Question

对于以下数据框，我想在city和district的每组中填写缺失的年份（从2015年到2017年）；然后通过按以下列分组来计算pct：city，district和year，最后一步，然后水平显示value和pct列吗？

  city district  value  year
0   sh        a      2  2015
1   sh        a      3  2016
2   sh        b      5  2015
3   sh        b      3  2016
4   bj        c      4  2015
5   bj        c      3  2017

到目前为止我所做的：

1。填写缺少的年份，但仍无法正常工作：

rng = pd.date_range('2015', '2017', freq='YS').dt.year
df = df.apply(lambda x: x.reindex(rng, fill_value = 0))

2。通过按pct和city分组来计算district：

df['pct'] = df.sort_values('year').groupby(['city', 'district']).value.pct_change()

3。水平显示value和pct列，但不希望显示顺序：

df.pivot_table(columns='year', index=['city','district'], values=['value', 'pct'], fill_value='NaN').reset_index()

到目前为止我得到的输出：

      city   district       pct            value          
year                  2015 2016  2017  2015 2016 2017
0      bj        c     NaN  NaN -0.25   4.0  NaN    3
1      sh        a     NaN  0.5   NaN   2.0    3  NaN
2      sh        b     NaN -0.4   NaN   5.0    3  NaN

我怎么能得到像这样的预期结果？

city  district      2015         2016         2017
                value  pct    value  pct  value   pct
bj     c          4                         3        
sh     a          2             3    0.5   
sh     b          5             3   -0.4

谢谢。

Answer 1

将DataFrame.swaplevel与DataFrame.sort_index一起使用，还为reindex添加了另一种解决方案：

rng = pd.date_range('2015', '2017', freq='YS').year
c = df['city'].unique()
d = df['district'].unique()
mux = pd.MultiIndex.from_product([c, d, rng], names=['city','district','year'])

df = df.set_index(['city','district','year']).reindex(mux)

df['pct'] = df.sort_values('year').groupby(['city', 'district']).value.pct_change()

df = df.pivot_table(columns='year', 
                    index=['city','district'],
                    values=['value', 'pct'],
                    fill_value='NaN')

df = df.swaplevel(0,1, axis=1).sort_index(axis=1, level=0)
print (df)
year          2015       2016        2017      
               pct value  pct value   pct value
city district                                  
bj   c         NaN   4.0  0.0   NaN -0.25     3
sh   a         NaN   2.0  0.5     3  0.00   NaN
     b         NaN   5.0 -0.4     3  0.00   NaN

编辑：错误：

ValueError：无法处理非唯一的多索引！

表示传递给groupby的每个列都有重复项，因此这里是['city','district','year']。解决方案是创建唯一值-例如总体而言：

print (df)
#  city district  value  year
#0   sh        a      2  2015
#0   sh        a     20  2015
#1   sh        a      3  2016
#2   sh        b      5  2015
#3   sh        b      3  2016
#4   bj        c      4  2015
#5   bj        c      3  2017

rng = pd.date_range('2015', '2017', freq='YS').year
c = df['city'].unique()
d = df['district'].unique()
mux = pd.MultiIndex.from_product([c, d, rng], names=['city','district','year'])

print (df.groupby(['city','district','year'])['value'].mean())
city  district  year
bj    c         2015     4
                2017     3
sh    a         2015    11
                2016     3
      b         2015     5
                2016     3
Name: value, dtype: int64

df = df.groupby(['city','district','year'])['value'].mean().reindex(mux)

print (df)
#city  district  year
#sh    a         2015    11.0
#                2016     3.0
#                2017     NaN
#      b         2015     5.0
#                2016     3.0
#                2017     NaN
#      c         2015     NaN
#                2016     NaN
#                2017     NaN
#bj    a         2015     NaN
#                2016     NaN
#                2017     NaN
#      b         2015     NaN
#                2016     NaN
#                2017     NaN
#      c         2015     4.0
#                2016     NaN
#                2017     3.0
#Name: value, dtype: float64

按多列填充缺少的年份分组并按顺序在熊猫中水平显示多列

1 个答案: