From ed2ff7c6e0e55bc67029a5055d8e4d2438305137 Mon Sep 17 00:00:00 2001
From: AnirudhVIyer <69951190+AnirudhVIyer@users.noreply.github.com>
Date: Fri, 14 Jul 2023 12:19:46 -0400
Subject: [PATCH] refactored is_snippet and with-clause removed
---
snippet-error (1).ipynb | 1833 ---------------------------------------
1 file changed, 1833 deletions(-)
delete mode 100644 snippet-error (1).ipynb
diff --git a/snippet-error (1).ipynb b/snippet-error (1).ipynb
deleted file mode 100644
index c8ba59464..000000000
--- a/snippet-error (1).ipynb
+++ /dev/null
@@ -1,1833 +0,0 @@
-{
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- "source": [
- "%load_ext sql\n",
- "%sql sqlite://"
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- "id": "a938708d-36f5-4702-93ba-eee0d781cfe9",
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- "text/html": [
- "Running query in 'sqlite://'"
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- "text/plain": [
- "Running query in 'sqlite://'"
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- "1 rows affected."
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- "%sql CREATE TABLE people (name varchar(50),age varchar(50),number int, country varchar(50),gender_1 varchar(50), gender_2 varchar(50)); INSERT INTO people VALUES ('joe', '48', 82, 'usa', '0', 'male'); INSERT INTO people VALUES ('paula', '50', 93, 'uk', '1', 'female');"
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- "text/plain": [
- "Running query in 'sqlite://'"
- ]
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- "
\n",
- " \n",
- " \n",
- " name | \n",
- " age | \n",
- " number | \n",
- " country | \n",
- " gender_1 | \n",
- " gender_2 | \n",
- "
\n",
- " \n",
- " \n",
- " \n",
- " joe | \n",
- " 48 | \n",
- " 82 | \n",
- " usa | \n",
- " 0 | \n",
- " male | \n",
- "
\n",
- " \n",
- "
\n",
- "ResultSet
: to convert to pandas, call .DataFrame()
or to polars, call .PolarsDataFrame()
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- "+------+-----+--------+---------+----------+----------+\n",
- "| name | age | number | country | gender_1 | gender_2 |\n",
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- "| joe | 48 | 82 | usa | 0 | male |\n",
- "+------+-----+--------+---------+----------+----------+"
- ]
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- "execution_count": 3,
- "metadata": {},
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- }
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- "source": [
- "%%sql --save joe\n",
- "select * from people where country = 'usa'"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "baf46de7-7eee-4dc2-99dc-514d6a08c95d",
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- "Generating CTE with stored snippets : 'joe'"
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- " gender_2 | \n",
- "
\n",
- " \n",
- " \n",
- " \n",
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\n",
- "ResultSet
: to convert to pandas, call .DataFrame()
or to polars, call .PolarsDataFrame()
"
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- "+------+-----+--------+---------+----------+----------+\n",
- "| name | age | number | country | gender_1 | gender_2 |\n",
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- "SELECT * FROM joe WHERE 1 = 0"
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\n",
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- "
"
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- "+--------+\n",
- "| Name |\n",
- "+--------+\n",
- "| people |\n",
- "+--------+"
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- "%sqlcmd tables"
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- "id": "50bdbff6-b02e-481b-982e-7ac7c2c39245",
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- "text": [
- "Profiling using saved snippet : joe\n"
- ]
- },
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- "data": {
- "text/html": [
- " Following statistics are not available in\n",
- " pysqlite: STD, 25%, 50%, 75%
\n",
- " \n",
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- " | \n",
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\n",
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\n",
- " \n",
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\n",
- " \n",
- " freq | \n",
- " 1 | \n",
- " nan | \n",
- " nan | \n",
- " 1 | \n",
- " nan | \n",
- " 1 | \n",
- "
\n",
- " \n",
- " mean | \n",
- " nan | \n",
- " 48.0000 | \n",
- " 82.0000 | \n",
- " nan | \n",
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\n",
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\n",
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- " nan | \n",
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\n",
- " \n",
- " 25% | \n",
- " nan | \n",
- " nan | \n",
- " nan | \n",
- " nan | \n",
- " nan | \n",
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\n",
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\n",
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- " nan | \n",
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\n",
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- " | \n",
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- " | \n",
- " nan | \n",
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\n",
- " \n",
- "
Warning: Columns age
gender_1
have a datatype mismatch -> numeric values stored as a string.
Cannot calculate mean/min/max/std/percentiles
"
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- "+--------+------+---------+---------+---------+----------+----------+\n",
- "| | name | age | number | country | gender_1 | gender_2 |\n",
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- "| count | 1 | 1 | 1 | 1 | 1 | 1 |\n",
- "| unique | 1 | 1 | 1 | 1 | 1 | 1 |\n",
- "| top | joe | nan | nan | usa | nan | male |\n",
- "| freq | 1 | nan | nan | 1 | nan | 1 |\n",
- "| mean | nan | 48.0000 | 82.0000 | nan | 0.0000 | nan |\n",
- "| std | nan | nan | nan | nan | nan | nan |\n",
- "| min | nan | | 82 | nan | | nan |\n",
- "| 25% | nan | nan | nan | nan | nan | nan |\n",
- "| 50% | nan | nan | nan | nan | nan | nan |\n",
- "| 75% | nan | nan | nan | nan | nan | nan |\n",
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- "Removing NULLs, if there exists any from age\n"
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",
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- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "%sqlplot bar -t joe -c age"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "id": "8de8bb0e-061c-4562-a489-a0f4e23fa88e",
- "metadata": {
- "execution": {
- "iopub.execute_input": "2023-07-14T15:58:54.380234Z",
- "iopub.status.busy": "2023-07-14T15:58:54.378248Z",
- "iopub.status.idle": "2023-07-14T15:58:54.880423Z",
- "shell.execute_reply": "2023-07-14T15:58:54.879436Z",
- "shell.execute_reply.started": "2023-07-14T15:58:54.380202Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- " \n",
- " \n",
- " Count | \n",
- "
\n",
- " \n",
- " \n",
- " \n",
- " 1 | \n",
- "
\n",
- " \n",
- "
\n",
- "ResultSet
: to convert to pandas, call .DataFrame()
or to polars, call .PolarsDataFrame()
"
- ],
- "text/plain": [
- "+-------+\n",
- "| Count |\n",
- "+-------+\n",
- "| 1 |\n",
- "+-------+"
- ]
- },
- "execution_count": 9,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql duckdb://\n",
- "CREATE TABLE people (name varchar(50), number int, country varchar(50));\n",
- "INSERT INTO people VALUES ('joe', 82, 'usa');\n",
- "INSERT INTO people VALUES ('paula', 93, 'uk');\n",
- "INSERT INTO people VALUES ('sam', 77, 'canada');\n",
- "INSERT INTO people VALUES ('emily', 65, 'usa');\n",
- "INSERT INTO people VALUES ('michael', 89, 'usa');\n",
- "INSERT INTO people VALUES ('sarah', 81, 'uk');\n",
- "INSERT INTO people VALUES ('james', 76, 'usa');\n",
- "INSERT INTO people VALUES ('angela', 88, 'usa');\n",
- "INSERT INTO people VALUES ('robert', 82, 'usa');\n",
- "INSERT INTO people VALUES ('lisa', 92, 'uk');\n",
- "INSERT INTO people VALUES ('mark', 77, 'usa');\n",
- "INSERT INTO people VALUES ('jennifer', 68, 'australia');"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "id": "64236d89-d7f0-4ad3-a062-06fa6d3a5cd5",
- "metadata": {
- "execution": {
- "iopub.execute_input": "2023-07-14T15:59:05.320367Z",
- "iopub.status.busy": "2023-07-14T15:59:05.319836Z",
- "iopub.status.idle": "2023-07-14T15:59:05.463663Z",
- "shell.execute_reply": "2023-07-14T15:59:05.463156Z",
- "shell.execute_reply.started": "2023-07-14T15:59:05.320334Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "Running query in 'duckdb://'"
- ],
- "text/plain": [
- "Running query in 'duckdb://'"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- "\n",
- " \n",
- " \n",
- " name | \n",
- " number | \n",
- " country | \n",
- "
\n",
- " \n",
- " \n",
- " \n",
- " joe | \n",
- " 82 | \n",
- " usa | \n",
- "
\n",
- " \n",
- " emily | \n",
- " 65 | \n",
- " usa | \n",
- "
\n",
- " \n",
- " michael | \n",
- " 89 | \n",
- " usa | \n",
- "
\n",
- " \n",
- " james | \n",
- " 76 | \n",
- " usa | \n",
- "
\n",
- " \n",
- " angela | \n",
- " 88 | \n",
- " usa | \n",
- "
\n",
- " \n",
- " robert | \n",
- " 82 | \n",
- " usa | \n",
- "
\n",
- " \n",
- " mark | \n",
- " 77 | \n",
- " usa | \n",
- "
\n",
- " \n",
- "
\n",
- "ResultSet
: to convert to pandas, call .DataFrame()
or to polars, call .PolarsDataFrame()
"
- ],
- "text/plain": [
- "+---------+--------+---------+\n",
- "| name | number | country |\n",
- "+---------+--------+---------+\n",
- "| joe | 82 | usa |\n",
- "| emily | 65 | usa |\n",
- "| michael | 89 | usa |\n",
- "| james | 76 | usa |\n",
- "| angela | 88 | usa |\n",
- "| robert | 82 | usa |\n",
- "| mark | 77 | usa |\n",
- "+---------+--------+---------+"
- ]
- },
- "execution_count": 10,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql --save citizen\n",
- "select * from people where country = 'usa'"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "id": "a762c8c7-c331-4715-a846-d71b8f086864",
- "metadata": {
- "execution": {
- "iopub.execute_input": "2023-07-14T15:59:16.778842Z",
- "iopub.status.busy": "2023-07-14T15:59:16.778418Z",
- "iopub.status.idle": "2023-07-14T15:59:16.981364Z",
- "shell.execute_reply": "2023-07-14T15:59:16.980877Z",
- "shell.execute_reply.started": "2023-07-14T15:59:16.778817Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Exploring using saved snippet : citizen\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
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- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "%sqlcmd explore -t citizen"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "8ee5fa89-40f6-4349-958d-526c023bdb09",
- "metadata": {},
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- "source": []
- }
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- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
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- "codemirror_mode": {
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- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.10.12"
- }
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- "nbformat": 4,
- "nbformat_minor": 5
-}