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python dataframe向下向上填充,fillna和ffill的方法

2018年12月04日  | 移动技术网IT编程  | 我要评论

e轮回,神兵天下,罗斯特的卵

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]: df
out[9]: 
   date house name
0 2010-01-01  1 a
1 2010-06-09  1 b
2 2011-12-03  2 c
3 2011-04-05  3 d
4 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_range
out[15]: 
empty dataframe
columns: []
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]: df
out[17]: 
   house    
name   a b c d
date       
2010-01-01 1.0 nan nan nan
2010-06-09 nan 1.0 nan nan
2011-04-05 nan nan nan 3.0
2011-12-03 nan nan 2.0 nan
2012-03-23 3.0 nan nan nan
in[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移动技术网。

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