Question

当我尝试进行包括两个或多个列的算术运算时，它们面临着空值问题。

我想在这里提及的另一件事是，我不想填写缺失/空值。

实际上，我想要类似1 + np.nan = 1的值，但它给出的是np.nan。我试图用np.nansum解决它，但是没有用。

Number_of_Elements = int(input("Enter number of intergers to be stored in the list: "))
print("Input", Number_of_Elements, "elements in the list: ")

Elements_List = []
for i in range(Number_of_Elements):
    data = int(input("Element -" + str(i) + " : "))
    Elements_List.append(data)
all_freq = {} 

for i in Elements_List: 
    if i in all_freq: 
        all_freq[i] += 1
    else: 
        all_freq[i] = 1

for key in all_freq:
  print(str(key) + " occurs " + str(all_freq[key]) + " times")

然后

df = pd.DataFrame({"a":[1,2,3,4],"b":[1,2,np.nan,np.nan]})
df
Out[6]: 
   a    b    c
0  1  1.0  2.0
1  2  2.0  4.0
2  3  NaN  NaN
3  4  NaN  NaN

但我实际上想要，

df["d"] = np.nansum([df.a + df.b])
df
Out[13]: 
   a    b    d
0  1  1.0  6.0
1  2  2.0  6.0
2  3  NaN  6.0
3  4  NaN  6.0

Answer 1

此处的np.nansum计算了整个列的总和。您不希望那样，您可能想在两列中调用np.nansum，例如：

df['d'] = np.nansum((df.a, df.b), axis=0)

然后产生预期的结果：

>>> df
   a    b    d
0  1  1.0  2.0
1  2  2.0  4.0
2  3  NaN  3.0
3  4  NaN  4.0

Answer 2

只需在DataFrame.sum上使用axis=1：

df['c'] = df.sum(axis=1)

输出

   a    b    c
0  1  1.0  2.0
1  2  2.0  4.0
2  3  NaN  3.0
3  4  NaN  4.0

对空值执行算术运算

2 个答案: