01.Numpy和原生Python用于数组计算的性能对比

### Numpy和原生Python用于数组计算的性能对比
需求：
* 实现两个数组的加法;
* 数组A是1--N数字的平方;
* 数组B是1--N数字的立方

### 引入Numpy的包


```python
import numpy as np
```


```python
np.__version__
```




    '1.18.1'



### 使用Python原生语法实现对比


```python
def python_sum(n):
    '''
    Python实现数组的加法
    n:数组的长度
    '''
    a = [i**2 for i in range(n)]
    b = [i**3 for i in range(n)]
    c = []
    for i in range(n):
        c.append(a[i] + b[i])
    return c
```


```python
#测试一下
python_sum(10)
```




    [0, 2, 12, 36, 80, 150, 252, 392, 576, 810]



### 使用Numpy实现


```python
def numpy_sum(n):
    '''
    numpy实现数组的加法
    n:数组的长度
    '''
    a = np.arange(n) ** 2
    b = np.arange(n) ** 3
    return a+b#对应元素相加
```


```python
#测试一下
numpy_sum(10)
```




    array([  0,   2,  12,  36,  80, 150, 252, 392, 576, 810], dtype=int32)



### 性能对比：执行1000次


```python
%timeit python_sum(1000)
```

    1.63 ms ± 492 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)



```python
%timeit numpy_sum(1000)
```

    11.7 µs ± 705 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)


### 性能对比：执行10W次


```python
%timeit python_sum(100000)
```

    141 ms ± 22.6 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)



```python
%timeit numpy_sum(100000)
```

    524 µs ± 22 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)


### 性能对比:执行1000W次


```python
%timeit python_sum(1000 * 10000)
```

    14.5 s ± 903 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)



```python
%timeit numpy_sum(1000 * 10000)
```

    184 ms ± 1.94 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)


### 绘制性能对比图


```python
pytimes = [1.06*1000, 104*1000, 10.4*1000*1000]
nptimes = [9.16, 424, 114*1000]
```


```python
import pandas as pd
```


```python
df = pd.DataFrame({
    "pytimes":pytimes,
    "nptimes":nptimes,
})
```


```python
df
```




<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
    
    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>pytimes</th>
      <th>nptimes</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>1060.0</td>
      <td>9.16</td>
    </tr>
    <tr>
      <th>1</th>
      <td>104000.0</td>
      <td>424.00</td>
    </tr>
    <tr>
      <th>2</th>
      <td>10400000.0</td>
      <td>114000.00</td>
    </tr>
  </tbody>
</table>
</div>




```python
%matplotlib inline
df.plot.bar()
```




    <matplotlib.axes._subplots.AxesSubplot at 0x1a6821dc388>




![png](output_24_1.png)



```python

```