当我通过以下方式修改熊猫数据框时会发生什么

Question

trying to understand this behavior (why it happens; and if it was intentional, then what was the motivation for it to be done this way)

So I create a dataframe

np.random.seed(0)
df = pd.DataFrame(np.random.random((4,2)))


          0         1
0  0.548814  0.715189
1  0.602763  0.544883
2  0.423655  0.645894
3  0.437587  0.891773

and I can reference columns like so

df.columns = ['a','b']
df.a
          0
0  0.548814
1  0.602763
2  0.423655
3  0.437587 

I can even make, what I think is a new column

 df.third = pd.DataFrame(np.random.random((4,1)))

but df is still

df
          0         1
0  0.548814  0.715189
1  0.602763  0.544883
2  0.423655  0.645894
3  0.437587  0.891773

however, df.third also exists (but i can't see it in my variable viewer in Spyder)

df.third
          0
0  0.118274
1  0.639921
2  0.143353
3  0.944669

if I wanted to add a third column, I'd have to do the following

df['third'] = pd.DataFrame(np.random.random((4,1)))

          a         b     third
0  0.548814  0.715189  0.568045
1  0.602763  0.544883  0.925597
2  0.423655  0.645894  0.071036
3  0.437587  0.891773  0.087129

So, my question is what's going on when I do df.third versus df['third']?

Answer 1

Because it added third as an attribute, you should stop accessing columns as an attribute and always use df['third'] to avoid ambiguous behaviour.

You should get into the habit of always accessing and assigning columns using df[col_name] , this is to avoid problems like

df.mean = some_calc()

well the problem here is that mean is a method for a DataFrame

So you've then overwritten a method with some computed value.

The problem here is that this was part of the design as a convenience and the pandas for data analysis book and some early online video presentations showed this as a way of assigning to a new column but the subtle errors can be so pervasive that it really should be banned and removed IMO

Seriously I can't stress this enough, stop referring to columns as an attribute , it's a serious bugbear of mine and unfortunately I still see lots of answers posted showing this usage

You can see that no new column is added:

In [97]:
df.third = pd.DataFrame(np.random.random((4,1)))
df.columns

Out[97]:
Index(['a', 'b'], dtype='object')

You can see that third was added as an attribute:

In [98]:
df.__dict__

Out[98]:
{'_data': BlockManager
 Items: Index(['a', 'b'], dtype='object')
 Axis 1: Int64Index([0, 1, 2, 3], dtype='int64')
 FloatBlock: slice(0, 2, 1), 2 x 4, dtype: float64,
 '_iloc': <pandas.core.indexing._iLocIndexer at 0x7e73b00>,
 '_item_cache': {},
 'is_copy': None,
 'third':           0
 0  0.844821
 1  0.286501
 2  0.459170
 3  0.243452}

You can see that you have an Items , __data , Axis 1 etc but then you also have 'third' which is an attribute

Answer 2

我认为您向熊猫数据框对象添加了属性第三 ，如果您想添加名称为“第三”的列，则必须这样做：

df['third'] = pd.DataFrame(np.random.random((4,1)))