How to get the integer portion of a float column in pandas

Question

Suppose I have a dataframe df as shown below

    qty
0   1.300
1   1.909

Now I want to extract only the integer portion of the qty column and the df should look like

   qty
0   1
1   1

Tried using df['qty'].round(0) but didn't get the desired result as it rounds of the number to the nearest integer.

Java has a function intValue() which does the desired operation. Is there a similar function in pandas ?

jezrael · Accepted Answer · 2020-02-17T07:47:50.817

6

Convert values to integers by Series.astype:

df['qty'] = df['qty'].astype(int)
print (df)
   qty
0    1
1    1

If not working above is possible use numpy.modf for extract values before .:

a, b = np.modf(df['qty'])
df['qty'] = b.astype(int)
print (df)
   qty
0    1
1    1

Or by split before ., but it should be slow if large DataFrame:

df['qty'] = b.astype(str).str.strip('.').str[0].astype(int)

df['qty'] = np.floor(df['qty']).astype(int)

edited Feb 17 '20 at 07:47

answered Feb 17 '20 at 07:16

jezrael

It rounds of the float to nearest integer. For ex: 1.909 is converted to 2 – Gopal Chandak Feb 17 '20 at 07:20
@GopalChandak - tested with `1.999999` and returned `1`, but added another 2 solutions. – jezrael Feb 17 '20 at 07:25
With astype solution it might be the version issue as suggested by @MykolaZotko – Gopal Chandak Feb 17 '20 at 07:29
@GopalChandak - Maybe simplier should be `df['qty'] = np.floor(df['qty']).astype(int)` – jezrael Feb 17 '20 at 07:48
Yeah @jezrael, the above solution seems much simpler. Thanks – Gopal Chandak Feb 17 '20 at 08:10
@GopalChandak - Only one aswer should be accepted, not both – jezrael Feb 17 '20 at 08:10

score 1 · Answer 2 · answered Feb 17 '20 at 07:45

1

You can use the method floordiv:

df['col'].floordiv(1).astype(int)

For example:

        col
0  9.748333
1  6.612708
2  2.888753
3  8.913470
4  2.354213

Output:

0    9
1    6
2    2
3    8
4    2
Name: col, dtype: int64

answered Feb 17 '20 at 07:45

Mykola Zotko

2 Answers2