df = pd.DataFrame(data, columns=list('ABCD')) print(df) 1. 2. 3. 4. 5. 报错信息截图如下所示: 报错翻译 报错信息翻译如下: 值错误:传递了4列,传递的数据有2列 报错原因 报错原因: 粉丝通过嵌套列表创建DataFrame,[1, 2]为两个元素,所以所对应的列也应该是两列,但是columns传递了4列,所以报错。
Python Pandas is a powerful library for data manipulation and analysis, designed to handle diverse datasets with ease. It provides a wide range of functions to perform various operations on data, such as cleaning, transforming, visualizing, and analyzing. The columns in a Pandas DataFrame can ...
如果只想删除指定列(例如第1、2、3列)中的重复项,那么可以使用下面的代码: Sub DeDupeColSpecific() Cells.RemoveDuplicates Columns:=Array...(1, 2, 3), Header:=xlYes End Sub 可以修改代码中代表列的数字,以删除你想要的列中的重复行。...注:本文学习整理自thesmallman.com,略有修改,供有兴趣的朋友...
data = DataFrame(np.arange(16).reshape(4,4),index = list('ABCD'),columns=list('wxyz')) df = pd.DataFrame({'a':[1,3,5,7,4,5,6,4,7,8,9], 'b':[3,5,6,2,4,6,7,8,7,8,9]}) df1 = pd.DataFrame({'lkey': ['foo', 'bar', 'baz', 'foo'],'value': [1, 2, ...
DataFrames are 2-dimensional data structures in pandas. DataFrames consist of rows, columns, and the data.Columns are the different fields that contain their particular values when we create a DataFrame. We can perform certain operations on both rows & column values....
一、构造 da=pd.read_csv(filepath_or_buffer='data.csv',sep='\t') print(da) datas=pd.DataFrame(da) 2、直接赋值 df = pd.DataFrame([[1.4, np.nan], [7, -4], [np.nan, np.nan], [0.75, -1.3]], index=[1, 2, 3, 4], columns=['one', 'two']) ...
df = pd.DataFrame(data) # 选择要转换的列 selected_columns = ["A", "B"] # 使用 tolist() 函数将所选列转换为列表 list_columns = df[selected_columns].tolist() print(list_columns) ``` 输出结果为: ``` [[1, 4], [2, 5], [3, 6]] ``` 4.结论 通过以上示例,我们可以看到如何使...
在创建DataFrame对象时,我们传入一个字典作为参数,字典的键表示列名,字典的值表示对应列的数据。 import pandas as pd df = pd.DataFrame({'X': [1, 2, 3, 4], 'Y': ['a', 'b', 'c', 'd']}, columns=['X', 'Y'], index=['a', 'b', 'c', 'd']) 接下来,我们使用....
data =DataFrame(np.arange(16).reshape(4,4),index =list('ABCD'),columns=list('wxyz')) df = pd.DataFrame({'a':[1,3,5,7,4,5,6,4,7,8,9],'b':[3,5,6,2,4,6,7,8,7,8,9]}) df1 = pd.DataFrame({'lkey': ['foo','bar','baz','foo'],'value': [1,2,3,5]}) ...
相同列名的话可以copy,要多少copy多少。i = pd.DataFrame(columns=["A","B"])j = i.copy()k = i.copy()不同列名的话,你就for循环一下vals = locals()columns = [["A"], ["A","B"], ["C"]]for i in range(len(columns)): vals[f'i{i}'] = pd.DataFrame(columns=columns[i])...