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Convert date column (string) to datetime and match the format

I'm trying to covert the next date column (str) to datetime64 and say that format doesn't match, can anyone help me pleas :)
Column:
df["Date"]
0 15/7/21
...
2541 13/9/21
dtype: object
What I try:
pd.to_datetime(df["Date"], format = "%d/%m/%Y")
ValueError: time data '15/7/21' does not match format '%d/%m/%Y' (match)
I also try:
pd.to_datetime(df["Date"].astype("datetime64"), format='%d/%m/%Y')
And it convert it as datetime but there is some date the day is in the month.
Anyone know what to do ?

%Y expects a 4-digit year. Use %y for a 2-digit year (See the docs):
>>> import pandas as pd
>>> df = pd.DataFrame({'Date':['15/7/21','13/9/21']})
>>> df['Date']
0 15/7/21
1 13/9/21
Name: Date, dtype: object
>>> pd.to_datetime(df['Date'].astype('datetime64'),format='%d/%m/%y')
0 2021-07-15
1 2021-09-13
Name: Date, dtype: datetime64[ns]
Note that pandas is pretty good at guessing the format:
>>> pd.to_datetime(df['Date'])
0 2021-07-15
1 2021-09-13

H2O python - How to let h2oframe to dataframe with correctly character and datetime

I have a csv file, and want to use H2O to do DeepLearning. But it has some Chinese and datetime that when I finish my Deeplearning need to save output to csv, it can't return to original data.
I use small data to show my problem here.
In[1]: df = pd.DataFrame({'datetime':['2016-12-17 00:00:00'],'time':['00:00:30'],'month':['月'], 'weekend':['周六']})
print(df.dtypes)
df
out[1]: datetime object
time object
month object
weekend object
dtype: object
datetime time month weekend
0 2016-12-17 00:00:00 00:00:30 月 周六
In[2]: h2o_frame = h2o.H2OFrame(df);h2o_frame ;h2o_frame.types ;h2o_frame
C:\Users\thi\Anaconda3\lib\site-packages\h2o\utils\shared_utils.py:170: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.
data = _handle_python_lists(python_obj.as_matrix().tolist(), -1)[1]
out[2]: Parse progress: |█████████████████████████████████████████████████████████| 100%
datetime time month weekend
2016-12-17 00:00:00 1970-01-01 00:00:30 <0xA4EB> <0xA9>P<0xA4BB>
the time I want it just only 00:00:30, any way to fix it?
month and weekends I don't find any way to let it show Chinese, but I still finish my deeplearning
But when I want to let h2oframe back to DataFrame and save to csv file, it save <0xA4EB> for me but not 月, and datetime change to int
In[3]: dff = h2o_frame.as_data_frame();dff
out[3]: datetime time month weekend
0 1481932800000 30000 <0xA4EB> <0xA9>P<0xA4BB>
How to correctly return character from h2oframe to DataFrame
How to correctly return datetime from h2oframe to DataFrame

One simplest way to solve this is, when you convet pandas frame to H2OFrame use argument column_types ,as below:
In [69]: col_types
Out[69]: ['categorical', 'categorical', 'categorical', 'categorical']
In [70]: h2o_frame = h2o.H2OFrame(df,column_types=col_types);h2o_frame ;h2o_frame.types ;h2o_frame
Parse progress: |█████████████████████████████████████████████████████████████████████████████| 100%
Out[70]:
datetime month time weekend
------------------- ------- -------- ---------
2016-12-17 00:00:00 月 00:00:30 周六
[1 row x 4 columns]
In [71]: dff = h2o_frame.as_data_frame();dff
Out[71]:
datetime month time weekend
0 2016-12-17 00:00:00 月 00:00:30 周六

allfiles = h2o.import_file(path='data/', pattern=".csv")
df = allfiles.as_data_frame()
df['datetime'] = pd.to_datetime(df["datetime"], unit='ms')

Convert Pandas Column to DateTime With Rare Date Format

I have one column on my dataframe that follows this date format:
17 MAY2016
I've tried to follow this reference: http://strftime.org/ and pandas.to_datetime reference: http://pandas.pydata.org/pandas-docs/version/0.20/generated/pandas.to_datetime.html
My code is as follows:
df1 =df1.apply(pandas.to_datetime, errors='ignore', format='%d %b%Y')
I also tried: format='%d/%b%Y' format='%d /%b%Y' and still doesn't work. The date column type is still and object.
Any ideas? Thanks in advance

You can use to_datetime only:
df = pd.DataFrame({'date':['17 MAY2016']})
df['date'] = pd.to_datetime(df['date'])
print (df)
date
0 2016-05-17
If want format parameter:
df['date'] = pd.to_datetime(df['date'], format='%d %b%Y')
print (df)
date
0 2016-05-17
If some non date values add errors='coerce' for convert them to NaT:
df['date'] = pd.to_datetime(df['date'], errors='coerce')
EDIT:
For check use dtypes:
print (df.dtypes)
date datetime64[ns]
dtype: object

You don't need to use .apply, the to_datetime function natively works on pandas Series objects.
df1['date column'] = pd.to_datetime(df1['date column'], errors='ignore')

Pandas Dataframe convert string to data without time

I have a Pandas Dataframe df:
a date
1 2014-06-29 00:00:00
df.types return:
a object
date object
I want convert column data to data without time but:
df['date']=df['date'].astype('datetime64[s]')
return:
a date
1 2014-06-28 22:00:00
df.types return:
a object
date datetime64[ns]
But value is wrong.
I'd have:
a date
1 2014-06-29
or:
a date
1 2014-06-29 00:00:00

I would start by putting your dates in pd.datetime:
df['date'] = pd.to_datetime(df.date)
Now, you can see that the time component is still there:
df.date.values
array(['2014-06-28T19:00:00.000000000-0500'], dtype='datetime64[ns]')
If you are ok having a date object again, you want:
df['date'] = [x.strftime("%y-%m-%d") for x in df.date]
Here would be ending with a datetime:
df['date'] = [x.date() for x in df.date]
df.date
datetime.date(2014, 6, 29)

Here you go. Just use this pattern:
df.to_datetime().date()

How do I convert strings in a Pandas data frame to a 'date' data type?

I have a Pandas data frame, one of the column contains date strings in the format YYYY-MM-DD
For e.g. '2013-10-28'
At the moment the dtype of the column is object.
How do I convert the column values to Pandas date format?

Essentially equivalent to #waitingkuo, but I would use pd.to_datetime here (it seems a little cleaner, and offers some additional functionality e.g. dayfirst):
In [11]: df
Out[11]:
a time
0 1 2013-01-01
1 2 2013-01-02
2 3 2013-01-03
In [12]: pd.to_datetime(df['time'])
Out[12]:
0 2013-01-01 00:00:00
1 2013-01-02 00:00:00
2 2013-01-03 00:00:00
Name: time, dtype: datetime64[ns]
In [13]: df['time'] = pd.to_datetime(df['time'])
In [14]: df
Out[14]:
a time
0 1 2013-01-01 00:00:00
1 2 2013-01-02 00:00:00
2 3 2013-01-03 00:00:00
Handling ValueErrors
If you run into a situation where doing
df['time'] = pd.to_datetime(df['time'])
Throws a
ValueError: Unknown string format
That means you have invalid (non-coercible) values. If you are okay with having them converted to pd.NaT, you can add an errors='coerce' argument to to_datetime:
df['time'] = pd.to_datetime(df['time'], errors='coerce')

Use astype
In [31]: df
Out[31]:
a time
0 1 2013-01-01
1 2 2013-01-02
2 3 2013-01-03
In [32]: df['time'] = df['time'].astype('datetime64[ns]')
In [33]: df
Out[33]:
a time
0 1 2013-01-01 00:00:00
1 2 2013-01-02 00:00:00
2 3 2013-01-03 00:00:00

I imagine a lot of data comes into Pandas from CSV files, in which case you can simply convert the date during the initial CSV read:
dfcsv = pd.read_csv('xyz.csv', parse_dates=[0]) where the 0 refers to the column the date is in.
You could also add , index_col=0 in there if you want the date to be your index.
See https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html

Now you can do df['column'].dt.date
Note that for datetime objects, if you don't see the hour when they're all 00:00:00, that's not pandas. That's iPython notebook trying to make things look pretty.

If you want to get the DATE and not DATETIME format:
df["id_date"] = pd.to_datetime(df["id_date"]).dt.date

Another way to do this and this works well if you have multiple columns to convert to datetime.
cols = ['date1','date2']
df[cols] = df[cols].apply(pd.to_datetime)

It may be the case that dates need to be converted to a different frequency. In this case, I would suggest setting an index by dates.
#set an index by dates
df.set_index(['time'], drop=True, inplace=True)
After this, you can more easily convert to the type of date format you will need most. Below, I sequentially convert to a number of date formats, ultimately ending up with a set of daily dates at the beginning of the month.
#Convert to daily dates
df.index = pd.DatetimeIndex(data=df.index)
#Convert to monthly dates
df.index = df.index.to_period(freq='M')
#Convert to strings
df.index = df.index.strftime('%Y-%m')
#Convert to daily dates
df.index = pd.DatetimeIndex(data=df.index)
For brevity, I don't show that I run the following code after each line above:
print(df.index)
print(df.index.dtype)
print(type(df.index))
This gives me the following output:
Index(['2013-01-01', '2013-01-02', '2013-01-03'], dtype='object', name='time')
object
<class 'pandas.core.indexes.base.Index'>
DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03'], dtype='datetime64[ns]', name='time', freq=None)
datetime64[ns]
<class 'pandas.core.indexes.datetimes.DatetimeIndex'>
PeriodIndex(['2013-01', '2013-01', '2013-01'], dtype='period[M]', name='time', freq='M')
period[M]
<class 'pandas.core.indexes.period.PeriodIndex'>
Index(['2013-01', '2013-01', '2013-01'], dtype='object')
object
<class 'pandas.core.indexes.base.Index'>
DatetimeIndex(['2013-01-01', '2013-01-01', '2013-01-01'], dtype='datetime64[ns]', freq=None)
datetime64[ns]
<class 'pandas.core.indexes.datetimes.DatetimeIndex'>

For the sake of completeness, another option, which might not be the most straightforward one, a bit similar to the one proposed by #SSS, but using rather the datetime library is:
import datetime
df["Date"] = df["Date"].apply(lambda x: datetime.datetime.strptime(x, '%Y-%d-%m').date())

# Column Non-Null Count Dtype
--- ------ -------------- -----
0 startDay 110526 non-null object
1 endDay 110526 non-null object
import pandas as pd
df['startDay'] = pd.to_datetime(df.startDay)
df['endDay'] = pd.to_datetime(df.endDay)
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 startDay 110526 non-null datetime64[ns]
1 endDay 110526 non-null datetime64[ns]

Try to convert one of the rows into timestamp using the pd.to_datetime function and then use .map to map the formular to the entire column