从 pandas DataFrame 中的日期列获取唯一年份

Question

I have a date column in my DataFrame say df_dob and it looks like -

id	DOB
23312	31-12-9999
1482	31-12-9999
807	#VALUE!
2201	06-12-1925
653	01/01/1855
108	01/01/1855
768	1967-02-20

What I want to print is a list of unique years like - `['9999', '1925', '1855', '1967']

basically through this list I just wanted to check whether there is some unwanted year is present or not. I have tried(pasted my code below) but getting ValueError: time data 01/01/1855 doesn't match format specified and could not resolve it.

df_dob['DOB'] = df_dob['DOB'].replace('01/01/1855 00:00:00', '1855-01-01')
df_dob['DOB'] = pd.to_datetime(df_dob.DOB, format='%Y-%m-%d')
df_dob['DOB'] = df_dob['DOB'].dt.strftime('%Y-%m-%d')
print(np.unique(df_dob['DOB']))
# print(list(df_dob['DOB'].year.unique()))

PS - when I print df_dob['DOB'] , I get values like - 1967-02-20 00:00:00

Answer 1

Use pandas' unique for this. And on year only.

So try:

print(df['DOB'].dt.year.unique())

Also, you don't need to stringify your time. Alse, you don't need to replace anything, pandas is smart enough to do it for you. So you overall code becomes:

df_dob['DOB'] = pd.to_datetime(df_dob.DOB)    # No need to pass format if there isn't some specific anomoly
print(df['DOB'].dt.year.unique())

Edit:

Another method: Since you have outofbounds problem, Another method you can try is not converting them to datetime, but rather find all the four digit numbers in each column using regex. So,

df['DOB'].str.extract(r'(\d{4})')[0].unique()

[0] because unique() is a function of pd.series not a dataframe . So taking the first series in the dataframe.

Answer 2

Can you try this?

df_dob["DOB"] = pd.to_datetime(df_DOB["Date"])

df_dob['YOB'] = df_dob['DOB'].dt.strftime('%Y')

Answer 3

The first thing you need to know is if the resulting values (which you said look like 1967-02-20 00:00:00 are datetimes or not. That's as simple as df_dob.info()

If the result says similar to datetime64[ns] for the DOB column, you're good. If not you'll need to cast it as a DateTime. You have a couple of different formats so that might be part of your problem. Also, because there're several ways of doing this and it's a separate question, I'm not addressing it.

We going to leverage the speed of sets, plus a bit of pandas, and then convert that back to a list as you wanted the final version to be.

years = list({i for i in df['date'].dt.year})

And just a side note, you can't use [] instead of list() as you'll end with a list with a single element that's a set.

That's a list as you indicated. If you want it as a column, you won't get unique values

Nitish's answer will also work but give you something like: array([9999, 1925, 1855, 1967])