Generate and analyze sales data using Python. Explore data visualization techniques using Dash, Pandas, and Plotly. Gain insights into sales performance with interactive charts and revenue breakdown analysis.
In this article, we will analyze the stock data of Apple Inc. (AAPL) using time series analysis and SARIMA modeling. We will use Python libraries such as pandas, matplotlib, statsmodels, and pmdarima to get historical stock data, check for stationarity, decompose the data, and fit a SARIMA model. This analysis can help investors make informed decisions regarding AAPL stock.
Learn how to perform better fundamental analysis for investment advice with the help of Python. This article covers the use of libraries such as pandas, matplotlib, yfinance, numpy_financial, yahoofinancials, and statsmodels to download financial data, calculate important metrics, fit time-series models, and visualize the data, ultimately saving professionals time and providing more accurate investment advice.
Learn how to forecast demand using Python with this comprehensive guide. We walk you through the process step-by-step, from importing libraries to creating mock data and generating forecasts using moving averages. Code examples and tips included!
Learn how to install libraries and use them to analyze stock data in Python and R. This article breaks down the steps needed to install and use libraries like yfinance and matplotlib in Python and yahoo.finance and ggplot2 in R. You will also find functions with explanations for retrieving and plotting stock data in both languages.
