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‎ PythonNotebook_CodingTutorialDocumentated.ipynb

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "71ea34bd",
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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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"import seaborn as sns\n",
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"import matplotlib.pyplot as plt"
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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": "3bd1661e",
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"metadata": {},
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"outputs": [],
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"source": [
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"df = pd.read_csv('FilterandFireData.csv')\n",
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"display(df)"
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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": "4887ed3c",
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"metadata": {},
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"outputs": [],
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"source": [
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"#extract those with 40ms to specifically extract the data\n",
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"forty_time = five_connections[five_connections['Time'] == 40]\n",
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"display(forty_time)"
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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": "ced5f652",
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"metadata": {},
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"outputs": [],
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"source": [
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"#barplot\n",
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"mean_accuracies = forty_time.groupby('digit')['Accuracy.FF'].mean()\n",
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"plt.figure(figsize=(10, 6)) # for changing the figure size\n",
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"mean_accuracies.plot(kind='bar', color= 'green')\n",
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"plt.title('Mean Accuracy for Each Digit (0-9)')\n",
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"plt.xlabel('Digits')\n",
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"plt.ylabel('Mean Accuracy.FF')\n",
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"plt.ylim(85,100) \n",
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"plt.grid(axis='y')\n",
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"plt.show()"
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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": "e09f0511",
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"metadata": {},
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"outputs": [],
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"source": [
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"#boxplot\n",
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"plt.figure(figsize=(10,6))\n",
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"#df.boxplot(column='Accuracy.FF', by='digit', grid = False)\n",
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"sns.boxplot(x='digit', y='Accuracy.FF', data=df, palette='pastel')\n",
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"plt.title('Boxplot of Accuracy for Each Digit (0-9)')\n",
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"plt.suptitle('') # Suppress the default title to avoid duplication\n",
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"plt.xlabel('Digits')\n",
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"plt.ylabel('Accuracy.FF')\n",
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"plt.ylim(85, 100) # Set the y-axis range\n",
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"plt.grid(axis='y')\n",
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"plt.grid(axis='x')\n",
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"plt.show()"
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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": "915a4518",
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"metadata": {},
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"outputs": [],
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"source": [
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"#histogram\n",
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"plt.figure(figsize=(10, 6))\n",
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"plt.hist(digit_9['Accuracy.FF'], bins=20, color='skyblue', edgecolor='black')\n",
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"plt.title('Histogram of Accuracy')\n",
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"plt.xlabel('Accuracy')\n",
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"plt.ylabel('Frequency')\n",
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"plt.grid(axis='y')\n",
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"plt.show()"
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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": "3dfdeb10",
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"metadata": {},
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"outputs": [],
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"source": [
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"#violin plot\n",
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"plt.figure(figsize=(10,6))\n",
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"sns.violinplot(x='digit', y='Accuracy.FF', data=df, inner=\"quartile\", palette='pastel')\n",
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"plt.title('Violin Plot of Accuracy for Each Digit (0-9)')\n",
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"plt.xlabel('Digits')\n",
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"plt.ylabel('Accuracy')\n",
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"plt.show()"
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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": "6941a7ce",
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"metadata": {},
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"outputs": [],
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"source": [
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"genes_data = pd.read_csv('heatmapExpressionData.csv')\n",
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"display(genes_data)\n",
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"genes = genes_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": null,
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"id": "33e310ca",
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"metadata": {},
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"outputs": [],
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"source": [
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"genes.set_index('GeneName', inplace=True)\n",
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"\n",
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"#heatmap\n",
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"plt.figure(figsize=(12, 8)) #figsize 25,30 adequate to display all genes here\n",
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"sns.heatmap(genes, cmap='viridis',linewidths=0.5) #,yticklabels=True \n",
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"plt.title('Heatmap of Gene Expression Data')\n",
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"plt.xlabel('Samples')\n",
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"plt.ylabel('Genes')\n",
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"plt.show()"
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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": "9521c6be",
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"metadata": {},
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"outputs": [],
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"source": [
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"plt.figure(figsize=(10, 6))\n",
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"sns.scatterplot(x='NAc1', y='NAc4', data=genes)\n",
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"plt.title('Scatter Plot of Gene Expression')\n",
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"plt.grid(True)\n",
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"\n",
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"plt.show()"
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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": "b2865082",
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"metadata": {},
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"outputs": [],
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"source": [
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"transposed_genes = genes.set_index('GeneName').transpose()\n",
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"\n",
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"# Plot a line plot of the gene expression data\n",
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"plt.figure(figsize=(12, 8))\n",
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"#for gene in transposed_genes.columns:\n",
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" #plt.plot(transposed_genes.index, transposed_genes[gene], label=gene)\n",
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"transposed_genes.plot(legend=False, alpha=0.5)\n",
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"plt.title('Line Plot of Gene Expression Data')\n",
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"plt.xlabel('Samples')\n",
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"plt.ylabel('Gene Expression')\n",
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"#plt.legend(loc='upper right', bbox_to_anchor=(1.25, 1))\n",
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"#plt.grid(True)\n",
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"plt.show()"
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]
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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": "3.9.12"
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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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