465 lines
8.6 KiB
Plaintext
465 lines
8.6 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"___\n",
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"\n",
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"<a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>\n",
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"___\n",
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"# Series"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The first main data type we will learn about for pandas is the Series data type. Let's import Pandas and explore the Series object.\n",
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"\n",
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"A Series is very similar to a NumPy array (in fact it is built on top of the NumPy array object). What differentiates the NumPy array from a Series, is that a Series can have axis labels, meaning it can be indexed by a label, instead of just a number location. It also doesn't need to hold numeric data, it can hold any arbitrary Python Object.\n",
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"\n",
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"Let's explore this concept through some examples:"
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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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"metadata": {
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"collapsed": true
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},
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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": "markdown",
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"metadata": {},
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"source": [
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"### Creating a Series\n",
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"\n",
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"You can convert a list,numpy array, or dictionary to a Series:"
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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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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"labels = ['a','b','c']\n",
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"my_list = [10,20,30]\n",
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"arr = np.array([10,20,30])\n",
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"d = {'a':10,'b':20,'c':30}"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"** Using Lists**"
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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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"metadata": {
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"collapsed": false
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},
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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 10\n",
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"1 20\n",
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"2 30\n",
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"dtype: int64"
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]
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},
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"execution_count": 4,
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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(data=my_list)"
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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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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"a 10\n",
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"b 20\n",
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"c 30\n",
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"dtype: int64"
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]
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},
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"execution_count": 5,
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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(data=my_list,index=labels)"
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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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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"a 10\n",
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"b 20\n",
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"c 30\n",
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"dtype: int64"
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]
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},
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"execution_count": 6,
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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(my_list,labels)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"** NumPy Arrays **"
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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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"metadata": {
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"collapsed": false
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},
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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 10\n",
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"1 20\n",
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"2 30\n",
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"dtype: int64"
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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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"pd.Series(arr)"
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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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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"a 10\n",
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"b 20\n",
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"c 30\n",
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"dtype: int64"
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]
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},
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"execution_count": 8,
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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(arr,labels)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"** Dictionary**"
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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": 9,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"a 10\n",
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"b 20\n",
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"c 30\n",
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"dtype: int64"
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]
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},
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"execution_count": 9,
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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(d)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Data in a Series\n",
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"\n",
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"A pandas Series can hold a variety of object types:"
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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": 10,
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"metadata": {
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"collapsed": false
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},
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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 a\n",
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"1 b\n",
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"2 c\n",
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"dtype: object"
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]
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},
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"execution_count": 10,
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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(data=labels)"
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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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"metadata": {
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"collapsed": false
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},
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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 <built-in function sum>\n",
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"1 <built-in function print>\n",
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"2 <built-in function len>\n",
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"dtype: object"
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]
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},
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"execution_count": 11,
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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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"# Even functions (although unlikely that you will use this)\n",
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"pd.Series([sum,print,len])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Using an Index\n",
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"\n",
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"The key to using a Series is understanding its index. Pandas makes use of these index names or numbers by allowing for fast look ups of information (works like a hash table or dictionary).\n",
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"\n",
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"Let's see some examples of how to grab information from a Series. Let us create two sereis, ser1 and ser2:"
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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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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"ser1 = pd.Series([1,2,3,4],index = ['USA', 'Germany','USSR', 'Japan']) "
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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": 13,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"USA 1\n",
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"Germany 2\n",
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"USSR 3\n",
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"Japan 4\n",
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"dtype: int64"
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]
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},
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"execution_count": 13,
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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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"ser1"
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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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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"ser2 = pd.Series([1,2,5,4],index = ['USA', 'Germany','Italy', 'Japan']) "
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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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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"USA 1\n",
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"Germany 2\n",
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"Italy 5\n",
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"Japan 4\n",
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"dtype: int64"
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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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"ser2"
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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": 16,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"1"
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]
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},
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"execution_count": 16,
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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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"ser1['USA']"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"collapsed": false
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},
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"source": [
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"Operations are then also done based off of index:"
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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": 17,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Germany 4.0\n",
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"Italy NaN\n",
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"Japan 8.0\n",
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"USA 2.0\n",
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"USSR NaN\n",
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"dtype: float64"
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]
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},
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"execution_count": 17,
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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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"ser1 + ser2"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Let's stop here for now and move on to DataFrames, which will expand on the concept of Series!\n",
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"# Great Job!"
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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": "3.5.1"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}
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