使用pandas查找特定行的最大值

Question

我有一个看起来像这样的csv（还有更多年）：

year,title_field,value
2009,Total Housing Units,39499
2009,Vacant Housing Units,3583
2009,Occupied Housing Units,35916
2008,Total Housing Units,41194
2008,Vacant Housing Units,4483
2008,Occupied Housing Units,36711
2009,Owner Occupied,18057
2009,Renter Occupied,17859
2008,Owner Occupied,17340
2008,Renter Occupied,19371
2009,Median Gross Rent,769
2008,Median Gross Rent,768

我需要找到所有空置住房单元的最大值。

到目前为止，我已经得到了这个：

将pandas导入为pd

df = pd.read_csv("denton_housing.csv", names=("year", "title_field", "value"))

inds = df.groupby(['title_field'])['value'].transform(max) == df['value']
df = df[inds]
df.reset_index(drop=True, inplace=True)
print(df)

那段代码给了我这个：

   year             title_field  value
0  year             title_field  value
1  2014     Total Housing Units  49109
2  2014  Occupied Housing Units  46295
3  2008    Vacant Housing Units   4483
4  2014          Owner Occupied  21427
5  2014         Renter Occupied  24868
6  2014       Median Gross Rent    905

我只需要它输出：

2008    Vacant Housing Units    4483

Answer 1

我认为你需要idxmax

df.loc[[df.groupby(['title_field'])['value'].idxmax().loc['Vacant Housing Units']]]
Out[92]: 
   year           title_field  value
4  2008  Vacant Housing Units   4483

Answer 2

您可以先过滤空置住房单位记录，对其进行排序并采取最大值

df.loc[df.title_field.eq('Vacant Housing Units')].sort_values(by='value').tail(1)
Out[96]: 
   year           title_field  value
4  2008  Vacant Housing Units   4483