Python .sum添加随机数

Question

我有以下python代码：

import pandas as pd
import numpy as np
import openpyxl

wb = openpyxl.load_workbook('MOA_Assignment.xlsx')
PD = pd.DataFrame(wb['Purchase Exit Survey Data'].values)
#drop unneeded rows and columns
PD=PD.iloc[6:,1:]
#sum columns into the 58th row
PD[58]=PD.sum()

为什么DataFrame.sum()会添加奇怪的数字？我使用DataFrame.astype('int')来确保数据框不是一个对象，但它仍然给我奇怪的结果。正确的结果应该是6, 1, 2, 1 ,15的顺序。 CNBC单独至少应该有3.0以上的结果，因为显示的五个数字加起来超过3.请帮忙，谢谢。

PD.head（）。to_dict（）

{1: {6: 'aapka_colors',
  7: 'baby_first',
  8: 'bloomberg',
  9: 'cbs_sports',
  10: 'cnbc'},
 2: {6: None, 7: None, 8: None, 9: None, 10: None},
 3: {6: None, 7: None, 8: None, 9: 1, 10: 1},
 4: {6: None, 7: None, 8: None, 9: None, 10: 1},
 5: {6: None, 7: None, 8: None, 9: None, 10: None},
 6: {6: None, 7: None, 8: None, 9: None, 10: None},
 7: {6: None, 7: None, 8: None, 9: None, 10: None},
 8: {6: None, 7: None, 8: None, 9: None, 10: 2},
 9: {6: None, 7: None, 8: None, 9: None, 10: None},
 10: {6: 1, 7: None, 8: 1, 9: None, 10: None},
 11: {6: None, 7: None, 8: None, 9: None, 10: None},
 12: {6: None, 7: None, 8: None, 9: None, 10: None},
 13: {6: None, 7: None, 8: None, 9: None, 10: None},
 14: {6: None, 7: None, 8: None, 9: None, 10: None},
 15: {6: None, 7: None, 8: None, 9: None, 10: None},
 16: {6: None, 7: None, 8: None, 9: None, 10: None},
 17: {6: None, 7: None, 8: None, 9: None, 10: None},
 18: {6: None, 7: None, 8: None, 9: None, 10: None},
 19: {6: None, 7: None, 8: None, 9: None, 10: None},
 20: {6: None, 7: None, 8: None, 9: None, 10: None},
 21: {6: None, 7: None, 8: None, 9: None, 10: None},
 22: {6: None, 7: None, 8: None, 9: None, 10: None},
 23: {6: None, 7: 1, 8: None, 9: None, 10: None},
 24: {6: None, 7: None, 8: None, 9: None, 10: None},
 25: {6: None, 7: None, 8: None, 9: None, 10: 1},
 26: {6: None, 7: None, 8: None, 9: None, 10: None},
 27: {6: None, 7: None, 8: None, 9: None, 10: None},
 28: {6: None, 7: None, 8: None, 9: None, 10: None},
 29: {6: 1, 7: None, 8: None, 9: None, 10: 1},
 30: {6: 1, 7: None, 8: None, 9: None, 10: None},
 31: {6: None, 7: None, 8: None, 9: None, 10: 1},
 32: {6: None, 7: None, 8: None, 9: None, 10: None},
 33: {6: None, 7: None, 8: None, 9: None, 10: None},
 34: {6: None, 7: None, 8: None, 9: None, 10: None},
 35: {6: None, 7: None, 8: None, 9: None, 10: None},
 36: {6: None, 7: None, 8: None, 9: None, 10: 1},
 37: {6: None, 7: None, 8: None, 9: None, 10: None},
 38: {6: None, 7: None, 8: 1, 9: None, 10: 1},
 39: {6: None, 7: None, 8: None, 9: None, 10: None},
 40: {6: None, 7: None, 8: None, 9: None, 10: None},
 41: {6: None, 7: None, 8: None, 9: None, 10: None},
 42: {6: None, 7: None, 8: None, 9: None, 10: None},
 43: {6: 1, 7: None, 8: None, 9: None, 10: 1},
 44: {6: None, 7: None, 8: None, 9: None, 10: None},
 45: {6: None, 7: None, 8: None, 9: None, 10: None},
 46: {6: None, 7: None, 8: None, 9: None, 10: None},
 47: {6: None, 7: None, 8: None, 9: None, 10: None},
 48: {6: None, 7: None, 8: None, 9: None, 10: None},
 49: {6: None, 7: None, 8: None, 9: None, 10: 1},
 50: {6: 2, 7: None, 8: None, 9: None, 10: None},
 51: {6: None, 7: None, 8: None, 9: None, 10: None},
 52: {6: None, 7: None, 8: None, 9: None, 10: None},
 53: {6: None, 7: None, 8: None, 9: None, 10: None},
 54: {6: None, 7: None, 8: None, 9: None, 10: None},
 55: {6: None, 7: None, 8: None, 9: None, 10: None},
 56: {6: None, 7: None, 8: None, 9: None, 10: 1},
 57: {6: None, 7: None, 8: None, 9: None, 10: 3},
 58: {6: 7, 7: 7, 8: 7, 9: 1, 10: 3}}

Answer 1

更改

PD[58]=PD.sum()

至

PD[58]=PD.sum(1)
# P[58] = PD.sum(axis=1) Or this either option does the same

您目前正在对列进行求和，您想要对行进行求和

Answer 2

我无法复制你的问题。

从您粘贴的字典中，我看到了预期的结果：

res = df.sum(axis=1).to_dict()

# {6: 13.0, 7: 8.0, 8: 9.0, 9: 2.0, 10: 18.0}

df = df.fillna(0.0).loc[:, df.sum(axis=0) != 0]

#               1    3    4    8    10   23   25   29   30   31   36   38   43  \
# 6   aapka_colors  0.0  0.0  0.0  1.0  0.0  0.0  1.0  1.0  0.0  0.0  0.0  1.0   
# 7     baby_first  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   
# 8      bloomberg  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0   
# 9     cbs_sports  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   
# 10          cnbc  1.0  1.0  2.0  0.0  0.0  1.0  1.0  0.0  1.0  1.0  1.0  1.0   

#      49   50   56   57  58  
# 6   0.0  2.0  0.0  0.0   7  
# 7   0.0  0.0  0.0  0.0   7  
# 8   0.0  0.0  0.0  0.0   7  
# 9   0.0  0.0  0.0  0.0   1  
# 10  1.0  0.0  1.0  3.0   3