python - Pandas数据框中的按行操作

我有一个具有这种格式的世界指标数据集

country     year    indicatorName       value
USA         1970    Agricultural Land   ...
USA         1970    Crop production     ...
...
USA         2000    Agricultural Land   ...
USA         2000    Crop production     ...
...
Mexico      1970    Agricultural Land   ...
Mexico      1970    Crop production     ...
...
Mexico      2000    Agricultural Land   ...
Mexico      2000    Crop production     ...

这里有一些指标我没有包括，但是这两个是我感兴趣的指标。我想将value的相应Crop production划分为每个Agricultural Land每个country的year。让我们将结果命名为crop_prod_density。

我不知道该怎么做

df.groupby(['country', 'year'])

如何从此处执行操作以产生以下输出：


添加新的行指示器


country year indicatorName value USA 1970 Agricultural Land ... USA 1970 Crop production ... USA 1970 crop_prod_density ...


为分组（国家/地区，年份）的所有行添加具有相同值的新列


country year indicatorName value crop_prod_density USA 1970 Agricultural Land ... us_value_1970 USA 1970 Crop production ... us_value_1970 ... Mexico 2000 Agricultural Land ... mx_value_2000 Mexico 2000 Crop production ... mx_value_2000


仅具有此列的新数据框


country year crop_prod_density USA 1970 us_value_1970 ... USA 2000 us_value_2000 ... Mexico 1970 mx_value_1970 ... Mexico 2000 mx_value_2000

最佳答案

您可以先用set_index用unstack重塑形状，然后再用div除以：

print (df)
  country  year      indicatorName  value
0     USA  1970  Agricultural Land     10
1     USA  1970    Crop production      2
2     USA  2000  Agricultural Land     10
3     USA  2000    Crop production      3
4  Mexico  1970  Agricultural Land     10
5  Mexico  1970    Crop production      5
6  Mexico  2000  Agricultural Land     10
7  Mexico  2000    Crop production      4

df = (df.set_index(['country','year','indicatorName'])['value']
       .unstack()
       .assign(crop_prod_density=lambda x: x['Crop production'].div(x['Agricultural Land'])))
print (df)
indicatorName  Agricultural Land  Crop production  crop_prod_density
country year
Mexico  1970                  10                5                0.5
        2000                  10                4                0.4
USA     1970                  10                2                0.2
        2000                  10                3                0.3

然后通过stack重新调整形状：

df1 = df.stack().reset_index(name='value')
print (df1)
   country  year      indicatorName  value
0   Mexico  1970  Agricultural Land   10.0
1   Mexico  1970    Crop production    5.0
2   Mexico  1970  crop_prod_density    0.5
3   Mexico  2000  Agricultural Land   10.0
4   Mexico  2000    Crop production    4.0
5   Mexico  2000  crop_prod_density    0.4
6      USA  1970  Agricultural Land   10.0
7      USA  1970    Crop production    2.0
8      USA  1970  crop_prod_density    0.2
9      USA  2000  Agricultural Land   10.0
10     USA  2000    Crop production    3.0
11     USA  2000  crop_prod_density    0.3

对于将原始新列追加到索引新列的索引，但是最后必须通过reindex更改列的顺序：

df2 =(df.set_index(['crop_prod_density'], append=True)
        .stack()
        .reset_index(name='value')
        .reindex(columns=['country','year','indicatorName','value','crop_prod_density']))
print (df2)
  country  year      indicatorName  value  crop_prod_density
0  Mexico  1970  Agricultural Land     10                0.5
1  Mexico  1970    Crop production      5                0.5
2  Mexico  2000  Agricultural Land     10                0.4
3  Mexico  2000    Crop production      4                0.4
4     USA  1970  Agricultural Land     10                0.2
5     USA  1970    Crop production      2                0.2
6     USA  2000  Agricultural Land     10                0.3
7     USA  2000    Crop production      3                0.3

最后删除不必要的列并从MultiIndex创建列：

df3 = (df.drop(['Crop production','Agricultural Land'], axis=1)
        .reset_index()
        .rename_axis(None, 1))
print (df3)
  country  year  crop_prod_density
0  Mexico  1970                0.5
1  Mexico  2000                0.4
2     USA  1970                0.2
3     USA  2000                0.3

关于python - Pandas数据框中的按行操作，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/47546355/