pandas converting floats to strings without decimals

For pandas >= 1.0:

<NA> type was introduced for 'Int64'. You can now do this:

df['your_column'].astype('Int64').astype('str')

And it will properly convert 1.0 to 1.

---

Alternative:

If you do not want to change the display options of all pandas, @maxymoo solution does, you can use apply:

df['your_column'].apply(lambda x: f'{x:.0f}')

Add a astype(int) in the mix:

df.astype(float).sum().astype(int).astype(str)

0     7
1     4
2    11
dtype: object

---

Demonstration of example with empty cells. This was not a requirement from the OP but to satisfy the detractors

df = pd.DataFrame([
        ['2', '3', 'nan', None],
        [None, None, None, None],
        ['0', '1', '4', None],
        ['5', 'nan', '7', None]
    ])

df

      0     1     2     3
0     2     3   nan  None
1  None  None  None  None
2     0     1     4  None
3     5   nan     7  None

Then

df.astype(float).sum().astype(int).astype(str)

0     7
1     4
2    11
3     0
dtype: object

Because the OP didn't specify what they'd like to happen when a column was all missing, presenting zero is a reasonable option.

However, we could also drop those columns

df.dropna(1, 'all').astype(float).sum().astype(int).astype(str)

0     7
1     4
2    11
dtype: object

Add astype(int) right before conversion to a string:

print (df.astype(float).sum().astype(int).astype(str))

Generates the desired result.

Converting to int (i.e. with .astype(int).astype(str)) won't work if your column contains nulls; it's often a better idea to use string formatting to explicitly specify the format of your string column; (you can set this in pd.options):

>>> pd.options.display.float_format = '{:,.0f}'.format
>>> df.astype(float).sum()
0     7
1     4
2    11
dtype: float64