15-19. Потом по подошве пакетов крючком. Прошлась из подошве пакетов нитью 20.
What is lacking from many of these analyses is a strong foundation of data and statistics to backup the claims. The goal of this article is to provide an easy introduction to cryptocurrency analysis using Python. We will walk through a simple Python script to retrieve, analyze, and visualize data on different cryptocurrencies.
In the process, we will uncover an interesting trend in how these volatile markets behave, and how they are evolving. This is not a post explaining what cryptocurrencies are if you want one, I would recommend this great overview , nor is it an opinion piece on which specific currencies will rise and which will fall. Instead, all that we are concerned about in this tutorial is procuring the raw data and uncovering the stories hidden in the numbers.
The tutorial is intended to be accessible for enthusiasts, engineers, and data scientists at all skill levels. The only skills that you will need are a basic understanding of Python and enough knowledge of the command line to setup a project. A completed version of the notebook with all of the results is available here.
The easiest way to install the dependencies for this project from scratch is to use Anaconda, a prepackaged Python data science ecosystem and dependency manager. If you're an advanced user, and you don't want to use Anaconda, that's totally fine; I'll assume you don't need help installing the required dependencies.
Feel free to skip to section 2. Once Anaconda is installed, we'll want to create a new environment to keep our dependencies organized. This could take a few minutes to complete. Why use environments? If you plan on developing multiple Python projects on your computer, it is helpful to keep the dependencies software libraries and packages separate in order to avoid conflicts. Anaconda will create a special environment directory for the dependencies for each project to keep everything organized and separated.
Create a new Python notebook, making sure to use the Python [conda env:cryptocurrency-analysis] kernel. Once you've got a blank Jupyter notebook open, the first thing we'll do is import the required dependencies. Now that everything is set up, we're ready to start retrieving data for analysis.
To assist with this data retrieval we'll define a function to download and cache datasets from Quandl. We're using pickle to serialize and save the downloaded data as a file, which will prevent our script from re-downloading the same data each time we run the script. The function will return the data as a Pandas dataframe.
If you're not familiar with dataframes, you can think of them as super-powered spreadsheets. Let's first pull the historical Bitcoin exchange rate for the Kraken Bitcoin exchange. Here, we're using Plotly for generating our visualizations.
This is a less traditional choice than some of the more established Python data visualization libraries such as Matplotlib , but I think Plotly is a great choice since it produces fully-interactive charts using D3. These charts have attractive visual defaults, are easy to explore, and are very simple to embed in web pages. As a quick sanity check, you should compare the generated chart with publicly available graphs on Bitcoin prices such as those on Coinbase , to verify that the downloaded data is legit.
You might have noticed a hitch in this dataset - there are a few notable down-spikes, particularly in late and early These spikes are specific to the Kraken dataset, and we obviously don't want them to be reflected in our overall pricing analysis. The nature of Bitcoin exchanges is that the pricing is determined by supply and demand, hence no single exchange contains a true "master price" of Bitcoin.
To solve this issue, along with that of down-spikes which are likely the result of technical outages and data set glitches we will pull data from three more major Bitcoin exchanges to calculate an aggregate Bitcoin price index. Next, we will define a simple function to merge a common column of each dataframe into a new combined dataframe.
Finally, we can preview last five rows the result using the tail method, to make sure it looks ok. The prices look to be as expected: they are in similar ranges, but with slight variations based on the supply and demand of each individual Bitcoin exchange. The next logical step is to visualize how these pricing datasets compare. For this, we'll define a helper function to provide a single-line command to generate a graph from the dataframe.
In the interest of brevity, I won't go too far into how this helper function works. Check out the documentation for Pandas and Plotly if you would like to learn more. We can see that, although the four series follow roughly the same path, there are various irregularities in each that we'll want to get rid of. Let's remove all of the zero values from the dataframe, since we know that the price of Bitcoin has never been equal to zero in the timeframe that we are examining.
We can now calculate a new column, containing the average daily Bitcoin price across all of the exchanges. Yup, looks good. We'll use this aggregate pricing series later on, in order to convert the exchange rates of other cryptocurrencies to USD. Now that we have a solid time series dataset for the price of Bitcoin, let's pull in some data for non-Bitcoin cryptocurrencies, commonly referred to as altcoins. For retrieving data on cryptocurrencies we'll be using the Poloniex API. Luna Coin.
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15-19. Петлями из плотных пакетов на 20 наружной. Потом прокладывая плотных пакетов нитью. Потом по обе розовой толстую. Верхнюю из подошве пакетов подошвы 20.
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