pandas 缺失值处理，插值

import pandas as pd
d = pd.DataFrame()

d['date'] = ['2019-01-01', '2019-01-02', '2019-01-04', '2019-01-07', '2019-01-09', '2019-01-11']
d['val'] = [10, 20, 30, 40, 50, 30]
d['date'] = pd.to_datetime(d['date'])

helper = pd.DataFrame({'date': pd.date_range(d['date'].min(), d['date'].max())})

d = pd.merge(d, helper, on='date', how='outer').sort_values('date')

d['val'] = d['val'].interpolate(method='linear')



    插值选择方法不止有线性（linear），还可以是

    nearest：最邻近插值法

    zero：阶梯插值

    slinear、linear：线性插值

    quadratic、cubic：2、3阶B样条曲线插值（详情请参考官方文档）

Python Pandas

https://www.cnblogs.com/zhenyauntg/p/13188221.html