how to merge month day year columns in date column? - python-3.x

Question

The date is in separate columns
Month Day Year
8 12 1993
8 12 1993
8 12 1993
I want to merge it in one column
Date
8/12/1993
8/12/1993
8/12/1993
I tried
df_date = df.Timestamp((df_filtered.Year*10000+df_filtered.Month*100+df_filtered.Day).apply(str),format='%Y%m%d')
I get this error
AttributeError: 'DataFrame' object has no attribute 'Timestamp'

Answer 1

Using pd.to_datetime with astype(str)
1. as string type:
df['Date'] = pd.to_datetime(df['Month'].astype(str) + df['Day'].astype(str) + df['Year'].astype(str), format='%d%m%Y').dt.strftime('%d/%m/%Y')
Month Day Year Date
0 8 12 1993 08/12/1993
1 8 12 1993 08/12/1993
2 8 12 1993 08/12/1993
2. as datetime type:
df['Date'] = pd.to_datetime(df['Month'].astype(str) + df['Day'].astype(str) + df['Year'].astype(str), format='%d%m%Y')
Month Day Year Date
0 8 12 1993 1993-12-08
1 8 12 1993 1993-12-08
2 8 12 1993 1993-12-08

Answer 2

Here is the solution:
df = pd.DataFrame({'Month': [8, 8, 8], 'Day': [12, 12, 12], 'Year': [1993, 1993, 1993]})
# This way dates will be a DataFrame
dates = df.apply(lambda row:
pd.Series(pd.Timestamp(row[2], row[0], row[1]), index=['Date']),
axis=1)
# And this way dates will be a Series:
# dates = df.apply(lambda row:
# pd.Timestamp(row[2], row[0], row[1]),
# axis=1)
apply method generates a new Series or DataFrame iteratively applying provided function (lambda in this case) and joining the results.
You can read about apply method in official documentation.
And here is the explanation of lambda expressions.
EDIT:
#JohnClements suggested a better solution, using pd.to_datetime method:
dates = pd.to_datetime(df).to_frame('Date')
Also, if you want your output to be a string, you can use
dates = df.apply(lambda row: f"{row[2]}/{row[0]}/{row[1]}",
axis=1)

Answer 3

You can try:
df = pd.DataFrame({'Month': [8,8,8], 'Day': [12,12,12], 'Year': [1993, 1993, 1993]})
df['date'] = pd.to_datetime(df)
Result:
Month Day Year date
0 8 12 1993 1993-08-12
1 8 12 1993 1993-08-12
2 8 12 1993 1993-08-12
Info:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3 entries, 0 to 2
Data columns (total 4 columns):
Month 3 non-null int64
Day 3 non-null int64
Year 3 non-null int64
date 3 non-null datetime64[ns]
dtypes: datetime64[ns](1), int64(3)
memory usage: 176.0 bytes