补全程序,完成以下功能:创建表示10×10的随机矩阵的DataFrame 对象,行索引名和列索引名都为1~10,元素取值在0~100,并求每列的最大值和最小值。输出参考如下运行示例:import pandas as pd import ______ as np data = np.random.randint(0, 101, (____, ____), np.int32) index = np.arange(1, 11, 1) matrix = pd.DataFrame(data, ____, index) # 创建DataFrame对象 print(matrix) matrix_max = matrix.____(axis=0) # 求每列最大值 print(matrix_max) matrix_min = matrix.min(axis=____) # 求每列最小值 print(matrix_min)
时间: 2024-03-28 12:37:36
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import pandas as pd
import numpy as np
[dat](https://geek.csdn.net/educolumn/965fd2cd81c8a495e4fbe430eceebb34?spm=1055.2569.3001.10083)a = np.random.randint(0, 101, (10, 10), np.int32)
index = np.arange(1, 11, 1)
matrix = pd.[dat](https://geek.csdn.net/educolumn/965fd2cd81c8a495e4fbe430eceebb34?spm=1055.2569.3001.10083)aFrame([dat](https://geek.csdn.net/educolumn/965fd2cd81c8a495e4fbe430eceebb34?spm=1055.2569.3001.10083)a, index, index) # 创建DataFrame[对象]([http](https://geek.csdn.net/educolumn/68f28ca3131d47699e5902c5d3d74d51?spm=1055.2569.3001.10083)s://geek.csdn.net/educolumn/04c51611e4b730957464192e0307b82c?spm=1055.2569.3001.10083)
print(matrix)
matrix_max = matrix.max(axis=0) # 求每列最大值
print(matrix_max)
matrix_min = matrix.min(axis=0) # 求每列最小值
print(matrix_min)
相关问题
用python 完成以下功能:创建表示10×10的随机矩阵的DataFrame 对象,行索引名和列索引名都为1~10,元素取值在0~100,并求每列的最大值和最小值。输出参考如下运行示例:
好的,以下是代码实现:
```python
import pandas as pd
import numpy as np
# 创建 DataFrame 对象
data = np.random.randint(0, 100, size=(10, 10))
df = pd.DataFrame(data, columns=[i for i in range(1, 11)], index=[i for i in range(1, 11)])
# 求每列的最大值和最小值
max_values = df.max()
min_values = df.min()
# 输出结果
print("每列的最大值:")
print(max_values)
print("每列的最小值:")
print(min_values)
```
用python 完成以下功能:创建表示5×5的随机矩阵的DataFrame 对象,行素引和列索引都为1~5,元素取值在1~50,对数据进行归一化和正则化。
好的,下面是Python代码实现:
```python
import numpy as np
import pandas as pd
# 创建随机矩阵
data = np.random.randint(1, 50, size=(5, 5))
# 将矩阵转换为DataFrame对象
df = pd.DataFrame(data=data, index=range(1, 6), columns=range(1, 6))
# 归一化和正则化数据
df_normalized = (df - df.mean()) / df.std()
df_regularized = (df - df.min()) / (df.max() - df.min())
print("原始数据:\n", df)
print("归一化数据:\n", df_normalized)
print("正则化数据:\n", df_regularized)
```
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