Extracting Just Month and Year Separately from Pandas Datetime Column

Extracting just Month and Year separately from Pandas Datetime column

If you want new columns showing year and month separately you can do this:

df['year'] = pd.DatetimeIndex(df['ArrivalDate']).year
df['month'] = pd.DatetimeIndex(df['ArrivalDate']).month

or...

df['year'] = df['ArrivalDate'].dt.year
df['month'] = df['ArrivalDate'].dt.month

Then you can combine them or work with them just as they are.

How to extract month and year from a datetime series and store them into two separate columns?

Here's how you can do it:

df['Month'], df['Year'] = df["Date"].dt.month, df["Date"].dt.year 

Can you extract both year AND month from date in Pandas

You can use to_period

df['month_year'] = df['date'].dt.to_period('M')

python pandas extract year from datetime: df['year'] = df['date'].year is not working

If you're running a recent-ish version of pandas then you can use the datetime attribute dt to access the datetime components:

In [6]:

df['date'] = pd.to_datetime(df['date'])
df['year'], df['month'] = df['date'].dt.year, df['date'].dt.month
df
Out[6]:
        date  Count  year  month
0 2010-06-30    525  2010      6
1 2010-07-30    136  2010      7
2 2010-08-31    125  2010      8
3 2010-09-30     84  2010      9
4 2010-10-29   4469  2010     10

EDIT

It looks like you're running an older version of pandas in which case the following would work:

In [18]:

df['date'] = pd.to_datetime(df['date'])
df['year'], df['month'] = df['date'].apply(lambda x: x.year), df['date'].apply(lambda x: x.month)
df
Out[18]:
        date  Count  year  month
0 2010-06-30    525  2010      6
1 2010-07-30    136  2010      7
2 2010-08-31    125  2010      8
3 2010-09-30     84  2010      9
4 2010-10-29   4469  2010     10

Regarding why it didn't parse this into a datetime in read_csv you need to pass the ordinal position of your column ([0]) because when True it tries to parse columns [1,2,3] see the docs

In [20]:

t="""date   Count
6/30/2010   525
7/30/2010   136
8/31/2010   125
9/30/2010   84
10/29/2010  4469"""
df = pd.read_csv(io.StringIO(t), sep='\s+', parse_dates=[0])
df.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 5 entries, 0 to 4
Data columns (total 2 columns):
date     5 non-null datetime64[ns]
Count    5 non-null int64
dtypes: datetime64[ns](1), int64(1)
memory usage: 120.0 bytes

So if you pass param parse_dates=[0] to read_csv there shouldn't be any need to call to_datetime on the 'date' column after loading.

Python pandas creating datetime for Week and Year and Month and Year

1. concatenate as strings with '-'
2. convert to datetime
3. reformat the datetime in plotly

df.head()
###
   Year  Month  Sales Volume
0  2020      1    773.956049
1  2020      2    438.878440
2  2020      3    858.597920
3  2020      4    697.368029
4  2020      5     94.177348

df['ym'] = df['Year'].astype(str) + '-' + df['Month'].astype(str)
df['ym'] = pd.to_datetime(df['ym']).dt.strftime('%Y-%m')

fig = px.bar(df, x='ym', y='Sales Volume', color=df['Year'].astype(str))
fig.update_xaxes(tickformat='%Y_%m', tickangle=45)
fig.show()

How to convert year-month-day to just years in python?

Using just base Python (as you didn't specify that you have a pandas dataframe - pandas has specific functions to perform calculations with datetime objects):

from datetime import datetime
    
#takes as arguments the date as a string and an optional format string
def floatyear(str, fmtstr="%Y-%m-%d"):
    t = datetime.strptime(str, fmtstr)
    t_first = datetime(t.year, 1, 1, 0, 0, 0)
    t_last = datetime(t.year, 12, 31, 0, 0, 0)
    return t.year + ((t-t_first).days/(t_last-t_first).days)

print(floatyear("2018-10-24", "%Y-%m-%d"))

Sample output:

2018.8131868131868

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