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Examples of how to create a datetime column from year, month and day columns in pandas:

[TOC]

### Create a dataframe

Let's first create a dataframe in pandas with 3 columns "Year", "Month" and "Day":

import pandas as pd
	import random
	import itertools

year_list = []
	month_list = []
	day_list = []

for combination in itertools.product([2015], [1,2,3,4], [5,15,25]):
			#print(combination) 
			year_list.append( combination[0] )
			month_list.append( combination[1] )
			day_list.append( combination[2] )

data = {'Year':year_list,
					'Month':month_list,
					'Day':day_list,
					'Random Integer':[random.randrange(1, 10) for i in range(len(year_list))]}

df = pd.DataFrame(data=data)

print(df)
	
returns for example
	
				Year  Month  Day  Random Integer
	0   2015      1    5               7
	1   2015      1   15               5
	2   2015      1   25               6
	3   2015      2    5               4
	4   2015      2   15               1
	5   2015      2   25               2
	6   2015      3    5               4
	7   2015      3   15               9
	8   2015      3   25               3
	9   2015      4    5               1
	10  2015      4   15               3
	11  2015      4   25               7
	
### Create a datetime column
	
To create a new datetime column using 'Year', 'Month' and 'Day' columns, a solution is to use [to_datetime()](https://pandas.pydata.org/docs/reference/api/pandas.to_datetime.html):
	
	df['Datetime'] = pd.to_datetime( df[['Year', 'Month', 'Day']])

returns

Year  Month  Day  Random Integer   Datetime
	0   2015      1    5               7 2015-01-05
	1   2015      1   15               5 2015-01-15
	2   2015      1   25               6 2015-01-25
	3   2015      2    5               4 2015-02-05
	4   2015      2   15               1 2015-02-15
	5   2015      2   25               2 2015-02-25
	6   2015      3    5               4 2015-03-05
	7   2015      3   15               9 2015-03-15
	8   2015      3   25               3 2015-03-25
	9   2015      4    5               1 2015-04-05
	10  2015      4   15               3 2015-04-15
	11  2015      4   25               7 2015-04-25

### Set datetime column as the dataframe index

Now, if you want to set the datetime column as the dataframe index,a solution is to use set_index():

df.set_index('Datetime', inplace=True)

returns

Year  Month  Day  Random Integer
	Datetime                                    
	2015-01-05  2015      1    5               7
	2015-01-15  2015      1   15               5
	2015-01-25  2015      1   25               6
	2015-02-05  2015      2    5               4
	2015-02-15  2015      2   15               1
	2015-02-25  2015      2   25               2
	2015-03-05  2015      3    5               4
	2015-03-15  2015      3   15               9
	2015-03-25  2015      3   25               3
	2015-04-05  2015      4    5               1
	2015-04-15  2015      4   15               3
	2015-04-25  2015      4   25               7

Can be used to plot a time series:

[image:pandas-datetime-column size:100 caption:How to create a datetime column from year, month and day columns in pandas ?]

Note to reset the index:

df.reset_index(inplace=True)

### References

- [to_datetime()](https://pandas.pydata.org/docs/reference/api/pandas.to_datetime.html)
- [How to convert columns into one datetime column in pandas?](https://stackoverflow.com/questions/19350806/how-to-convert-columns-into-one-datetime-column-in-pandas)
- [pandas.DataFrame.set_index](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.set_index.html)

How to create a datetime column from year, month and day columns in pandas ?

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