I have a Python array in the form:
K = [ [2022,1,16], [2022,1,18], [2022,2,12], [2022,3,24]]
This array contains dates within sub-arrays.
How can I turn it into a Pandas DataFrame with 1 column of dates in standard format (%d/%m/%Y)?
-
1In Python, those are called lists, not arrays. Anyway - did you try reading the documentation? Did you try to write any kind of call to the DataFrame constructor? What happened when you tried this, and how was the result different from what you wanted? Also, what does this have to do with machine learning? Please only tag things that are relevant to the problem you are actively asking about, not the project that motivates the question.Karl Knechtel– Karl Knechtel2022-02-02 23:09:23 +00:00Commented Feb 2, 2022 at 23:09
-
Welcome back to Stack Overflow. As a refresher, please read How to Ask and meta.stackoverflow.com/questions/261592.Karl Knechtel– Karl Knechtel2022-02-02 23:10:11 +00:00Commented Feb 2, 2022 at 23:10
Add a comment
|
2 Answers
import pandas as pd
date_array = [ [2022,1,16], [2022,1,18], [2022,2,12], [2022,3,24]]
date_df = pd.DataFrame(date_array, columns=['year', 'month', 'day'])
date_df['date'] = pd.to_datetime(date_df[['year', 'month', 'day']], format='%d/%m/%Y')
And if you'd like you can drop extra columns
Comments
import datetime
K = [ [2022,1,16], [2022,1,18], [2022,2,12], [2022,3,24]]
dict_t ={"date": []}
import pandas as pd
for time_list in K:
dict_t["date"] += [datetime.datetime(*time_list)]
pd.DataFrame(dict_t)
#output
date
0 2022-01-16
1 2022-01-18
2 2022-02-12
3 2022-03-24
you can change the format this way
import datetime
K = [ [2022,1,16], [2022,1,18], [2022,2,12], [2022,3,24]]
dict_t ={"date": []}
import pandas as pd
for time_list in K:
dict_t["date"] += [datetime.datetime(*time_list)]
df = pd.DataFrame(dict_t)
df.style.format({"date": lambda t: t.strftime("%d/%m/%Y")})
date
0 16/01/2022
1 18/01/2022
2 12/02/2022
3 24/03/2022
