Python|熊猫 dataframe.rolling()
Python是一种用于进行数据分析的出色语言，主要是因为以数据为中心的Python包的奇妙生态系统。 Pandas是使导入和分析数据变得更加容易的软件包之一。
Pandas dataframe.rolling()函数提供了滚动窗口计算的功能。滚动窗口计算的概念主要用于信号处理和时间序列数据。用非常简单的话来说，我们一次取一个大小为 k 的窗口，并对其执行一些所需的数学运算。大小为 k 的窗口意味着一次有 k 个连续值。在一个非常简单的情况下，所有“k”值的权重相同。
Syntax : DataFrame.rolling(window, min_periods=None, freq=None, center=False, win_type=None, on=None, axis=0, closed=None)
Parameters :
window : Size of the moving window. This is the number of observations used for calculating the statistic. Each window will be a fixed size. If its an offset then this will be the time period of each window. Each window will be a variable sized based on the observations included in the time-period. This is only valid for datetimelike indexes.
min_periods : Minimum number of observations in window required to have a value (otherwise result is NA). For a window that is specified by an offset, this will default to 1.
freq : Frequency to conform the data to before computing the statistic. Specified as a frequency string or DateOffset object.
center : Set the labels at the center of the window.
win_type : Provide a window type. See the notes below.
on : For a DataFrame, column on which to calculate the rolling window, rather than the index
closed : Make the interval closed on the ‘right’, ‘left’, ‘both’ or ‘neither’ endpoints. For offset-based windows, it defaults to ‘right’. For fixed windows, defaults to ‘both’. Remaining cases not implemented for fixed windows.
axis : int or string, default 0
注意： freq 关键字用于通过重新采样数据使时间序列数据符合指定的频率。这是使用 resample() 的默认参数完成的（即使用平均值）。
如果 win_type=none，则窗口中的所有值均加权。还有各种其他类型的滚动窗口类型。要了解有关其他滚动窗口类型的更多信息，请参阅此 scipy 文档。
有关代码中使用的 CSV 文件的链接，请单击此处。这是苹果公司从 (13-11-17) 到 (13-11-18) 为期 1 年的股价数据
示例 #1：在股票收盘价列上使用大小为 3 的窗口的滚动总和
Python3
# importing pandas as pd
import pandas as pd
# By default the "date" column was in string format,
# we need to convert it into date-time format
# parse_dates =["date"], converts the "date" column to date-time format
# Resampling works with time-series data only
# so convert "date" column to index
# index_col ="date", makes "date" column
df = pd.read_csv("apple.csv", parse_dates =["date"], index_col ="date")
# Printing the first 10 rows of dataframe
df[:10]Python3
# 3 indicates the window size
# we have selected 'triang' type window
# which returns triangular type window
# sum() function find the sum over
# all the windows in our data frame
df.close.rolling(3, win_type ='triang').sum()Python3
# importing pandas as pd
import pandas as pd
df = pd.read_csv("apple.csv", parse_dates =["date"], index_col ="date")
# close is the column on which
# we are performing the operation
# mean() function finds the mean over each window
df.close.rolling(3).mean()_0.png)
Python3
# 3 indicates the window size
# we have selected 'triang' type window
# which returns triangular type window
# sum() function find the sum over
# all the windows in our data frame
df.close.rolling(3, win_type ='triang').sum()
输出 ：
_1.png)
示例#2：滚动窗口意味着窗口大小为 3。我们使用默认窗口类型，即无。所以所有的值都会被平均加权。
Python3
# importing pandas as pd
import pandas as pd
df = pd.read_csv("apple.csv", parse_dates =["date"], index_col ="date")
# close is the column on which
# we are performing the operation
# mean() function finds the mean over each window
df.close.rolling(3).mean()
输出 ：
_2.png)