Python如何计算日期后30天的月数

Question

I have a dataframe containing date, and I would like to process the data as follow for feature engineering

df

date
2016/1/1
2015/2/10
2016/4/5

after process I would like to make the df looks like

date      Jan    Feb   Mar   Apr    
2016/1/1  30     0    0      0    //date from 1/1 to 1/30 : the number of dates in jan
2015/2/10  0     19   11     0    //date from 2/10 to 3/11 : the number of dates in feb and no of dates in mar
2016/3/25  0     0    7     21    //date from 3/25 to 4/21 : the number of dates in mar and no of dates in apr

1. get 30 days after the df["date"]

   df["date"] + timedelta(month=1)

2. count the frequency of months which belong to the specific 30 days

Is there any method to do this quickly?

Thanks.

Answer 1

Just go step by step. First you offset your original date by + pd.to_timedelta('30d') . Then create a column indicating the month only by df.date.dt.month . Then create a column with the end-of-month date for each date - some ideas for that are here: Want the last day of each month for a data frame in pandas . Finally, fill in a matrix where the columns are the 12 months, setting the values in the columns for the month and month+1.

By enriching your DataFrame one column at a time, you can easily move from your input to your desired output. There is not likely to be a magic method that does everything in a single call.

Read all about date/time functions in Pandas here: https://pandas.pydata.org/pandas-docs/stable/timeseries.html - there are a lot!

Answer 2

You can use custom function with date_range and groupby with size :

date = df[['date']]
names = ['Jan', 'Feb','Mar','Apr','May']

def f(x):
    print (x['date'])
    a = pd.date_range(x['date'], periods=30)
    a = pd.Series(a).groupby(a.month).size()
    return (a)


df = df.apply(f, axis=1).fillna(0).astype(int)
df = df.rename(columns = {k:v for k,v in enumerate(names)})
df = date.join(df)
print (df)
        date  Feb  Mar  Apr  May
0 2016-01-01   30    0    0    0
1 2015-02-10    0   19   11    0
2 2016-03-25    0    0    7   23

---

Similar solution with value_counts :

date = df[['date']]
names = ['Jan', 'Feb','Mar','Apr','May']

df = df.apply(lambda x: pd.date_range(x['date'], periods=30).month.value_counts(), axis=1)
       .fillna(0)
       .astype(int)
df = df.rename(columns = {k:v for k,v in enumerate(names)})
df = date.join(df)
print (df)

Another solution:

names = ['Jan', 'Feb','Mar','Apr','May']
date = df[['date']]

df["date1"] = df["date"] + pd.Timedelta(days=29)
df = df.reset_index().melt(id_vars='index', value_name='date').set_index('date')
df = df.groupby('index').resample('D').asfreq()
df = df.groupby([df.index.get_level_values(0), df.index.get_level_values(1).month])
      .size()
      .unstack(fill_value=0)
df = df.rename(columns = {k+1:v for k,v in enumerate(names)})
df = date.join(df)
print (df)
        date  Jan  Feb  Mar  Apr
0 2016-01-01   30    0    0    0
1 2015-02-10    0   19   11    0
2 2016-03-25    0    0    7   23