panda_stats_test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from panda_stats import panda_stats\n",
    "from IPython.display import display\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test Case 1\n",
    "Run the following cell to produce the panda_stats display. At the top, you will see your input data (similar to df.head()). In the middle, you will see a series of statistical tests organized in accordions. After running a statistical test, the output will appear in the output section below. \n",
    "\n",
    "With this data, I recommend the following tests - \n",
    "1. Independent t-test with grouping variable `condition` and dependent variable `systolic bp`. \n",
    "2. Linear regression with independent variable `systolic bp` and dependent variable `diastolic bp`.\n",
    "3. Wilcoxon signed-rank test with grouping variable `gender` and dependent variable `resting heart rate`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d23ca9ecf9164df7ac0f0414a3052ba1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HTML(value='<h1>PandaStats</h1>', placeholder=''), HTML(value='<h3>Input Data</h3>', placeholde…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# test dataset 1 - bp + hr experiment\n",
    "df = pd.DataFrame({\"condition\": pd.Series([\"placebo\",\"placebo\",\"control\",\"placebo\",\"control\", \"control\"], dtype=\"category\"), \\\n",
    "                \"systolic bp\": [140,135,138,122, 125, 146], \\\n",
    "                \"diastolic bp\": [85,90,93,102, 100, 101], \\\n",
    "                \"resting heart rate\": [55, 60, 80, 90, 47, 59], \\\n",
    "                \"age\": pd.Series([\"young\", \"mid\", \"mid\", \"old\", \"young\", \"old\"], dtype=\"category\"), \\\n",
    "                \"gender\": pd.Series([\"M\", \"F\", \"M\", \"M\", \"F\", \"F\"], dtype=\"category\")})\n",
    "\n",
    "# call pandastats\n",
    "widget = panda_stats(df)\n",
    "display(widget)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test Case 2\n",
    "Run the following cell to produce the panda_stats display.\n",
    "\n",
    "With this data, I recommend the following tests - \n",
    "1. ANOVA with grouping variable `species` and dependent variable `sepal length`. \n",
    "2. Correlation (Spearman) with `sepal length` and `sepal width`.\n",
    "3. Correlation (Pearson) `sepal width` and `petal width`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a544fa9f39504e4e9d569ac6a90fc694",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HTML(value='<h1>PandaStats</h1>', placeholder=''), HTML(value='<h3>Input Data</h3>', placeholde…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# test dataset 2 - iris dataset\n",
    "df = pd.read_csv('https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv')\n",
    "df['species'] = df['species'].astype('category')\n",
    "# call pandastats\n",
    "widget = panda_stats(df)\n",
    "display(widget)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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