pandas 按时间序列填充缺失的日期 [英] pandas fill missing dates in time series
问题描述
我有一个数据框架，该数据框架已经汇总了几天的数据.我想补充缺少的日子
I have a dataframe which has aggregated data for some days. I want to add in the missing days
我正在关注另一篇帖子，向熊猫数据框添加缺少的日期，不幸的是，它覆盖了我的结果(也许功能有所改变?)...代码如下
I was following another post, Add missing dates to pandas dataframe, unfortunately, it overwrote my results (maybe functionality was changed slightly?)... the code is below
import random
import datetime as dt
import numpy as np
import pandas as pd
def generate_row(year, month, day):
while True:
date = dt.datetime(year=year, month=month, day=day)
data = np.random.random(size=4)
yield [date] + list(data)
# days I have data for
dates = [(2000, 1, 1), (2000, 1, 2), (2000, 2, 4)]
generators = [generate_row(*date) for date in dates]
# get 5 data points for each
data = [next(generator) for generator in generators for _ in range(5)]
df = pd.DataFrame(data, columns=['date'] + ['f'+str(i) for i in range(1,5)])
# df
groupby_day = df.groupby(pd.PeriodIndex(data=df.date, freq='D'))
results = groupby_day.sum()
idx = pd.date_range(min(df.date), max(df.date))
results.reindex(idx, fill_value=0)
填写缺失的日期索引之前的结果
Results before filling in missing date indices
之后的结果
Results after
推荐答案
您需要使用 period_range 而不是 date_range :
You need to use period_range rather than date_range:
In [11]: idx = pd.period_range(min(df.date), max(df.date))
...: results.reindex(idx, fill_value=0)
...:
Out[11]:
f1 f2 f3 f4
2000-01-01 2.049157 1.962635 2.756154 2.224751
2000-01-02 2.675899 2.587217 1.540823 1.606150
2000-01-03 0.000000 0.000000 0.000000 0.000000
2000-01-04 0.000000 0.000000 0.000000 0.000000
2000-01-05 0.000000 0.000000 0.000000 0.000000
2000-01-06 0.000000 0.000000 0.000000 0.000000
2000-01-07 0.000000 0.000000 0.000000 0.000000
2000-01-08 0.000000 0.000000 0.000000 0.000000
2000-01-09 0.000000 0.000000 0.000000 0.000000
2000-01-10 0.000000 0.000000 0.000000 0.000000
2000-01-11 0.000000 0.000000 0.000000 0.000000
2000-01-12 0.000000 0.000000 0.000000 0.000000
2000-01-13 0.000000 0.000000 0.000000 0.000000
2000-01-14 0.000000 0.000000 0.000000 0.000000
2000-01-15 0.000000 0.000000 0.000000 0.000000
2000-01-16 0.000000 0.000000 0.000000 0.000000
2000-01-17 0.000000 0.000000 0.000000 0.000000
2000-01-18 0.000000 0.000000 0.000000 0.000000
2000-01-19 0.000000 0.000000 0.000000 0.000000
2000-01-20 0.000000 0.000000 0.000000 0.000000
2000-01-21 0.000000 0.000000 0.000000 0.000000
2000-01-22 0.000000 0.000000 0.000000 0.000000
2000-01-23 0.000000 0.000000 0.000000 0.000000
2000-01-24 0.000000 0.000000 0.000000 0.000000
2000-01-25 0.000000 0.000000 0.000000 0.000000
2000-01-26 0.000000 0.000000 0.000000 0.000000
2000-01-27 0.000000 0.000000 0.000000 0.000000
2000-01-28 0.000000 0.000000 0.000000 0.000000
2000-01-29 0.000000 0.000000 0.000000 0.000000
2000-01-30 0.000000 0.000000 0.000000 0.000000
2000-01-31 0.000000 0.000000 0.000000 0.000000
2000-02-01 0.000000 0.000000 0.000000 0.000000
2000-02-02 0.000000 0.000000 0.000000 0.000000
2000-02-03 0.000000 0.000000 0.000000 0.000000
2000-02-04 1.856158 2.892620 2.986166 2.793448
这是因为您的groupby使用PeriodIndex而不是datetime:
This is because your groupby uses PeriodIndex, rather than datetime:
df.groupby(pd.PeriodIndex(data=df.date, freq='D'))
您可以改用pd.Grouper:
df.groupby(pd.Grouper(key="date", freq='D'))
将会给出日期时间索引.
which would have give a datetime index.
这篇关于 pandas 按时间序列填充缺失的日期的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
