如何使用python 3.7（Spyder）计算数据读数的移动平均值？

Question

我有每个月的销售数据3年。我必须进行回归分析，需要计算移动平均线和居中移动平均线。我已经能够绘制销售价值了。现在我需要绘制移动平均线和居中移动平均线并存储这些值以供进一步分析。以下是我能够做到的。

我试过取平均值但只能计算MA和CMA并存储它。

import matplotlib.pyplot as plt
from collections import Counter

def make_chart_simple_line_chart(plt):
    period = ['201601', '201602', 201603, 201604, 201605, 201606, 201607, 201608, 201609, 201610, 201611, 201612, 201701, 201702, 201703, 201704, 201705, 201706, 201707, 201708, 201709, 201710, 201711, 201712, 201801, 201802, 201803, 201804, 201805, 201806, 201807, 201808, 201809, 201810, 201811, 201812]
    sale = [9478, 9594, 14068, 9692, 9718, 14144, 9294, 10072, 14254, 10508, 11224, 17640, 11300, 11656, 17360, 11342, 12300, 17334, 11296, 12452, 16886, 11878, 13482, 19260, 13932, 13600, 20122, 13134, 14564, 19354, 13104, 13562, 17350, 12486, 12570, 17716]

    # create a line chart, period on x-axis, sale on y-axis
    plt.plot(period, sale, color='green', marker='o', linestyle='solid')

    # add a title
    plt.title("Sales Chart")

    # add a label to the y-axis
    plt.ylabel("number of contracts sold")
    plt.show()

if __name__ == "__main__":

    make_chart_simple_line_chart(plt)

我想使用可用数据预测2019年的销售额。

Answer 1

移动平均：

pd.rolling_mean(df['column'], n)

母亲：

pd.ewma(df['column'], span = n, min_periods = n - 1)

Answer 2

您的数据似乎有两个单独的销售趋势线。以下是我用来将日期格式转换为月份的代码，销售量为数千，易读性：

import matplotlib
import matplotlib.pyplot as plt

period = [201601.0, 201602.0, 201603.0, 201604.0, 201605.0, 201606.0, 201607.0, 201608.0, 201609.0, 201610.0, 201611.0, 201612.0, 201701.0, 201702.0, 201703.0, 201704.0, 201705.0, 201706.0, 201707.0, 201708.0, 201709.0, 201710.0, 201711.0, 201712.0, 201801.0, 201802.0, 201803.0, 201804.0, 201805.0, 201806.0, 201807.0, 201808.0, 201809.0, 201810.0, 201811.0, 201812.0]
sale = [9478.0, 9594.0, 14068.0, 9692.0, 9718.0, 14144.0, 9294.0, 10072.0, 14254.0, 10508.0, 11224.0, 17640.0, 11300.0, 11656.0, 17360.0, 11342.0, 12300.0, 17334.0, 11296.0, 12452.0, 16886.0, 11878.0, 13482.0, 19260.0, 13932.0, 13600.0, 20122.0, 13134.0, 14564.0, 19354.0, 13104.0, 13562.0, 17350.0, 12486.0, 12570.0, 17716.0]

months = []
sales = []

for i in range(len(period)):
    if period[i] < 201700.0:
        month = period[i] - 201600.0
    elif period[i] < 201800.0:
        month = period[i] - 201700.0 + 12.0
    elif period[i] < 201900.0:
        month = period[i] - 201800.0 + 24.0
    months.append(month)
    sales.append(sale[i] / 1000.0)

plt.plot(months, sale,  'D')
plt.xlabel('Month')
plt.ylabel('Sales (thousands)')
plt.show()