ASPP 2024 material
This commit is contained in:
Pietro Berkes
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exercises/pandas_intro/.DS_Store
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exercises/pandas_intro/.DS_Store
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "560f48cd",
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"metadata": {},
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"source": [
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"# Exercise: Have a look at the neural data using Pandas"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "f7777604",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"\n",
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"# Set some Pandas options: maximum number of rows/columns it's going to display\n",
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"pd.set_option('display.max_rows', 1000)\n",
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"pd.set_option('display.max_columns', 100)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "6494fb41",
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"metadata": {},
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"source": [
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"# Load electrophysiology data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "9ca3bec6",
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"metadata": {},
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"outputs": [],
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"source": [
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"df = pd.read_csv('../../data/QC_passed_2024-07-04_collected.csv')"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0d78a63e",
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"metadata": {},
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"source": [
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"## 1. How many rows/columns does the data set have?"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "4b68e5a6",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"id": "0fab635e",
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"metadata": {},
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"source": [
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"## 2. Display the first 5 rows of the DataFrame"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "4adcd5bf",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"id": "deecdfa0",
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"metadata": {},
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"source": [
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"## 3. Display the names and dtypes of all the columns"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "64df567c",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"id": "411f4228",
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"metadata": {},
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"source": [
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"## 4. Display the unique values of the `high K concentration` and of the `treatment` columns"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b90ce541",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"id": "1e395e8d",
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"metadata": {},
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"source": [
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"## 5. Display the main statistics of the `max_spikes` column"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e2b86159",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"id": "c8c9f6b2",
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"metadata": {},
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"source": [
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"## 6. Show all the rows where the max number of spikes is larger than 50"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c449e9ff",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"id": "ce9ff32b",
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"metadata": {},
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"source": [
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"## 7. Display the main statistics of `'max_spikes'`, for the rows where `high K concentration` is `8 mM` and `15 mM` (separately)\n",
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"\n",
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"Are the distributions any different?"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8b84faa2",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"id": "8b2d1c2b",
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"metadata": {},
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"source": [
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"## 8. Display the statistics of `max_spikes` when `high K concentration` is `8 mM`, and the maximum number of spikes is <= 100\n",
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"\n",
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"Does that change your conclusion?"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1201f7d1",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"id": "6dbbf6c8",
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"metadata": {},
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"source": [
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"## 9. Transform the `high K concentration` column into a numerical column\n",
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"\n",
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"a) Discard the last three characters of the columns (`' mM'`)\n",
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"\n",
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"b) Use `.astype(float)` to convert to floating point numbers\n",
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"\n",
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"c) Save the result in a column `K (mM)`"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1cf5c15d",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ecc0cad1",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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227
exercises/pandas_intro/pandas_intro.ipynb
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227
exercises/pandas_intro/pandas_intro.ipynb
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@ -0,0 +1,227 @@
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{
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"cells": [
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{
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||||
"cell_type": "markdown",
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"id": "560f48cd",
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"metadata": {},
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"source": [
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"# Exercise: Have a look at the neural data using Pandas"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "f7777604",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"\n",
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"# Set some Pandas options: maximum number of rows/columns it's going to display\n",
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"pd.set_option('display.max_rows', 1000)\n",
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"pd.set_option('display.max_columns', 100)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "6494fb41",
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"metadata": {},
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"source": [
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"# Load electrophysiology data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "9ca3bec6",
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"metadata": {},
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"outputs": [],
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"source": [
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"df = pd.read_csv('../../data/QC_passed_2024-07-04_collected.csv')"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0d78a63e",
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"metadata": {},
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"source": [
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"## 1. How many rows/columns does the data set have?"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "4b68e5a6",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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||||
{
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||||
"cell_type": "markdown",
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"id": "0fab635e",
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||||
"metadata": {},
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||||
"source": [
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"## 2. Display the first 5 rows of the DataFrame"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "4adcd5bf",
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"metadata": {},
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||||
"outputs": [],
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"source": []
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},
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{
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||||
"cell_type": "markdown",
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||||
"id": "deecdfa0",
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||||
"metadata": {},
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||||
"source": [
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"## 3. Display the names and dtypes of all the columns"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "64df567c",
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||||
"metadata": {},
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||||
"outputs": [],
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||||
"source": []
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},
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||||
{
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||||
"cell_type": "markdown",
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"id": "411f4228",
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||||
"metadata": {},
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"source": [
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"## 4. Display the unique values of the `high K concentration` and of the `treatment` columns"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b90ce541",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"id": "1e395e8d",
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"metadata": {},
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"source": [
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"## 5. Display the main statistics of the `max_spikes` column"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e2b86159",
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||||
"metadata": {},
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||||
"outputs": [],
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||||
"source": []
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},
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{
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"cell_type": "markdown",
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"id": "c8c9f6b2",
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"metadata": {},
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"source": [
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"## 6. Show all the rows where the max number of spikes is larger than 50"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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||||
"id": "c449e9ff",
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||||
"metadata": {},
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||||
"outputs": [],
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||||
"source": []
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||||
},
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||||
{
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||||
"cell_type": "markdown",
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||||
"id": "ce9ff32b",
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||||
"metadata": {},
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"source": [
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"## 7. Display the main statistics of `'max_spikes'`, for the rows where `high K concentration` is `8 mM` and `15 mM` (separately)\n",
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"\n",
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"Are the distributions any different?"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8b84faa2",
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||||
"metadata": {},
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||||
"outputs": [],
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||||
"source": []
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},
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{
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"cell_type": "markdown",
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"id": "8b2d1c2b",
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"metadata": {},
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"source": [
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"## 8. Display the statistics of `max_spikes` when `high K concentration` is `8 mM`, and the maximum number of spikes is <= 100\n",
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"\n",
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"Does that change your conclusion?"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1201f7d1",
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||||
"metadata": {},
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||||
"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"id": "6dbbf6c8",
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||||
"metadata": {},
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||||
"source": [
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"## 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",
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"c) Save the result in a column `K (mM)`"
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||||
]
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||||
},
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{
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"cell_type": "code",
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"execution_count": null,
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||||
"id": "1cf5c15d",
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||||
"metadata": {},
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||||
"outputs": [],
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||||
"source": []
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||||
},
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||||
{
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||||
"cell_type": "code",
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||||
"execution_count": null,
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||||
"id": "ecc0cad1",
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||||
"metadata": {},
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||||
"outputs": [],
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||||
"source": []
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||||
}
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||||
],
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"metadata": {
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"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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||||
"language_info": {
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||||
"codemirror_mode": {
|
||||
"name": "ipython",
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||||
"version": 3
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||||
},
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||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.3"
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||||
}
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||||
},
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||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
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||||
}
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||||
1487
exercises/pandas_intro/pandas_intro_solution.ipynb
Normal file
1487
exercises/pandas_intro/pandas_intro_solution.ipynb
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