309 lines
44 KiB
Plaintext
309 lines
44 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "ec2a032c",
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"metadata": {},
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"source": [
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"---\n",
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"date: '2026-03-12T20:00:07+08:00'\n",
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"title: 'Using Pandas For Getting Data And Analise Them'\n",
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"feature_link: \"https://www.midjourney.com/home/\"\n",
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"feature_text: \"by IA Midjourney\"\n",
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"description: 'Using Pandas For Getting Data And Analise Them'\n",
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"isStarred: false\n",
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"tags:\n",
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"- pandas\n",
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"categories:\n",
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"- dev\n",
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"images:\n",
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"keywords:\n",
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"series:\n",
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"- Data and Data Tools\n",
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"draft: true\n",
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"---"
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]
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},
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{
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"cell_type": "markdown",
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"id": "762356ba-8dd6-4e7d-98bc-413980ba2ba4",
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"metadata": {},
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"source": [
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"## The idea"
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]
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},
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{
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"cell_type": "markdown",
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"id": "51dbea95-75af-4def-a8e8-eb95ed8258eb",
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"metadata": {},
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"source": [
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"I want to test some of the pandas functionality so I try the import from HTML table for make some data analisys.\n",
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"So I choose a web page with data in a table (or two in this case)"
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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": 1,
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"id": "d8948cc6-0a35-4d05-a44c-401ee8ab4523",
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"metadata": {},
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"outputs": [],
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"source": [
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"import matplotlib\n",
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"import pandas as pd\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "26008d72-af7f-4684-8bad-b829c2d774d3",
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"metadata": {},
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"source": [
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"Here we have the basic import for the needed package for the project."
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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": "f4451b98-aff7-4dad-9788-f7cbff92ce21",
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"metadata": {},
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"outputs": [],
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"source": [
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"url = \"https://www.mangacodex.com/oricon_yearly.php?title_series=&year_series=All&title_volumes=&year_volumes=All\"\n",
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"\n",
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"pd.set_option(\"display.precision\", 2)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "53634b74-7b71-45d7-a12d-ca4c96cfac05",
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"metadata": {},
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"source": [
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"Some basic config (the log, the url,...) for my little script. I allwayse put at the top of the file for easy edit of them, if needed."
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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": "b6fb3973-dff2-40c9-b3fb-467ff859c9e9",
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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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"Downloading data from the page...\n",
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"Found 2 tables on the page.\n",
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"<class 'pandas.DataFrame'>\n",
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"<class 'pandas.DataFrame'>\n"
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]
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}
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],
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"source": [
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"print(\"Downloading data from the page...\")\n",
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"tables = pd.read_html(url, thousands='.', decimal =',')\n",
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"print(f\"Found {len(tables)} tables on the page.\")\n",
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"\n",
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"df1 = pd.DataFrame(tables[0])\n",
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"print(type(df1))\n",
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"df2 = pd.DataFrame(tables[1])\n",
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"print(type(df2))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7e03d8bb-e520-4a1d-8c24-ccd0863faa0b",
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"metadata": {},
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"source": [
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"Starting with the scrape of the page with Pandas. In this case it returnes 2 table in pandas.DataFrame"
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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": "5206887b-37b5-4d23-b7ad-805637b07a3a",
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"metadata": {},
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"outputs": [],
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"source": [
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"if len(tables) >= 2:\n",
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" table_series = tables[0]\n",
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" table_volumes = tables[1]\n",
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"\n",
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"else:\n",
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" print(\"Error: The page does not contain enough tables.\")\n",
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" raise Error"
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]
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},
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{
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"cell_type": "markdown",
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"id": "54ae5555-b0cb-42f7-9b80-de907e9be379",
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"metadata": {},
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"source": [
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"And now we have the two table as pandas Dataframe. Are there some empty 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": 5,
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"id": "fcfe611f-a87b-49ff-bc6e-c7daa52bb9a0",
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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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"-*--*--*--*--*--*--*--*--*--*--*--*--*--*--*--*--*--*--*--*-\n",
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"Missing value stats for Series:\n",
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"Ranking 0\n",
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"Title 0\n",
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"Sales 0\n",
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"Year 0\n",
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"dtype: int64\n",
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"\n",
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"-*--*--*--*--*--*--*--*--*--*--*--*--*--*--*--*--*--*--*--*-\n",
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"Missing value stats for Volumes:\n",
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"Ranking 0\n",
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"Volume 0\n",
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"Sales 0\n",
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"Year 0\n",
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"dtype: int64\n"
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]
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}
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],
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"source": [
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"print(\"-*-\" * 20)\n",
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"print(\"Missing value stats for Series:\")\n",
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"print(table_series.isnull().sum())\n",
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"print()\n",
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"print(\"-*-\" * 20)\n",
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"print(\"Missing value stats for Volumes:\")\n",
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"print(table_volumes.isnull().sum())"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e94ecf50-2b48-4b7b-91a3-3baea5823308",
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"metadata": {},
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"source": [
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"## Start the analysis"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4d49a38b-3a42-4a61-b6d3-8cc65305ab6e",
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"metadata": {},
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"source": [
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"So we know the data is consistant so we need to know some generic data about this two dataset."
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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": 11,
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"id": "433f803c-dd0b-434e-9425-1d46b6526a4b",
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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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"-*-\n",
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" Ranking Title Sales Year\n",
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"0 1 One Piece #50 1678208 2008\n",
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"1 2 One Piece #51 1646978 2008\n",
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"2 3 Nana #19 1645128 2008\n",
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"3 4 One Piece #49 1544000 2008\n",
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"4 5 Nana #20 1431335 2008\n",
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"<class 'pandas.DataFrame'>\n",
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"RangeIndex: 3413 entries, 0 to 3412\n",
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"Columns: 4 entries, Ranking to Year\n",
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"dtypes: int64(3), str(1)\n",
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"memory usage: 106.8 KB\n",
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"None\n",
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"\n",
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"-*-\n",
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" Ranking Volume Sales Year\n",
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"0 1 One Piece 5956540 2008\n",
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"1 2 Naruto 4261054 2008\n",
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"2 3 20th Century Boys 3710054 2008\n",
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"3 4 Hitman Reborn! 3371618 2008\n",
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"4 5 Bleach 3161825 2008\n",
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"<class 'pandas.DataFrame'>\n",
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"RangeIndex: 860 entries, 0 to 859\n",
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"Columns: 4 entries, Ranking to Year\n",
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"dtypes: int64(3), str(1)\n",
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"memory usage: 27.0 KB\n",
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"None\n"
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]
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}
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],
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"source": [
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"print(\"-*-\")\n",
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"print(table_series.head())\n",
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"print(table_series.info(verbose=False))\n",
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"print()\n",
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"print(\"-*-\")\n",
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"print(table_volumes.head())\n",
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"print(table_volumes.info(verbose=False))\n"
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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": 12,
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"id": "aedc41b6-a7c4-442e-b56c-fe2fe9a85f51",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<Axes: title={'center': 'Manga Series'}>"
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]
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},
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"execution_count": 12,
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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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"data": {
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"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"table_series[[\"Title\",\"Sales\",\"Year\"]].plot(title=\"Manga Series\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "c0d88d19-decb-4555-9459-c66060f65e9c",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"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": "3.12.9"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|