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Us baby names finished and wind_stats
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‎README.md‎

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| | | |
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|:-------------:|:-------------:|:-----:|
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|[Getting and knowing](#getting-and-knowing)|[Merge](#merge) |Exporting|
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|[Filtering and Sorting](#filtering-and-sorting)| Stats | Visualization |
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|[Filtering and Sorting](#filtering-and-sorting)| [Stats](#stats) | Visualization |
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|[Grouping](#grouping)| Deleting and Droping | Indexing |
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|[Apply](#apply)| Creating Series and DataFrames| Using Dates|
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### [Merge](https://github.com/guipsamora/pandas_exercises/tree/master/Merge)
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[Auto_MPG](https://github.com/guipsamora/pandas_exercises/tree/master/Merge/Auto_MPG)
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[House Market](https://github.com/guipsamora/pandas_exercises/tree/master/Merge/Housing%20Market)
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### [Stats](https://github.com/guipsamora/pandas_exercises/tree/master/Stats)
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[US_Baby_Names](https://github.com/guipsamora/pandas_exercises/tree/master/Stats/US_Baby_Names)
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[Wind_Stats](https://github.com/guipsamora/pandas_exercises/tree/master/Stats/Wind_Stats)

‎Stats/US_Baby_Names/Exercises.ipynb‎

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{
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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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"# US - Baby Names"
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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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"### Introduction:\n",
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"\n",
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"We are going to use a subset of [US Baby Names](https://www.kaggle.com/kaggle/us-baby-names) from Kaggle. \n",
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"In the file it will be names from 2004 until 2014\n",
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"\n",
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"\n",
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"### Step 1. Import the necessary libraries"
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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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"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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},
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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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"### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/Stats/US_Baby_Names/US_Baby_Names_right.csv). "
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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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"### Step 3. Assign it to a variable called baby_names."
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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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"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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},
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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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"### Step 4. See the first 10 entries"
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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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"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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},
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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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"### Step 5. Delete the column 'Unnamed: 0' and 'Id'"
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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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"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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},
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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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"### Step 6. Is there more male or female names in the 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": null,
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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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},
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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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"### Step 7. Group the dataset by name and assign to names"
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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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"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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},
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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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"### Step 8. How many different names exist in the 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": null,
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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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},
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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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"### Step 9. What is the name with most occurrences?"
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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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"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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},
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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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"### Step 10. How many different names have the least occurrences?"
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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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"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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},
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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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"### Step 11. What is the median name occurrence?"
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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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"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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},
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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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"### Step 12. What is the standard deviation of names?"
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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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"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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},
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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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"### Step 13. Get a summary with the mean, min, max, std and quartiles."
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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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"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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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 2",
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"language": "python",
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"name": "python2"
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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": 2
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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": "ipython2",
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"version": "2.7.11"
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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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