超级好用的移动窗口函数
最近经常使用移动窗口函数,觉得很方便,功能强大,代码简单,故将pandas中的移动窗口函数都做介绍。它都是以rolling打头的函数,后接具体的函数,来显示该移动窗口函数的功能。
函数
pandas.rolling_count(arg, window, freq=None, center=False, how=None)
arg : DataFrame 或 numpy的ndarray 数组格式 window : 指移动窗口的大小,为整数 freq : center : 布尔型,默认为False, 指取中间的 how : 字符串,默认为“mean”,为down- 或re-sampling
import pandas as pd
import numpy as np
df = pd.DataFrame({'key1':['a','a','b','b','a'],
'key2':['one','two','one','two','one'],
'data1':np.nan,
'data2':np.random.randn(5)})
df
pd.rolling_count(df[['data1','data2']],window = 3)
pandas.rolling_sum(arg, window, min_periods=None, freq=None, center=False, how=None, **kwargs)
arg : 为Series或DataFrame window : 窗口的大小 min_periods : 最小的观察数值个数 freq : center : 布尔型,默认为False, 指取中间的 how : 取值的方式,默认为None
pd.rolling_sum(df,window = 2,min_periods = 1)
pandas.rolling_mean(arg, window, min_periods=None, freq=None, center=False, how=None, **kwargs)
pandas.rolling_median(arg, window, min_periods=None, freq=None, center=False, how='median', **kwargs)
pandas.rolling_var(arg, window, min_periods=None, freq=None, center=False, how=None, **kwargs)
pandas.rolling_std(arg, window, min_periods=None, freq=None, center=False, how=None, **kwargs)
pandas.rolling_min(arg, window, min_periods=None, freq=None, center=False, how='min', **kwargs)
pandas.rolling_min(arg, window, min_periods=None, freq=None, center=False, how='min', **kwargs)
pandas.rolling_corr(arg1, arg2=None, window=None, min_periods=None, freq=None, center=False, pairwise=None, how=None)
等价于: rolling_corr(…, pairwise=True)
pandas.rolling_corr_pairwise(df1, df2=None, window=None, min_periods=None, freq=None, center=False)
pandas.rolling_cov(arg1, arg2=None, window=None, min_periods=None, freq=None, center=False, pairwise=None, how=None, ddof=1)
pandas.rolling_skew(arg, window, min_periods=None, freq=None, center=False, how=None, **kwargs)
pandas.rolling_kurt(arg, window, min_periods=None, freq=None, center=False, how=None, **kwargs)
pandas.rolling_apply(arg, window, func, min_periods=None, freq=None, center=False, args=(), kwargs={})
pandas.rolling_quantile(arg, window, quantile, min_periods=None, freq=None, center=False)
pandas.rolling_window(arg, window=None, win_type=None, min_periods=None, freq=None, center=False, mean=True, axis=0, how=None, **kwargs)
ewma(arg[, com, span, halflife, ...])
ewmstd(arg[, com, span, halflife, ...])
ewmvar(arg[, com, span, halflife, ...])
ewmcorr(arg1[, arg2, com, span, halflife, ...])
ewmcov(arg1[, arg2, com, span, halflife, ...])