ASPP 2024 material

exercises/pandas_intro/.ipynb_checkpoints/pandas_intro-checkpoint.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "560f48cd",
+   "metadata": {},
+   "source": [
+    "# Exercise: Have a look at the neural data using Pandas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "f7777604",
+   "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": "6494fb41",
+   "metadata": {},
+   "source": [
+    "# Load electrophysiology data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "9ca3bec6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv('../../data/QC_passed_2024-07-04_collected.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0d78a63e",
+   "metadata": {},
+   "source": [
+    "## 1. How many rows/columns does the data set have?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4b68e5a6",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0fab635e",
+   "metadata": {},
+   "source": [
+    "## 2. Display the first 5 rows of the DataFrame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4adcd5bf",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "deecdfa0",
+   "metadata": {},
+   "source": [
+    "## 3. Display the names and dtypes of all the columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "64df567c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "411f4228",
+   "metadata": {},
+   "source": [
+    "## 4. Display the unique values of the `high K concentration` and of the `treatment` columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b90ce541",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1e395e8d",
+   "metadata": {},
+   "source": [
+    "## 5. Display the main statistics of the `max_spikes` column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e2b86159",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c8c9f6b2",
+   "metadata": {},
+   "source": [
+    "## 6. Show all the rows where the max number of spikes is larger than 50"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c449e9ff",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ce9ff32b",
+   "metadata": {},
+   "source": [
+    "## 7. Display the main statistics of `'max_spikes'`, for the rows where `high K concentration` is `8 mM` and `15 mM` (separately)\n",
+    "\n",
+    "Are the distributions any different?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8b84faa2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b2d1c2b",
+   "metadata": {},
+   "source": [
+    "## 8. Display the statistics of `max_spikes` when `high K concentration` is `8 mM`, and the maximum number of spikes is <= 100\n",
+    "\n",
+    "Does that change your conclusion?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1201f7d1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6dbbf6c8",
+   "metadata": {},
+   "source": [
+    "## 9. Transform the `high K concentration` column into a numerical column\n",
+    "\n",
+    "a) Discard the last three characters of the columns (`' mM'`)\n",
+    "\n",
+    "b) Use `.astype(float)` to convert to floating point numbers\n",
+    "\n",
+    "c) Save the result in a column `K (mM)`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1cf5c15d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ecc0cad1",
+   "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
+}