'pandasdataframe.com','pandasdataframe.com']})# 使用 apply 和 lambda 来创建一个新列,根据条件修改值df['New Column']=df.apply(lambdarow:row['A']+row['B']ifrow['A']>150elserow['B'],axis=1)print(df)
(self, key, value) 1284 ) 1285 1286 check_dict_or_set_indexers(key) 1287 key = com.apply_if_callable(key, self) -> 1288 cacher_needs_updating = self._check_is_chained_assignment_possible() 1289 1290 if key is Ellipsis: 1291 key = slice(None) ~/work/pandas/pandas/pandas/core/seri...
importpandasaspd data={'A':[1,2,3]}df=pd.DataFrame(data)# Creating anewcolumn'D'based on a conditionincolumn'A'df['D']=df['A'].apply(lambda x:'Even'ifx%2==0else'Odd')print(df)Output:AD01Odd12Even23Odd 使用lambda函数来检查' a '中的每个元素是偶数还是奇数,并将结果分配给' D ...
pandas.DataFrame.apply() can be used along with the Python lambda function to apply a custom operation to all columns in a DataFrame. A lambda function is a small anonymous function that can take any number of arguments and execute an expression....
df[['First Name', 'Last Name']] = df["Student_details"].apply(lambda x: pd.Series(str(x).split(","))) # Example 5: Split single column into two columns use apply() df[['First Name', 'Last Name']] = df["Student_details"].apply(lambda x: pd.Series(str(x).split("_"))...
(4, 9): df.insert(loc=col_num, column=f'列{col_num-3}', value=None) # 如果A列【学号】<10,则E列【列1】填写:是;否则填写:否, df['列1'] = df['学号'].apply(lambda x: '是' if x < 10 else '否') # 保存修改后的DataFrame到新的Excel文件 df.to_excel('结果.xlsx', index=...
# df.columns是一个Index对象,也可使用.str # 成员资格:.isin() df.columns=df.columns.str.upper() print(df) 2.字符串常用方法 # 字符串常用方法(1) -lower,upper,len,startswith,endswith s= pd.Series(['A','b','bbhello','123',np.nan]) ...
df = pd.DataFrame([['A',1],['A',3],['A',2],['B',5],['B',9]], columns = ['name','score'])介绍两种高效地组内排序的方法。df.sort_values(['name','score'], ascending = [True,False])df.groupby('name').apply(lambda x: x.sort_values('score', ascending=False)).reset_...
如果你需要一次性重新命令所有的列名，更简单的方式就是重写DataFrame的columns属性：In [15]: df....
apply() apply()会将待处理的对象拆分成多个片段,然后对各片段调用传入的函数,最后尝试将各片段组合到一起。 用DataFrame的apply方法,可以将函数应用到由各列或行所形成的一维数组中。 In [44]: f =lambdax : x.max()-x.min() In [45]: df.apply(f) ...