Ano*_*nov 71 python time-series pandas
我是Pandas的新手....我有一堆民意调查数据; 我想计算一个滚动均值,以便根据三天的窗口得出每天的估计值.当我从理解这个问题,在rolling_*函数计算基于值指定数量的窗口,而不是具体的日期时间范围.
是否有实现此功能的不同功能？还是我坚持写自己的？
编辑:
样本输入数据:
polls_subset.tail(20)
Out[185]:
favorable unfavorable other
enddate
2012-10-25 0.48 0.49 0.03
2012-10-25 0.51 0.48 0.02
2012-10-27 0.51 0.47 0.02
2012-10-26 0.56 0.40 0.04
2012-10-28 0.48 0.49 0.04
2012-10-28 0.46 0.46 0.09
2012-10-28 0.48 0.49 0.03
2012-10-28 0.49 0.48 0.03
2012-10-30 0.53 0.45 0.02
2012-11-01 0.49 0.49 0.03
2012-11-01 0.47 0.47 0.05
2012-11-01 0.51 0.45 0.04
2012-11-03 0.49 0.45 0.06
2012-11-04 0.53 0.39 0.00
2012-11-04 0.47 0.44 0.08
2012-11-04 0.49 0.48 0.03
2012-11-04 0.52 0.46 0.01
2012-11-04 0.50 0.47 0.03
2012-11-05 0.51 0.46 0.02
2012-11-07 0.51 0.41 0.00
每个日期的输出只有一行.
编辑x2:修正错字
Zel*_*ny7 47
这样的事情怎么样:
首先将数据帧重采样为1D间隔.这取所有重复日期的值的平均值.使用该fill_method选项填写缺少的日期值.接下来,将重采样帧传递pd.rolling_mean给窗口3和min_periods = 1:
pd.rolling_mean(df.resample("1D", fill_method="ffill"), window=3, min_periods=1)
favorable unfavorable other
enddate
2012-10-25 0.495000 0.485000 0.025000
2012-10-26 0.527500 0.442500 0.032500
2012-10-27 0.521667 0.451667 0.028333
2012-10-28 0.515833 0.450000 0.035833
2012-10-29 0.488333 0.476667 0.038333
2012-10-30 0.495000 0.470000 0.038333
2012-10-31 0.512500 0.460000 0.029167
2012-11-01 0.516667 0.456667 0.026667
2012-11-02 0.503333 0.463333 0.033333
2012-11-03 0.490000 0.463333 0.046667
2012-11-04 0.494000 0.456000 0.043333
2012-11-05 0.500667 0.452667 0.036667
2012-11-06 0.507333 0.456000 0.023333
2012-11-07 0.510000 0.443333 0.013333
更新:正如Ben在评论中指出的那样,使用pandas 0.18.0语法已经改变了.使用新语法,这将是:
df.resample("1d").sum().fillna(0).rolling(window=3, min_periods=1).mean()
小智 46
与此同时,增加了时间窗功能.请参阅以下链接:
https://github.com/pydata/pandas/pull/13513
In [1]: df = DataFrame({'B': range(5)})
In [2]: df.index = [Timestamp('20130101 09:00:00'),
...: Timestamp('20130101 09:00:02'),
...: Timestamp('20130101 09:00:03'),
...: Timestamp('20130101 09:00:05'),
...: Timestamp('20130101 09:00:06')]
In [3]: df
Out[3]:
B
2013-01-01 09:00:00 0
2013-01-01 09:00:02 1
2013-01-01 09:00:03 2
2013-01-01 09:00:05 3
2013-01-01 09:00:06 4
In [4]: df.rolling(2, min_periods=1).sum()
Out[4]:
B
2013-01-01 09:00:00 0.0
2013-01-01 09:00:02 1.0
2013-01-01 09:00:03 3.0
2013-01-01 09:00:05 5.0
2013-01-01 09:00:06 7.0
In [5]: df.rolling('2s', min_periods=1).sum()
Out[5]:
B
2013-01-01 09:00:00 0.0
2013-01-01 09:00:02 1.0
2013-01-01 09:00:03 3.0
2013-01-01 09:00:05 3.0
2013-01-01 09:00:06 7.0
use*_*410 33
我只是有同样的问题,但数据点不规则.Resample在这里不是一个真正的选择.所以我创建了自己的功能.也许它对其他人也有用:
from pandas import Series, DataFrame
import pandas as pd
from datetime import datetime, timedelta
import numpy as np
def rolling_mean(data, window, min_periods=1, center=False):
''' Function that computes a rolling mean
Parameters
----------
data : DataFrame or Series
If a DataFrame is passed, the rolling_mean is computed for all columns.
window : int or string
If int is passed, window is the number of observations used for calculating
the statistic, as defined by the function pd.rolling_mean()
If a string is passed, it must be a frequency string, e.g. '90S'. This is
internally converted into a DateOffset object, representing the window size.
min_periods : int
Minimum number of observations in window required to have a value.
Returns
-------
Series or DataFrame, if more than one column
'''
def f(x):
'''Function to apply that actually computes the rolling mean'''
if center == False:
dslice = col[x-pd.datetools.to_offset(window).delta+timedelta(0,0,1):x]
# adding a microsecond because when slicing with labels start and endpoint
# are inclusive
else:
dslice = col[x-pd.datetools.to_offset(window).delta/2+timedelta(0,0,1):
x+pd.datetools.to_offset(window).delta/2]
if dslice.size < min_periods:
return np.nan
else:
return dslice.mean()
data = DataFrame(data.copy())
dfout = DataFrame()
if isinstance(window, int):
dfout = pd.rolling_mean(data, window, min_periods=min_periods, center=center)
elif isinstance(window, basestring):
idx = Series(data.index.to_pydatetime(), index=data.index)
for colname, col in data.iterkv():
result = idx.apply(f)
result.name = colname
dfout = dfout.join(result, how='outer')
if dfout.columns.size == 1:
dfout = dfout.ix[:,0]
return dfout
# Example
idx = [datetime(2011, 2, 7, 0, 0),
datetime(2011, 2, 7, 0, 1),
datetime(2011, 2, 7, 0, 1, 30),
datetime(2011, 2, 7, 0, 2),
datetime(2011, 2, 7, 0, 4),
datetime(2011, 2, 7, 0, 5),
datetime(2011, 2, 7, 0, 5, 10),
datetime(2011, 2, 7, 0, 6),
datetime(2011, 2, 7, 0, 8),
datetime(2011, 2, 7, 0, 9)]
idx = pd.Index(idx)
vals = np.arange(len(idx)).astype(float)
s = Series(vals, index=idx)
rm = rolling_mean(s, window='2min')
user2689410的代码正是我所需要的.提供我的版本(信用到user2689410),由于在DataFrame中为整行计算平均值,因此速度更快.
希望我的后缀约定是可读的:_s:string,_ i:int,_b:bool,_ser:Series和_df:DataFrame.如果找到多个后缀,则可以同时键入两个类型.
import pandas as pd
from datetime import datetime, timedelta
import numpy as np
def time_offset_rolling_mean_df_ser(data_df_ser, window_i_s, min_periods_i=1, center_b=False):
""" Function that computes a rolling mean
Credit goes to user2689410 at http://stackoverflow.com/questions/15771472/pandas-rolling-mean-by-time-interval
Parameters
----------
data_df_ser : DataFrame or Series
If a DataFrame is passed, the time_offset_rolling_mean_df_ser is computed for all columns.
window_i_s : int or string
If int is passed, window_i_s is the number of observations used for calculating
the statistic, as defined by the function pd.time_offset_rolling_mean_df_ser()
If a string is passed, it must be a frequency string, e.g. '90S'. This is
internally converted into a DateOffset object, representing the window_i_s size.
min_periods_i : int
Minimum number of observations in window_i_s required to have a value.
Returns
-------
Series or DataFrame, if more than one column
>>> idx = [
... datetime(2011, 2, 7, 0, 0),
... datetime(2011, 2, 7, 0, 1),
... datetime(2011, 2, 7, 0, 1, 30),
... datetime(2011, 2, 7, 0, 2),
... datetime(2011, 2, 7, 0, 4),
... datetime(2011, 2, 7, 0, 5),
... datetime(2011, 2, 7, 0, 5, 10),
... datetime(2011, 2, 7, 0, 6),
... datetime(2011, 2, 7, 0, 8),
... datetime(2011, 2, 7, 0, 9)]
>>> idx = pd.Index(idx)
>>> vals = np.arange(len(idx)).astype(float)
>>> ser = pd.Series(vals, index=idx)
>>> df = pd.DataFrame({'s1':ser, 's2':ser+1})
>>> time_offset_rolling_mean_df_ser(df, window_i_s='2min')
s1 s2
2011-02-07 00:00:00 0.0 1.0
2011-02-07 00:01:00 0.5 1.5
2011-02-07 00:01:30 1.0 2.0
2011-02-07 00:02:00 2.0 3.0
2011-02-07 00:04:00 4.0 5.0
2011-02-07 00:05:00 4.5 5.5
2011-02-07 00:05:10 5.0 6.0
2011-02-07 00:06:00 6.0 7.0
2011-02-07 00:08:00 8.0 9.0
2011-02-07 00:09:00 8.5 9.5
"""
def calculate_mean_at_ts(ts):
"""Function (closure) to apply that actually computes the rolling mean"""
if center_b == False:
dslice_df_ser = data_df_ser[
ts-pd.datetools.to_offset(window_i_s).delta+timedelta(0,0,1):
ts
]
# adding a microsecond because when slicing with labels start and endpoint
# are inclusive
else:
dslice_df_ser = data_df_ser[
ts-pd.datetools.to_offset(window_i_s).delta/2+timedelta(0,0,1):
ts+pd.datetools.to_offset(window_i_s).delta/2
]
if (isinstance(dslice_df_ser, pd.DataFrame) and dslice_df_ser.shape[0] < min_periods_i) or \
(isinstance(dslice_df_ser, pd.Series) and dslice_df_ser.size < min_periods_i):
return dslice_df_ser.mean()*np.nan # keeps number format and whether Series or DataFrame
else:
return dslice_df_ser.mean()
if isinstance(window_i_s, int):
mean_df_ser = pd.rolling_mean(data_df_ser, window=window_i_s, min_periods=min_periods_i, center=center_b)
elif isinstance(window_i_s, basestring):
idx_ser = pd.Series(data_df_ser.index.to_pydatetime(), index=data_df_ser.index)
mean_df_ser = idx_ser.apply(calculate_mean_at_ts)
return mean_df_ser