relief=GROOVE) # or pass configs to Demo() part.pack(side=LEFT, expand=YES, fill=BOTH) # grow, stretch with window parts.append(part) # change list in-place
当x、y 为list时, += 会自动调用 extend 方法进行合并运算,in-place change。这个属于共享引用。 >>> L = [1,2] >>> M = L >>> L = L + [3,4] >>> print(L,M) [1, 2, 3, 4] [1, 2] >>> L = [1,2] >>> M = L >>> L += [3,4] >>> print(L,M) [1, 2, ...
x += y,x 只出现一次,也只会计算一次,性能好,不生成新对象,只在内存块末尾增加元素。 当x、y 为list时, += 会自动调用 extend 方法进行合并运算,in-place change。 属于共享引用 代码语言:javascript 代码运行次数:0 运行 AI代码解释 L=[1,2]M=LL=L+[3,4]printL,Mprint"---"L=[1,2]M=LL+=[...
level:可填入int, str, tuple, list类型, 默认 None, 仅从索引中删除给定级别。默认情况下删除所有级别。 drop:默认False,索引列会被还原为普通列,行索引被重置为默认的int索引 设置为True,索引列丢失。 inplace:默认为False,就地修改DataFrame,不再创建新对象 col_level:int或str,默认值为0,如果列有多个级别,...
stat_data.reset_index(drop=True, inplace=True) stat_data = pd.concat([stat_data.tail(1), lr], axis=1) stat_data.insert(0, 'code', stock_code) stat_data = stat_data.set_index('code') # 导出数据到CSV文件 csv_filename = f'{stock_code}_stat_data.csv' ...
set_index('account number', inplace=True) df_all_changes = pd.concat([change_old, change_new], axis='columns', keys=['old', 'new'], join='outer') df_all_changes 接下来我们定义一个函数来展示从一列到另一列的变化 def report_diff(x): return x[0] if x[0] == x[1] else '{...
def drop_multiple_col(col_names_list, df): ''' AIM -> Drop multiple columns based on their column names INPUT -> List of column names, df OUTPUT -> updated df with dropped columns --- ''' df.drop(col_names_list, axis=1, inplace=True) return df 转换数...
·可变数据(3个):List(列表)、Dictionary(字典)、Set(集合)。 数字:python3 支持 int、float、bool 1.1整型(Int)- 通常被称为整型或者整数,是正或负整数,不带小数点 1.2浮点型(float)-浮点型由整数部分与小数部分组成 1.3布尔型(bool)-True False ...
app_train['DAYS_EMPLOYED'].replace({365243: np.nan}, inplace = True) 同样对测试集进行异常值处理: app_test['DAYS_EMPLOYED_ANOM'] = app_test["DAYS_EMPLOYED"] == 365243 app_test["DAYS_EMPLOYED"].replace({365243: np.nan}, inplace = True) ...
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