python - 为什么重新索引后会显示 nan 值？

Question

我正在尝试重新索引列，但它显示的是 nan 值。我不明白为什么？

data = {
    "age": [50, 40, 30, 40],
    "qualified": [True, False, False, False]
    }
index = ["P", "Q", "R", "S"]
df = pd.DataFrame(data, index=index)

new = ["A", "B", "C", "D"]
newdf = df.reindex(new)
print(newdf)

输出:

   age qualified
A  NaN       NaN
B  NaN       NaN
C  NaN       NaN
D  NaN       NaN

Answer 1

我认为你需要DataFrame.set_index ，如果需要用新值替换索引值，则使用嵌套列表:

new = ["A", "B", "C", "D"]
newdf = df.set_index([new])
#alternative
#newdf.index = new
print(newdf)
   age  qualified
A   50       True
B   40      False
C   30      False
D   40      False

---

方法DataFrame.reindex工作方式不同 - 它通过带有对齐数据的列表创建新索引 - 这意味着首先通过新列表 new 的值匹配索引的现有值，对于不匹配的值创建 NaN:

data = {
    "age": [50, 40, 30, 40],
    "qualified": [True, False, False, False]
    }
index = ["A", "Q", "D", "C"]
df = pd.DataFrame(data, index=index)

new = ["A", "B", "C"]
newdf = df.reindex(new)
print(newdf)

    age qualified
A  50.0      True
B   NaN       NaN
C  40.0     False