在DataFrame中组合多个列 [英] Combining multiple columns in a DataFrame

问题描述

我有一个DataFrame有40列（列0到39），我想一次将它们分组四个：

  import numpy as np 
 import pandas as pd 
 df = pd.DataFrame（np.random.binomial（1，0.2，（100，40）））
  

---

  new_df [0-3] = df [ 0] + df [1] + df [2] + df [3] 
 new_df [4-7] = df [4] + df [5] + df [6] + df [7] 
 ... 
 new_df [36-39] = df [36] + df [37] + df [38] + df [39] 
  / pre> 
 
 
我可以在单个语句中执行此操作（还是以比单独求和更好的方式）？新的DataFrame中的列名不重要。
解决方案
这是另一种方法：
  new_df = df.transpose（）
 new_df ['Group'] = new_df.index / 4 
 new_df = new_df.groupby 'group'）。sum（）。transpose（）
  
请注意，这里的分割操作是整数除法，而不是浮点除法。
 
I have a DataFrame with 40 columns (columns 0 through 39) and I want to group them four at a time: 
import numpy as np
import pandas as pd
df = pd.DataFrame(np.random.binomial(1, 0.2, (100, 40)))




new_df["0-3"] = df[0] + df[1] + df[2] + df[3]
new_df["4-7"] = df[4] + df[5] + df[6] + df[7]
...
new_df["36-39"] = df[36] + df[37] + df[38] + df[39]
Can I do this in a single statement (or in a better way than summing them separately)? The column names in the new DataFrame are not important.
解决方案
Here's another way to do it:
new_df = df.transpose()  
new_df['Group'] = new_df.index / 4  
new_df = new_df.groupby('Group').sum().transpose()
Note that the divide-by operation here is integer division, not floating-point division.

                        
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