Convert dataframe string column to numeric in Python

[darpInd]

Hello,
I have taken a sample data as dataframe from an url and then added columns in that. While I try to perform some calculations, I realised that column 'Dage' and 'Cat_Ind' are not numeric but string. So, how to convert them to numeric so as to do next level of analysis?

df2=pd.read_csv("http://users.stat.ufl.edu/~winner/data/agedeath.dat", header = None)
df2.columns =["col1"]
print (df2)

df3 = df2.col1.str.split(expand = True)
df3.columns = ["Cat","Dage", "Cat_Ind"]
print (df3)

Resulting dataframe is:

Output:
       Cat Dage Cat_Ind
0     aris   21       1
1     aris   21       2
2     aris   21       3
3     aris   21       4
4     aris   21       5
...    ...  ...     ...
6181  sovr   95    1436
6182  sovr   95    1437
6183  sovr   97    1438
6184  sovr  100    1439
6185  sovr  101    1440

[6186 rows x 3 columns]

Here is the problem: I use below code -

df3['Dage'].min()

getting output as:

Output:
'100'

I suspect that this must be due to the fact that column 'Dage' is not here as the integer- but string.

Q. How to get this column converted to integer values? Please help

[ndc85430]

Did you even look at the docs? read_csv takes a parameter dtype that lets you specify the types of the columns: https://pandas.pydata.org/docs/user_guid...-csv-table.