The main reason to use Python is that you get a lot more options than what's included in most spreadsheets. Spreadsheets are ...

Syndication 10 simple Python tips to speed up your data analysis October 12, 2020 - 11:39 am Tips and tricks, especially in the programming world, can be very useful.

object is handled as a Python str type. int64 is handled as a Python int. Note that not all Python int s can be converted to int64 types; anything larger than (2 ** 63)-1 will not convert to int64.

TL;DR Key Takeaways : Python integration in Excel enhances data analysis by combining Python’s flexibility with Excel’s accessibility, allowing advanced analytics and workflow optimization ...

Unlock deeper analytical capabilities by integrating BQL, Bloomberg’s most advanced data API, with Python via the BQL Object Model. This session will feature practical demonstrations, code ...

6 min read Scenario Analysis: Python Code Snippets for Forecasting Investment Performance Copy and paste these code snippets to forecast investment performance in any market conditions.

import pandas as pd import statsmodels.formula.api as smf First, fetch the data needed for style analysis of equity funds. Obtain the unique tickers for the funds you plan to analyze.

Backlink analysis can be done on a spreadsheet but Python has certain advantages. Learn how to use it and customize reports with this script. Chances are, you’ve used one of the more popular ...

Useful Libraries for Data Analysis Whenever I start a data analysis project, I like to have at a minimum the following libraries installed: Requests. Matplotlib. Requests-html. Pandas.

JetBrains survey shows Python 3 adoption growing and data analysis and web development to be the leading Python use cases JetBrains, maker of the PyCharm IDE for Python, have released the results ...