英文:
Vectorize a For Statement - Zeros In A Diagonal
问题
想要用一个向量化的理解语句替换Python中的'for'循环，将pandas DataFrame 的左对角线替换为零。
DataFrame 是使用标准的 numpy.random.randint() 方法实例化的。
df = pd.DataFrame(np.random.randint(1, 100, 100).reshape(10, -1))
我尝试应用一个lambda函数，使用df.iloc[] 方法定位/替换对角线上的单元格。期望左对角线被替换为零。
df = df.apply(lambda i: [df.iloc[i, i] * 0 for i in range(df.shape[0])])
结果是整个DataFrame被加载为零。需要帮助来编写lambda函数，因为lambda函数中不允许赋值。
英文:
Want to replace a Python 'for' loop with a vectorized comprehension statement to replace the left diagonal in a pandas DataFrame with zeroes.
The dataframe is instantiated using a standard numpy.random.randint() method.
df = pd.DataFrame(np.random.randint(1,100, 100).reshape(10, -1))
I tried to apply a lambda function that locates/replaces the diagonal cells using the df.iloc[] method. Was expecting the left diagonal to be replaced with zeroes.
df = df.apply(lambda i : [df.iloc[i, i] * 0 for i in range(df.shape[0])])
The result yields an entire DataFrame loaded with zeroes. Need help on how to write the lambda function since an assignment is not allowed in the lambda function.
答案1
得分: 1
如果您使用 iloc，您将不会得到一个矢量化的函数，因为您实际上是在遍历索引。
相反，您应该使用NumPy自己的fill_diagonal()方法
arr = np.random.randint(1, 100, 100).reshape(10, -1)
np.fill_diagonal(arr, 0)
df = pd.DataFrame(arr)
英文:
If you use iloc you won't get a vectorized function since you're essentially iterating over indices.
What you should do instead is to use NumPy's own fill_diagonal() method
arr = np.random.randint(1,100, 100).reshape(10, -1)
np.fill_diagonal(arr, 0)
df = pd.DataFrame(arr)
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