np.insert(temp_cap_diff, 0, values=np.array(0), axis=0)
时间: 2024-04-21 19:12:08 浏览: 20
This code inserts a value of 0 at the beginning of the 1-dimensional NumPy array "temp_cap_diff" and returns the resulting array. The "axis=0" argument specifies that the insertion should be done along the first axis (which is the only axis in a 1-dimensional array).
For example, if temp_cap_diff = [1, 2, 3], the code would return the array [0, 1, 2, 3].
相关问题
np.insert(df_charge_mile_diff, 0, values=np.array(0), axis=0)什么意思
这行代码的作用是在 pandas 的 DataFrame 对象 df_charge_mile_diff 的第一行插入一个值为 0 的新行,并返回插入后的新 DataFrame 对象。具体参数解释如下:
- df_charge_mile_diff:要插入新行的 DataFrame 对象。
- 0:要插入的新行的位置,这里是第一行。
- values:要插入的新行的值,这里是一个只包含一个元素为 0 的一维数组。
- axis:要插入的新行的方向,这里是沿着行方向插入,所以值为 0。
写一个c函数复现以下代码 # 求每一列的均值 col_mean = np.mean(temp_arr, axis=0) # 每个元素减去所在列的均值 temp_arr = (temp_arr - col_mean).astype(int) n_largest = max_min_num n_smallest = max_min_num max_values = np.apply_along_axis(lambda x: np.sort(x)[-n_largest:], axis=0, arr=temp_arr) min_values = np.apply_along_axis(lambda x: np.sort(x)[:n_smallest], axis=0, arr=temp_arr) max_mean = np.mean(max_values, axis=0).astype(int) min_mean = np.abs(np.mean(min_values, axis=0)).astype(int) mean = (max_mean + min_mean) // 2
以下是复现上述代码功能的C函数:
```c
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
void compute_mean(int rows, int cols, int temp_arr[rows][cols], int max_min_num) {
int col_mean[cols];
int i, j, k;
int n_largest = max_min_num;
int n_smallest = max_min_num;
int max_values[cols][n_largest];
int min_values[cols][n_smallest];
int max_mean[cols];
int min_mean[cols];
int mean[cols];
// 求每一列的均值
for (j = 0; j < cols; j++) {
int sum = 0;
for (i = 0; i < rows; i++) {
sum += temp_arr[i][j];
}
col_mean[j] = round(1.0 * sum / rows);
}
// 每个元素减去所在列的均值
for (i = 0; i < rows; i++) {
for (j = 0; j < cols; j++) {
temp_arr[i][j] -= col_mean[j];
}
}
// 求每列的最大值
for (j = 0; j < cols; j++) {
for (i = 0; i < rows; i++) {
int val = temp_arr[i][j];
for (k = 0; k < n_largest; k++) {
if (k == 0 || val > max_values[j][k-1]) {
max_values[j][k] = val;
break;
}
}
}
}
// 求每列的最小值
for (j = 0; j < cols; j++) {
for (i = 0; i < rows; i++) {
int val = temp_arr[i][j];
for (k = 0; k < n_smallest; k++) {
if (k == 0 || val < min_values[j][k-1]) {
min_values[j][k] = val;
break;
}
}
}
}
// 求每列最大值的均值
for (j = 0; j < cols; j++) {
int sum = 0;
for (k = 0; k < n_largest; k++) {
sum += max_values[j][k];
}
max_mean[j] = round(1.0 * sum / n_largest);
}
// 求每列最小值的均值的绝对值
for (j = 0; j < cols; j++) {
int sum = 0;
for (k = 0; k < n_smallest; k++) {
sum += min_values[j][k];
}
min_mean[j] = round(fabs(1.0 * sum / n_smallest));
}
// 求每列最大值均值和最小值均值的平均值
for (j = 0; j < cols; j++) {
mean[j] = round(0.5 * (max_mean[j] + min_mean[j]));
}
// 输出结果
printf("col_mean: ");
for (j = 0; j < cols; j++) {
printf("%d ", col_mean[j]);
}
printf("\n");
printf("max_values: ");
for (j = 0; j < cols; j++) {
printf("[ ");
for (k = 0; k < n_largest; k++) {
printf("%d ", max_values[j][k]);
}
printf("] ");
}
printf("\n");
printf("min_values: ");
for (j = 0; j < cols; j++) {
printf("[ ");
for (k = 0; k < n_smallest; k++) {
printf("%d ", min_values[j][k]);
}
printf("] ");
}
printf("\n");
printf("max_mean: ");
for (j = 0; j < cols; j++) {
printf("%d ", max_mean[j]);
}
printf("\n");
printf("min_mean: ");
for (j = 0; j < cols; j++) {
printf("%d ", min_mean[j]);
}
printf("\n");
printf("mean: ");
for (j = 0; j < cols; j++) {
printf("%d ", mean[j]);
}
printf("\n");
}
```
假设输入的数据存储在一个二维数组 `temp_arr` 中,其大小为 `rows` 行 `cols` 列。函数的第二个参数 `max_min_num` 表示求每列的最大值和最小值时,分别选取的个数。函数会依次输出 `col_mean`,`max_values`,`min_values`,`max_mean`,`min_mean` 和 `mean`,分别对应上述代码中的变量。