I have the following dataframe:
| data |
|---|
| A(1.2,2) |
| B(1,5) |
| A(5.8, 9) |
| B(8.9,0.9) |
I would like to convert these float (str) objects to int. How do I do that?
Desired Output:
| data |
|---|
| A(1,2) |
| B(1,5) |
| A(6, 9) |
| B(9,1) |
What I tried so far?
pd.to_numeric(df['data'])
But I get the following error: ValueError: Unable to parse string "A(1,2)" at position 0 How do I solve this?
Your strings are effectively namedtuples. Treat them as such
pd.eval()df = pd.read_csv(
io.StringIO(
"""df
A(1.2,2)
B(1,5)
A(5.8, 9)
B(8.9,0.9)"""
),
sep="\t",
).rename(columns={"df": "data"})
from collections import namedtuple
A = namedtuple("A", "x y")
B = namedtuple("B", "x y")
df = pd.DataFrame(
{
"data": [
f"{type(nt).__name__}({round(nt.x,0):.0f},{round(nt.y):.0f})"
for nt in pd.eval(df["data"])
]
}
)
map() for roundingdf["data"] = pd.Series(pd.eval(df["data"])).map(
lambda nt: str(nt._replace(x=int(round(nt.x, 0)), y=int(round(nt.y, 0))))
)
| data | |
|---|---|
| 0 | A(1,2) |
| 1 | B(1,5) |
| 2 | A(6,9) |
| 3 | B(9,1) |
You have a string and need first to seperate the numbers from each other, propbaly a custom function might be the easiest way:
def round_string(s):
start = s.index('(') +1
stop = s.index(')')
l = s[start:stop].split(',')
lst = [str(int(round(float(i)))) for i in l]
return s[:start] + ','.join(lst) + s[stop:]
s = "B(8.9,0.9)"
round_string(s)
# 'B(9,1)'
Map function to dataframe:
df['data'].map(round_string)
If you don't need to round off, following should work, replacing the decimal and digits after decimal with empty string
df['data'].str.replace('\.\d+', '', regex=True)
0 A(1,2)
1 B(1,5)
2 A(5, 9)
3 B(8,0)
Name: col, dtype: object
For rounding the values, a bit more effort is required, just extract the parenthesis part using regex and assign it to a temporary column, then use comprehension to round each values calling the eval finally replace with new value in the given column.
df.assign(tup=df['data'].str.extract('(\(.*\))')).apply(lambda x: x['data'].replace(x['tup'], str(tuple(round(i) for i in eval(x['tup'])))), axis=1)
0 A(1, 2)
1 B(1, 5)
2 A(6, 9)
3 B(9, 1)
dtype: object
SyntaxError: invalid syntax Sorry for the silly question, how to I solve it?
So what you want to do is convert the floats nested inside of strings into ints.
Furthermore, your output suggests you don't want to use the int function but probably round(x,0) (I say this because int(5.8) evaluates to 5, not 6.
So a function like this applied to the dataframe will work:
def convert_fn_strs(fn):
val_list = re.split('[(,)]',fn)
val_list.remove('')
fn_name = val_list.pop(0)
val_list = [round(float(x)) for i,x in enumerate(val_list)]
return fn_name + str(tuple(val_list))```