如何在pandas数据帧中向下调整每行的行值？

Question

我的数据框：

    Name  Percent Subject1 Subject2
  ramesh       85    Maths  Science
     ram       42    Maths      
     Raj       85      NaN  Science

输出数据框：

     Name  Percent  Subject
   ramesh       85    Maths
   ramesh       85  Science
      ram       42    Maths
      Raj       85  Science

Answer 1

pd.lreshape可以将多列中的值合并为一个：

import numpy as np
import pandas as pd
nan = np.nan
df = pd.DataFrame({'Name': ['ramesh', 'ram', 'Raj'],
                   'Percent': [85, 42, 85],
                   'Subject1': ['Maths', 'Maths', nan],
                   'Subject2': ['Science', nan, 'Science']})

print(pd.lreshape(df, {'Subject':['Subject1', 'Subject2']}))

产量

     Name  Percent  Subject
0  ramesh       85    Maths
1     ram       42    Maths
2  ramesh       85  Science
3     Raj       85  Science

pd.lreshape似乎没有在online docs中记录（但是？）。这是它的文档字符串：

In [40]: help(pd.lreshape)
Help on function lreshape in module pandas.core.reshape:

lreshape(data, groups, dropna=True, label=None)
    Reshape long-format data to wide. Generalized inverse of DataFrame.pivot

    Parameters
    ----------
    data : DataFrame
    groups : dict
        {new_name : list_of_columns}
    dropna : boolean, default True

    Examples
    --------
    >>> import pandas as pd
    >>> data = pd.DataFrame({'hr1': [514, 573], 'hr2': [545, 526],
    ...                      'team': ['Red Sox', 'Yankees'],
    ...                      'year1': [2007, 2008], 'year2': [2008, 2008]})
    >>> data
       hr1  hr2     team  year1  year2
    0  514  545  Red Sox   2007   2008
    1  573  526  Yankees   2007   2008

    >>> pd.lreshape(data, {'year': ['year1', 'year2'], 'hr': ['hr1', 'hr2']})
          team   hr  year
    0  Red Sox  514  2007
    1  Yankees  573  2007
    2  Red Sox  545  2008
    3  Yankees  526  2008

    Returns
    -------
    reshaped : DataFrame

Answer 2

您可以使用stack，reset_index和rename列0：

print df.set_index(['Name', 'Percent']).stack().reset_index(level=[0,1])
                                       .reset_index(drop=True).rename(columns={0:'Subject'})
     Name  Percent  Subject
0  ramesh       85    Maths
1  ramesh       85  Science
2     ram       42    Maths
3     Raj       85  Science