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Diff for: ‎1. Getting Started with Pandas/Getting Started with Pandas.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": 14,
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"id": "d3d27258",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import pandas as pd"
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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": "da448c8c",
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"metadata": {},
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"outputs": [],
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"source": [
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"lst = [1,2,3,4,5]"
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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": 15,
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"id": "c6bd0c9a",
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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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"array([1, 2, 3, 4, 5])"
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]
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},
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"execution_count": 15,
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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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"np.array(lst)"
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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": 22,
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"id": "0b65a039",
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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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"0 [1, 2, 3, 4, 5]\n",
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"1 [1, 2, 3, 4, 5]\n",
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"2 [1, 2, 3, 4, 5]\n",
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"dtype: object"
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]
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},
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"execution_count": 22,
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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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"pd.Series([lst,lst,lst])"
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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": 23,
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"id": "88558f91",
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"metadata": {},
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"outputs": [],
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"source": [
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"df = pd.DataFrame([lst,lst,lst,lst,lst,lst,lst,lst,lst], columns = ['A','B','C','D','E'])"
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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": 25,
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"id": "690e6ed8",
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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>A</th>\n",
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" <th>B</th>\n",
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" <th>C</th>\n",
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" <th>D</th>\n",
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" <th>E</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>1</td>\n",
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" <td>4</td>\n",
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" <td>5</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>1</td>\n",
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" <td>2</td>\n",
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" <td>3</td>\n",
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" <td>4</td>\n",
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" <td>5</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>1</td>\n",
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" <td>2</td>\n",
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" <td>3</td>\n",
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" <td>4</td>\n",
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" <td>5</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>1</td>\n",
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" <td>2</td>\n",
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" <td>3</td>\n",
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" <td>4</td>\n",
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" <td>5</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>1</td>\n",
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" <td>2</td>\n",
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" <td>3</td>\n",
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" <td>4</td>\n",
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" <td>5</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>5</th>\n",
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" <td>1</td>\n",
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" <td>2</td>\n",
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" <td>3</td>\n",
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" <td>4</td>\n",
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" <td>5</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>6</th>\n",
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" <td>1</td>\n",
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" <td>2</td>\n",
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" <td>3</td>\n",
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" <td>4</td>\n",
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" <td>5</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>7</th>\n",
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" <td>1</td>\n",
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" <td>2</td>\n",
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" <td>3</td>\n",
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" <td>4</td>\n",
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" <td>5</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>8</th>\n",
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" <td>1</td>\n",
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" <td>2</td>\n",
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" <td>3</td>\n",
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" <td>4</td>\n",
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" <td>5</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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" A B C D E\n",
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"0 1 2 3 4 5\n",
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"1 1 2 3 4 5\n",
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"2 1 2 3 4 5\n",
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"3 1 2 3 4 5\n",
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"4 1 2 3 4 5\n",
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"5 1 2 3 4 5\n",
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"6 1 2 3 4 5\n",
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"7 1 2 3 4 5\n",
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"8 1 2 3 4 5"
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]
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},
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"execution_count": 25,
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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"
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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": 26,
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"id": "c0b939e2",
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"metadata": {},
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"outputs": [],
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"source": [
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"del 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": 27,
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"id": "ce679a64",
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"metadata": {},
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"outputs": [
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{
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"ename": "NameError",
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"evalue": "name 'df' is not defined",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m<ipython-input-27-00cf07b74dcd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
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"\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
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]
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}
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],
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"source": [
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"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": null,
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"id": "975234d4",
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"metadata": {},
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"outputs": [],
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"source": [
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"pd.array()"
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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": "a1029b5e",
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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": "82d26a17",
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"metadata": {},
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{
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},
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{
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"metadata": {},
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"outputs": [],
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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": "5caba000",
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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",
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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": "3.8.8"
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}
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"nbformat": 4,
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}

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