python-3.x - Python - 计算数据框列的标准偏差(行级)

Question

我创建了一个 Pandas 数据框，并且能够确定该数据框的一列或多列(列级别)的标准偏差。我需要确定特定列的所有行的标准偏差。以下是我迄今为止尝试过的命令

# Will determine the standard deviation of all the numerical columns by default.
inp_df.std()

salary         8.194421e-01
num_months     3.690081e+05
no_of_hours    2.518869e+02

# Same as above command. Performs the standard deviation at the column level.
inp_df.std(axis = 0)

# Determines the standard deviation over only the salary column of the dataframe.
inp_df[['salary']].std()

salary         8.194421e-01

# Determines Standard Deviation for every row present in the dataframe. But it
# does this for the entire row and it will output values in a single column.
# One std value for each row.
inp_df.std(axis=1)

0       4.374107e+12
1       4.377543e+12
2       4.374026e+12
3       4.374046e+12
4       4.374112e+12
5       4.373926e+12

当我执行以下命令时，所有记录都得到“NaN”。有没有办法解决这个问题？

# Trying to determine standard deviation only for the "salary" column at the
# row level.
inp_df[['salary']].std(axis = 1)

0      NaN
1      NaN
2      NaN
3      NaN
4      NaN

Answer 1

这是预期的，因为如果检查 DataFrame.std :

Normalized by N-1 by default. This can be changed using the ddof argument

如果您有一个元素，您就是在除以 0。因此，如果您有一个列并且想要跨列的样本标准差，请获取所有缺失值。
sample :

inp_df = pd.DataFrame({'salary':[10,20,30],
                       'num_months':[1,2,3],
                       'no_of_hours':[2,5,6]})
print (inp_df)
   salary  num_months  no_of_hours
0      10           1            2
1      20           2            5
2      30           3            6

一栏一选 []为 Series :

print (inp_df['salary'])
0    10
1    20
2    30
Name: salary, dtype: int64

获取 std的 Series - 得到一个标量:

print (inp_df['salary'].std())
10.0

选择一列双 []为 one column DataFrame :

print (inp_df[['salary']])
   salary
0      10
1      20
2      30

获取 std的 DataFrame每个索引(默认值) - 获取一个元素 Series :

print (inp_df[['salary']].std())
#same like
#print (inp_df[['salary']].std(axis=0))
salary    10.0
dtype: float64

获取 std的 DataFrame每列(轴 = 1) - 获取所有 NaN:

print (inp_df[['salary']].std(axis = 1))
0   NaN
1   NaN
2   NaN
dtype: float64

如果更改默认 ddof=1至 ddof=0 :

print (inp_df[['salary']].std(axis = 1, ddof=0))
0    0.0
1    0.0
2    0.0
dtype: float64

如果你想要 std按两列或更多列:

#select 2 columns
print (inp_df[['salary', 'num_months']])
   salary  num_months
0      10           1
1      20           2
2      30           3

#std by index
print (inp_df[['salary','num_months']].std())
salary        10.0
num_months     1.0
dtype: float64

#std by columns
print (inp_df[['salary','no_of_hours']].std(axis = 1))
0     5.656854
1    10.606602
2    16.970563
dtype: float64