pandas dataframe: how to aggregate a subset of rows based on value of a column

Question

I have a pandas dataframe structured like this:

      value
lab        
A        50
B        35
C         8
D         5
E         1
F         1

This is just an example, the actual dataframe is bigger, but follows the same structure.
The sample dataframe has been created with this two lines:

df = pd.DataFrame({'lab':['A', 'B', 'C', 'D', 'E', 'F'], 'value':[50, 35, 8, 5, 1, 1]})
df = df.set_index('lab')

I would like to aggregate the rows whose value is smaller that a given threshold: all these rows should be substituted by a single row whose value is the sum of the substituted rows.

例如，如果我选择阈值= 6，则预期结果应为：

      value
lab        
A        50
B        35
C         8
X         7 #sum of D, E, F

我怎样才能做到这一点？

我以为可以使用groupby()，但是我所看到的所有示例都涉及到使用单独的列进行分组，因此在这种情况下我不知道如何使用它。通过执行操作，
我可以选择小于阈值的行loc，df.loc[df['value'] < threshold]但是我不知道如何只对这些行求和，而其余数据框保持不变。

Answer 1

您可以“单线” 使用lambda并DataFrame.append实现此目标：

thresh = 6

(df[lambda x: x['value'] >= thresh]
 .append(df[lambda x: x['value'] < thresh].sum().rename('X')))

或者，如果您愿意

mask = df['value'].ge(thresh)

df[mask].append(df[~mask].sum().rename('X'))

[出]

     value
lab       
A       50
B       35
C        8
X        7

Answer 2

使用具有放大设置有过滤DataFrame：

threshold = 6
m = df['value'] < threshold
df1 = df[~m].copy()
df1.loc['Z'] = df.loc[m, 'value'].sum()

print (df1)
     value
lab       
A       50
B       35
C        8
Z        7

另一个解决方案：

m = df['value'] < threshold
df1 = df[~m].append(df.loc[m, ['value']].sum().rename('Z'))
print (df1)
     value
lab       
A       50
B       35
C        8
Z        7