在 pandas 中，将 float64（具有 NaN 值）转换为字符串（不显示小数）

Question

We are trying to go from

zed = pd.DataFrame(data = {'a': [33.0, 67.0, np.nan]})

to

pd.DataFrame(data = {'a': ['33', '67', '']})

We've tried zed['a'].astype(np.int).astype(str) however this throws the error Cannot convert non-finite values (NA or inf) to integer because of the NaN value. Is it possible to go from floats to strings with no decimals, and with NaN values replaced by empty strings?

Answer 1

First idea is use Int64 for integer NaN s and then set empty string:

zed['a'] = zed['a'].astype('Int64').astype(str).replace('<NA>','')
print (zed)
    a
0  33
1  67
2    

Or for old pandas version is possible this alternative:

zed['a'] = zed['a'].fillna(0).astype(int).astype(str).mask(zed['a'].isna(),'')

---

If need missing values insted empty strings:

zed['a'] = zed['a'].fillna(0).astype(int).astype(str).mask(zed['a'].isna())
print (zed)
     a
0   33
1   67
2  NaN

Or:

zed.loc[zed['a'].notna(), 'a'] = zed['a'].astype('Int64').astype(str)
zed['a'] = zed['a'].astype('Int64').astype(str).replace('<NA>', np.nan)

Answer 2

You can handle the trimming of the decimal at the string level:

zed['b'] = zed['a'].fillna('').astype(str).str.replace(r'\..*', '', regex=True)

or:

zed['b'] = zed['a'].fillna('').astype(str).str.split('.').str[0]

or:

zed['b'] = zed['a'].map('{:.0f}'.format).replace('nan', '')

output:

      a   b
0  33.0  33
1  67.0  67
2   NaN