Hi my use case is I have dynamic names of datetime field and date will be some thing like unix timestamp . so every time there could be different column name and there can be multiple date filed . so how I can do this ? for now if I do hardcode for column name this works for me
df['date'] = pandas.to_datetime(df['date'], unit='s')
but not sure how I can make this for dynamic names and multiple fields with pandas
As suggested on my comment, you can try to convert all columns as DatetimeIndex then just keep one where conversion succeeded?
# Inspired from @mozway, https://stackoverflow.com/a/75106101/15239951
def find_datetime_col(df):
mask = df.astype(str).apply(pd.to_datetime, errors='coerce').notna()
return mask.mean().idxmax()
col = find_datetime_col(df)
print(col)
# Output
Heading 1
Input dataframe:
>>> df
Heading 1 Heading 2 Heading 3 Heading 4
0 2023-01-01 34 12 34
1 2023-01-02 42 99 42
2 2023-01-03 42 99 42
3 2023-01-04 42 99 42
