如何使用Python和/或R在数据帧之间插值

Question

我有一个如下所示的数据集:

我使用pandas.read_csv将"年份"和"国家/地区"列作为索引导入到pandas数据框中.我需要做的是将时间步长从每5年改为每年,并插入所述值,我真的不知道如何做到这一点.我正在学习R和python,所以对这两种语言的帮助都会受到高度赞赏.

Answer 1

• 如果您为DataFrame提供DatetimeIndex,那么您可以利用df.resample和df.interpolate('time')方法.

• 要制作df.indexDatetimeIndex,您可能会想要使用它set_index('Year').然而,Year由于每个都重复,因此它本身并不是唯一的Country.为了打电话,resample我们需要一个独特的索引.所以df.pivot改用:

  # convert integer years into `datetime64` values
  In [441]: df['Year'] = (df['Year'].astype('i8')-1970).view('datetime64[Y]')
  In [442]: df.pivot(index='Year', columns='Country')
  Out[442]: 
                  Avg1                      Avg2                
  Country    Australia Austria Belgium Australia Austria Belgium
  Year                                                          
  1950-01-01         0       0       0         0       0       0
  1955-01-01         1       1       1        10      10      10
  1960-01-01         2       2       2        20      20      20
  1965-01-01         3       3       3        30      30      30
  

• 然后,您可以使用df.resample('A').mean()到重采样数据与每年的频率.您可以将其resample('A')视为df切入1年间隔的组. resample返回一个DatetimeIndexResampler对象,该 mean方法的方法通过取均值来聚合每个组中的值.因此 mean(),每年返回一行DataFrame.由于您的原始版本 df每5年有一个数据,因此大多数1年组都是空的,因此这些年份的均值返回NaN.如果您的数据间隔为5年,那么.mean()您可以使用.first()或 .last()代替.他们都会返回相同的结果.

  In [438]: df.resample('A').mean()
  Out[438]: 
                  Avg1                      Avg2                
  Country    Australia Austria Belgium Australia Austria Belgium
  Year                                                          
  1950-12-31       0.0     0.0     0.0       0.0     0.0     0.0
  1951-12-31       NaN     NaN     NaN       NaN     NaN     NaN
  1952-12-31       NaN     NaN     NaN       NaN     NaN     NaN
  1953-12-31       NaN     NaN     NaN       NaN     NaN     NaN
  1954-12-31       NaN     NaN     NaN       NaN     NaN     NaN
  1955-12-31       1.0     1.0     1.0      10.0    10.0    10.0
  1956-12-31       NaN     NaN     NaN       NaN     NaN     NaN
  1957-12-31       NaN     NaN     NaN       NaN     NaN     NaN
  1958-12-31       NaN     NaN     NaN       NaN     NaN     NaN
  1959-12-31       NaN     NaN     NaN       NaN     NaN     NaN
  1960-12-31       2.0     2.0     2.0      20.0    20.0    20.0
  1961-12-31       NaN     NaN     NaN       NaN     NaN     NaN
  1962-12-31       NaN     NaN     NaN       NaN     NaN     NaN
  1963-12-31       NaN     NaN     NaN       NaN     NaN     NaN
  1964-12-31       NaN     NaN     NaN       NaN     NaN     NaN
  1965-12-31       3.0     3.0     3.0      30.0    30.0    30.0
  

• 然后df.interpolate(method='time')将根据最近的非NaN值及其相关的日期时间索引值线性插入缺失的NaN值.

import numpy as np
import pandas as pd

countries = 'Australia Austria Belgium'.split()
year = np.arange(1950, 1970, 5)
df = pd.DataFrame(
    {'Country': np.repeat(countries, len(year)),
     'Year': np.tile(year, len(countries)),
     'Avg1': np.tile(np.arange(len(year)), len(countries)),
     'Avg2': 10*np.tile(np.arange(len(year)), len(countries))})
df['Year'] = (df['Year'].astype('i8')-1970).view('datetime64[Y]')
df = df.pivot(index='Year', columns='Country')

df = df.resample('A').mean()
df = df.interpolate(method='time')

df = df.stack('Country')
df = df.reset_index()
df = df.sort_values(by=['Country', 'Year'])
print(df)

产量

         Year    Country      Avg1       Avg2
0  1950-12-31  Australia  0.000000   0.000000
3  1951-12-31  Australia  0.199890   1.998905
6  1952-12-31  Australia  0.400329   4.003286
9  1953-12-31  Australia  0.600219   6.002191
12 1954-12-31  Australia  0.800110   8.001095
15 1955-12-31  Australia  1.000000  10.000000
18 1956-12-31  Australia  1.200328  12.003284
21 1957-12-31  Australia  1.400109  14.001095
...