{"id":13655994,"url":"https://github.com/beltashazzer/jmpy","last_synced_at":"2025-04-23T17:30:56.830Z","repository":{"id":36941091,"uuid":"41248523","full_name":"beltashazzer/jmpy","owner":"beltashazzer","description":"Quick plotting and data visualization of pandas and numpy data.","archived":false,"fork":false,"pushed_at":"2017-01-27T22:50:12.000Z","size":10998,"stargazers_count":57,"open_issues_count":0,"forks_count":2,"subscribers_count":4,"default_branch":"master","last_synced_at":"2025-04-07T23:07:14.067Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/beltashazzer.png","metadata":{"files":{"readme":"README.ipynb","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2015-08-23T12:18:29.000Z","updated_at":"2024-02-10T20:36:40.000Z","dependencies_parsed_at":"2022-07-09T03:31:42.546Z","dependency_job_id":null,"html_url":"https://github.com/beltashazzer/jmpy","commit_stats":null,"previous_names":[],"tags_count":1,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/beltashazzer%2Fjmpy","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/beltashazzer%2Fjmpy/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/beltashazzer%2Fjmpy/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/beltashazzer%2Fjmpy/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/beltashazzer","download_url":"https://codeload.github.com/beltashazzer/jmpy/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":250480339,"owners_count":21437525,"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":[],"created_at":"2024-08-02T04:00:45.168Z","updated_at":"2025-04-23T17:30:54.478Z","avatar_url":"https://github.com/beltashazzer.png","language":"Python","funding_links":[],"categories":["Python","Uncategorized"],"sub_categories":["Uncategorized"],"readme":"{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## JMPY  \\n\",\n    \"Jmpy is an analysis library created to simplify common plotting and modeling tasks.  Simplicity, a common plotting signature, and ease of use are preferred over flexibility.\\n\",\n    \"\\n\",\n    \"The goal is to create mini-reports with each function for better visualization of data.\\n\",\n    \"___\\n\",\n    \"Currently, there are three modules:  plotting, modeling, and bayes.  \\n\",\n    \"Plotting is used to make pretty graphs, and modeling is used to anaylyze data with visual output.  \\n\",\n    \"Bayes is currenlty in development to support easy bayesian analysis and plotting, relying on significant assumptions about the underlaying data.\\n\",\n    \"\\n\",\n    \"This module relies heavily on statsmodels, patsy, pymc, and pandas.\\n\",\n    \"\\n\",\n    \"___\\n\",\n    \"__Overview__  \\n\",\n    \"[Plotting](#plotting)  \\n\",\n    \"* [Histogram](#histogram)  \\n\",\n    \"* [Cumprob](#cumprob)  \\n\",\n    \"* [Boxplot](#boxplot)\\n\",\n    \"* [ScatterPlot](#scatter)\\n\",\n    \"* [Contour](#contour)\\n\",\n    \"\\n\",\n    \"[Grid Layout](#grid)  \\n\",\n    \"[Modeling](#modeling)  \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"The autoreload extension is already loaded. To reload it, use:\\n\",\n      \"  %reload_ext autoreload\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%load_ext autoreload\\n\",\n    \"%autoreload 2\\n\",\n    \"    \\n\",\n    \"%matplotlib inline\\n\",\n    \"import numpy as np\\n\",\n    \"import pandas as pd\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"plt.style.use('bmh')\\n\",\n    \"\\n\",\n    \"import jmpy.plotting as jp\\n\",\n    \"import jmpy.modeling as jm\\n\",\n    \"\\n\",\n    \"import warnings\\n\",\n    \"warnings.filterwarnings('ignore')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\u003ca id='plotting'\u003e\u003c/a\u003e\\n\",\n    \"### Plotting  \\n\",\n    \"___\\n\",\n    \"Create some artifical data for our analysis:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"\u003cdiv\u003e\\n\",\n       \"\u003ctable border=\\\"1\\\" class=\\\"dataframe\\\"\u003e\\n\",\n       \"  \u003cthead\u003e\\n\",\n       \"    \u003ctr style=\\\"text-align: right;\\\"\u003e\\n\",\n       \"      \u003cth\u003e\u003c/th\u003e\\n\",\n       \"      \u003cth\u003exc\u003c/th\u003e\\n\",\n       \"      \u003cth\u003exc2\u003c/th\u003e\\n\",\n       \"      \u003cth\u003exd\u003c/th\u003e\\n\",\n       \"      \u003cth\u003exe\u003c/th\u003e\\n\",\n       \"      \u003cth\u003exf\u003c/th\u003e\\n\",\n       \"      \u003cth\u003exg\u003c/th\u003e\\n\",\n       \"      \u003cth\u003ey\u003c/th\u003e\\n\",\n       \"      \u003cth\u003eytrue\u003c/th\u003e\\n\",\n       \"    \u003c/tr\u003e\\n\",\n       \"  \u003c/thead\u003e\\n\",\n       \"  \u003ctbody\u003e\\n\",\n       \"    \u003ctr\u003e\\n\",\n       \"      \u003cth\u003e0\u003c/th\u003e\\n\",\n       \"      \u003ctd\u003e0.000000\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e0.000000\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e3\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e50\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e0.1\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e12.310639\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e41.696174\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e51.050000\u003c/td\u003e\\n\",\n       \"    \u003c/tr\u003e\\n\",\n       \"    \u003ctr\u003e\\n\",\n       \"      \u003cth\u003e1\u003c/th\u003e\\n\",\n       \"      \u003ctd\u003e0.401606\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e0.161288\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e5\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e50\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e0.1\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e-4.350195\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e96.030224\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e85.453219\u003c/td\u003e\\n\",\n       \"    \u003c/tr\u003e\\n\",\n       \"    \u003ctr\u003e\\n\",\n       \"      \u003cth\u003e2\u003c/th\u003e\\n\",\n       \"      \u003ctd\u003e0.803213\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e0.645151\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e5\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e50\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e0.4\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e24.779776\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e90.837742\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e85.859664\u003c/td\u003e\\n\",\n       \"    \u003c/tr\u003e\\n\",\n       \"    \u003ctr\u003e\\n\",\n       \"      \u003cth\u003e3\u003c/th\u003e\\n\",\n       \"      \u003ctd\u003e1.204819\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e1.451589\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e7\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e10\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e0.4\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e-19.704997\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e105.596364\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e120.229335\u003c/td\u003e\\n\",\n       \"    \u003c/tr\u003e\\n\",\n       \"    \u003ctr\u003e\\n\",\n       \"      \u003cth\u003e4\u003c/th\u003e\\n\",\n       \"      \u003ctd\u003e1.606426\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e2.580604\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e3\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e50\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e0.4\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e3.845414\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e38.828264\u003c/td\u003e\\n\",\n       \"      \u003ctd\u003e52.682232\u003c/td\u003e\\n\",\n       \"    \u003c/tr\u003e\\n\",\n       \"  \u003c/tbody\u003e\\n\",\n       \"\u003c/table\u003e\\n\",\n       \"\u003c/div\u003e\"\n      ],\n      \"text/plain\": [\n       \"         xc       xc2  xd  xe   xf         xg           y       ytrue\\n\",\n       \"0  0.000000  0.000000   3  50  0.1  12.310639   41.696174   51.050000\\n\",\n       \"1  0.401606  0.161288   5  50  0.1  -4.350195   96.030224   85.453219\\n\",\n       \"2  0.803213  0.645151   5  50  0.4  24.779776   90.837742   85.859664\\n\",\n       \"3  1.204819  1.451589   7  10  0.4 -19.704997  105.596364  120.229335\\n\",\n       \"4  1.606426  2.580604   3  50  0.4   3.845414   38.828264   52.682232\"\n      ]\n     },\n     \"execution_count\": 11,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"nsamples = 250\\n\",\n    \"xc = np.linspace(0, 100, nsamples)\\n\",\n    \"xc2 = xc**2\\n\",\n    \"xd = np.random.choice([1, 3, 5, 7], nsamples)\\n\",\n    \"xe = np.random.choice([10, 30, 50], nsamples)\\n\",\n    \"xf = np.random.choice([.1, .4], nsamples)\\n\",\n    \"xg = np.random.normal(size=nsamples)*15\\n\",\n    \"\\n\",\n    \"X = np.column_stack((xc, xc2, xd, xe))\\n\",\n    \"beta = np.array([1, .01, 17, .001])\\n\",\n    \"\\n\",\n    \"e = np.random.normal(size=nsamples)*10\\n\",\n    \"ytrue = np.dot(X, beta)\\n\",\n    \"y = ytrue + e\\n\",\n    \"\\n\",\n    \"data = {}\\n\",\n    \"data['xc'] = xc\\n\",\n    \"data['xc2'] = xc2\\n\",\n    \"data['xd'] = xd\\n\",\n    \"data['xe'] = xe\\n\",\n    \"data['xf'] = xf\\n\",\n    \"data['xg'] = xg\\n\",\n    \"data['y'] = y\\n\",\n    \"data['ytrue'] = ytrue\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame.from_dict(data)\\n\",\n    \"df.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\u003ca id='histogram'\u003e\u003c/a\u003e\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAA2kAAAGoCAYAAADco62eAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\n\",\n      \"text/plain\": [\n       \"\u003cmatplotlib.figure.Figure at 0xbcddac8\u003e\"\n      ]\n     },\n     \"execution_count\": 12,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"jp.histogram('y', df)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Lets start to visualize how our artifical data looks.  First, plot a histogram of the results.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"If you want to look at the data color coded by a categorical variable, you need to specify \\\"legend\\\":\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAA2MAAAGoCAYAAADLgT1XAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\ngv8HUaj935eirOwAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"\u003cmatplotlib.figure.Figure at 0xc82b1d0\u003e\"\n      ]\n     },\n     \"execution_count\": 13,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"jp.histogram('y', df, legend='xd', bins=50, cumprob=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\u003ca id='cumprob'\u003e\u003c/a\u003e\\n\",\n    \"You can also create cumprob plots:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAA2YAAAGoCAYAAAAtqPYTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\nh4z11ZVCWTanOTcRp3sySbhiyZffbuaD22v4","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbeltashazzer%2Fjmpy","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbeltashazzer%2Fjmpy","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbeltashazzer%2Fjmpy/lists"}