import pandas as pd def test(): # 读取Excel文件 df = pd.read_excel('测试数据.xlsx') # 插入列 df.insert(loc=2, column='爱好', value=None) # 保存修改后的DataFrame到新的Excel文件 df.to_excel('结果.xlsx', index=False) test() 3、插入多列 假设我需要在D列(班级)后面插入5列,表头名...
io1=r"F:\文档存放区\pandas_exercise\exercise_data\second_cars_info_aft.xlsx" df1=pd.read_excel(io1,sheet_name='欧宝',parse_dates = True) done_io=r"F:\课程资料\Python机器学习\TEST.xlsx" with pd.ExcelWriter(done_io,mode="a", engine="openpyxl") as writer: df1.to_excel(writer,she...
df[:5].groupby(lambda x:print(x)).head(0) 根据奇偶行分组。 代码语言:javascript 代码运行次数:0 运行 AI代码解释 df.groupby(lambda x:'奇数行'ifnot df.index.get_loc(x)%2==1else'偶数行').groups 如果是多层索引,那么lambda表达式中的输入就是元组,下面实现的功能为查看两所学校中男女生分别均分...
df3 = pd.DataFrame(data, index=['first','second'], columns=['a','b']) df4= pd.DataFrame(data, index=['first','second'], columns=['a','b1'])print(f'df3的列名在字典键中存在\n{df3}')print(f'df4的列名b1在字典键不中存在\n{df4}')'''df3的列名在字典键中存在 a b first...
Works with non-floating point data as well (detects NaN and None) Parameters: axis : {0 or ‘index’, 1 or ‘columns’}, default 0 0 or ‘index’ for row-wise, 1 or ‘columns’ for column-wise level : int or level name, default None If the axis is a MultiIndex (hierarchical),...
'bool' = True) -> 'FrameOrSeriesUnion'Concatenate pandas objects along a particular axis with optional set logicalong the other axes.Can also add a layer of hierarchical indexing on the concatenation axis,which may be useful if the labels are the same (or overlapping) onthe passed axis numb...
df_1.to_excel(writer,sheet_name='第一个', index=False) df_2.to_excel(writer,sheet_name='第二个', index=False) writer.save()# 必须运行writer.save(),不然不能输出到本地 # 写法2 with pd.ExcelWriter('new.xlsx') as writer:
df.head(3)#任务三:查看DataFrame数据的每列名称df.columns#任务四:查看“Cabin”这列数据的所有值df['Cabin'].head(3) #第一种方法读取df.Cabin.head(3) #第二种方法读取#任务五:加载数据集“test_1.csv”,对比train.csv,test_1 = pd.read_csv('test_1.csv')...
df = pd.DataFrame(technologies,index=index_labels) print("Create DataFrame:\n",df) Yields below output. Pandas Add Column with Constant Value to DataFrame You have an existing DataFrame where you need to add an additional column with the same constant value for every row.df["Discount_Percentag...
df.head() 1. 2. 3. 4. SAC过程 1. 内涵 SAC指的是分组操作中的split-apply-combine过程。其中split指基于某一些规则,将数据拆成若干组;apply是指对每一组独立地使用函数;combine指将每一组的结果组合成某一类数据结构。 2. apply过程 在apply过程中,我们实际往往会遇到四类问题: ...