# 习题解答2.1 ndarray 快速入门

这里是2.1节的习题。我们先在这里`import`所有需要用到的库。


```python
import numpy as np
import pandas as pd

arr1 = np.load("data/1-task-arr1.npy")
arr2 = np.load("data/1-task-arr2.npy")

iris_df = pd.read_csv("data/iris.csv")
```

## 基础练习

1. 请创建形状为`(3, 4, 5)`的全0，全1，全2 数组.


```python
shape = (3, 4, 5)
arr_0 = np.zeros(shape)   # 全0 数组
arr_1 = np.ones(shape)    # 全1 数组
arr_2 = np.full(shape, 2) # 全2 数组
```

2. 请创建长度为16的一维数组，其中数据为 24,22,20,18...,-6.


```python
arr = np.arange(24, -8, -2)
arr
```




    array([24, 22, 20, 18, 16, 14, 12, 10,  8,  6,  4,  2,  0, -2, -4, -6])



3. 请获取数组`arr1`的维度、形状与数据类型.


```python
print("维度:", arr1.ndim)
print("形状:", arr1.shape)
print("数据类型:", arr1.dtype)
```

    维度: 5
    形状: (1, 2, 3, 4, 5)
    数据类型: datetime64[s]
    

4. 请获取`arr2`的简略字符串与详细字符串表示.


```python
print("简略字符串:", str(arr2))
print("详细字符串:\n", repr(arr2))
```

    简略字符串: [[1. 2. 3.]
     [4. 5. 6.]]
    详细字符串:
     array([[1., 2., 3.],
           [4., 5., 6.]])
    

5. 请计算将`arr2`中的每个元素加100的平方根后的数组, 并求出它的最大值、均值与标准差.


```python
arr = np.sqrt(arr2 + 100)
print("最大值:", np.max(arr))
print("均值:", np.mean(arr))
print("标准差:", np.std(arr))
arr
```

    最大值: 10.295630140987
    均值: 10.173148676451225
    标准差: 0.08394049570182746
    




    array([[10.04987562, 10.09950494, 10.14889157],
           [10.19803903, 10.24695077, 10.29563014]])



6. 请遍历`arr2`中的所有元素并存储到一个集合`set`中.


```python
result = set()
for i in arr2.flat:
    result.add(i)
result
```




    {np.float64(1.0),
     np.float64(2.0),
     np.float64(3.0),
     np.float64(4.0),
     np.float64(5.0),
     np.float64(6.0)}



7. 请获取一个数值为1 ~ 9 ,形状为 (3, 3)的数组，将其存储至`arr3`中.


```python
arr3 = np.arange(1,10).reshape(3,3)
arr3
```




    array([[1, 2, 3],
           [4, 5, 6],
           [7, 8, 9]])



8. 请计算`arr2`与`arr3`在第一个轴上的堆叠，`arr2`在`arr3`之前.


```python
np.concatenate((arr2, arr3), axis=0)
```




    array([[1., 2., 3.],
           [4., 5., 6.],
           [1., 2., 3.],
           [4., 5., 6.],
           [7., 8., 9.]])



## 实战演练

这里我们使用了经典的鸢尾花数据集（Iris Dataset）。已知数组`iris_arr`存储的是`iris_df`的特征，即其中第2-5列的数据(按照顺序)，数组`iris_index`存储的是这四列的名称，`iris_target`存储的是目标特征，即`species`列中的数据。请完成下述问题。


```python
iris_arr = iris_df.to_numpy()[:,1:5].astype(np.float64)
iris_index = iris_df.columns[1:5].to_list().copy()
iris_target = iris_df.to_numpy()[:, 5].copy()
iris_df.head()
```




<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>Unnamed: 0</th>
      <th>sepal_length</th>
      <th>sepal_width</th>
      <th>petal_length</th>
      <th>petal_width</th>
      <th>species</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>0</td>
      <td>5.1</td>
      <td>3.5</td>
      <td>1.4</td>
      <td>0.2</td>
      <td>Iris-setosa</td>
    </tr>
    <tr>
      <th>1</th>
      <td>1</td>
      <td>4.9</td>
      <td>3.0</td>
      <td>1.4</td>
      <td>0.2</td>
      <td>Iris-setosa</td>
    </tr>
    <tr>
      <th>2</th>
      <td>2</td>
      <td>4.7</td>
      <td>3.2</td>
      <td>1.3</td>
      <td>0.2</td>
      <td>Iris-setosa</td>
    </tr>
    <tr>
      <th>3</th>
      <td>3</td>
      <td>4.6</td>
      <td>3.1</td>
      <td>1.5</td>
      <td>0.2</td>
      <td>Iris-setosa</td>
    </tr>
    <tr>
      <th>4</th>
      <td>4</td>
      <td>5.0</td>
      <td>3.6</td>
      <td>1.4</td>
      <td>0.2</td>
      <td>Iris-setosa</td>
    </tr>
  </tbody>
</table>
</div>



1. 请计算每个特征的均值和方差


```python
means = np.mean(iris_arr, axis=0)
variances = np.var(iris_arr, axis=0)

for col_name, mean, variance in zip(iris_index, means, variances):
    print(f"特征{col_name}的均值为:", mean)
    print(f"特征{col_name}的方差为:", variance)
```

    特征sepal_length的均值为: 5.843333333333334
    特征sepal_length的方差为: 0.6811222222222223
    特征sepal_width的均值为: 3.0540000000000003
    特征sepal_width的方差为: 0.18675066666666668
    特征petal_length的均值为: 3.758666666666666
    特征petal_length的方差为: 3.092424888888889
    特征petal_width的均值为: 1.1986666666666668
    特征petal_width的方差为: 0.5785315555555555
    

2. 请将`iris`数据标准化（均值为0，方差为1）


```python
iris_arr = (iris_arr - means) / np.sqrt(variances)
iris_df.iloc[:, 1:5] = iris_arr
iris_df.head()
```




<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>Unnamed: 0</th>
      <th>sepal_length</th>
      <th>sepal_width</th>
      <th>petal_length</th>
      <th>petal_width</th>
      <th>species</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>0</td>
      <td>-0.900681</td>
      <td>1.032057</td>
      <td>-1.341272</td>
      <td>-1.312977</td>
      <td>Iris-setosa</td>
    </tr>
    <tr>
      <th>1</th>
      <td>1</td>
      <td>-1.143017</td>
      <td>-0.124958</td>
      <td>-1.341272</td>
      <td>-1.312977</td>
      <td>Iris-setosa</td>
    </tr>
    <tr>
      <th>2</th>
      <td>2</td>
      <td>-1.385353</td>
      <td>0.337848</td>
      <td>-1.398138</td>
      <td>-1.312977</td>
      <td>Iris-setosa</td>
    </tr>
    <tr>
      <th>3</th>
      <td>3</td>
      <td>-1.506521</td>
      <td>0.106445</td>
      <td>-1.284407</td>
      <td>-1.312977</td>
      <td>Iris-setosa</td>
    </tr>
    <tr>
      <th>4</th>
      <td>4</td>
      <td>-1.021849</td>
      <td>1.263460</td>
      <td>-1.341272</td>
      <td>-1.312977</td>
      <td>Iris-setosa</td>
    </tr>
  </tbody>
</table>
</div>



3. 请计算每个类别的平均特征值


```python
unique_classes = np.unique(iris_target)
class_means = {}

for cls in unique_classes:
    class_data = iris_arr[iris_target == cls]
    class_means[cls] = np.mean(class_data, axis=0)

print("每个类别的平均特征值:")
for class_name, mean_values in class_means.items():
    print(f"{class_name}: {mean_values}")
```

    每个类别的平均特征值:
    Iris-setosa: [-1.01457897  0.84230679 -1.30487835 -1.25512862]
    Iris-versicolor: [ 0.11228223 -0.65718442  0.28508673  0.16740892]
    Iris-virginica: [ 0.90229674 -0.18512237  1.01979162  1.08771971]