method="apply") File ~/work/pandas/pandas/pandas/core/apply.py:916, in FrameApply.apply(self) 913 elif self.raw: 914 return self.apply_raw(engine=self.engine, engine_kwargs=self.engine_kwargs) --> 916 return self.apply_standard() File ~/work/pandas/pandas/pandas/core/apply.py:1063,...
1. Series Series是一种类似于一维数组的对象,它由一组数据(不同数据类型)以及一组与之相关的数据标签(即索引)组成。 1.1 仅有数据列表即可产生最简单的Series In [2]: 代码语言:javascript 复制 s1 = pd.Series([1,'a',5.2,7]) In [3]: 代码语言:javascript 复制 # 左侧为索引,右侧是数据 s1 Out[...
ifscore>=90: return'优秀' elifscore>=80: return'良好' elifscore>=60:
去除重复值:df.drop_duplicates() 填充缺失值:df.fillna(value) 按照特定列的值填充缺失值:df['column'].fillna(df['column'].mean()) 删除包含缺失值的行:df.dropna() 替换特定值:df.replace(old_value, new_value) 去除字符串中的空格:df['column'] = df['column'].str.strip() 数据转换 更改列...
importswifterdeftarget_function(row): returnrow*10deftraditional_way(data): data['out']=data['in'].apply(target_function)defswifter_way(data): data['out']=data['in'].swifter.apply(target_function) Pandarallel importpandasaspd frompandarallelimportpandaralleldeftarget_function(row): ...
(self, key) 1118 return self._values[key] 1120 elif key_is_scalar: -> 1121 return self._get_value(key) 1123 # Convert generator to list before going through hashable part 1124 # (We will iterate through the generator there to check for slices) 1125 if is_iterator(key): File ~/work...
(self, labels, index, columns, axis, method, copy, level, fill_value, limit, tolerance)5607returnself._reindex_multi(axes, copy, fill_value)5609# perform the reindex on the axes->5610returnself._reindex_axes(5611axes, level, limit, tolerance, method, fill_value, copy5612).__finalize__...
Series 结构,也称 Series 序列,是 Pandas 常用的数据结构之一,它是一种类似于一维数组的结构,由一组数据值(value)和一组标签组成,其中标签与数据值之间是一一对应的关系。 Series 可以保存任何数据类型,比如整数、字符串、浮点数、Python 对象等,它的标签默认为整数,从 0 开始依次递增。Series 的结构图,如下所示...
return create_series(row_data, emoji) elif emoji in emoji_labels and bins in emoji_labelsemoji: labels = emoji_labelsemojibins return pd.cut(row_data, bins=len(labels), labels=labels, ordered=False) else: return row_data def create_series(row_data, emoji): ...
A pandas user-defined function (UDF)—also known as vectorized UDF—is a user-defined function that uses Apache Arrow to transfer data and pandas to work with the data. pandas UDFs allow vectorized operations that can increase performance up to 100x compared to row-at-a-time Python UDFs.For...