabelError Traceback (most recent call last) Cell In[19], line 1 ---> 1 pd.Series([0, 1, 2], index=["a", "b", "b"]).set_flags(allows_duplicate_labels=...
In [83]: df.insert(1, "bar", df["one"]) In [84]: df Out[84]: one bar flag foo one_trunc a 1.0 1.0 False bar 1.0 b 2.0 2.0 False bar 2.0 c 3.0 3.0 True bar NaN d NaN NaN False bar NaN ```### 在方法链中分配新列 灵感源于[dplyr 的](https://dplyr.tidyverse.org/refe...
Cell In[26], line1--->1s["f"] File ~/work/pandas/pandas/pandas/core/series.py:1121,inSeries.__getitem__(self, key)1118returnself._values[key]1120elifkey_is_scalar: ->1121returnself._get_value(key)1123# Convert generator to list before going through hashable part1124# (We will iter...
(mostrecentcalllast)CellIn[26],line1--->1s["f"]File~/work/pandas/pandas/pandas/core/series.py:1121,inSeries.__getitem__(self,key)1118returnself._values[key]1120elifkey_is_scalar:->1121returnself._get_value(key)1123# Convert generator to list before going through hashable part1124# (We...
(v, *self.args, **self.kwargs) 1082 if isinstance(results[i], ABCSeries): 1083 # If we have a view on v, we need to make a copy because 1084 # series_generator will swap out the underlying data 1085 results[i] = results[i].copy(deep=False) Cell In[25], line 2, in f(s...
Cell In[4], line1--->1s1.reindex(["a","b","c"]) File ~/work/pandas/pandas/pandas/core/series.py:5153,inSeries.reindex(self, index, axis, method, copy, level, fill_value, limit, tolerance)5136@doc(5137NDFrame.reindex,# type: ignore[has-type]5138klass=_shared_doc_kwargs["klass...
选择某个元素,不输出索引:finished.iloc[1, 1]; 选择第2行和第2列交叉的那个元素——这里加上="new_value",就可以修改某个cell的取值了; 检索符合某个条件值的多行:df = df[ df['ID'].isin(['102', '301', '402']) ]; 删除符合条件的多行:df.loc[~df['column_name'].isin(some_values)]...
.loc[] uses a label to point to a row, column or cell, whereas .iloc[] uses the numeric position. To understand the difference between the two, let’s modify the index of df2 created earlier. df2.index = range(1,769) Powered By The below example returns a pandas Series instead ...