优化代码以查找DataFrame中每行过去30天的值的中位数

我想找到更快的代码来实现相同的目标：对于每行，计算过去30天中所有数据的中位数。但是少于5个数据点，然后返回np.nan。

import pandas as pd
import numpy as np
import datetime

def findPastVar(df, var='var' ,window=30, method='median'):
    # window= # of past days    
    def findPastVar_apply(row):
        pastVar = df[var].loc[(df['timestamp'] - row['timestamp'] < datetime.timedelta(days=0)) & (df['timestamp'] - row['timestamp'] > datetime.timedelta(days=-window))]
        if len(pastVar) < 5:
            return(np.nan)            
        if method == 'median':
            return(np.median(pastVar.values))
    df['past{}d_{}_median'.format(window,var)] = df.apply(findPastVar_apply,axis=1)
    return(df)


df = pd.DataFrame()
df['timestamp'] = pd.date_range('1/1/2011', periods=100, freq='D')
df['timestamp'] = df.timestamp.astype(pd.Timestamp)
df['var'] = pd.Series(np.random.randn(len(df['timestamp'])))

数据看起来像这样。在我的真实数据中，时间上存在差距，一天中可能会有更多数据点。

In [47]: df.head()
Out[47]: 
             timestamp       var
0  2011-01-01 00:00:00 -0.670695
1  2011-01-02 00:00:00  0.315148
2  2011-01-03 00:00:00 -0.717432
3  2011-01-04 00:00:00  2.904063
4  2011-01-05 00:00:00 -1.092813

所需的输出：

In [55]: df.head(10)
Out[55]: 
             timestamp       var  past30d_var_median
0  2011-01-01 00:00:00 -0.670695                 NaN
1  2011-01-02 00:00:00  0.315148                 NaN
2  2011-01-03 00:00:00 -0.717432                 NaN
3  2011-01-04 00:00:00  2.904063                 NaN
4  2011-01-05 00:00:00 -1.092813                 NaN
5  2011-01-06 00:00:00 -2.676784           -0.670695
6  2011-01-07 00:00:00 -0.353425           -0.694063
7  2011-01-08 00:00:00 -0.223442           -0.670695
8  2011-01-09 00:00:00  0.162126           -0.512060
9  2011-01-10 00:00:00  0.633801           -0.353425

但是，我当前的代码运行速度：

In [49]: %timeit findPastVar(df)
1 loop, best of 3: 755 ms per loop

我需要不时地运行大型数据框，因此我想优化此代码。

欢迎任何建议或评论。