Filter on month and date irrespective of year in python

Question

I have a column of data one of them being a date and am expected to drop the rows that have leap dates. It is a range of years so I was hoping to drop any that matched the 02-29 filter.

The one way I used is to add additional columns, extract the month and date separately and then filter on the data as shown below. It serves the purpose but obviously not good from an efficiency perspective

df['Yr'], df['Mth-Dte'] = zip(*df['Date'].apply(lambda x: (x[:4], x[5:])))
df = df[df['Mth-Dte'] != '02-29']

Is there a better way to implement this by directly applying the filter on the column in the dataframe?

Adding the data

        ID          Date
22398   IDM00096087 1/1/2005
22586   IDM00096087 1/1/2005
21790   IDM00096087 1/2/2005
21791   IDM00096087 1/2/2005
14727   IDM00096087 1/3/2005

Thanks in advance

Answer 1

Convert to datetime and use boolean mask.

import pandas as pd

data = {'Date': {14727: '1/3/2005',
  21790: '1/2/2005',
  21791: '1/2/2005',
  22398: '1/1/2005',
  22586: '29/2/2008'},
 'ID': {14727: 'IDM00096087',
  21790: 'IDM00096087',
  21791: 'IDM00096087',
  22398: 'IDM00096087',
  22586: 'IDM00096087'}}

df = pd.DataFrame(data)

Option1, convert + dt:

df.Date = pd.to_datetime(df.Date)

# Filter away february 29
df[~((df.Date.dt.month == 2) & (df.Date.dt.day == 29))] # ~ for not equal to

Option2, convert + strftime:

df.Date = pd.to_datetime(df.Date)

# Filter away february 29
df[df.Date.dt.strftime('%m%d') != '0229']

Option3, without conversion:

mask = pd.to_datetime(df.Date).dt.strftime('%m%d') != '0229'
df[mask]