Adding a new column with specific dtype in pandas

Question

Can we assign a new column to pandas and also declare the datatype in one fell scoop?

df = pd.DataFrame({'BP': ['100/80'],'Sex': ['M']})
df2 = (df.drop('BP',axis=1)
       .assign(BPS =  lambda x: df.BP.str.extract('(?P<BPS>\d+)/'))
       .assign(BPD =  lambda x: df.BP.str.extract('/(?P<BPD>\d+)'))
        )

print(df2)
df2.dtypes

Can we have dtype as np.float using only the chained expression?

Answer 1

Obviously, you don't have to do this, but you can.

df.drop('BP', 1).join(
    df['BP'].str.split('/', expand=True)
            .set_axis(['BPS', 'BPD'], axis=1, inplace=False)
            .astype(float))

  Sex    BPS   BPD
0   M  100.0  80.0

Your two str.extract calls can be done away with in favour of a single str.split call. You can then make one astype call.

---

Personally, if you ask me about style, I would say this looks more elegant:

u = (df['BP'].str.split('/', expand=True)
             .set_axis(['BPS', 'BPD'], axis=1, inplace=False)
             .astype(float))
df.drop('BP', 1).join(u)


  Sex    BPS   BPD
0   M  100.0  80.0

Answer 2

Adding astype when you assign the values

df2 = (df.drop('BP',axis=1)
       .assign(BPS =  lambda x: df.BP.str.extract('(?P<BPS>\d+)/').astype(float))
       .assign(BPD =  lambda x: df.BP.str.extract('/(?P<BPD>\d+)').astype(float))
       )
df2.dtypes
Sex     object
BPS    float64
BPD    float64
dtype: object

What I will do

df.assign(**df.pop('BP').str.extract(r'(?P<BPS>\d+)/(?P<BPD>\d+)').astype(float))
  Sex    BPS   BPD
0   M  100.0  80.0