147 pyecharts Python options, make charting easier 10.7k 148 tushare A utility for crawling historical and Real-time Quotes data of China s... 10.6k 149 mlagents Unity Machine Learning Agents 10.6k 150 gym-unity Unity Machine Learning Agents Gym Interface 10.6k 151 click Composable command lin...
We have, so we createdChartify, an open-source Python library that wrapsBokehto make it easier for data scientists to create charts. Chartify is more intuitive than other charting tools Back in 2017, we took a good look how data scientists at Spotify created charts. Unsurprisingly, there...
# To make development easier, faster, and less expensive, downsample for now sampled_taxi_df = filtered_df.sample(True, 0.001, seed=1234) # The charting package needs a Pandas DataFrame or NumPy array to do the conversion sampled_taxi_pd_df = sampled_taxi_df.toPandas() 可以了解数据集中...
Visualisation and reporting:Many backtesting software tools offer advanced charting and visualisation capabilities, making it easier to analyse and interpret backtest results. These tools often provide interactive charts, performance metrics, and customizable reports, enabling traders to gain valuable insights...
A list of AI autonomous agents. Contribute to electron-python/awesome-ai-agents development by creating an account on GitHub.
Make a feature detection. Find particular items in the movies or pictures, such as faces, eyes, or automobiles. 17. Seaborn Seabornis a Python package designed for charting and data visualization, much like Matplotlib. In actuality, Seaborn is an open-source library that was built on Matplotlib...
approach, making it easier to manage strategies. Risk management techniquesfor price action trading Risk management is a aspect of trading that ensures capital preservation and sustainability in the markets. In price action trading while the focus is primarily on interpreting price movements and ...
In biological research, we're currently in agolden ageof data. It's never been easier to assemble large datasets to probe biological questions. But these large datasets come with their own problems: How to clean and validate data? How to combine datasets from multiple sources?
In this case the graph is much simpler (and easier to read!) Figure 3. Task graph for x+x.T with 2×2 chunking of data The second step for Dask is to send the graph to the scheduler to schedule the subtasks and execute them on the available resources. That step is accomplished ...
it may be easiest (and easier to read later) to edit a cell and rerun it. Keep in mind that, as you learned inthe first lesson, you should always be able to run your notebook from top to bottom and achieve the desired results. Make sure as you re-run cells that they still work ...