[英]How to complete time series data with some missing dates with pandas
I have dataset with missing dates like this. 我有像这样的缺少日期的数据集。
date,value
2015-01-01,7392
2015-01-03,4928
2015-01-06,8672
This is what I expect to achieve. 这是我期望实现的目标。
date,value
2015-01-01,7392
2015-01-02,7392 # ffill 1st
2015-01-03,4928
2015-01-04,4928 # ffill 3rd
2015-01-05,4928 # ffill 3rd
2015-01-06,8672
I tried a lot, I read the documentation, but I could not find a solutioni. 我尝试了很多,我阅读了文档,但我找不到解决方案。 I guessed using df.resample('d',fill_method='ffill'), but I am not still reaching here.
我猜测使用df.resample('d',fill_method ='ffill'),但我还没到达这里。 Could anyone help me to solve the problem?
谁能帮我解决问题?
This is what I did. 这就是我做的。
>>> import pandas as pd
>>> df = pd.read_csv(text,sep="\t",index_col='date')
>>> df.index = df.index.to_datetime()
>>> index = pd.date_range(df.index[1],df.index.max())
Here I get the DatetimeIndex from 2015-01-01 to 2015-01-06. 这里我从2015-01-01到2015-01-06获得DatetimeIndex。
>>> values = [ x for x in range(len(index)) ]
>>> df2 = pd.DataFrame(values,index=index)
Next I am going to merge the original data and DatetimeIndex. 接下来,我将合并原始数据和DatetimeIndex。
>>> df + df2
0 value
2015-01-01 NaN NaN
2015-01-02 NaN NaN
2015-01-03 NaN NaN
2015-01-04 NaN NaN
2015-01-05 NaN NaN
2015-01-06 NaN NaN
NaN? 喃? I am puzzled.
我很困惑。
>>> df3 = df + df2
>>> df3.info()
DatetimeIndex: 10 entries, 2015-01-01 to 2015-01-10
Data columns (total 2 columns):
value 0 non-null float64
dtypes: float64(1)
The original value was int, but it converted into float. 原始值为int,但它转换为float。
What is my mistake? 我的错是什么?
Try this: 尝试这个:
import numpy as np
df2 = pd.DataFrame(np.nan, index=index)
df.combine_first(df2).fillna(method='ffill')
combine_first
will replace nan
values in df2
with values from the original df
when they exist. combine_first
将df2
nan
值替换为原始df
存在的值。 You can then fill the remaining nan
values with fillna
. 然后,您可以使用
fillna
填充剩余的nan
值。
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