I have extracted a dataframe from an excel file that has a datetime column but with a few values in the excel Date format like so:
import pandas as pd
import numpy as np
import xlrd
rnd = np.random.randint(0,1000,size=(10, 1))
test = pd.DataFrame(data=rnd,index=range(0,10),columns=['rnd'])
test['Date'] = pd.date_range(start='1/1/1979', periods=len(test), freq='D')
r1 = np.random.randint(0,5)
r2 = np.random.randint(6,10)
test.loc[r1, 'Date'] = 44305
test.loc[r2, 'Date'] = 44287
test
rnd Date
0 56 1979-01-01 00:00:00
1 557 1979-01-02 00:00:00
2 851 1979-01-03 00:00:00
3 553 44305
4 258 1979-01-05 00:00:00
5 946 1979-01-06 00:00:00
6 930 1979-01-07 00:00:00
7 805 1979-01-08 00:00:00
8 362 44287
9 705 1979-01-10 00:00:00
When I attempt to convert the errant dates using the xlrd.xldate_as_datetime function in isolation I get a series with the correct format:
# Identifying the index of dates in int format
idx_ints = test[test['Date'].map(lambda x: isinstance(x, int))].index
test.loc[idx_ints, 'Date'].map(lambda x: xlrd.xldate_as_datetime(x, 0))
3 2021-04-19
8 2021-04-01
Name: Date, dtype: datetime64[ns]
However when I attempt to apply the change in place I get a wildly different int:
test.loc[idx_ints,'Date'] = test.loc[idx_ints, 'Date'].map(lambda x: xlrd.xldate_as_datetime(x, 0))
test
rnd Date
0 56 1979-01-01 00:00:00
1 557 1979-01-02 00:00:00
2 851 1979-01-03 00:00:00
3 553 1618790400000000000
4 258 1979-01-05 00:00:00
5 946 1979-01-06 00:00:00
6 930 1979-01-07 00:00:00
7 805 1979-01-08 00:00:00
8 362 1617235200000000000
9 705 1979-01-10 00:00:00
Any idea, or perhaps an alternative solution to my date int conversion problem, thanks!
Reversing the logic from the answer I linked, this works fine for your test df:
# where you have numeric values, i.e. "excel datetime format":
nums = pd.to_numeric(test['Date'], errors='coerce') # timestamps will give NaN here
# now first convert the excel dates:
test.loc[nums.notna(), 'datetime'] = pd.to_datetime(nums[nums.notna()], unit='d', origin='1899-12-30')
# ...and the other, "parseable" timestamps:
test.loc[nums.isna(), 'datetime'] = pd.to_datetime(test['Date'][nums.isna()])
test
rnd Date datetime
0 840 44305 2021-04-19
1 298 1979-01-02 00:00:00 1979-01-02
2 981 1979-01-03 00:00:00 1979-01-03
3 806 1979-01-04 00:00:00 1979-01-04
4 629 1979-01-05 00:00:00 1979-01-05
5 540 1979-01-06 00:00:00 1979-01-06
6 499 1979-01-07 00:00:00 1979-01-07
7 155 1979-01-08 00:00:00 1979-01-08
8 208 44287 2021-04-01
9 737 1979-01-10 00:00:00 1979-01-10
If your input already has datetime objects instead of timestamp strings, you could skip the conversion and just transfer the values to the new column I think.