ASPP 2024 material

exercises/tabular_join/.ipynb_checkpoints/tabular_join-checkpoint.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f11a76bf",
+   "metadata": {},
+   "source": [
+    "# Exercise: Add experiment information to electrophysiology data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b6f2742b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# Set some Pandas options: maximum number of rows/columns it's going to display\n",
+    "pd.set_option('display.max_rows', 1000)\n",
+    "pd.set_option('display.max_columns', 100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2967c84e",
+   "metadata": {},
+   "source": [
+    "# Load electrophysiology data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "ed626ee3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv('../../data/QC_passed_2024-07-04_collected.csv')\n",
+    "info = pd.read_csv('../../data/op_info.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2fef4d37",
+   "metadata": {},
+   "source": [
+    "# 1. Add experiment information to the electrophysiology results\n",
+    "\n",
+    "* Is there information for every experiment?\n",
+    "* How many experiments did each patcher perform?  (i.e., individual OPs, or rows in `info`)\n",
+    "* How many samples did each patcher analyze?  (i.e., individual rows in `df`)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1f3f57eb",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "44031178",
+   "metadata": {},
+   "source": [
+    "# 2. Remove outliers from the table\n",
+    "\n",
+    "1. Load the list of outliers in `outliers.csv`\n",
+    "2. Use an anti-join to remove the outliers from the table\n",
+    "3. How many samples (rows) are left in the data?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7fa953af",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "84270332",
+   "metadata": {},
+   "source": [
+    "# 3. Save final result in `processed_QC_passed_2024-07-04_collected_v1.csv`\n",
+    "\n",
+    "1. Using the `.to_csv` method of Pandas DataFrames"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c7bcff45",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}