首先新建一个dataframe：

In[8]: df = pd.DataFrame({'name':list('ABCDA'),'house':[1,1,2,3,3],'date':['2010-01-01','2010-06-09','2011-12-03','2011-04-05','2012-03-23']})In[9]: dfOut[9]: date house name0 2010-01-01 1 A1 2010-06-09 1 B2 2011-12-03 2 C3 2011-04-05 3 D4 2012-03-23 3 A

将date列改为时间类型：

In[12]: df.date = pd.to_datetime(df.date)

数据的含义是这样的，我们有ABCD四个人的数据，已知A在2010-01-01的时候，名下有1套房，B在2010-06-09的时候，名下有1套房，C在2011-12-03的时候，有2套房，D在2011-04-05的时候有3套房，A在2012-02-23的时候，数据更新了，有两套房。

要求在有姓名和时间的情况下，能给出其名下有几套房：

比如A在2010-01-01与2012-03-23期间任意一天，都应该是1套房，在2012-03-23之后，都是3套房。

我们使用pandas的fillna方法，选择ffill。

首先我们获得一个2010-01-01到2017-12-01的dataframe

In[14]: time_range = pd.DataFrame( pd.date_range('2010-01-01','2017-12-01',freq='D'), columns=['date']).set_index("date")In[15]: time_rangeOut[15]: Empty DataFrameColumns: []Index: [2010-01-01 00:00:00, 2010-01-02 00:00:00, 2010-01-03 00:00:00, 2010-01-04 00:00:00, 2010-01-05 00:00:00, 2010-01-06 00:00:00, 2010-01-07 00:00:00, 2010-01-08 00:00:00, 2010-01-09 00:00:00, 2010-01-10 00:00:00, 2010-01-11 00:00:00, 2010-01-12 00:00:00, 2010-01-13 00:00:00, 2010-01-14 00:00:00, 2010-01-15 00:00:00, 2010-01-16 00:00:00, 2010-01-17 00:00:00, 2010-01-18 00:00:00, 2010-01-19 00:00:00, 2010-01-20 00:00:00, 2010-01-21 00:00:00, 2010-01-22 00:00:00, 2010-01-23 00:00:00, 2010-01-24 00:00:00, 2010-01-25 00:00:00, 2010-01-26 00:00:00, 2010-01-27 00:00:00, 2010-01-28 00:00:00, 2010-01-29 00:00:00, 2010-01-30 00:00:00, 2010-01-31 00:00:00, 2010-02-01 00:00:00, 2010-02-02 00:00:00, 2010-02-03 00:00:00, 2010-02-04 00:00:00, 2010-02-05 00:00:00, 2010-02-06 00:00:00, 2010-02-07 00:00:00, 2010-02-08 00:00:00, 2010-02-09 00:00:00, 2010-02-10 00:00:00, 2010-02-11 00:00:00, 2010-02-12 00:00:00, 2010-02-13 00:00:00, 2010-02-14 00:00:00, 2010-02-15 00:00:00, 2010-02-16 00:00:00, 2010-02-17 00:00:00, 2010-02-18 00:00:00, 2010-02-19 00:00:00, 2010-02-20 00:00:00, 2010-02-21 00:00:00, 2010-02-22 00:00:00, 2010-02-23 00:00:00, 2010-02-24 00:00:00, 2010-02-25 00:00:00, 2010-02-26 00:00:00, 2010-02-27 00:00:00, 2010-02-28 00:00:00, 2010-03-01 00:00:00, 2010-03-02 00:00:00, 2010-03-03 00:00:00, 2010-03-04 00:00:00, 2010-03-05 00:00:00, 2010-03-06 00:00:00, 2010-03-07 00:00:00, 2010-03-08 00:00:00, 2010-03-09 00:00:00, 2010-03-10 00:00:00, 2010-03-11 00:00:00, 2010-03-12 00:00:00, 2010-03-13 00:00:00, 2010-03-14 00:00:00, 2010-03-15 00:00:00, 2010-03-16 00:00:00, 2010-03-17 00:00:00, 2010-03-18 00:00:00, 2010-03-19 00:00:00, 2010-03-20 00:00:00, 2010-03-21 00:00:00, 2010-03-22 00:00:00, 2010-03-23 00:00:00, 2010-03-24 00:00:00, 2010-03-25 00:00:00, 2010-03-26 00:00:00, 2010-03-27 00:00:00, 2010-03-28 00:00:00, 2010-03-29 00:00:00, 2010-03-30 00:00:00, 2010-03-31 00:00:00, 2010-04-01 00:00:00, 2010-04-02 00:00:00, 2010-04-03 00:00:00, 2010-04-04 00:00:00, 2010-04-05 00:00:00, 2010-04-06 00:00:00, 2010-04-07 00:00:00, 2010-04-08 00:00:00, 2010-04-09 00:00:00, 2010-04-10 00:00:00, ...] [2892 rows x 0 columns]

然后用上上篇博客中提到的pivot_table将原本的df转变之后，与time_range进行merger操作。

In[16]: df = pd.pivot_table(df, columns='name', index='date') In[17]: dfOut[17]: house name A B C Ddate 2010-01-01 1.0 NaN NaN NaN2010-06-09 NaN 1.0 NaN NaN2011-04-05 NaN NaN NaN 3.02011-12-03 NaN NaN 2.0 NaN2012-03-23 3.0 NaN NaN NaNIn[18]: df = df.merge(time_range,how="right", left_index=True, right_index=True)

然后再进行向下填充操作：

In[20]: df = df.fillna(method='ffill')

最后：

df = df.stack().reset_index()

结果太长，这里就不粘贴了。如果想向上填充，可选择method = 'bfill‘

以上这篇python dataframe向下向上填充,fillna和ffill的方法就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持。