CODEWITHSUNDEEP
pythonpandasstringtype-conversion
gmarais
gmarais

Reputation: 1891

Pandas convert string to int

I have a large dataframe with ID numbers:

ID.head()
Out[64]: 
0    4806105017087
1    4806105017087
2    4806105017087
3    4901295030089
4    4901295030089

These are all strings at the moment.

I want to convert to int without using loops - for this I use ID.astype(int).

The problem is that some of my lines contain dirty data which cannot be converted to int, for e.g.

ID[154382]
Out[58]: 'CN414149'

How can I (without using loops) remove these type of occurrences so that I can use astype with peace of mind?

Upvotes: 67

Views: 269666

Answers (3)

creator person
creator person

Reputation: 1

I solved it Jan-2024 in the latest version of jupyter notebook by doing this.

Always use try and catch to see if its not working than what the error. I checked the "Price" data type and previously it was "o" and now its showing "int(64)". That's what we all looking for.

try:
    car_sales["Price"] = car_sales["Price"].str.replace('[\$\,]|\.\d*', '', regex=True).astype(int)
except ValueError as e:
    print(f"Error: {e}")

Upvotes: 0

cottontail
cottontail

Reputation: 23459

  1. If you're here because you got
OverflowError: Python int too large to convert to C long

use .astype('int64') for 64-bit signed integers:

df['ID'] = df['ID'].astype('int64')

If you don't want to lose the values with letters in them, use str.replace() with a regex pattern to remove the non-digit characters.

df['ID'] = df['ID'].str.replace('[^0-9]', '', regex=True).astype('int64')

Then input

0    4806105017087
1    4806105017087
2         CN414149
Name: ID, dtype: object

converts into

0    4806105017087
1    4806105017087
2           414149
Name: ID, dtype: int64

Upvotes: 10

jezrael
jezrael

Reputation: 863731

You need add parameter errors='coerce' to function to_numeric:

ID = pd.to_numeric(ID, errors='coerce')

If ID is column:

df.ID = pd.to_numeric(df.ID, errors='coerce')

but non numeric are converted to NaN, so all values are float.

For int need convert NaN to some value e.g. 0 and then cast to int:

df.ID = pd.to_numeric(df.ID, errors='coerce').fillna(0).astype(np.int64)

Sample:

df = pd.DataFrame({'ID':['4806105017087','4806105017087','CN414149']})
print (df)
              ID
0  4806105017087
1  4806105017087
2       CN414149

print (pd.to_numeric(df.ID, errors='coerce'))
0    4.806105e+12
1    4.806105e+12
2             NaN
Name: ID, dtype: float64

df.ID = pd.to_numeric(df.ID, errors='coerce').fillna(0).astype(np.int64)
print (df)
              ID
0  4806105017087
1  4806105017087
2              0

EDIT: If use pandas 0.25+ then is possible use integer_na:

df.ID = pd.to_numeric(df.ID, errors='coerce').astype('Int64')
print (df)
              ID
0  4806105017087
1  4806105017087
2            NaN

Upvotes: 120

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