Commit fa1cda8f authored by Eva Zangerle's avatar Eva Zangerle
Browse files

updated naming of notebooks

parent 43c80758
......@@ -10,11 +10,22 @@
"\n",
"Eva Zangerle\n",
"\n",
"## Overview of Notebooks\n",
"* Datasets: [3_datasets.ipynb](3_datasets.ipynb)\n",
"* Data Preparation and Quality: [4_data_preparation_quality.ipynb](4_data_preparation_quality.ipynb)\n",
"* Feature Engineering: [5_feature_engineering.ipynb](5_feature_engineering.ipynb)\n",
"* Dataset Analyses: [6_dataset_analyses.ipynb](6_dataset_analyses.ipynb)\n",
"* Hypotheses and Evaluation: [7_hypotheses_evaluation.ipynb](7_hypotheses_evaluation.ipynb)\n",
"* Modeling and Prediction: [8_modeling_prediction.ipynb](8_modeling_prediction.ipynb)\n",
"* Reproducible Research: [9_reproducible_research.ipynb](9_reproducible_research.ipynb)\n",
"\n",
"\n",
"## General Notes\n",
"* Code is partly taken from further sources, such as books.\n",
"* Sources are annotated (and acknowledged!) as follows:\n",
" * (CleaningData): Cleaning Data for Effective Data Science: Doing the other 80% of the work with Python, R, and command-line tools; David Mertz; Packt Publishing, 2021; [Github repo](https://github.com/PacktPublishing/Cleaning-Data-for-Effective-Data-Science/)\n",
"* Unless marked otherwise, code was written by Eva Zangerle.\n",
"* I deliberately mix different Python packages (e.g., for visualization matplotlib, pandas and seaborn) to showcase their use.\n",
"\n",
"\n",
"\n",
......@@ -36,6 +47,16 @@
"## Further tools\n",
"* jq command linen json processor: https://stedolan.github.io/jq/\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f6860d72-11e4-4574-b20b-7884a6653abc",
"metadata": {},
"outputs": [],
"source": [
" "
]
}
],
"metadata": {
......
{
"cells": [],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}
{
"cells": [
{
"cell_type": "markdown",
"id": "b5576d1e-7912-4308-ae7d-d6b0571ade38",
"metadata": {},
"source": [
"# Visualization"
]
},
{
"cell_type": "markdown",
"id": "072aa23b-1c58-4b80-a8d4-28db8e1d60fc",
"metadata": {},
"source": [
"todo:\n",
"* plotly and handcuffs (also add to start.py) as follows:\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "557c1313-8fb5-4222-9064-961d8f51f744",
"metadata": {},
"outputs": [
{
"ename": "ModuleNotFoundError",
"evalue": "No module named 'plotly'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-1-3bacd8ca2d3f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Visualization\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mplotly\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplotly\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mplotly\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraph_objs\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mgo\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mplotly\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moffline\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0miplot\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minit_notebook_mode\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0minit_notebook_mode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconnected\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'plotly'"
]
}
],
"source": [
"\n",
"# Visualization\n",
"import plotly.plotly as py\n",
"import plotly.graph_objs as go\n",
"from plotly.offline import iplot, init_notebook_mode\n",
"init_notebook_mode(connected=True)\n",
"import cufflinks as cf\n",
"cf.go_offline(connected=True)\n",
"cf.set_config_file(theme='pearl')\n",
"\n",
"print('Your favorite libraries have been loaded.')\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "68da3423-ec0c-4f31-866f-01288666cbe6",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "42cf0d5f-6c8d-4fbd-9f08-37dc11f27fc9",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.9.5"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "be4ee762-b4c6-421b-beec-ec63bd8c13a5",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.9.5"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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