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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": 2,
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"id": "97177ccf",
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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 numpy as np"
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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": 8,
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"id": "8cf560c1",
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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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"DataFrame from dictionary:\n",
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" Product Price\n",
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"P1 Apple 1.00\n",
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"P2 Banana 0.50\n",
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"P3 Orange 0.75\n"
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]
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}
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],
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"source": [
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"# From a dictionary\n",
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"data = {\n",
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" 'Product': ['Apple', 'Banana', 'Orange'], \n",
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" 'Price': [1.0, 0.5, 0.75]\n",
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" }\n",
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"indexx = ['P1', 'P2', 'P3']\n",
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"df = pd.DataFrame(data, index=indexx)\n",
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"print(\"DataFrame from dictionary:\\n\", df)"
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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": 14,
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"id": "d80bd1cf",
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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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"Merged DataFrame:\n",
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" Product Price Region\n",
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"0 Apple 1.00 North\n",
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"1 Banana 0.50 South\n",
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"2 Books NaN North\n",
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"3 Orange 0.75 NaN\n"
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]
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}
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],
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"source": [
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"# Creating another DataFrame\n",
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"data2 = {\n",
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" 'Product': ['Apple', 'Banana','Books'], \n",
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" 'Region': ['North', 'South','North']\n",
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" }\n",
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"df2 = pd.DataFrame(data2)\n",
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"\n",
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"# Merging DataFrames\n",
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"merged = pd.merge(df, df2, on='Product', how='outer')\n",
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"print(\"Merged DataFrame:\\n\", merged)\n",
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"# how{‘left’, ‘right’, ‘outer’, ‘inner’, ‘cross’}, default ‘inner’"
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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": "Python 3",
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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": "3.11.4"
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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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