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{
"cells": [
{
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"source": [
"# US - Baby Names"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Introduction:\n",
"\n",
"We are going to use a subset of [US Baby Names](https://www.kaggle.com/kaggle/us-baby-names) from Kaggle. \n",
"In the file it will be names from 2004 until 2014\n",
"\n",
"\n",
"### Step 1. Import the necessary libraries"
]
},
{
"cell_type": "code",
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"metadata": {
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"outputs": [],
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},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 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). "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 3. Assign it to a variable called baby_names."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 4. See the first 10 entries"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 5. Delete the column 'Unnamed: 0' and 'Id'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 6. Is there more male or female names in the dataset?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 7. Group the dataset by name and assign to names"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 8. How many different names exist in the dataset?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 9. What is the name with most occurrences?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 10. How many different names have the least occurrences?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 11. What is the median name occurrence?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 12. What is the standard deviation of names?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 13. Get a summary with the mean, min, max, std and quartiles."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
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}
],
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"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
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