Question

给定another question的数据集：

    user                             item  \
0  b80344d063b5ccb3212f76538f3d9e43d87dca9e          The Cove - Jack Johnson   
1  b80344d063b5ccb3212f76538f3d9e43d87dca9e  Entre Dos Aguas - Paco De Lucia   
2  b80344d063b5ccb3212f76538f3d9e43d87dca9e            Stronger - Kanye West   
3  b80344d063b5ccb3212f76538f3d9e43d87dca9e    Constellations - Jack Johnson   
4  b80344d063b5ccb3212f76538f3d9e43d87dca9e      Learn To Fly - Foo Fighters   

rating  
0       1  
1       2  
2       1  
3       1  
4       1

是否有任何方式以预期的格式加载此类数据而无需手动将所有内容移动到同一行中？

Answer 1

其中一种方法是基于\n\n进行拆分，然后创建单独的数据帧，然后将它们连接起来。即

#Bit of code from https://stackoverflow.com/questions/45740537/copying-multiindex-dataframes-with-pd-read-clipboard

def read_clipboard_split(index_names_row=None, **kwargs):
    encoding = kwargs.pop('encoding', 'utf-8')

    # only utf-8 is valid for passed value because that's what clipboard
    # supports
    if encoding is not None and encoding.lower().replace('-', '') != 'utf8':
        raise NotImplementedError(
            'reading from clipboard only supports utf-8 encoding')

    from pandas import compat, read_fwf
    from pandas.io.clipboard import clipboard_get
    from pandas.io.common import StringIO

    data = clipboard_get()
    items = data.split("\n\n")
    k = []
    for i in items:
        k.append(read_fwf(StringIO(i), **kwargs))
    df = pd.concat(k,axis=1)
    return df

read_clipboard_split()

示例运行：

     user                       \      
0  b80344d063b5ccb3212f76538f3d9e43d87dca9e
1  b80344d063b5ccb3212f76538f3d9e43d87dca9e  
2  b80344d063b5ccb3212f76538f3d9e43d87dca9e   
3  b80344d063b5ccb3212f76538f3d9e43d87dca9e   
4  b80344d063b5ccb3212f76538f3d9e43d87dca9e   

   rating  
0       1  
1       2  
2       1  
3       1  
4       1

输出：

   Unnamed: 0              user                       \  Unnamed: 0  rating
0  0           b80344d063b5ccb3212f76538f3d9e43d87dca9e  0           1     
1  1           b80344d063b5ccb3212f76538f3d9e43d87dca9e  1           2     
2  2           b80344d063b5ccb3212f76538f3d9e43d87dca9e  2           1     
3  3           b80344d063b5ccb3212f76538f3d9e43d87dca9e  3           1     
4  4           b80344d063b5ccb3212f76538f3d9e43d87dca9e  4           1

使用pd.read_clipboard加载包含跨越多行的列的数据框

1 个答案: