Question

我有以下numpy数组：

arr= [[  0.          0.1046225518   0.          0.8953774482   0.        ]]

目前我有

values= str(np.around([arr*100],decimals=2))

返回：

 [[  0.          10.46   0.          89.53  0.        ]]

如果我+ %为值，则返回

 [[  0.          10.46   0.          89.53  0.        ]]%

所需的输出是：

[[  0.          10.46%   0.          89.53%  0.        ]]

Answer 1

由于您在评论中提到过您想将其转换为数据框（我假设您的意思是Pandas数据框）...

import numpy as np
import pandas as pd

# Reproduce your numpy array
arr= np.array([[  0.0, 0.1046225518, 0.0, 0.8953774482, 0.0]])

# Convert to 1-Column DataFrame of % Strings 
# (use pd.Series() instead if you'd prefer this as a Pandas Series)
as_strings = pd.DataFrame(["{0:.2f}%".format(val * 100) for val in arr[0]])

# Assign column name
as_strings.columns = ['Numbers as Strings'] 

print(as_strings)

  Numbers as Strings
0              0.00%
1             10.46%
2              0.00%
3             89.54%
4              0.00%

感谢大多数代码行的this SO answer。

Answer 2

如果你正在使用熊猫：

(pd.Series([  0.0, 0.1046225518, 0.0, 0.8953774482, 0.0]) * 10).round(2).astype(str) + " %"

导致

0     0.0 %
1    1.05 %
2     0.0 %
3    8.95 %
4     0.0 %
dtype: object

Answer 3

如果仅需要0，还可以解决方法：

where + mul + round + astype

arr = np.array([[0.,0.1046225518,0., 0.8953774482, 0.]])

#DataFrame by constructor
df = pd.DataFrame(arr.reshape(-1, len(arr)), columns=['A'])

#convert 0 to string also for avoid mixed types - floats and strings 
df['B'] = df['A'].astype(str).where(df['A'] == 0, 
                                    df['A'].mul(100).round(2).astype(str).add('%'))
print (df)
          A       B
0  0.000000     0.0
1  0.104623  10.46%
2  0.000000     0.0
3  0.895377  89.54%
4  0.000000     0.0

我怎么能将％添加到numpy数组中的每个值？

3 个答案: