我现在有些用pandas dataframe写的两个for循环对三万条数据处理起来非常慢，大概需要各四十秒 我需要将其加速到处理速度在十秒之内 这样是整个代码可以带的动最多五十万条数据（十分钟内）
有谁明晚或者后晚有空帮我看一下可以不？ 谢谢！
话说我希望解释一下我上一条SQL付费询问最后谁也没采纳的原因，是因为所有的方法都不对，我后来重新卸载SSRS 得到错误日志，我看了一下错误日志上面显示SSRS安装路径不对，要安装在根目录，我后来据此改好了，所以因此我谁也没采纳，我不是白嫖党…我和几个回答者取得联系 他们后来不理我或者回答的不对，望知悉…就告诉一下大家我不是白嫖党 要是有帮助我肯定会采纳，谢谢！
1其实
就两个双for循环 不用很久的…
给你一个实例，对着看，修改，望采纳，谢谢
# -*- coding: utf-8 -*-
"""
@File : xxx
@Time : 2021/06/28 09:20
@Author : xxx
@Software: PyCharm
"""
import time
import numpy as np
import pyrealsense2 as rs
import cv2
import sys
from numba import jit, vectorize, int64, int32, autojit
class ObstacleDetection(object):
def __init__(self):
# self.cam_serials = ['838212073161', '827312071726']
self.cam_serials = ['838212073161']
# @jit(nopython=True)
# @jit
# @vectorize([int64(int64, int64)], target='parallel')
@autojit
def traversing_pixels(self, depth_image):
num_black = 0
all_pixels = 0
for pixel in depth_image.ravel():
all_pixels += 1
if pixel == 0:
num_black += 1
return [all_pixels, num_black]
def obstacle_detection(self):
# 摄像头个数（在这里设置所需使用摄像头的总个数）
cam_num = 6
ctx = rs.context()
'''连续验证机制'''
# D·C 1911202：创建最大验证次数max_veri_times；创建连续稳定值continuous_stable_value，用于判断设备重置后是否处于稳定状态
max_veri_times = 100
continuous_stable_value = 10
print('\n', end='')
print('开始连续验证，连续验证稳定值：{}，最大验证次数：{}：'.format(continuous_stable_value, max_veri_times))
continuous_value = 0
veri_times = 0
while True:
devices = ctx.query_devices()
connected_cam_num = len(devices)
if connected_cam_num == cam_num:
continuous_value += 1
if continuous_value == continuous_stable_value:
break
else:
continuous_value = 0
veri_times += 1
if veri_times == max_veri_times:
print("检测超时，请检查摄像头连接！")
sys.exit()
print('摄像头个数：{}'.format(connected_cam_num))
'''循环reset摄像头'''
# hardware_reset()后是不是应该延迟一段时间？不延迟就会报错
print('\n', end='')
print('开始初始化摄像头：')
for dev in ctx.query_devices():
# 先将设备的序列号放进一个变量里，免得在下面for循环里访问设备的信息过多（虽然不知道它会不会每次都重新访问）
dev_serial = dev.get_info(rs.camera_info.serial_number)
# 匹配序列号，重置我们需重置的特定摄像头（注意两个for循环顺序，哪个在外哪个在内很重要，不然会导致刚重置的摄像头又被访问导致报错）
for serial in self.cam_serials:
if serial == dev_serial:
dev.hardware_reset()
# 像下面这条语句居然不会报错，不是刚刚才重置了dev吗？莫非区别在于没有通过for循环ctx.query_devices()去访问？
# 是不是刚重置后可以通过ctx.query_devices()去查看有这个设备，但是却没有存储设备地址？如果是这样，
# 也就能够解释为啥能够通过len(ctx.query_devices())函数获取设备数量，但访问序列号等信息就会报错的原因了
print('摄像头{}初始化成功'.format(dev.get_info(rs.camera_info.serial_number)))
'''连续验证机制'''
# D·C 1911202：创建最大验证次数max_veri_times；创建连续稳定值continuous_stable_value，用于判断设备重置后是否处于稳定状态
print('\n', end='')
print('开始连续验证，连续验证稳定值：{}，最大验证次数：{}：'.format(continuous_stable_value, max_veri_times))
continuous_value = 0
veri_times = 0
while True:
devices = ctx.query_devices()
connected_cam_num = len(devices)
if connected_cam_num == cam_num:
continuous_value += 1
if continuous_value == continuous_stable_value:
break
else:
continuous_value = 0
veri_times += 1
if veri_times == max_veri_times:
print("检测超时，请检查摄像头连接！")
sys.exit()
print('摄像头个数：{}'.format(connected_cam_num))
'''配置各个摄像头的基本对象'''
for i in range(len(self.cam_serials)):
locals()['pipeline' + str(i + 1)] = rs.pipeline(ctx)
locals()['config' + str(i + 1)] = rs.config()
locals()['config' + str(i + 1)].enable_device(self.cam_serials[i])
locals()['config' + str(i + 1)].enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30)
locals()['config' + str(i + 1)].enable_stream(rs.stream.color, 640, 480, rs.format.bgr8, 30)
locals()['pipeline' + str(i + 1)].start(locals()['config' + str(i + 1)])
# 创建对齐对象（深度对齐颜色）
locals()['align' + str(i + 1)] = rs.align(rs.stream.color)
'''运行摄像头'''
try:
while True:
start_time = time.time()
for i in range(len(self.cam_serials)):
locals()['frames' + str(i + 1)] = locals()['pipeline' + str(i + 1)].wait_for_frames()
# 获取对齐帧集
locals()['aligned_frames' + str(i + 1)] = locals()['align' + str(i + 1)].process(
locals()['frames' + str(i + 1)])
# 获取对齐后的深度帧和彩色帧
locals()['aligned_depth_frame' + str(i + 1)] = locals()[
'aligned_frames' + str(i + 1)].get_depth_frame()
locals()['color_frame' + str(i + 1)] = locals()['aligned_frames' + str(i + 1)].get_color_frame()
if not locals()['aligned_depth_frame' + str(i + 1)] or not locals()['color_frame' + str(i + 1)]:
continue
# 获取颜色帧内参
locals()['color_profile' + str(i + 1)] = locals()['color_frame' + str(i + 1)].get_profile()
locals()['cvsprofile' + str(i + 1)] = rs.video_stream_profile(
locals()['color_profile' + str(i + 1)])
locals()['color_intrin' + str(i + 1)] = locals()['cvsprofile' + str(i + 1)].get_intrinsics()
locals()['color_intrin_part' + str(i + 1)] = [locals()['color_intrin' + str(i + 1)].ppx,
locals()['color_intrin' + str(i + 1)].ppy,
locals()['color_intrin' + str(i + 1)].fx,
locals()['color_intrin' + str(i + 1)].fy]
# 【空间过滤器】
locals()['spatial' + str(i + 1)] = rs.spatial_filter()
locals()['spatial' + str(i + 1)].set_option(rs.option.filter_magnitude, 5)
locals()['spatial' + str(i + 1)].set_option(rs.option.filter_smooth_alpha, 1)
locals()['spatial' + str(i + 1)].set_option(rs.option.filter_smooth_delta, 50)
locals()['spatial' + str(i + 1)].set_option(rs.option.holes_fill, 3)
locals()['filtered_depth' + str(i + 1)] = locals()['spatial' + str(i + 1)].process(
locals()['aligned_depth_frame' + str(i + 1)])
locals()['depth_image' + str(i + 1)] = np.asanyarray(
locals()['filtered_depth' + str(i + 1)].get_data())
locals()['color_image' + str(i + 1)] = np.asanyarray(
locals()['color_frame' + str(i + 1)].get_data())
# locals()['depth_image' + str(i + 1)] = np.asanyarray(
# locals()['aligned_depth_frame' + str(i + 1)].get_data())
# 【打印深度值看看、全部打印显示】
# np.set_printoptions(threshold=np.inf)
# print(locals()['depth_image' + str(i + 1)])
# 【计算深度图数据中的0值】
[locals()['all_pixels' + str(i + 1)], locals()['num_black' + str(i + 1)]] = self.traversing_pixels(
locals()['depth_image' + str(i + 1)])
# num_black = 0
# all_pixels = 0
# for row in range(480):
# for colume in range(640):
# all_pixels += 1
# if locals()['depth_image' + str(i + 1)][row, colume] == 0:
# num_black += 1
print('depth_image分辨率：{}'.format(locals()['depth_image' + str(i + 1)].shape))
# print('depth_image:{}'.format(num_black))
# print('depth_image:{}'.format(num_black / all_pixels))
print('depth_image:{}'.format(locals()['num_black' + str(i + 1)]))
print('depth_image:{}'.format(
locals()['num_black' + str(i + 1)] / locals()['all_pixels' + str(i + 1)]))
# 以下这种卡的不行(get_distance()函数会把窗口搞崩溃(即使不很卡))
# for row in range(locals()['aligned_depth_frame' + str(i + 1)].get_height()):
# for colume in range(locals()['aligned_depth_frame' + str(i + 1)].get_width()):
# all_pixels += 1
# if locals()['depth_image' + str(i + 1)][row, colume] == 0:
# # if locals()[
# # 'aligned_depth_frame' + str(i + 1)].get_distance(row, colume) == 0:
# num_black += 1
# for pixel in locals()['depth_image' + str(i + 1)].ravel():
# all_pixels += 1
# if pixel == 0:
# num_black += 1
# print('depth_image分辨率：{}'.format(locals()['depth_image' + str(i + 1)].shape))
# print('depth_image:{}'.format(num_black))
# print('depth_image:{}'.format(num_black / all_pixels))
locals()['depth_colormap' + str(i + 1)] = cv2.applyColorMap(
cv2.convertScaleAbs(locals()['depth_image' + str(i + 1)], alpha=0.0425),
cv2.COLORMAP_JET)
locals()['image' + str(i + 1)] = np.hstack(
(locals()['color_image' + str(i + 1)], locals()['depth_colormap' + str(i + 1)]))
cv2.imshow('win{}'.format(i + 1), locals()['image' + str(i + 1)])
cv2.waitKey(1)
end_time = time.time()
print('单帧运行时间：{}'.format(end_time - start_time))
finally:
for i in range(len(self.cam_serials)):
locals()['pipeline' + str(i + 1)].stop()
if __name__ == '__main__':
ObstacleDetection().obstacle_detection()
代码和数据私发来看看
给我康康你的循环怎么写的
题主要是得到解决了，我可以有偿学习一下，我这种运用场景蛮多的，目前我没找到快的
要是还没解决的话，可以把你的代码和数据私信一下，我看看能不能解决