CODEWITHSUNDEEP
pythonpandasdatedatetime
Arturo Sbr
Arturo Sbr

Reputation: 6333

Change type of multiple columns from datetime to date simultaneously (pandas)

I have multiple columns in the format yyyy-mm-dd hh:mm:ss and I want to transform all of them to yyyy-mm-dd format with a single (preferably vectorized) line of code.

In R, I would do something along the lines of:

df[, c("col1", "col2") := list(col1 = as.Date(col1), col2 = as.Date(col2))]

I want to avoid doing this individually for every column. So far I can only think of the following trivial solution:

df["col1"] = df["col1"].dt.date
df["col2"] = df["col2"].dt.date

So I have two questions: how do you operate on multiple columns in a single line of code and how do you turn datetime objects to date.

Upvotes: 2

Views: 2901

Answers (2)

Andy L.
Andy L.

Reputation: 25249

You may use select_dtypes to select only datetime dtype columns and stack, convert to dt.date and unstack and update back to df

df.update(df.select_dtypes('datetime').stack().dt.date.unstack())

Upvotes: 2

Hryhorii Pavlenko
Hryhorii Pavlenko

Reputation: 3910

# sample data
df = pd.DataFrame({
    'a': np.random.choice(pd.date_range("2019-01-01", "2019-12-31", freq="2H").strftime('Y%-%m-%d %H:%M:%S'), size=5),
    'b': np.random.choice(pd.date_range("2019-01-01", "2019-12-31", freq="2H").strftime('Y%-%m-%d %H:%M:%S'), size=5),
    'c': np.random.choice(pd.date_range("2019-01-01", "2019-12-31", freq="2H").strftime('Y%-%m-%d %H:%M:%S'), size=5)
})

df.loc[:, 'a':'c'] = df.loc[:, 'a':'c'].apply(lambda x: pd.to_datetime(x).dt.date)

    a           b           c 
0   2019-04-19  2019-08-11  2019-11-01
1   2019-04-02  2019-07-04  2019-08-29
2   2019-03-02  2019-06-02  2019-11-25
3   2019-08-22  2019-04-27  2019-04-28
4   2019-05-10  2019-09-21  2019-12-07

Upvotes: 2

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