快捷導(dǎo)航

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

更新時間：2018年11月28日 12:19:50 作者：chenKFKevin

今天小編就為大家分享一篇python dataframe向下向上填充,fillna和ffill的方法，具有很好的參考價值，希望對大家有所幫助。一起跟隨小編過來看看吧

首先新建一個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

將date列改為時間類型：

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

數(shù)據(jù)的含義是這樣的，我們有ABCD四個人的數(shù)據(jù)，已知A在2010-01-01的時候，名下有1套房，B在2010-06-09的時候，名下有1套房，C在2011-12-03的時候，有2套房，D在2011-04-05的時候有3套房，A在2012-02-23的時候，數(shù)據(jù)更新了，有兩套房。

要求在有姓名和時間的情況下，能給出其名下有幾套房：

比如A在2010-01-01與2012-03-23期間任意一天，都應(yīng)該是1套房，在2012-03-23之后，都是3套房。

我們使用pandas的fillna方法，選擇ffill。

首先我們獲得一個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將原本的df轉(zhuǎn)變之后，與time_range進行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)

然后再進行向下填充操作：