In this short guide, I'll show you how to extract Month and Year from a DateTime column in Pandas DataFrame. You can also find how to convert string data to a DateTime. So at the end you will get:
01/08/2021 -> 2021-08
DD/MM/YYYY -> YYYY-MM
or any other date format. We will also cover MM/YYYY.
To start, here is the syntax that you may apply in order extract concatenation of year and month:
.dt.to_period('M')
In the next section, I'll review the steps to apply the above syntax in practice.
Step 1: Create a DataFrame with Datetime values
Lets create a DataFrame which has a single column StartDate:
dates = ['2021-08-01', '2021-08-02', '2021-08-03']
df = pd.DataFrame({'StartDate': dates})
result:
| StartDate |
|---|
| 2021-08-01 |
| 2021-08-02 |
| 2021-08-03 |
In order to convert string to Datetime column we are going to use:
df['StartDate'] = pd.to_datetime(df['StartDate'])
Step 2: Extract Year and Month with .dt.to_period('M') - format YYYY-MM
In order to extract from a full date only the year plus the month: 2021-08-01 -> 2021-08 we need just this line:
df['StartDate'].dt.to_period('M')
result:
0 2021-08
1 2021-08
2 2021-08
Step 3: Extract Year and Month other formats MM/YYYY
What if you like to get the month first and then the year? In this case we will use .dt.strftime in order to produce a column with format: MM/YYYY or any other format.
df['StartDate'].dt.strftime('%m/%Y')
0 08/2021
1 08/2021
2 08/2021
Note: have in mind that this solution might be really slow in case of a huge DataFrame.
Step 4: Extracting Year and Month separately and combine them
A bit faster solution than step 3 plus a trace of the month and year info will be:
- extract month and date to separate columns
- combine both columns into a single one
df['yyyy'] = pd.to_datetime(df['StartDate']).dt.year
df['mm'] = pd.to_datetime(df['StartDate']).dt.month
| StartDate | yyyy | mm |
|---|---|---|
| 2021-08-01 | 2021 | 8 |
| 2021-08-02 | 2021 | 8 |
| 2021-08-03 | 2021 | 8 |
and then:
df['yyyy'].astype(str) + '-'+ df['mm'].astype(str)
Note: If you don't need extra columns you can just do:
df['StartDate'].dt.year.astype(str) + "-" + df['StartDate'].dt.month.astype(str)
Notebook with all examples: Extract Month and Year from DateTime column
