Though Jupyter Notebook has been continuously improved, still its basic design doesn’t satisfy the majority of developers who got used to modern feature-rich IDEs. This is where notebook extensions (aka nbextensions) come in. Being not officially related to the Jupyter team, they still can make data scientist’s life much-much easier. Below I’ll describe how to install and enable Jupyter Notebook extensions with conda.Continue reading How to manage Jupyter Notebook extensions
The rumor is that installing Tensorflow with CUDA on Windows is a hard task but this time for me everything went more or less smoothly, thank God, and I’d like to record this path for the future.Continue reading Windows + TensorFlow 2 + CUDA + Anaconda
Today’s data analysis process is more than gathering and processing information. It encompasses arranging and explaining data as well as making conclusions to draw an objective and itemized picture of where the business is heading. And while 20 years ago it was a very time-consuming process, modern analysis tools allow for better efficiency as well as greater insight into the business processes.
The ultimate significance of data analysis is the ability to track the progress and changes in your business performance. This information serves a reliable foundation for informed business decisions to achieve further development. In other words, using data analysis is crucial for business success. Based on the current projections, the importance of business data analytics will only grow. Below you will find the list of tools that will help you retrieve, explore, visualize, and analyze data to have a greater insight into how your business is doing. Continue reading Tools for Data Analysis
Suppose, you have a column named ‘date’ in pandas data frame df. It represents a date and/or time, but pandas thinks it just a string (or an object) and you can’t apply all date/time-specific operations on it. The solution is simple:
df['date'] = pandas.to_datetime(df['date'])
df['date'] = pandas.to_datetime(df['date'], format='%d%b%Y:%H:%M:%S.%f')
How to get a brief view on what data poses? Continue reading Early Data Analysis with Pandas