Python 比特币编程实用指南（全）

比特币挖掘的一些特点如下：

Some of the features of Bitcoin excavation are as follows xff1a;

• 比特币挖矿是将比特币交易数据添加到比特币全球公共账本的过程。每个比特币矿工都与其他矿工一起合作，通过处理专门的分析和算术问题，将未完成的交易汇总到一个区块中。

  Bitcoin mining is the process of adding bitcoin trading data to the Bitcoin global public ledger. Each bitcoin miners work with other miners xff0c; by dealing with specialized analytical and arithmetic issues xff0c; and by synthesizing unfinished transactions into a block.

• 为了获得准确性并解决问题，比特币矿工获取他们处理的所有交易。

  To obtain accuracy and solve problems xff0c; Bitcoin miners get all the transactions they handle.

• 除了交易费用，矿工还会收到每个他们挖掘的区块的额外奖励。任何人都可以通过运行计算机程序参与比特币挖矿。除了在传统计算机上运行外，一些公司设计了专门的比特币挖矿硬件，可以更快地处理交易并构建区块。

  In addition to transaction costs xff0c; miners receive additional incentives for each of the blocks they dig. Anyone can participate in bitcoin mining by running a computer program. xff0c, apart from running on traditional computers; some companies have designed specialized bitcoin mining hardware xff0c; and they can process transactions and build blocks faster.

这些计划的过程表明，比特币挖矿的难度正在增加，随着时间的推移变得更加昂贵。

The process of these plans shows that xff0c; that bitcoin mining is becoming more difficult xff0c; and that it is becoming more expensive over time.

一些公司购买专门的硬件来挖掘比特币。其中一种硬件是来自 21.co 公司的 21 比特币计算机。因此，这种硬件预先安装了必要的软件。

Some companies purchase specialized hardware to dig for bitcoins. One of the hardware is a 21 bitco computers. xff0c; this hardware is pre-installed with the necessary software.

由于竞争和困难的增加，挖掘比特币时必须牢记许多因素，如下列表所示：

xff0c due to increased competition and difficulties; many factors must be borne in mind when digging bitcoin xff0c; xff1a as shown in the table below;

• 由于竞争，比特币的价格日益昂贵

  Because of competition & #xff0c; Bitcoin is becoming more expensive

• 全球许多超级计算机正在竞争挖掘下一个区块和比特币。

  Many supercomputers around the world are competing to dig the next block and bitcoin.

• 随着比特币矿工数量的增加，开始挖掘新比特币变得更加困难和昂贵

  As the number of bitcoin miners increases xff0c; starting to dig for new bitcoin becomes more difficult and expensive

在本章中，我们学习了如何使用 Python 开始编程比特币。我们探索了使用 Python 进行 Blockchain.info API 编程，以获取统计数据和其他比特币市场数据。

In this chapter xff0c; we learned how to use Python to start programming bitcoin. We explored the use of Python for Blockchain.info API programming xff0c; to get statistics and other bitcoin market data.

我们还学习了如何开始挖掘比特币。我们看了看挖掘比特币的各种方式，并了解到由于竞争日益激烈和难度增加，比特币挖矿可能并不适合每个人。

We have also learned how to start digging bitcoin. We have looked at ways to dig bitcoin & #xff0c; we have learned that it may not be suitable for everyone because of increasing competition and difficulty #xff0c; bitcoin digs.

在下一章中，我们将学习如何通过在网站上接受比特币、运行基于 API 的微服务，或者构建比特币交易机器人来开始以编程方式运行比特币。

In the next chapter xff0c; we will learn how to start running bitcoin in a programmed manner by accepting bitcoin on the website, running API-based microservices xff0c; or building bitcoin trading robots.

在本章中，我们将学习如何在我们的网站上开始接受比特币作为支付方式。我们还将学习如何构建基于 API 的微服务以赚取比特币，并探索比特币交易机器人。

In this chapter xff0c; we will learn how to start accepting bitcoin as a payment on our website. We will also learn how to build API-based microservices to earn bitcoinxff0c; and explore bitcoin trading robots.

在本节中，我们将学习以下主题：

In this section xff0c; we will study the following themes xff1a;

• 如何在我们的网站上启用比特币支付

  How to Enable Bitcoin Payments on our website

• 介绍 BitPay，第三方比特币 API 服务

  Introduction Bitpay， third-party Bitcoin API service

• 如何生成比特币支付按钮

  How to Generate Bitcoin Payment Buttons

• 如何在我们的网站上添加比特币支付按钮

  How to add a bitcoin payment button to our website

有很多第三方 API 可用于网站上快速启用比特币支付，其中最流行的之一是 BitPay。

There are many third-party APIs that can be used to fast-track Bitcoin payments xff0c on the website; one of the most popular of these is Bitpay.

BitPay 可用于以多种不同的方式接受付款，包括以下方式：

Bitpay can be used to receive payments in a number of different ways xff0c; including the following mode xff1a;

• 您可以使用比特币在电子商务网站上接受在线付款

  You can accept online payments using bitcoin on the e-commerce website.

• 将比特币与许多不同的电子商务解决方案集成

  Integrating bitcoin with many different e-commerce solutions

• 与购物车集成

  Combine with the shopping car.

• 您可以显示启用比特币的支付按钮，这对于在博客或播客上接受捐赠非常有效

  You can show the payment button that enabled Bitcoin & #xff0c; this is very effective for accepting donations on blogs or podcasts

按照以下步骤生成比特币支付按钮：

Generate a bitcoin payment button based on the following steps & #xff1a;

1. 要将此按钮添加到网站，只需复制 HTML 代码并粘贴到所需的网页中。

按照以下步骤将支付按钮添加到您的网站页面：

Add payment buttons to your website page xff1a in accordance with the following steps;

1. 在代码编辑器中打开您网站页面的源代码。

   Opens the source code for your website page in the code editor.

2. 粘贴我们在上一节从 BitPay 网站复制的 HTML 代码，保存文件并重新加载网页。

   Paste the HTML code & #xff0c that we copied from the Bitpay website in the previous section; save files and reload pages.

3. 以下截图显示了网页上的支付按钮，用户可以使用它发送付款：

   The following screenshot shows the payment button on the web page xff0c; users can use it to send payments xff1a;

我们将演示如何加入这个市场并出售微服务以赚取比特币。

We will demonstrate how to join the market and sell micro-services to earn bitcoin.

为此，我们将使用 21.co 的 SDK。

For this xff0c; we'll use 21.co's SDK.

21.co SDK 目前支持 Ubuntu 和 macOS。本节将演示在 AWS 上运行 Ubuntu 的情况。

21. Co SDK currently supports Ubuntu and macos. This section shows how Ubuntu is running on AWS.

连接到 AWS 实例后，安装 21.co SDK。您可以通过执行以下命令来完成：

After connecting to the AWS instance xff0c; install 21.co SDK. You can complete xff1a by executing the following commands;

安装 SDK 后，通过执行以下命令登录到您的 21.co 帐户：

Install SDK after xff0c; login to your 21.co account xff1a by executing the following command;

如果用户没有 21.co 登录，则必须在 21.co 网站上创建帐户。登录后，首先加入 21.co 节点到 21.co 虚拟市场。您可以通过执行以下命令来完成：

If the user does not have 21.co login & #xff0c; an account must be created on the 21.co website. After login & #xff0c; first joins the 21.co node to the 21.co virtual market. You can complete xff1a by executing the following command;

通过执行以下命令可以实现用户加入请求的状态：

The status of the user's request can be achieved by executing the following command xff1a;

接下来，通过执行以下命令测试安装：

Next xff0c; install xff1a by performing the following command tests;

上述命令将显示所有测试都已通过，并且节点已设置并加入了 21.co 市场。

The above command will show that all tests have passed xff0c; and nodes have been set and joined in the 21.co market.

为了获取比特币余额，执行以下命令：

To obtain the bitcoin balance xff0c; execute the following command xff1a;

上述代码显示了在 21.co 账户中持有的比特币余额。

The above codes show the bitco balance held in the 21.co account.

21.co 的 SDK 捆绑了一些服务。要启动所有这些服务，请执行以下命令：

21. The SDK of Co binds a number of services. To start all these services xff0c; please execute the following command xff1a;

可能会提示安装依赖项。如果是这样，您应该继续并执行。

Could be a reminder to install a dependent item. If so, xff0c; you should continue and execute it.

有时，用户可能需要注销并重新登录以进行更改。

Sometimes xff0c; users may need to write off and re-log for change.

在 21.co 市场上出售微服务，请执行以下步骤：

Sale of micro-services on the 21.co market xff0c; please implement the following steps xff1a;

1. 执行以下命令：

它将显示所有可在虚拟市场上开始销售的微服务的列表。

It will show a list of all micro-services that can be sold on the virtual market.

1. 要查看服务的状态，请执行以下命令：

1. 完成服务后，或者如果要停止它，请运行以下命令：

1. 要查看节点上发生的所有活动，请使用以下命令：

这是如何通过在 21.co 市场上出售和列出微服务来赚取比特币的演示。

This is a demonstration of how to earn bitco by selling and listing micro-services on the 21.co market.

在本节中，我们将学习以下主题：

In this section xff0c; we will study the following themes xff1a;

• 如何获取比特币的当前出价和要价

  How to get the current bid and bid for bitcoin

• 如何决定是否买入或卖出比特币

  How to decide whether to buy or sell bitcoin

• 触发比特币交易建议警报

  Activate bitcoin trade advice alert.

实际的比特币买卖不会被涵盖，因为涉及实际货币。但是，我们将专注于根据我们设置的条件尝试买入或卖出比特币时发送电子邮件警报。

Actual bitcoin sales will not be covered by xff0c; because it involves real currency. But xff0c; we will focus on sending e-mail alerts when trying to buy or sell bitcoin under the conditions we set.

为了设置比特币交易建议警报，请按照以下步骤进行：

To set the recommended Bitcoin transaction alert xff0c; please follow the following steps xff1a;

1. 首先，通过导入名为的比特币价格 API 开始：

1. 还要导入，我们将用它来触发比特币价格警报。在这里，我们定义了一个名为的函数。然后设置服务器用户和电子邮件详细信息：

1. 使用，发送函数发送价格警报电子邮件，如下面的代码所示：

1. 接下来，为比特币定义买入和卖出价格阈值。使用这些阈值来决定是否卖出或买入比特币：

1. 接下来，我们从 Bitfinex 比特币交易所使用我们在脚本中导入的模块获取当前比特币价格和当前出价。我们也可以使用其他交易所，如 CoinDesk，但目前我们将使用 Bitfinex。我们将在和中获取这些价格。

1. 一旦我们得到了当前的价格，我们可以将它们与之前设置的阈值价格进行比较。

   Once we get the current price xff0c; we can compare them with the previously set threshold price.

2. 如果买入价格低于卖出阈值，我们调用函数发送买入触发电子邮件警报：

   If the purchase price is below the selling threshold xff0c; we call the function to send the purchase to trigger e-mail alert xff1a;

1. 如果卖出价格高于卖出阈值，我们调用函数发送卖出触发电子邮件警报：

谷歌搜索是搜索当前出价的最简单方法。为了实现比特币的买卖，应相应地触发两者。

Google search is the easiest way to search for the current bid. To achieve the purchase and sale of bitcoin xff0c; both should be triggered accordingly.

以下是获取当前出价的步骤：

Below are the steps to obtain the current bid #xff1a;

1. 首先，检查比特币价格在线。

   First xff0c; check bitcoin prices online.

2. 修改脚本，使买入警报首先触发。将买入阈值设置为高于当前价格。在这里，我们将买入阈值设置为，如下面的代码所示：

   Modify the script & #xff0c; triggers the purchase alarm first. Set the purchase threshold to higher than the current price. Here xff0c; we set the purchase threshold to xff0c; xff1a as shown in the code below;

1. 保存脚本并执行它。以下屏幕截图显示了执行的脚本：

脚本已执行，买入警报应该已经发出。在电子邮件中检查。

Script executed xff0c; purchase alarm should have been sent. Check in email.

以下屏幕截图显示，根据我们在脚本中设置的标准，我们已收到比特币警报电子邮件，建议我们购买比特币：

The screen screenshot below shows xff0c; according to the standards we set in the script xff0c; we have received the Bitcoin alert e-mail xff0c; we are advised to purchase bitcoinxff1a;

1. 最初，我们应该将出售门槛设置为低于当前价格。例如，让我们将作为门槛，并再次执行脚本。以下代码显示了设置为：

现在，出售警报应该执行。再次在电子邮件中验证。

Now xff0c; sales alarms should be executed. Verify again in e-mail.

1. 验证后，我们应该看到我们已收到电子邮件警报，建议我们出售比特币，因为当前的要价高于我们愿意出售的价格：

1. 脚本已准备好。您现在可以将其设置为在各种操作系统上自动运行。在 Windows 上，请使用任务计划程序。

   Script is ready. You can now set it to run automatically on various operating systems. On Windows & #xff0c; please use the task plan program.

2. 从“操作”菜单中，选择“创建任务…”，并将其命名为“比特币交易警报”，如下面的屏幕截图所示：

   From the Operation menu & #xff0c; select "Creating Tasks..." & #xff0c; and name it "bitcoin Transaction Alert" & #xff0c; xff1a as shown in the screen screenshot below;

您还可以选择直接从脚本中触发交易，使用任何比特币交易所 API，例如 coinbase.com。由于涉及实际资金，用户需要小心处理。

You can also choose to trigger a transaction from a script directly xff0c; use any bitcoin exchange API， e. g. coinbase.com. Due to actual funds xff0c; users need to be careful.

在本章中，我们探讨了如何在网站上启用比特币支付，向您介绍了 BitPay，学习了如何生成比特币支付按钮，以及如何将支付按钮添加到我们的网站上。我们还介绍了 21.co 市场和比特币的买卖服务，以及编写了一个简单的比特币交易机器人。我们学会了如何获取比特币的当前竞价和要价。我们还学会了如何决定是买入还是卖出比特币，以及如何发送电子邮件警报，建议我们是否执行该线程。

In this chapter xff0c; we have explored how to use Bitcoin to pay xff0c; we have introduced BitPay， to you; we have learned how to generate bitco payment buttons xff0c; and how to add payment buttons to our website. We have also introduced 21.co and Bitco's sales services xff0c; and we have developed a simple bitco trading robot. We have learned how to get Bitco's current bid and bid. We have also learned how to decide whether to buy or sell bitco xff0c; and how to send e-mail alerts xff0c; we have suggested whether to execute the thread.

在下一章中，我们将学习如何对比特币数据进行数据分析。

In the next chapter, xff0c; we will learn how to compare the data with the data.

在本章中，我们将使用 Python 探索比特币价格数据的操作和可视化。我们还将探索比特币交易图表，以及使用 Python 收集和分析比特币骰子游戏数据。

In this chapter xff0c; we will use Python to explore the operation and visualization of bitcoin price data. We will also explore bitcoin trade charts xff0c; and use Python to collect and analyse bitco dice game data.

在本节中，我们将介绍以下主题：

In this section xff0c; we will introduce the following theme xff1a;

• 为数据分析做好准备

  Preparing for data analysis

• 获取、读取和清理比特币价格数据

  Obtain, read and clean bitcoin price data

• 探索、操作和可视化清理后的数据

  Explore, operate and visualize post-cleaning data

我们首先需要安装几个 Python 库，其中包括安装模块用于读取数据，以及进行一些探索性分析。我们还将安装用于创建图表和图表，以及 Jupyter 笔记本，因为它们是进行数据分析的最佳工具。

We first need to install several Python libraries & #xff0c; these include modules to read data & #xff0c; and some exploratory analysis. We will also install charts and graphs & #xff0c; and Jupyter notebooks & #xff0c; they are the best tools for data analysis.

要安装 Python 模块，请打开命令行程序。在命令行中，要安装，请执行以下命令：

To install the Python module & #xff0c; to open the command line. In the command line xff0c; to install xff0c; to execute the following command xff1a;

类似地，要安装，请执行以下命令：

Similar to xff0c; to install xff0c; please execute the following command xff1a;

要安装 Jupyter，请执行以下命令：

To install Jupyter， please execute the following command xff1a;

完成安装所需的模块后，通过执行命令启动 Jupyter Notebook。这将在新的浏览器窗口或选项卡中打开，显示已经存在于我们执行命令的文件夹中的文件列表。以下屏幕截图显示了命令：

xff0c after completion of installation of the required module; start the Jupyter Notebook by executing the command. This opens xff0c in the new browser window or tab; displays the list of files that already exist in the folder where we execute the command. The screenshot below shows the command xff1a;

接下来，选择创建一个新的 Python 3 笔记本，如下屏幕截图所示：

Next xff0c; Select to create a new Python 3 notebook xff0c; xff1a as shown in screenshot below;

还要导入以从数据中绘制图表。

Import to draw a chart from the data.

我们需要为和设置一些选项。以下屏幕截图显示了导入的命令：

We need to set some options. The screenshot below shows the imported command #xff1a;

我们将设置的第一个选项称为。

We'll call the first option set.

前面的选项是确保清理操作是在 pandas DataFrame 对象上执行的；我们希望清理操作发生在原始 DataFrame 对象上，而不是在副本上。

The first option is to ensure that the clean-up operation is performed on a pandas DataFrame object xff1b; we want the clean-up operation to take place on the original DataFrame object xff0c; not on a copy.

以下屏幕截图显示了选项：

The screenshot below shows options & #xff1a;

还要设置以可视化和显示以下屏幕截图中显示的所有图表：

Also set to visualize and display all charts & #xff1a shown in screen screenshots below;

我们的价格数据来自，如下屏幕截图所示，它可以免费下载：

Our price data are from xff0c; xff0c as shown on screen screen screen below; it can be downloaded free of charge xff1a;

为了打印底部的记录，调用方法。该方法显示最后两条记录不应该存在，因为它们不是日期或价格。在进行进一步分析之前，我们需要删除这些记录。

This method shows that the last two records should not exist xff0c; they are not dates or prices. Before further analysis xff0c; we need to delete these records.

我们可以看到收盘价有 NaN 值，这意味着它有缺失值。我们可以利用这一因素从 DataFrame 中删除这两条记录。我们在价格上调用方法，这将删除具有一个或多个列为空或缺失值的记录。

We can see that the collected price is NaN & #xff0c; this means that it has missing values. We can use this factor to delete both records from DataFrame. We call the price method & #xff0c; this removes one or more records with empty or missing values.

请记住，我们只是从 DataFrame 价格中删除它，而不是从我们读取数据的 CSV 文件中删除它。

Remember & #xff0c; we just delete it from DataFrame prices & #xff0c; not from the CSV file where we read the data.

以下截图中的代码显示了方法的实现：

The code in the screenshot below shows how the method is achieved xff1a;

此外，再次查看底部行，看看我们想要删除的记录是否已经被删除。我们可以在以下截图中看到，实际上已经被删除了：

In addition, xff0c; again check the bottom line xff0c; see if the records we want to delete have been deleted. We can see xff0c in the following screenshot; xff1a has actually been deleted;

.

我们需要做的另一个数据清理任务是将列从对象或文本格式转换为日期时间格式。我们使用 pandas 的方法来实现这一点。

Another data cleansing task we need to do is to convert columns from object or text format to date time format. We use the pandas method to do this.

在这里，我们要求方法将字段或 DataFrame 转换，并且我们还提供了格式。然后我们将字段重新分配给 DataFrame，如下截图所示：

Here's xff0c; we're asking for ways to convert fields or DataFrame xff0c; and we're also providing formatting. Then we reassign the field to DataFrame， see xff1a in the screenshot below;

我们还需要将索引设置为列，并将列删除为单独的列。这将帮助我们对日期数据运行一些有趣的查询。

We also need to set the index to column #xff0c; and delete the column as a separate column. This will help us run some interesting queries about date data.

以下截图中的代码显示了如何将索引设置为列：

The code in the following screenshot shows how the index is set to column #xff1a;

数据清理完成后，开始进行数据探索任务。我们可以使用 pandas 日期时间功能运行一些有趣的查询。

After data cleansing xff0c; data exploration tasks are started. We can run some interesting queries using the pandas date time function.

例如，如果我们想要获取特定年份的所有记录，将该年份传递给 DataFrame 内的方括号。以下截图显示了 2010 年的价格数据：

For example xff0c; if we want to get all records for a given year xff0c; pass that year to the square brackets in DataFrame. The following screenshot shows price data for 2010 xff1a;

我们还可以指定我们是否要从特定日期获取数据。

We can also specify whether we want to obtain data from a specific date.

以下截图显示了 2017 年 8 月 1 日的比特币价格：

The following screenshot shows the Bitcoin price of xff1a on 1 August 2017;

要获取最高价格，使用方法，如下图所示：

To obtain the maximum price xff0c; method of use xff0c; xff1a as shown in the figure below;

我们还可以放大到特定的时间段。例如，为了仅绘制 2017 年的数据，首先选择来自 2017 年的数据，然后在数据子集上调用方法。

We can also zoom into specific time periods. For example, xff0c; to draw data only for 2017 xff0c; first to select data from 2017 xff0c; and then to call methods on the subset of data.

在下图中，我们有 2017 年以后的价格数据的图表：

In the figure below xff0c; we have price data after 2017 xff1a;

为了探索这些数据，我们使用方法查看顶部的记录，并在 DataFrame 上调用方法以获取更多信息，例如有多少记录、有多少空记录或缺失记录，或各列的数据类型是什么。

To explore these data xff0c; we use methods to view top records xff0c; and to call on DataFrame to get more information xff0c; e.g. how many records, how many blank records or missing records xff0c; or what type of data is in each column.

我们可以看到列显示为对象。我们将其更改为日期时间以可视化这些数据。为此，我们使用方法，并将转换后的值分配回同一列——DataFrame。

We can see the columns as objects. We change them to date time to visualize the data. To this end, we use method & #xff0c; and assign the converted values back to the same column, DataFrame.

以下屏幕截图描述了比特币的日期格式：

The following screenshot depicts the date format of Bitcoin xff1a;

将 DataFrame 的索引设置为列，并删除列作为单独的列。执行此步骤以利用 pandas 的时间序列功能。

Sets the DataFrame index to column & #xff0c; and removes the column as a separate column. Executes this step to use the time series function for pandas.

现在，通过在 DataFrame 上调用和来再次检查是否发生了更改。

& #xff0c; Checks if changes have occurred again by calling and calling on DataFrame.

以下屏幕截图显示了特定日期范围内的比特币：

The screenshot below shows bitcoin #xff1a within a given date range;

现在我们准备从这些数据创建图表。在 DataFrame 上调用方法，然后调用方法显示图表。

Now we're going to create a chart from these data. Call method & #xff0c on DataFrame; then call method to display the chart.

它显示了已经在我们有记录的时间段内挖出的比特币总数。

It shows the total number of bitcoins that have been excavated within the time period we have recorded.

以下截图描述了前述数据的图表：

The following screenshot depicts the chart of the aforementioned data xff1a;

最初，我们使用和方法对这些数据进行可视化探索，如下截图所示：

Initially xff0c; we used and used methods to visualize the data xff0c; xff1a as shown in the screenshot below;

接下来，我们清理、转换和重塑数据，如下截图所示：

Next xff0c; we clean, convert and reconstruct data xff0c; xff1a as shown in the screenshot below;

最后，我们将在区块数据中可视化我们的交易，如下截图所示：

Finally xff0c; we will visualize our transactions in block data xff0c; xff1a as shown in the screenshot below;

同样，还有另一个例子，我们应该看一下关于显示挖矿难度的数据。挖矿数据的步骤如下：

Also xff0c; there's another example xff0c; we should look at the data on the difficulty of mining. The steps for mining data are as follows xff1a;

1. 读取数据，如下截图所示：

1. 清理数据，如下截图所示：

我们可以看到多年来挖矿难度逐渐增加，并呈上升趋势。这些只是交易图表的几个例子。比特币和区块链生态系统中还有很多其他可供您探索的数据。

These are just a few examples of trade charts. There are a lot of other data in Bitcoin and block chain ecosystems that you can explore.

在下一个模块中，我们将学习如何收集和分析比特币骰子游戏数据。

In the next module xff0c; we will learn how to collect and analyse bitcoin dice game data.

在本节中，我们将查看来自骰子游戏门户的数据，从 API 中读取数据，并使用将其转换为表格格式。我们还将导出数据并找到需要清理的内容。我们将清理、操作和重塑数据，使其准备好进行分析，最后，我们将从干净的数据中绘制一个简单的图表。

In this section xff0c; we will look at data from the dice game portal xff0c; read data from API xff0c; and use it to convert to table format. We will also export data and find what needs to be cleaned up. We will clean, operate and reshape data xff0c; prepare them for analysis xff0c; last xff0c; and we will draw a simple chart from clean data.

我们将使用方法从 MegaDice API 链接中读取个人赢家历史数据。我们从这些数据创建了一个名为的，并调用方法来查看这些数据的样子。

We will use the method to read individual winner history data from the MegaDice API link. From this data we create a xff0c; and the method to look at the data.

以下截图中的代码显示了 的创建：

The code in the following screenshot shows the creation of xff1a;

我们导入的数据有 703 列，其中大部分是个别日期。我们将删除最后一列，这不是实际数据的一部分，也不应该存在。该列显示在以下截图中：

The data we import are 703 columns & #xff0c; most of them are individual dates. We will delete the last column & #xff0c; this is not part of the actual data & #xff0c; nor should it exist. This column is shown in the following screenshot & #xff1a;

由于列太多，行太少，我们希望将列变成行，其中每个状态都有一行记录。为了执行此操作，我们需要将列翻转为行，反之亦然。为此，我们需要执行以下步骤：

Because there are too many rows xff0c; too few rows xff0c; we want to turn the column into row xff0c; each of these states is recorded in a row. For this operation xff0c; we need to turn the column to row xff0c; and vice versa. For this purpose xff0c; we need to implement the following steps xff1a;

1. 首先，我们重置索引，以便将当前索引作为另一列引入。当我们翻转时，此列的值将成为列名。使用方法确认，如下截图所示：

1. 接下来，使用称为或转置的方法翻转 DataFrame，如下截图所示：

1. 现在，在以下截图中，我们可以看到我们的行索引已成为日期，我们之前的行已成为列：

我们已经得到了想要的列和行，但还有一些其他事情需要做。我们希望日期值以日期时间格式显示，但目前它是文本格式。然而，数据框的转置已经将日期值更改为索引。我们需要首先使用方法将它们转换为列，如下面的屏幕截图所示：

We've already got the desired columns and rows #xff0c; but there are other things to do. We want date values to be displayed in date time format xff0c; but it is currently text format. xff0c; the conversion of data boxes has changed date values to indexes. We need to first use the method to convert them to column xff0c; see xff1a in screen screenshots below;

现在我们可以将这些文本数据转换为正确的日期时间格式，如下面的屏幕截图所示：

Now we can convert these text data into the correct date time format xff0c; the screen screen screen displays xff1a below;

调用“info()”方法来确认这一点。我们在适当的日期时间格式中有了新的列“日期”。日期值需要以文本格式显示在列名索引中，如下面的屏幕截图所示：

Call info() to confirm this. We have a new column, Date, in the appropriate date time format. The date value needs to be displayed in text format in the list index & #xff0c; see xff1a on screen screen below;

我们将把索引设置回适当的“日期”列，并将“日期”列作为单独的列删除，如下面的屏幕截图所示：

We will set the index back to the appropriate “date” column xff0c; and remove xff0c from the “date” column as a separate column; see xff1a in the screen screenshot below;

现在，我们已经正确地在“日期”字段上建立了数据框的索引：

xff0c; we have correctly created a data frame index xff1a on the “date” field;

现在，这些数据可以用作时间序列函数，或者可以为不同日期子集可视化多个块等。

xff0c; these data can be used as a time sequence function xff0c; or they can visualize multiple blocks for different date subsets, etc.

在本章中，我们学习了如何准备数据分析的设置。我们看到了如何获取、读取和清理价格数据。我们还学习了如何探索、操作和可视化清理后的数据。

In this chapter xff0c; we learn how to prepare data analysis settings. We see how to get, read and clean price data. We also learn how to explore, operate and visualize post-cleaning data.

我们还探索了一些比特币和区块链图表，我们可以创建。我们学习了从哪里获取相关数据，并在 Jupyter Notebook 中读取了这些数据并导入了必要的模块。我们清理和操作了这些数据，最后，我们从这些数据和笔记本中创建了图表，但没有使用 Python。

We also explored some bitcoin and block chain charts & #xff0c; we can create. We learned where to get the relevant data & #xff0c; and we read the data in Jupyter Notebook and imported the necessary modules. We cleaned and operated the data & xff0c; xff0c; we created the chart xff0c from these data and notebooks; but did not use Python.

我们还探索了来自骰子游戏门户的数据。我们添加了来自 API 的数据，并使用 pandas 将其转换为表格格式。我们探索了数据，并找到了需要清理的内容。我们清理和操作了数据，并使其准备好进行分析。

We also explored data from the dice portal. We added data from API & #xff0c; we converted them to table format using pandas. We explored data & #xff0c; we found what needed to be cleaned up. We cleaned and operated data xff0c; and we prepared them for analysis.