join, axis, level, …])Align two object on their axes with theDataFrame.drop(labels[, axis, level, …])返回删除的列DataFrame.drop_duplicates([subset, keep, …])Return DataFrame with duplicate rows removed, optionally onlyDataFrame.duplicated([subset, keep])Return boolean Series ...
DataFrame.drop_duplicates([subset, keep, …])Return DataFrame with duplicate rows removed, optionally only DataFrame.duplicated([subset, keep])Return boolean Series denoting duplicate rows, optionally only DataFrame.equals(other)两个数据框是否相同 DataFrame.filter([items, like, regex, axis])过滤特定的...
(self) 1489 ref = self._get_cacher() 1490 if ref is not None and ref._is_mixed_type: 1491 self._check_setitem_copy(t="referent", force=True) 1492 return True -> 1493 return super()._check_is_chained_assignment_possible() ~/work/pandas/pandas/pandas/core/generic.py in ?(self) ...
defdrop_duplicates(self,subset=None,keep='first',inplace=False):""" Return DataFrame with duplicate rows removed, optionally only considering certain columns Parameters --- subset : column label or sequence of labels, optional Only consider certain columns for identifying duplicates, by default use ...
return [players.shape[0], players.shape[1]] 1. 2. 显示前三行 def selectFirstRows(employees: pd.DataFrame) -> pd.DataFrame: return employees.head(3) 1. 2. 数据选取 def selectData(students: pd.DataFrame) -> pd.DataFrame: return students.loc[students["student_id"] == 101, ["name",...
(start=None, end=None, periods: 'int | None' = None, freq='B', tz=None, normalize: 'bool' = True, name: 'Hashable' = None, weekmask=None, holidays=None, closed=None, **kwargs) -> 'DatetimeIndex'Return a fixed frequency DatetimeIndex, with business day as the defaultfrequency....
print ("Axis indexes with duplicate values") obj=Series(range(5),index =['a','a','b','b','c']) print("obj is \n", obj) print("obj.index.is_unique is ",obj.index.is_unique) print("obj['a'] is \n", obj['a']) ...
x = df[mask] # `mask` should help us to find changed rows... # make sure `x` DF has a Primary Key column as index x = x.set_index('a') # dump a slice with changed rows to temporary MySQL table x.to_sql('my_tmp', engine, if_exists='replace', index=True) ...
duplicated([subset, keep]) #Return boolean Series denoting duplicate rows, optionally only DataFrame选取以及标签操作 代码语言:javascript 代码运行次数:0 运行 AI代码解释 DataFrame.equals(other) #两个数据框是否相同 DataFrame.filter([items, like, regex, axis]) #过滤特定的子数据框 DataFrame.first(...
columns.is_unique: -> 1971 return self._get_item_cache(key) 1972 1973 # duplicate columns & possible reduce dimensionality /Users/Ted/anaconda/lib/python3.6/site-packages/pandas/core/generic.py in _get_item_cache(self, item) 1643 res = cache.get(item) 1644 if res is None: -> 1645 ...