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Added SQL-related notebooks
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‎PyData/UsefulPackages.txt‎

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@@ -29,6 +29,7 @@ sweetviz: https://github.com/fbdesignpro/sweetviz
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dataprep: https://github.com/sfu-db/dataprep
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sidetable: https://github.com/chris1610/sidetable
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dataprofiler: https://github.com/capitalone/DataProfiler
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skimpy: https://github.com/aeturrell/skimpy
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# Database profiling
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https://github.com/darenasc/auto-eda
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boto3: Interface to S3
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PyHive: https://github.com/dropbox/PyHive
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Amazon S3 with SQL syntax: PyAthena - use plain 'ol SQL: https://github.com/laughingman7743/PyAthena
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Pandas syntax with Apache Spark: koalas - https://github.com/databricks/koalas
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PySpark: https://jacobcelestine.com/knowledge_repo/colab_and_pyspark/
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Dask/Coiled
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Dask-SQL: https://github.com/nils-braun/dask-sql

‎PyData/bookmarks.txt‎

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@@ -12,8 +12,8 @@ https://hakibenita.com/postgresql-unknown-features
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https://jacobcelestine.com/knowledge_repo/colab_and_pyspark/
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https://sparkbyexamples.com/
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chaining: https://mungingdata.com/pyspark/chaining-dataframe-transformations/
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https://phoenixnap.com/kb/install-spark-on-ubuntu
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https://www.how2shout.com/linux/installing-apache-spark-on-ubuntu-20-04-or-18-04/
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installing: https://phoenixnap.com/kb/install-spark-on-ubuntu
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installing: https://www.how2shout.com/linux/installing-apache-spark-on-ubuntu-20-04-or-18-04/
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installing: https://computingforgeeks.com/how-to-install-apache-spark-on-ubuntu-debian/
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installing, win10: https://phoenixnap.com/kb/install-spark-on-windows-10
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installing, win10: https://kontext.tech/column/spark/450/install-spark-300-on-windows-10
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# Understanding Imports/Packaging
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https://tenthousandmeters.com/blog/python-behind-the-scenes-11-how-the-python-import-system-works/
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https://towardsdatascience.com/on-writing-clean-jupyter-notebooks-abdf6c708c75
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# Cheat Sheets
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Python: https://github.com/gto76/python-cheatsheet

‎sql/DB2/Connecting_To_zOS_ODBC.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": 1,
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"id": "306bfc7c-da99-4221-aba4-1932c2810ed2",
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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 pyodbc\n",
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"from getpass import getpass"
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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": "c1b19fba-c9ea-4eec-8e8e-0c0cdac05608",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Python 3.8.10 (default, May 19 2021, 13:12:57) [MSC v.1916 64 bit (AMD64)]\n"
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]
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}
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],
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"source": [
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"!python -V -V"
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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": 3,
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"id": "12816fcf-4778-45d9-8265-3d0966f24a5b",
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"metadata": {},
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"outputs": [],
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"source": [
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"dsn = 'SRMDB_P'"
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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": 4,
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"id": "8051234e-1432-4bed-9001-8c27b2b826f0",
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"metadata": {},
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"outputs": [
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{
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"name": "stdin",
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"output_type": "stream",
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"text": [
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"Enter your mainframe password: ········\n"
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]
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}
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],
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"source": [
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"user = 'nmdpur'\n",
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"pwd = getpass(\"Enter your mainframe password:\")"
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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": 5,
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"id": "b5ca1cec-8a48-47e7-bcc1-dd3fe378f3e3",
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"metadata": {},
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"outputs": [],
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"source": [
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"sql = \"\"\"\n",
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"SELECT\n",
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" CURRENT TIMESTAMP as DATETIME_NOW\n",
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"FROM\n",
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" SYSIBM.SYSDUMMY1\n",
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"\"\"\""
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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": 6,
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"id": "ca61b9c4-0ab4-4ebd-a975-a0ee94a6b1a1",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Execute query and save results as a pandas dataframe\n",
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"with pyodbc.connect(f'DSN={dsn};UID={user};PWD={pwd}') as conn:\n",
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" df = pd.read_sql(sql, conn)"
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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": 7,
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"id": "e70e4228-6405-4c63-9c13-75a6013a8988",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>DATETIME_NOW</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>2021-05-21 17:52:06.436452</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" DATETIME_NOW\n",
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"0 2021-05-21 17:52:06.436452"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df.head()"
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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": "Py3.9 (data_eval)",
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"language": "python",
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"name": "data_eval"
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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": "3.9.8"
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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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