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I have a pandas data frame with values as below
ProcessID1 UserID Date Month Year Time
248 Tony 29 4 2017 23:30:56
436 Jeff 28 4 2017 20:02:19
500 Greg 4 5 2017 11:48:29
I would like to know is there any way I can combine columns of Date,Month&Year & time to a pd.datetimeformat?
583
Pandas Combine() Function combine() function which allows us to take a date and time string values and combine them to a single Pandas timestamp object. The function accepts two main parameters: Date – refers to the datetime. date object denoting the date string.
To merge two pandas DataFrames on multiple columns use pandas. merge() method. merge() is considered more versatile and flexible and we also have the same method in DataFrame.
to_datetime() can be used to assemble a datetime from multiple columns as well. The keys (columns label) can be common abbreviations like ['year', 'month', 'day', 'minute', 'second', 'ms', 'us', 'ns']) or plurals of the same.
Using DataFrame. insert() method, we can add new columns at specific position of the column name sequence. Although insert takes single column name, value as input, but we can use it repeatedly to add multiple columns to the DataFrame.
Use to_datetime with automatic convert column Day,Month,Year with add times converted to_timedelta:
df['Datetime'] = pd.to_datetime(df.rename(columns={'Date':'Day'})[['Day','Month','Year']]) + \
pd.to_timedelta(df['Time'])
Another solutions are join all column converted to strings first:
df['Datetime'] = pd.to_datetime(df[['Date','Month','Year', 'Time']]
.astype(str).apply(' '.join, 1), format='%d %m %Y %H:%M:%S')
df['Datetime'] = (pd.to_datetime(df['Year'].astype(str) + '-' +
df['Month'].astype(str) + '-' +
df['Date'].astype(str) + ' ' +
df['Time']))
print (df)
ProcessID1 UserID Date Month Year Time Datetime
0 248 Tony 29 4 2017 23:30:56 2017-04-29 23:30:56
1 436 Jeff 28 4 2017 20:02:19 2017-04-28 20:02:19
2 500 Greg 4 5 2017 11:48:29 2017-05-04 11:48:29
Last if need remove these columns:
df = df.drop(['Date','Month','Year', 'Time'], axis=1)
print (df)
ProcessID1 UserID Datetime
0 248 Tony 2017-04-29 23:30:56
1 436 Jeff 2017-04-28 20:02:19
2 500 Greg 2017-05-04 11:48:29
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