Consider a sample dataframe
df11 = pd.DataFrame({'A': ['1'+'_'+'2']})
I want to split column values to list in the respective rows and convert into integer. I am using the below code, the output values are in the form of string. How can i convert into integer
df11["Timetaken"] = df11['A'].str.split('_')
current output column i have is like below
I want to convert the string of values to integer or float
Using list comprehension, simply add:
[int(x) for x in df11['A'].str.split('_')]
This will give you a list of integers.
For the edited question, you can change the type of those values using
df11['Timetaken'] = df11['Timetaken'].apply(lambda x: [int(y) for y in x])
You can use expand=True then use astype and use agg like below:
As int
>>> df['Timetaken'] = df11['A'].str.split('_', expand=True).astype(int).apply(lambda x: x.dropna().tolist(), axis=1)
As float
>>> df['Timetaken'] = df11['A'].str.split('_', expand=True).astype(float).apply(lambda x: x.dropna().tolist(), axis=1)
>>> df
Timetaken
0 [9.0, 6.0, 36.0]
1 [3.0, 1.0]
2 [9.0, 2.0]
3 [6856.0, 4870.0]
4 [6864.0]
5 [6873.0]
You can use only pandas methods like so:
df = pd.DataFrame(
{
"Timetaken": [
"9.0_6.0_36.0",
"3.0_1.0",
"9.0_2.0",
"6856.0_4870.0",
"6864.0",
"6873.0",
]
}
)
df["Timetaken"] = (
df["Timetaken"]
.str.split("_")
.explode()
.astype("float")
.astype("int")
.groupby(level=0)
.apply(list)
)
Which will:
split the strings.astype('float').astype('int')).groupby(...).apply(...))