使用Pandas构建数据密集型应用实战指南

综合文档
需积分: 0 1 下载量 164 浏览量 更新于2024-07-17 收藏 1.14MB PDF 举报
"Instant Data Intensive Apps with Pandas How-to (PacktPub 2013) Trent Hauck.pdf.pdf" 这本书"Instant Data Intensive Apps with Pandas How-to"是Trent Hauck于2013年由Packt Publishing出版的一本指南，主要面向希望使用Pandas库处理大量数据的开发者和分析师。Pandas是一个强大的Python数据分析工具包，它提供了高效的数据结构和数据分析工具，特别适合用于数据清洗、操作和可视化。 在本书中，作者Trent Hauck详细介绍了如何利用Pandas进行数据密集型应用的开发。以下是一些关键知识点的概述： 1. **Pandas基础**：首先，书中会介绍Pandas的基础知识，包括DataFrame和Series这两个核心数据结构。DataFrame是一个二维表格型数据结构，可以存储各种类型的数据，而Series是一维的标量数据结构，类似于带索引的数组。 2. **数据导入与预处理**：Pandas支持多种数据格式的导入，如CSV、Excel、SQL数据库等。书中会讲解如何加载这些数据并进行初步的预处理，如缺失值处理、异常值检测、数据类型转换等。 3. **数据清洗**：Pandas提供了丰富的功能来处理脏数据，包括去除重复值、处理缺失值、字符串操作和日期时间操作。这些方法对于构建干净、准确的数据集至关重要。 4. **数据操作与分析**：书中的内容将涵盖数据的切片、排序、分组、聚合等操作，以及统计分析函数的使用，帮助读者理解数据的分布和关系。 5. **数据可视化**：Pandas可以与Matplotlib和Seaborn等图形库结合，实现数据的可视化。书中会讲解如何创建各种图表，如折线图、柱状图、散点图，以便更好地理解和传达数据洞察。 6. **高级功能**：更深入的话题可能包括时间序列分析、数据重塑（pivot操作）、数据合并和连接，以及使用Pandas进行复杂的统计建模。 7. **性能优化**：对于大数据处理，书中可能会讨论如何利用Pandas的并行计算和内存管理技巧来提高效率。 8. **实战案例**：通过实际应用示例，读者可以学习如何将所学应用于解决数据密集型应用的问题，如数据分析报告、机器学习模型的预处理等。 这本书是Python数据科学初学者和专业人士的宝贵资源，它将帮助读者快速上手并精通使用Pandas处理复杂的数据任务。无论是数据清洗、分析还是可视化，这本书都提供了详尽的指导和支持。

没有GPU，优化程序class point_cloud_generator(): def init(self, rgb_file, depth_file, save_ply, camera_intrinsics=[312.486, 243.928, 382.363, 382.363]): self.rgb_file = rgb_file self.depth_file = depth_file self.save_ply = save_ply self.rgb = cv2.imread(rgb_file) self.depth = cv2.imread(self.depth_file, -1) print("your depth image shape is:", self.depth.shape) self.width = self.rgb.shape[1] self.height = self.rgb.shape[0] self.camera_intrinsics = camera_intrinsics self.depth_scale = 1000 def compute(self): t1 = time.time() depth = np.asarray(self.depth, dtype=np.uint16).T self.Z = depth / self.depth_scale fx, fy, cx, cy = self.camera_intrinsics X = np.zeros((self.width, self.height)) Y = np.zeros((self.width, self.height)) for i in range(self.width): X[i, :] = np.full(X.shape[1], i) self.X = ((X - cx / 2) * self.Z) / fx for i in range(self.height): Y[:, i] = np.full(Y.shape[0], i) self.Y = ((Y - cy / 2) * self.Z) / fy data_ply = np.zeros((6, self.width * self.height)) data_ply[0] = self.X.T.reshape(-1)[:self.width * self.height] data_ply[1] = -self.Y.T.reshape(-1)[:self.width * self.height] data_ply[2] = -self.Z.T.reshape(-1)[:self.width * self.height] img = np.array(self.rgb, dtype=np.uint8) data_ply[3] = img[:, :, 0:1].reshape(-1)[:self.width * self.height] data_ply[4] = img[:, :, 1:2].reshape(-1)[:self.width * self.height] data_ply[5] = img[:, :, 2:3].reshape(-1)[:self.width * self.height] self.data_ply = data_ply t2 = time.time() print('calcualte 3d point cloud Done.', t2 - t1) def write_ply(self): start = time.time() float_formatter = lambda x: "%.4f" % x points = [] for i in self.data_ply

2023-05-24 上传
Here is how you can modify the code to use `numba`: 1. Import the `numba` library by adding the following line at the top of your code: ```python import numba ``` 2. Add the `@numba.jit(no...

学习后端应该准备什么书籍

2023-07-08 上传
4. "Designing Data-Intensive Applications" - Martin Kleppmann 5. "RESTful Web Services" - Leonard Richardson and Sam Ruby 6. "Building Microservices" - Sam Newman 7. "Docker Deep Dive" - Nigel Poulton...

CopyOnWriteArrayList

2023-03-31 上传
CopyOnWriteArrayList is a thread-safe variant of ArrayList in Java. It provides a way to ... It is also not suitable for scenarios where the data needs to be updated in real-time or with low latency.

mapreduce相关文献

2023-06-06 上传
"Data-intensive text processing with mapreduce." Synthesis Lectures on Human Language Technologies 3.1 (2010): 1-177. 3. Zaharia, Matei, et al. "Resilient distributed datasets: A fault-tolerant ...

designing data-intensive applications pdf

2023-06-20 上传
《Designing Data-Intensive Applications》这本书是一本关于设计和构建大型数据应用程序的指南。它重点关注数据存储、处理和传输方面的现代技术。这本书由三部分组成：第一部分围绕着数据存储系统展开，包括传统的...

linux keras

2023-05-17 上传
Linux is an operating system that is often used for developing and running machine learning models and other data-intensive applications. To use Keras on Linux, you can install it using pip, the ...

whu should we use double int pointer in queue

2023-03-29 上传
Double int pointer is used in queue to facilitate dynamic memory ... This can be useful when working with large queues, as creating a copy of the queue can be time-consuming and memory-intensive.

Cable Modem

2023-04-05 上传
It connects to a cable outlet and converts the digital signal from the cable network into data that can be used by a computer or other devices to access the internet. Cable modems typically offer ...

write a C example use Newton-Raphson to get Fractrals for f(z)=z^3-1=0 and save the result into "Problem1.txt"

2023-03-24 上传
This code performs a double loop over the region from -2 to 2 in both the x and y dimensions with a step size of 0.01. For each point (x, y), the code applies the Newton-Raphson method to find the ...

Optimizing Multi-UAV Deployment in 3D Space to Minimize Task Completion Time in UAV-Enabled Mobile Edge Computing Systems

2023-04-07 上传
In such systems, UAVs are deployed to perform tasks such as data collection, processing, and communication, which are computationally intensive and require low-latency data transmission. One of the ...
weixin_38744207
  • 粉丝: 344
  • 资源: 2万+
上传资源 快速赚钱

最新资源