{"id":25664435,"url":"https://github.com/kingsley-ezenwaka/mean-var-std","last_synced_at":"2026-05-09T07:40:20.857Z","repository":{"id":278223508,"uuid":"934921559","full_name":"kingsley-ezenwaka/mean-var-std","owner":"kingsley-ezenwaka","description":"This is a (mini) Python project, completed as part of the requirements for the Data Analysis with Python certification from freeCodeCamp.com.","archived":false,"fork":false,"pushed_at":"2025-02-18T16:25:52.000Z","size":5,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-23T00:26:41.935Z","etag":null,"topics":["numpy","python"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/kingsley-ezenwaka.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2025-02-18T16:08:32.000Z","updated_at":"2025-02-22T23:27:52.000Z","dependencies_parsed_at":"2025-02-18T17:37:11.449Z","dependency_job_id":null,"html_url":"https://github.com/kingsley-ezenwaka/mean-var-std","commit_stats":null,"previous_names":["kingsley-3z3nw4k4/mean-var-std","kingsley-ezenwaka/mean-var-std"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kingsley-ezenwaka%2Fmean-var-std","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kingsley-ezenwaka%2Fmean-var-std/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kingsley-ezenwaka%2Fmean-var-std/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kingsley-ezenwaka%2Fmean-var-std/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/kingsley-ezenwaka","download_url":"https://codeload.github.com/kingsley-ezenwaka/mean-var-std/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":240427315,"owners_count":19799471,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["numpy","python"],"created_at":"2025-02-24T06:18:42.634Z","updated_at":"2026-05-09T07:40:15.816Z","avatar_url":"https://github.com/kingsley-ezenwaka.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Mean-Variance-Standard Deviation Calculator\n\nThis is a (mini) Python project, completed as part of the requirements for the **Data Analysis with Python** certification from [freeCodeCamp.com](https://www.freecodecamp.org/learn/data-analysis-with-python/).\n\n## Project Instructions\n\nCreate a function named `calculate()` in mean_var_std.py that uses Numpy to output the mean, variance, standard deviation, max, min, and sum of the rows, columns, and elements in a 3 x 3 matrix.\n\nThe input of the function should be a list containing 9 digits. The function should convert the list into a 3 x 3 Numpy array, and then return a dictionary containing the mean, variance, standard deviation, max, min, and sum along both axes and for the flattened matrix.\n\nThe returned dictionary should follow this format:\n```\n{\n  'mean': [axis1, axis2, flattened],\n  'variance': [axis1, axis2, flattened],\n  'standard deviation': [axis1, axis2, flattened],\n  'max': [axis1, axis2, flattened],\n  'min': [axis1, axis2, flattened],\n  'sum': [axis1, axis2, flattened]\n}\n```\nIf a list containing less than 9 elements is passed into the function, it should raise a ValueError exception with the message: \"List must contain nine numbers.\" The values in the returned dictionary should be lists and not Numpy arrays.\n\nThe instructions for building the project can be found at [freeCodeCamp](https://www.freecodecamp.org/learn/data-analysis-with-python/data-analysis-with-python-projects/mean-variance-standard-deviation-calculator).","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkingsley-ezenwaka%2Fmean-var-std","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkingsley-ezenwaka%2Fmean-var-std","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkingsley-ezenwaka%2Fmean-var-std/lists"}