本文介绍了Python Pandas DataFrame插值丢失的数据的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
我有一个类似以下的数据集.我们只有一个月的最后一天的数据，我想插值其余数据，这是正确的方法吗?
I have a data set like the following. We only have data for the last day of a month I am trying to interpolate rest of it, is it the right way of doing it?
Date Australia China
2011-01-01 NaN NaN
2011-01-02 NaN NaN
- - -
- - -
2011-01-31 4.75 5.81
2011-02-01 NaN NaN
2011-02-02 NaN NaN
- - -
- - -
2011-02-28 4.75 5.81
2011-03-01 NaN NaN
2011-03-02 NaN NaN
- - -
- - -
2011-03-31 4.75 6.06
2011-04-01 NaN NaN
2011-04-02 NaN NaN
- - -
- - -
2011-04-30 4.75 6.06
要对这个数据帧进行插值以查找缺失的NaN值，我正在使用以下代码
For interpolate this dataframe to find missing NaN values I am using the following code
import pandas as pd
df = pd.read_csv("data.csv", index_col="Date")
df.index = pd.DatetimeIndex(df.index)
df.interpolate(method='linear', axis=0).ffill().bfill()
但是我收到一个错误"TypeError:无法对所有NaN进行插值."
But I am getting an error "TypeError: Cannot interpolate with all NaNs."
这里可能出什么问题，我该如何解决?
What might be wrong here, how I can fix this?
谢谢.
推荐答案
您可以尝试通过 astype :
You can try convert dataframe to float by astype:
import pandas as pd
df = pd.read_csv("data.csv", index_col=['Date'], parse_dates=['Date'])
print df
Australia China
Date
2011-01-31 4.75 5.81
2011-02-28 4.75 5.81
2011-03-31 4.75 6.06
2011-04-30 4.75 6.06
df = df.reindex(pd.date_range("2011-01-01", "2011-10-31"), fill_value="NaN")
#convert to float
df = df.astype(float)
df = df.interpolate(method='linear', axis=0).ffill().bfill()
print df
Australia China
2011-01-01 4.75 5.81
2011-01-02 4.75 5.81
2011-01-03 4.75 5.81
2011-01-04 4.75 5.81
2011-01-05 4.75 5.81
2011-01-06 4.75 5.81
2011-01-07 4.75 5.81
2011-01-08 4.75 5.81
2011-01-09 4.75 5.81
2011-01-10 4.75 5.81
2011-01-11 4.75 5.81
2011-01-12 4.75 5.81
2011-01-13 4.75 5.81
2011-01-14 4.75 5.81
2011-01-15 4.75 5.81
2011-01-16 4.75 5.81
2011-01-17 4.75 5.81
2011-01-18 4.75 5.81
2011-01-19 4.75 5.81
2011-01-20 4.75 5.81
2011-01-21 4.75 5.81
2011-01-22 4.75 5.81
2011-01-23 4.75 5.81
2011-01-24 4.75 5.81
2011-01-25 4.75 5.81
2011-01-26 4.75 5.81
2011-01-27 4.75 5.81
2011-01-28 4.75 5.81
2011-01-29 4.75 5.81
2011-01-30 4.75 5.81
... ... ...
2011-10-02 4.75 6.06
2011-10-03 4.75 6.06
2011-10-04 4.75 6.06
2011-10-05 4.75 6.06
2011-10-06 4.75 6.06
2011-10-07 4.75 6.06
2011-10-08 4.75 6.06
2011-10-09 4.75 6.06
2011-10-10 4.75 6.06
2011-10-11 4.75 6.06
2011-10-12 4.75 6.06
2011-10-13 4.75 6.06
2011-10-14 4.75 6.06
2011-10-15 4.75 6.06
2011-10-16 4.75 6.06
2011-10-17 4.75 6.06
2011-10-18 4.75 6.06
2011-10-19 4.75 6.06
2011-10-20 4.75 6.06
2011-10-21 4.75 6.06
2011-10-22 4.75 6.06
2011-10-23 4.75 6.06
2011-10-24 4.75 6.06
2011-10-25 4.75 6.06
2011-10-26 4.75 6.06
2011-10-27 4.75 6.06
2011-10-28 4.75 6.06
2011-10-29 4.75 6.06
2011-10-30 4.75 6.06
2011-10-31 4.75 6.06
[304 rows x 2 columns]
您可以省略ffill()，因为NaN仅位于dataframe的第一行:
And you can omit ffill(), because NaN are only in first rows of dataframe:
df = df.interpolate(method='linear', axis=0).ffill().bfill()
收件人:
df = df.interpolate(method='linear', axis=0).bfill()
这篇关于Python Pandas DataFrame插值丢失的数据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！