python:使用 .iterrows() 创建列

Question

我正在尝试使用循环函数来创建一个矩阵，该矩阵表示某个产品是否在特定的一周内被看到。

df 中的每一行(代表产品)都有一个 close_date(产品关闭的日期)和一个 week_diff(产品上市的周数)。

import pandas
mydata = [{'subid' : 'A', 'Close_date_wk': 25, 'week_diff':3},
          {'subid' : 'B', 'Close_date_wk': 26, 'week_diff':2},
          {'subid' : 'C', 'Close_date_wk': 27, 'week_diff':2},]
df = pandas.DataFrame(mydata)

我的目标是查看每个日期范围内每种产品列出了多少替代产品

我已经设置了以下循环:

for index, row in df.iterrows():
    i = 0
    max_range = row['Close_date_wk']    
    min_range = int(row['Close_date_wk'] - row['week_diff'])
    for i in range(min_range,max_range):
        col_head = 'job_week_'  +  str(i)
        row[col_head] = 1

您能否帮助解释为什么“row[col_head] = 1”行既没有添加列，也没有为该行的该列添加值。

例如，如果:

row A has date range 1,2,3 
row B has date range 2,3  
row C has date range 3,4,5'

那么理想情况下我想结束

row A has 0 alternative products in week 1
          1 alternative products in week 2
          2 alternative products in week 3
row B has 1 alternative products in week 2
          2 alternative products in week 3
&c..

Answer 1

您不能在此处使用 row 更改 df 以添加新列，您可以引用原始 df 或使用 .loc, .iloc，或.ix，例如:

In [29]:

df = pd.DataFrame(columns=list('abc'), data = np.random.randn(5,3))
df
Out[29]:
          a         b         c
0 -1.525011  0.778190 -1.010391
1  0.619824  0.790439 -0.692568
2  1.272323  1.620728  0.192169
3  0.193523  0.070921  1.067544
4  0.057110 -1.007442  1.706704
In [30]:

for index,row in df.iterrows():
    df.loc[index,'d'] = np.random.randint(0, 10)
df
Out[30]:
          a         b         c  d
0 -1.525011  0.778190 -1.010391  9
1  0.619824  0.790439 -0.692568  9
2  1.272323  1.620728  0.192169  1
3  0.193523  0.070921  1.067544  0
4  0.057110 -1.007442  1.706704  9

您可以修改现有行:

In [31]:
# reset the df by slicing
df = df[list('abc')]
for index,row in df.iterrows():
    row['b'] = np.random.randint(0, 10)
df
Out[31]:
          a  b         c
0 -1.525011  8 -1.010391
1  0.619824  2 -0.692568
2  1.272323  8  0.192169
3  0.193523  2  1.067544
4  0.057110  3  1.706704

但是使用 row 添加新列将不起作用:

In [35]:

df = df[list('abc')]
for index,row in df.iterrows():
    row['d'] = np.random.randint(0,10)
df
Out[35]:
          a  b         c
0 -1.525011  8 -1.010391
1  0.619824  2 -0.692568
2  1.272323  8  0.192169
3  0.193523  2  1.067544
4  0.057110  3  1.706704