QST: is the new behavior of df.apply(my_func, axis=1) in v1.1.0 intended?
- [x] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.

- [x] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).

---

#### Question about pandas




```python
import pandas as pd
def test_func(row):
    row['c'] = str(row['a']) + str(row['b'])
    row['d'] = row['a'] + 1
    return row

df = pd.DataFrame({'a': [1,2,3], 'b': ['i','j', 'k']})
df.apply(test_func, axis=1)
```

The above code ran on pandas 1.1.0 returns:

```
   a  b   c  d
0  1  i  1i  2
1  1  i  1i  2
2  1  i  1i  2
```
While in pandas 1.0.5 it returns:

```
   a   b    c  d
0  1   i   1i  2
1  2   j   2j  3
2  3   k   3k  4
```


Using python 3.8.3 and IPython 7.16.1. 

## The Question: 

:question: What is the right way of getting the v1.0.5  behavior in v1.1.0? 

I did see this release note but honestly can't figure out if this is an intended/unintended side effect of it: https://pandas.pydata.org/pandas-docs/stable/whatsnew/v1.1.0.html#apply-and-applymap-on-dataframe-evaluates-first-row-column-only-once


thanks