Empty Blocks in the Bitcoin Blockchain? In [1]: # First we import blocksci import blocksci # We import Counter from collections, which we will use to count items in the lists we’ll create from collections import Counter # we instantiate the chain object chain = blocksci.Blockchain(“/BlockSci/config_file”) # we also import import pandas as pd import matplotlib.pyplot …
Hallo, for this tutorial we’ll see how to display information about a single block with PlutoHash Jupyter Notebook. First thing to do is to import BlockSci module. #import BlockSci moduleimport blocksci The second thing we need to do is to instantiate the chain object: # instantiate the chain objectchain = blocksci.Blockchain(“/BlockSci/config_file”) Now we select the …
Bitcoin Blockchain specific block data with Jupyter Notebook and Python Read More »
In this post we’ll see how it’s possible, with just a few lines in Python, to analyze how the average daily cost of fees changes over time, from one year to the next but also from one month to the next. As a reminder, we are analyzing the bitcoin blockchain. Let’s start by importing the …
In this simple tutorial we will see how you can observe the fees paid in a specific block by knowing the location of the block in the blockchain. We start by importing the blocksci library and instantiating the blockchain. We import the pandas library to store the data in a pandas.DataFrame, and the seaborn library …
BlockSci was designed and built as an open-source software platform for analyzing blockchains. The use cases in blockchain analysis are many, useful applications can be made in both research and business fields. However, BlockSci requires a local setup, which means having to parse the blockchain you want to analyze, which takes time and resources. The …
