I have a dataframe like this:
housing_deals.head()
Out[2]:
price sale_date
0 477,000,000 1396/10/30
1 608,700,000 1396/11/25
2 580,000,000 1396/10/03
3 350,000,000 1396/12/05
4 328,000,000 1396/03/18
how can I convert sale_date column to pandas datetime
i see below
How to work around Python Pandas DataFrame's "Out of bounds nanosecond timestamp" error?
but yet i cannot do that for my dataframe
You can convert values to daily periods, check docs:
df['sale_date'] = df['sale_date'].apply(lambda x: pd.Period(x, freq='D'))
print (df)
price sale_date
0 477,000,000 1396-10-30
1 608,700,000 1396-11-25
2 580,000,000 1396-10-03
3 350,000,000 1396-12-05
4 328,000,000 1396-03-18
EDIT: You can convert values to numbers and then use function with docs:
print (df['sale_date'].str.replace('/','').astype(int))
0 13961030
1 13961125
2 13961003
3 13961205
4 13960318
Name: sale_date, dtype: int32
def conv(x):
return pd.Period(year=x // 10000,
month=x // 100 % 100,
day=x % 100, freq='D')
df['sale_date'] = df['sale_date'].str.replace('/','').astype(int).apply(conv)
print (df)
price sale_date
0 477,000,000 1396-10-30
1 608,700,000 1396-11-25
2 580,000,000 1396-10-03
3 350,000,000 1396-12-05
4 328,000,000 1396-03-18