mirror of
https://github.com/wassname/cookiecutter-data-science.git
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156 lines
3.5 KiB
Plaintext
156 lines
3.5 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "1b44551e",
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"metadata": {},
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"source": [
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"# Exploratory Data Analysis\n",
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"\n",
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"Hypothesis: What is this notebook about?"
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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": "198de680",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2022-06-28T02:34:01.879987Z",
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"start_time": "2022-06-28T02:34:01.864103Z"
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}
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},
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"outputs": [],
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"source": [
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"# autoreload your package\n",
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"%load_ext autoreload\n",
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"%autoreload 2\n",
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"import {{ cookiecutter.project_name.lower().replace(' ', '_') }}"
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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": "a372ed7c",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2022-06-28T02:34:02.470436Z",
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"start_time": "2022-06-28T02:34:02.424826Z"
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}
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},
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"outputs": [],
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"source": [
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"## secrets\n",
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"from dotenv import load_dotenv\n",
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"load_dotenv() # take environment variables from .env.\n",
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"\n",
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"import warnings\n",
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"# warnings.simplefilter(\"ignore\")\n",
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"warnings.filterwarnings(\"ignore\", \".*does not have many workers.*\")\n",
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"warnings.filterwarnings(\"ignore\", \".*divide by zero.*\")\n",
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"\n",
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"## numeric, plotting\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"%matplotlib inline\n",
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"plt.style.use('ggplot')\n",
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"plt.rcParams['figure.figsize'] = (7.0, 4)\n",
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"\n",
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"## utils\n",
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"from pathlib import Path\n",
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"from tqdm.auto import tqdm\n",
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"import logging, os, re\n",
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"import collections, functools, itertools\n",
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"\n",
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"# torch\n",
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"import pytorch_lightning as pl\n",
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"from einops import rearrange, repeat, reduce\n",
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"import torch\n",
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"import torch.nn as nn\n",
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"\n",
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"# logging\n",
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"from loguru import logger\n",
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"logger.remove()\n",
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"logger.add(os.sys.stdout, level=\"ERROR\", colorize=True, format=\"<level>{time} | {message}</level>\")"
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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": "54a03c3a",
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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": "64890012",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2022-06-28T02:34:02.890216Z",
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"start_time": "2022-06-28T02:34:02.882249Z"
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}
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},
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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": "1d4da6fa",
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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": "6d102e3d",
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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 3.10.4 64-bit",
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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": "3.10.4"
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},
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"toc": {
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"base_numbering": 1,
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"nav_menu": {},
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"number_sections": true,
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"sideBar": true,
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"skip_h1_title": false,
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"title_cell": "Table of Contents",
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"title_sidebar": "Contents",
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"toc_cell": false,
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"toc_position": {},
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"toc_section_display": true,
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"toc_window_display": false
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"vscode": {
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"interpreter": {
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"nbformat": 4,
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