scatterplots
There are 11 repositories under scatterplots topic.
melindamalone/World_Weather_Analysis
The World Weather Analysis repo utilizes Python and Jupyter Notebook in conjunction with decision and repetition statements, data structures, Pandas, Matplotlib, NumPy, CitiPy, and SciPy statistics to retrieve and use data from OpenWeatherMap and Google Map API. The APIs are used to "get" requests from a server, retrieve and store values from a JSON array, use try and except blocks to resolve errors, create and format scatter plots using Matplotlib, perform linear regression and add regression lines to scatter plots while simultaneously determining favorable vacation destinations for customers based on weather conditions.
ehuelsda/skills-vs-interests
Basic scatterplot visualising skills vs interests
GiorjeanM/Plotting-the-Big-Apple
Advanced analytics skills for geographic visualisation using Python and Tableau.
raquelbaeta/r-hdi-religion-script
This script analyses the relationship between the Human Development Index (HDI), population, and non-religious groups in various countries. Plots visualise relationships between HDI, population, and non-religious groups and using scatterplots and a linear regression model to predict.
sarahtischer/WorldRiskIndex-Python_Project_CareerFoundry
Advanced exploratory analysis in Python using supervised and unsupervised machine learning to understand the dimensions of global disaster risk.
antonio-f/dimensionality_curse_plots
Quick plots in Python as a visual support for the Curse of Dimensionality phenomenon.
Develop-Packt/Introduction-to-NumPy-Pandas-and-Matplotlib
Implement advanced operations and data handling techniques on essential Python libraries to perform statistical descriptive analysis
JRigh/Data-visualization-in-Python
Elements of data visualization with Python 3
KenSaville/python-api-challenge
This repo consists of the scripts and results files associated with the pythhon api challenge done as homework for the MSU Data Science Boot Camp
melindamalone/PyBer_Analysis
The PyBer Analysis repo contains an analysis of ridesharing and city data using Python, NumPy, Matplotlib, and SciPy by creating line charts, bar charts, scatter plots, bubble charts, pie charts, and box-and-whisker plots. Using Pandas DataFrames and groupby, pivot, and resample functions, the data has been analyzed to determine total rides, total drivers, total fares, and average fare per ride and driver by rural, suburban, or urban city type.