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joqu86joqu86
committedNov 30, 2022
adding sprint challenge
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‎sprint_challenge/DS_Unit1_Sprint1_AG.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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"id": "j4EFYnn4MixR",
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"nbgrader": {
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"schema_version": 3,
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}
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},
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"source": [
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"## Sprint Challenge: Data Wrangling and Storytelling\n",
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"### Notebook points total: 14\n"
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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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"locked": true,
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}
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},
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"source": [
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"## Python Fundamentals"
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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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"deletable": false,
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"editable": false,
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"cell_type": "markdown",
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"checksum": "dc8877d44d9b265b9d66a579582b88cd",
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"locked": true,
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"schema_version": 3,
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"solution": false,
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"task": false
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}
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},
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"source": [
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"**Task 1** - Python Objects\n",
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"* Create a list object called `list_practice` using the following three strings: `bloom`, `data`, `python` "
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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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"checksum": "2c2a3c1d8e5f4ea0a783bfa9d00218e7",
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"grade": false,
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"grade_id": "cell-6ee0685279505899",
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"locked": false,
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"schema_version": 3,
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"solution": true,
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"task": false
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}
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},
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"outputs": [],
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"source": [
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"# YOUR CODE HERE\n",
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"raise NotImplementedError()"
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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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"deletable": false,
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"editable": false,
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"nbgrader": {
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"cell_type": "markdown",
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"checksum": "6e15fd8ad40d40895af5017592188d86",
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"grade": false,
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"grade_id": "cell-254c30333f63dc0e",
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"locked": true,
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"schema_version": 3,
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"solution": false,
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"task": false
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}
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},
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"source": [
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"**Task 1 Test**"
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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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"deletable": false,
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"nbgrader": {
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"cell_type": "code",
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"checksum": "248c1e6a40bea014a9f130cee72c1a46",
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"grade": true,
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"grade_id": "cell-7f5ed8ccc5b15f71",
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"locked": true,
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"points": 1,
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"schema_version": 3,
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"solution": false,
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"task": false
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}
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},
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"outputs": [],
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"source": [
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"#Task 1 - Test\n",
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"assert isinstance(list_practice, list), \"Make sure you created a list object\"\n"
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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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"deletable": false,
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"editable": false,
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"nbgrader": {
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"cell_type": "markdown",
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"checksum": "30d91f27e533bf95f3afd44e8d9cda1b",
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"grade": false,
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"grade_id": "cell-bd07e140b06f24b6",
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"locked": true,
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"schema_version": 3,
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"solution": false,
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"task": false
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}
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},
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"source": [
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"\n",
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"**Task 2** - Dictionaries\n",
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"* Create a dictionary object called `diction_practice`. \n",
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"* Assign the following values to their respective keys listed above: `tech`, `science`, `language`\n",
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"\n",
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"*Hint:* There are multiple ways you can accomplish this task. You can either write out the key:values pairs manually, or iteratively combine two lists using the [`zip` function](https://docs.python.org/3/library/functions.html)."
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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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"deletable": false,
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"nbgrader": {
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"cell_type": "code",
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"checksum": "f9948f7da71e452ccc2cb3fd5550ac09",
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"grade": false,
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"grade_id": "cell-d2aac83e74b8fb89",
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"locked": false,
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"schema_version": 3,
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"solution": true,
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"task": false
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}
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},
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"outputs": [],
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"source": [
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"# YOUR CODE HERE\n",
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"raise NotImplementedError()"
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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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"deletable": false,
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"editable": false,
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"nbgrader": {
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"cell_type": "markdown",
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"checksum": "89adce679550e000e75a3cd0351721b1",
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"grade": false,
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"grade_id": "cell-95f85a283f98ef20",
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"locked": true,
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"schema_version": 3,
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"solution": false,
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"task": false
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}
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},
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"source": [
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"**Task 2 - Test**"
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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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"deletable": false,
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"editable": false,
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"nbgrader": {
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"cell_type": "code",
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"checksum": "eb3d302695cce47d57ee41774cb9aa05",
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"grade": true,
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"grade_id": "cell-6f3011fa357d270f",
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"locked": true,
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"points": 1,
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"schema_version": 3,
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"solution": false,
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"task": false
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}
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},
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"outputs": [],
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"source": [
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"#Task 2 - Test\n",
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"\n",
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"assert isinstance(diction_practice, dict), \"Did you use the correct syntax?\"\n"
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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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"deletable": false,
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"editable": false,
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"nbgrader": {
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"cell_type": "markdown",
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"checksum": "500b06dc305643e357cca230f16dce04",
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"grade": false,
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"grade_id": "cell-4a31ee084e18453a",
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"locked": true,
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"schema_version": 3,
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"solution": false,
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"task": false
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}
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},
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"source": [
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"\n",
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"**Task 3** - Dictionaries\n",
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"* Reassign the value of the `python` key in your dictionary to `'programming_language'`"
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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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"deletable": false,
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"nbgrader": {
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"cell_type": "code",
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"checksum": "968e8893d06b1276dac72f7a0db78d95",
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"grade": false,
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"grade_id": "cell-59c6653a1a1faae6",
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"locked": false,
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"schema_version": 3,
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"solution": true,
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"task": false
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}
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},
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"outputs": [],
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"source": [
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"# YOUR CODE HERE\n",
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"raise NotImplementedError()"
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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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"deletable": false,
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"editable": false,
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"nbgrader": {
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"cell_type": "markdown",
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"checksum": "b3c824a5462497e6fc4d903f762303c9",
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"grade": false,
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"grade_id": "cell-b0a297189d9a7dd3",
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"locked": true,
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"schema_version": 3,
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"solution": false,
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"task": false
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}
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},
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"source": [
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"**Task 3 Tests**"
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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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"deletable": false,
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"editable": false,
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"nbgrader": {
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"cell_type": "code",
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"checksum": "8fc04dbb280a746dbac3ef75ee9de31b",
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"grade": true,
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"grade_id": "cell-94bd4331d6cb29c9",
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"locked": true,
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"points": 1,
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"schema_version": 3,
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"solution": false,
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"task": false
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}
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},
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"outputs": [],
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"source": [
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"#Task 3 Tests\n",
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"\n",
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"assert diction_practice['data'] == 'science', \"Make sure your values are assigned to their correct keys\"\n"
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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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"deletable": false,
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"editable": false,
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"id": "qSvL3CeTFk9F",
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"nbgrader": {
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"cell_type": "markdown",
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"checksum": "71b4d138b7b263261d663d1b41d6add4",
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"grade": false,
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"grade_id": "cell-40090434cb0736b0",
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"locked": true,
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"schema_version": 3,
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"solution": false,
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"task": false
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}
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},
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"source": [
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"## Use the following information to complete Tasks \n",
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"\n",
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"\n",
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"\n",
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"In this Sprint Challenge you will first \"wrangle\" some data from [Gapminder](https://www.gapminder.org/about-gapminder/), a Swedish non-profit co-founded by Hans Rosling. \"Gapminder produces free teaching resources making the world understandable based on reliable statistics.\"\n",
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"- [Cell phones (total), by country and year](https://raw.githubusercontent.com/open-numbers/ddf--gapminder--systema_globalis/master/countries-etc-datapoints/ddf--datapoints--cell_phones_total--by--geo--time.csv)\n",
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"- [Population (total), by country and year](https://raw.githubusercontent.com/open-numbers/ddf--gapminder--systema_globalis/master/countries-etc-datapoints/ddf--datapoints--population_total--by--geo--time.csv)\n",
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"- [Geo country codes](https://github.com/open-numbers/ddf--gapminder--systema_globalis/blob/master/ddf--entities--geo--country.csv)\n",
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"\n",
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"These two links have everything you need to successfully complete the first part of this sprint challenge.\n",
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"- [Pandas documentation: Working with Text Data](https://pandas.pydata.org/pandas-docs/stable/text.html) (one question)\n",
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"- [Pandas Cheat Sheet](https://github.com/pandas-dev/pandas/blob/master/doc/cheatsheet/Pandas_Cheat_Sheet.pdf) (everything else)\n"
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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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"deletable": false,
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"editable": false,
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"id": "0ZklksziMixS",
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"nbgrader": {
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"cell_type": "markdown",
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"checksum": "235860cb511ea847389f2096adf6cc6e",
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"grade": false,
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"grade_id": "cell-ae2312e461921817",
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"locked": true,
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"schema_version": 3,
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"solution": false,
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"task": false
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}
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},
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"source": [
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"**Task 4** - Load and print the cell phone data. Pandas and numpy import statements have been included for you.\n",
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"\n",
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"* Load your CSV file found at `cell_phones_url` into a DataFrame object named `cell_phones`\n"
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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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"deletable": false,
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"id": "FFO8QNJ7MixS",
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"nbgrader": {
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"cell_type": "code",
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"checksum": "82e3c74df1cb9321caff9035dd2e9409",
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"grade": false,
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"grade_id": "cell-6f16afb9d271f949",
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"locked": false,
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"schema_version": 3,
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"solution": true,
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"task": false
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}
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},
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"outputs": [],
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"source": [
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"# Task 4\n",
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"\n",
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"# Imports \n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"\n",
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"cell_phones_url = 'https://raw.githubusercontent.com/bloominstituteoftechnology/data-science-practice-datasets/main/unit_1/Cell__Phones/cell_phones.csv'\n",
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"\n",
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"# Load the dataframe and print the top 5 rows\n",
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"\n",
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"# YOUR CODE HERE\n",
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"raise NotImplementedError()\n"
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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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"id": "ymCLkZMJMixT"
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},
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"source": [
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"**Task 4 Test**"
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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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"deletable": false,
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"editable": false,
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"id": "btcEJXxCMixT",
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"nbgrader": {
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"cell_type": "code",
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"checksum": "597bda1cfbeb2d3def97e7c47f6971c7",
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"grade": true,
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"grade_id": "cell-226f7bf8e9ea24f9",
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"locked": true,
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"points": 1,
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"schema_version": 3,
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"solution": false,
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"task": false
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}
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},
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"outputs": [],
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"source": [
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"# Task 4 - Test\n",
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"\n",
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"assert isinstance(cell_phones, pd.DataFrame), 'Have you created a DataFrame named `cell_phones`?'\n",
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"assert len(cell_phones) == 9574\n"
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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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"deletable": false,
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"editable": false,
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"id": "9YPW16tmT2J_",
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"nbgrader": {
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"cell_type": "markdown",
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"checksum": "26b84eb6f8694fc80894fec62cb92e2f",
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"grade": false,
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"grade_id": "cell-905dd5d05e5cebb7",
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"locked": true,
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"schema_version": 3,
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"solution": false,
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"task": false
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}
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},
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"source": [
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"**Task 5** - Load and print the population data. \n",
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"\n",
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"* Load the CSV file found at `population_url` into a DataFrame named `population`\n",
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"\n"
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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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"deletable": false,
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"id": "SNWpDAvyUYa2",
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"nbgrader": {
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"cell_type": "code",
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"checksum": "9874647278a76399d4f5547222d9dc02",
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"grade": false,
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"grade_id": "cell-561c2d59728188a9",
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"locked": false,
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"schema_version": 3,
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"solution": true,
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"task": false
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}
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},
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"outputs": [],
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"source": [
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"# Task 5\n",
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"\n",
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"population_url = 'https://raw.githubusercontent.com/bloominstituteoftechnology/data-science-practice-datasets/main/unit_1/Population/population.csv'\n",
483+
"\n",
484+
"# Load the dataframe and print the first 5 records\n",
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"\n",
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"# YOUR CODE HERE\n",
487+
"raise NotImplementedError()"
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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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"id": "RDOcC0FdVjIz"
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},
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"source": [
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"**Task 5 Test**"
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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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"deletable": false,
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"editable": false,
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"id": "jcaZ5W5cVjI_",
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"nbgrader": {
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"cell_type": "code",
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"checksum": "01d084b75b701322c49f0aecda45802a",
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"grade": true,
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"grade_id": "cell-59d01cd695becd74",
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"locked": true,
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"points": 1,
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"schema_version": 3,
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"solution": false,
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"task": false
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}
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},
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"outputs": [],
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"source": [
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"# Task 5 - Test\n",
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"\n",
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"assert isinstance(population, pd.DataFrame), 'Have you created a DataFrame named `population`?'\n",
523+
"assert len(population) == 59297\n"
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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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"id": "9acXXTiEV5uJ"
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},
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"source": [
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"**Task 6** - Load and print the geo country codes data. \n",
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"\n",
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"* Load the CSV file found at `geo_codes_url` into a DataFrame named `geo_codes`\n"
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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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"deletable": false,
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"id": "Obm4p8WXV5uJ",
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"nbgrader": {
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"cell_type": "code",
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"checksum": "8247001411ff10e160febf6472e84ce8",
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"grade": false,
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"grade_id": "cell-eb4d290384535503",
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"locked": false,
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"schema_version": 3,
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"solution": true,
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"task": false
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}
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},
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"outputs": [],
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"source": [
556+
"# Task 6\n",
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"\n",
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"geo_codes_url = 'https://raw.githubusercontent.com/bloominstituteoftechnology/data-science-practice-datasets/main/unit_1/GEO_codes/geo_country_codes.csv'\n",
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"\n",
560+
"# Load the dataframe and print out the first 5 records\n",
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"\n",
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"# YOUR CODE HERE\n",
563+
"raise NotImplementedError()"
564+
]
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},
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{
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"cell_type": "markdown",
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"metadata": {
569+
"deletable": false,
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"editable": false,
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"id": "_WR-4MbmV5uK",
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"nbgrader": {
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"cell_type": "markdown",
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"checksum": "a88bbf49e8714b89d0e0fa46c06f2217",
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"grade": false,
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"grade_id": "cell-4a0f7ebd4c9931a7",
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"locked": true,
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"schema_version": 3,
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"solution": false,
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"task": false
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}
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},
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"source": [
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"**Task 6 Test**"
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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,
590+
"metadata": {
591+
"deletable": false,
592+
"editable": false,
593+
"id": "Z3Tza5NWV5uK",
594+
"nbgrader": {
595+
"cell_type": "code",
596+
"checksum": "6bf7335d2dd565fbef6619579e0ddf59",
597+
"grade": true,
598+
"grade_id": "cell-39240405659c0c19",
599+
"locked": true,
600+
"points": 1,
601+
"schema_version": 3,
602+
"solution": false,
603+
"task": false
604+
}
605+
},
606+
"outputs": [],
607+
"source": [
608+
"# Task 6 - Test\n",
609+
"\n",
610+
"assert geo_codes is not None, 'Have you created a DataFrame named `geo_codes`?'\n",
611+
"assert len(geo_codes) == 273\n"
612+
]
613+
},
614+
{
615+
"cell_type": "markdown",
616+
"metadata": {
617+
"deletable": false,
618+
"editable": false,
619+
"id": "5DbACESjYxpV",
620+
"nbgrader": {
621+
"cell_type": "markdown",
622+
"checksum": "fbbb18f1bcf72e28cfd967ddb3d36732",
623+
"grade": false,
624+
"grade_id": "cell-817781e1dc5827c6",
625+
"locked": true,
626+
"schema_version": 3,
627+
"solution": false,
628+
"task": false
629+
}
630+
},
631+
"source": [
632+
"**Task 7** - Check for missing values\n",
633+
"\n",
634+
"Let's check for missing values in each of these DataFrames: `cell_phones`, `population` and `geo_codes`\n",
635+
"\n",
636+
"* Check for missing values in the following DataFrames:\n",
637+
" * Assign the total number of missing values in `cell_phones` to the variable `cell_phones_missing`\n",
638+
" * Assign the total number of missing values in `population` to the variable `population_missing`\n",
639+
" * Assign the total number of missing values in `geo_codes` to the variable `geo_codes_missing` \n",
640+
" * Hint: you will need to do a sum of a sum for this last task."
641+
]
642+
},
643+
{
644+
"cell_type": "code",
645+
"execution_count": null,
646+
"metadata": {
647+
"deletable": false,
648+
"id": "SwmSvUySJjXc",
649+
"nbgrader": {
650+
"cell_type": "code",
651+
"checksum": "462f481fe420ad2c37fe5a262dec6816",
652+
"grade": false,
653+
"grade_id": "cell-9426cd5765574e07",
654+
"locked": false,
655+
"schema_version": 3,
656+
"solution": true,
657+
"task": false
658+
}
659+
},
660+
"outputs": [],
661+
"source": [
662+
"# Task 7\n",
663+
"\n",
664+
"# Check for missing data in each of the DataFrames\n",
665+
"\n",
666+
"# YOUR CODE HERE\n",
667+
"raise NotImplementedError()"
668+
]
669+
},
670+
{
671+
"cell_type": "markdown",
672+
"metadata": {
673+
"deletable": false,
674+
"editable": false,
675+
"id": "cREZV7g0aLGC",
676+
"nbgrader": {
677+
"cell_type": "markdown",
678+
"checksum": "24b8d3d5ff21f6c4cfa3e634f00113aa",
679+
"grade": false,
680+
"grade_id": "cell-47ee1692d471f9ea",
681+
"locked": true,
682+
"schema_version": 3,
683+
"solution": false,
684+
"task": false
685+
}
686+
},
687+
"source": [
688+
"**Task 7 Test**"
689+
]
690+
},
691+
{
692+
"cell_type": "code",
693+
"execution_count": null,
694+
"metadata": {
695+
"deletable": false,
696+
"editable": false,
697+
"id": "eaQwM15IaLGD",
698+
"nbgrader": {
699+
"cell_type": "code",
700+
"checksum": "da0a8c532d46ac776b2609e8f439f601",
701+
"grade": true,
702+
"grade_id": "cell-cf6ab3b4b1e8afc1",
703+
"locked": true,
704+
"points": 1,
705+
"schema_version": 3,
706+
"solution": false,
707+
"task": false
708+
}
709+
},
710+
"outputs": [],
711+
"source": [
712+
"# Task 7 - Test\n",
713+
"\n",
714+
"if geo_codes_missing == 21: print('ERROR: Make sure to use a sum of a sum for the missing geo codes!') \n",
715+
"\n",
716+
"# Hidden tests - you will see the results when you submit to Canvas"
717+
]
718+
},
719+
{
720+
"cell_type": "markdown",
721+
"metadata": {
722+
"deletable": false,
723+
"editable": false,
724+
"id": "P54itLGveF5p",
725+
"nbgrader": {
726+
"cell_type": "markdown",
727+
"checksum": "c6054c87e1ff95c1767b855e2149568c",
728+
"grade": false,
729+
"grade_id": "cell-aad431149f1868a7",
730+
"locked": true,
731+
"schema_version": 3,
732+
"solution": false,
733+
"task": false
734+
}
735+
},
736+
"source": [
737+
"**Task 8** - Merge the `cell_phones` and `population` DataFrames.\n",
738+
"\n",
739+
"* Merge the `cell_phones` and `population` dataframes with an **inner** merge on both the `geo` and `time` columns.\n",
740+
"* Call the resulting dataframe `cell_phone_population`"
741+
]
742+
},
743+
{
744+
"cell_type": "code",
745+
"execution_count": null,
746+
"metadata": {
747+
"deletable": false,
748+
"id": "KL_NCL7heF51",
749+
"nbgrader": {
750+
"cell_type": "code",
751+
"checksum": "1ee15cfe45ac7986f9cb323dd1d52fbb",
752+
"grade": false,
753+
"grade_id": "cell-decaebaa844aa3a5",
754+
"locked": false,
755+
"schema_version": 3,
756+
"solution": true,
757+
"task": false
758+
}
759+
},
760+
"outputs": [],
761+
"source": [
762+
"# Task 8\n",
763+
"\n",
764+
"# Merge the cell_phones and population dataframes\n",
765+
"\n",
766+
"# YOUR CODE HERE\n",
767+
"raise NotImplementedError()"
768+
]
769+
},
770+
{
771+
"cell_type": "markdown",
772+
"metadata": {
773+
"deletable": false,
774+
"editable": false,
775+
"id": "9vFSumbkfqr_",
776+
"nbgrader": {
777+
"cell_type": "markdown",
778+
"checksum": "9daf332b99212b89d3e248fef54079ac",
779+
"grade": false,
780+
"grade_id": "cell-00202b83d4d54973",
781+
"locked": true,
782+
"schema_version": 3,
783+
"solution": false,
784+
"task": false
785+
}
786+
},
787+
"source": [
788+
"**Task 8 Test**"
789+
]
790+
},
791+
{
792+
"cell_type": "code",
793+
"execution_count": null,
794+
"metadata": {
795+
"deletable": false,
796+
"editable": false,
797+
"id": "85-p_0UGfkZJ",
798+
"nbgrader": {
799+
"cell_type": "code",
800+
"checksum": "9bd0a79e9049acd8ced9d23b68d54d9a",
801+
"grade": true,
802+
"grade_id": "cell-dd2473ea91f15f30",
803+
"locked": true,
804+
"points": 1,
805+
"schema_version": 3,
806+
"solution": false,
807+
"task": false
808+
}
809+
},
810+
"outputs": [],
811+
"source": [
812+
"# Task 8 - Test\n",
813+
"\n",
814+
"assert cell_phone_population is not None, 'Have you merged created a DataFrame named cell_phone_population?'\n",
815+
"assert len(cell_phone_population) == 8930\n"
816+
]
817+
},
818+
{
819+
"cell_type": "markdown",
820+
"metadata": {
821+
"deletable": false,
822+
"editable": false,
823+
"id": "oByYSkC7hB05",
824+
"nbgrader": {
825+
"cell_type": "markdown",
826+
"checksum": "49df153f02f79995801f03a60b34c838",
827+
"grade": false,
828+
"grade_id": "cell-01ad09608fc02d0c",
829+
"locked": true,
830+
"schema_version": 3,
831+
"solution": false,
832+
"task": false
833+
}
834+
},
835+
"source": [
836+
"**Task 9** - Merge the `cell_phone_population` and `geo_codes` DataFrames\n",
837+
"\n",
838+
"* Merge the `cell_phone_population` and `geo_codes` DataFrames with an inner merge using the `geo` column.\n",
839+
"* **Only merge the `country` and `geo` columns from the `geo_codes` dataframe.** \n",
840+
"* Call the resulting DataFrame `geo_cell_phone_population`\n"
841+
]
842+
},
843+
{
844+
"cell_type": "code",
845+
"execution_count": null,
846+
"metadata": {
847+
"deletable": false,
848+
"id": "NcO8-JpQhB1F",
849+
"nbgrader": {
850+
"cell_type": "code",
851+
"checksum": "3450df7dea31e4dee7ceb126a2d07f6a",
852+
"grade": false,
853+
"grade_id": "cell-1ce5a2360ee6fd20",
854+
"locked": false,
855+
"schema_version": 3,
856+
"solution": true,
857+
"task": false
858+
}
859+
},
860+
"outputs": [],
861+
"source": [
862+
"# Task 9\n",
863+
"\n",
864+
"# Merge the cell_phone_population and geo_codes dataframes\n",
865+
"# Only include the country and geo columns from geo_codes\n",
866+
"\n",
867+
"# YOUR CODE HERE\n",
868+
"raise NotImplementedError()"
869+
]
870+
},
871+
{
872+
"cell_type": "markdown",
873+
"metadata": {
874+
"deletable": false,
875+
"editable": false,
876+
"id": "zAKDLSV-hB1G",
877+
"nbgrader": {
878+
"cell_type": "markdown",
879+
"checksum": "a5634357f45ca81f6651395a42cc6341",
880+
"grade": false,
881+
"grade_id": "cell-935fc7dc053d368e",
882+
"locked": true,
883+
"schema_version": 3,
884+
"solution": false,
885+
"task": false
886+
}
887+
},
888+
"source": [
889+
"**Task 9 Test**"
890+
]
891+
},
892+
{
893+
"cell_type": "code",
894+
"execution_count": null,
895+
"metadata": {
896+
"deletable": false,
897+
"editable": false,
898+
"id": "eQgHSsLihB1G",
899+
"nbgrader": {
900+
"cell_type": "code",
901+
"checksum": "7005259d49844adab8beb565aaf5eed5",
902+
"grade": true,
903+
"grade_id": "cell-764d5b72ae382339",
904+
"locked": true,
905+
"points": 1,
906+
"schema_version": 3,
907+
"solution": false,
908+
"task": false
909+
}
910+
},
911+
"outputs": [],
912+
"source": [
913+
"# Task 9 - Test\n",
914+
"assert len(geo_cell_phone_population) == 8930\n",
915+
"assert type(geo_cell_phone_population) == pd.DataFrame\n"
916+
]
917+
},
918+
{
919+
"cell_type": "markdown",
920+
"metadata": {
921+
"deletable": false,
922+
"editable": false,
923+
"id": "-CZF39BWivc2",
924+
"nbgrader": {
925+
"cell_type": "markdown",
926+
"checksum": "2373ace4287e3e77f2e1c4b86d9be106",
927+
"grade": false,
928+
"grade_id": "cell-bbacb1043d1c0990",
929+
"locked": true,
930+
"schema_version": 3,
931+
"solution": false,
932+
"task": false
933+
}
934+
},
935+
"source": [
936+
"**Task 10** - Calculate the number of cell phones per person.\n",
937+
"\n",
938+
"* Use the `cell_phones_total` and `population_total` columns to calculate the number of cell phones per person for every year. (In other words, for every row). \n",
939+
"* Create a new column: Call this new feature (column) `phones_per_person` and add it to the `geo_cell_phone_population` DataFrame (you'll be adding the column to the DataFrame).\n",
940+
"\n",
941+
"*Hint: You can find a refresher on how to create a new column in Module 2 of this sprint.*"
942+
]
943+
},
944+
{
945+
"cell_type": "code",
946+
"execution_count": null,
947+
"metadata": {
948+
"deletable": false,
949+
"id": "vBeXa3LTivdC",
950+
"nbgrader": {
951+
"cell_type": "code",
952+
"checksum": "ca0c58c434dc1be273c34f79f9cdc4db",
953+
"grade": false,
954+
"grade_id": "cell-bd0bce2920604643",
955+
"locked": false,
956+
"schema_version": 3,
957+
"solution": true,
958+
"task": false
959+
}
960+
},
961+
"outputs": [],
962+
"source": [
963+
"# Task 10\n",
964+
"\n",
965+
"# YOUR CODE HERE\n",
966+
"raise NotImplementedError()"
967+
]
968+
},
969+
{
970+
"cell_type": "markdown",
971+
"metadata": {
972+
"id": "4IiDB6f6ivdD"
973+
},
974+
"source": [
975+
"**Task 10 Test**"
976+
]
977+
},
978+
{
979+
"cell_type": "code",
980+
"execution_count": null,
981+
"metadata": {
982+
"deletable": false,
983+
"editable": false,
984+
"id": "L3UgjCfFivdD",
985+
"nbgrader": {
986+
"cell_type": "code",
987+
"checksum": "2c7a7142a39c84e1d18daa6c160cae60",
988+
"grade": true,
989+
"grade_id": "cell-45c955c43e471400",
990+
"locked": true,
991+
"points": 1,
992+
"schema_version": 3,
993+
"solution": false,
994+
"task": false
995+
}
996+
},
997+
"outputs": [],
998+
"source": [
999+
"# Task 10 - Test\n",
1000+
"\n",
1001+
"# Hidden tests - you will see the results when you submit to Canvas"
1002+
]
1003+
},
1004+
{
1005+
"cell_type": "markdown",
1006+
"metadata": {
1007+
"id": "b_CZNPZAlw71"
1008+
},
1009+
"source": [
1010+
"**Task 11** - Identify the number of cell phones per person in the US in 2017\n",
1011+
"\n",
1012+
"* Create a one-row subset of `geo_cell_phone_population` with data on cell phone ownership in the United States for the year 2017.\n",
1013+
"* Call this subset DataFrame `US_2017`.\n",
1014+
"* Print `US_2017`."
1015+
]
1016+
},
1017+
{
1018+
"cell_type": "code",
1019+
"execution_count": null,
1020+
"metadata": {
1021+
"deletable": false,
1022+
"id": "Y0hRRvc1lw8B",
1023+
"nbgrader": {
1024+
"cell_type": "code",
1025+
"checksum": "a0c775ef5f419d97070af229fe40c354",
1026+
"grade": false,
1027+
"grade_id": "cell-665e83d11e594d90",
1028+
"locked": false,
1029+
"schema_version": 3,
1030+
"solution": true,
1031+
"task": false
1032+
}
1033+
},
1034+
"outputs": [],
1035+
"source": [
1036+
"# Task 11\n",
1037+
"\n",
1038+
"# Determine the number of cell phones per person in the US in 2017\n",
1039+
"\n",
1040+
"# YOUR CODE HERE\n",
1041+
"raise NotImplementedError()\n",
1042+
"\n",
1043+
"# View the DataFrame\n",
1044+
"US_2017"
1045+
]
1046+
},
1047+
{
1048+
"cell_type": "markdown",
1049+
"metadata": {
1050+
"id": "mIDryQfKlw8C"
1051+
},
1052+
"source": [
1053+
"**Task 11 Test**"
1054+
]
1055+
},
1056+
{
1057+
"cell_type": "code",
1058+
"execution_count": null,
1059+
"metadata": {
1060+
"deletable": false,
1061+
"editable": false,
1062+
"id": "wL0MypzFlw8C",
1063+
"nbgrader": {
1064+
"cell_type": "code",
1065+
"checksum": "912e932a02c4192868d095b526a831e4",
1066+
"grade": true,
1067+
"grade_id": "cell-ea08fdda80cf9731",
1068+
"locked": true,
1069+
"points": 1,
1070+
"schema_version": 3,
1071+
"solution": false,
1072+
"task": false
1073+
}
1074+
},
1075+
"outputs": [],
1076+
"source": [
1077+
"# Task 11 - Test\n",
1078+
"\n",
1079+
"# Hidden tests - you will see the results when you submit to Canvas"
1080+
]
1081+
},
1082+
{
1083+
"cell_type": "markdown",
1084+
"metadata": {
1085+
"deletable": false,
1086+
"editable": false,
1087+
"id": "HTl_zamAtfJa",
1088+
"nbgrader": {
1089+
"cell_type": "markdown",
1090+
"checksum": "815cb8dd3b19d79fd553f23d9a773b03",
1091+
"grade": false,
1092+
"grade_id": "cell-4acad6efd0ae146c",
1093+
"locked": true,
1094+
"schema_version": 3,
1095+
"solution": false,
1096+
"task": false
1097+
}
1098+
},
1099+
"source": [
1100+
"**Task 12** - Describe the numeric variables in `geo_cell_phone_population`\n",
1101+
"\n",
1102+
"* Calculate the summary statistics for the quantitative variables in `geo_cell_phone_population` using `.describe()`.\n",
1103+
"* Find the mean value for `phones_per_person` and assign it to the variable `mean_phones`. Define your value out to two decimal points.\n"
1104+
]
1105+
},
1106+
{
1107+
"cell_type": "code",
1108+
"execution_count": null,
1109+
"metadata": {
1110+
"deletable": false,
1111+
"id": "HGKKIqAktfJn",
1112+
"nbgrader": {
1113+
"cell_type": "code",
1114+
"checksum": "8773b60013ce17644af742cd4c6a356d",
1115+
"grade": false,
1116+
"grade_id": "cell-181c9805c52dfda8",
1117+
"locked": false,
1118+
"schema_version": 3,
1119+
"solution": true,
1120+
"task": false
1121+
}
1122+
},
1123+
"outputs": [],
1124+
"source": [
1125+
"# Task 12\n",
1126+
"\n",
1127+
"# YOUR CODE HERE\n",
1128+
"raise NotImplementedError()"
1129+
]
1130+
},
1131+
{
1132+
"cell_type": "markdown",
1133+
"metadata": {
1134+
"id": "4Nh0qP1ptfJn"
1135+
},
1136+
"source": [
1137+
"**Task 12 Test**"
1138+
]
1139+
},
1140+
{
1141+
"cell_type": "code",
1142+
"execution_count": null,
1143+
"metadata": {
1144+
"deletable": false,
1145+
"editable": false,
1146+
"id": "cBZg6p1VtfJo",
1147+
"nbgrader": {
1148+
"cell_type": "code",
1149+
"checksum": "012a6f5c818c64f8ed1570906eea15b3",
1150+
"grade": true,
1151+
"grade_id": "cell-0a0cfb1ac7acc279",
1152+
"locked": true,
1153+
"points": 1,
1154+
"schema_version": 3,
1155+
"solution": false,
1156+
"task": false
1157+
}
1158+
},
1159+
"outputs": [],
1160+
"source": [
1161+
"# Task 12 - Test\n",
1162+
"\n",
1163+
"# Hidden tests - you will see the results when you submit to Canvas"
1164+
]
1165+
},
1166+
{
1167+
"cell_type": "markdown",
1168+
"metadata": {
1169+
"id": "wBQCMGrkw69w"
1170+
},
1171+
"source": [
1172+
"**Task 13** - Subset the DataFrame for 2017\n",
1173+
"\n",
1174+
"* Create a new dataframe called `df2017` that includes **only** records from `geo_cell_phone_population` that ocurred in 2017."
1175+
]
1176+
},
1177+
{
1178+
"cell_type": "code",
1179+
"execution_count": null,
1180+
"metadata": {
1181+
"deletable": false,
1182+
"id": "_EKPYqW-w698",
1183+
"nbgrader": {
1184+
"cell_type": "code",
1185+
"checksum": "50dd62f311ea30c217ec74206b8b42b9",
1186+
"grade": false,
1187+
"grade_id": "cell-f3fa17e15b5174ac",
1188+
"locked": false,
1189+
"schema_version": 3,
1190+
"solution": true,
1191+
"task": false
1192+
}
1193+
},
1194+
"outputs": [],
1195+
"source": [
1196+
"# Task 13\n",
1197+
"\n",
1198+
"# Create a new dataframe called df2017 that includes only records from geo_cell_phone_population that ocurred in 2017.\n",
1199+
"\n",
1200+
"# YOUR CODE HERE\n",
1201+
"raise NotImplementedError()"
1202+
]
1203+
},
1204+
{
1205+
"cell_type": "markdown",
1206+
"metadata": {
1207+
"id": "QuLy4qwrw698"
1208+
},
1209+
"source": [
1210+
"**Task 13 Test**"
1211+
]
1212+
},
1213+
{
1214+
"cell_type": "code",
1215+
"execution_count": null,
1216+
"metadata": {
1217+
"deletable": false,
1218+
"editable": false,
1219+
"id": "S0rU2pGWw698",
1220+
"nbgrader": {
1221+
"cell_type": "code",
1222+
"checksum": "3ec1b21a811f55002c03f4f9f54fcf3e",
1223+
"grade": true,
1224+
"grade_id": "cell-b19a6956b5dddadb",
1225+
"locked": true,
1226+
"points": 1,
1227+
"schema_version": 3,
1228+
"solution": false,
1229+
"task": false
1230+
}
1231+
},
1232+
"outputs": [],
1233+
"source": [
1234+
"# Task 13 - Test\n",
1235+
"\n",
1236+
"# Hidden tests - you will see the results when you submit to Canvas"
1237+
]
1238+
},
1239+
{
1240+
"cell_type": "markdown",
1241+
"metadata": {
1242+
"id": "Rww_2EpHyd4K"
1243+
},
1244+
"source": [
1245+
"**Task 14** - Identify the five countries with the most cell phones per person in 2017\n",
1246+
"\n",
1247+
"* Sort the `df2017` DataFrame by `phones_per_person` in descending order and assign the result to `df2017_top`. Your new DataFrame should only have **five** rows (Hint: use `.head()` to return only five rows).\n",
1248+
"* Print the first 5 records of `df2017_top`."
1249+
]
1250+
},
1251+
{
1252+
"cell_type": "code",
1253+
"execution_count": null,
1254+
"metadata": {
1255+
"deletable": false,
1256+
"id": "h6ym9Dwhyd4V",
1257+
"nbgrader": {
1258+
"cell_type": "code",
1259+
"checksum": "7a116d8d8ab46d5494cec232cfc25c12",
1260+
"grade": false,
1261+
"grade_id": "cell-c9b701bfe897edf5",
1262+
"locked": false,
1263+
"schema_version": 3,
1264+
"solution": true,
1265+
"task": false
1266+
}
1267+
},
1268+
"outputs": [],
1269+
"source": [
1270+
"# Task 14\n",
1271+
"\n",
1272+
"# Sort the df2017 dataframe by phones_per_person in descending order\n",
1273+
"# Return only five (5) rows\n",
1274+
"\n",
1275+
"# YOUR CODE HERE\n",
1276+
"raise NotImplementedError()\n",
1277+
"\n",
1278+
"# View the df2017_top DataFrame\n",
1279+
"df2017_top"
1280+
]
1281+
},
1282+
{
1283+
"cell_type": "markdown",
1284+
"metadata": {
1285+
"id": "7WBox_Axyd4W"
1286+
},
1287+
"source": [
1288+
"**Task 14 Test**"
1289+
]
1290+
},
1291+
{
1292+
"cell_type": "code",
1293+
"execution_count": null,
1294+
"metadata": {
1295+
"deletable": false,
1296+
"editable": false,
1297+
"id": "ePj-a6rLyd4W",
1298+
"nbgrader": {
1299+
"cell_type": "code",
1300+
"checksum": "7baa090a5b1136f9576ed19a1424a180",
1301+
"grade": true,
1302+
"grade_id": "cell-5f6fffc9db1b9492",
1303+
"locked": true,
1304+
"points": 1,
1305+
"schema_version": 3,
1306+
"solution": false,
1307+
"task": false
1308+
}
1309+
},
1310+
"outputs": [],
1311+
"source": [
1312+
"# Task 14 - Test\n",
1313+
"\n",
1314+
"assert df2017_top.shape == (5,6), 'Make sure you return only five rows'\n"
1315+
]
1316+
},
1317+
{
1318+
"cell_type": "code",
1319+
"execution_count": null,
1320+
"metadata": {},
1321+
"outputs": [],
1322+
"source": []
1323+
}
1324+
],
1325+
"metadata": {
1326+
"colab": {
1327+
"collapsed_sections": [],
1328+
"name": "LS_DS_Sprint1_AG.ipynb",
1329+
"provenance": []
1330+
},
1331+
"kernelspec": {
1332+
"display_name": "Python 3",
1333+
"language": "python",
1334+
"name": "python3"
1335+
},
1336+
"language_info": {
1337+
"codemirror_mode": {
1338+
"name": "ipython",
1339+
"version": 3
1340+
},
1341+
"file_extension": ".py",
1342+
"mimetype": "text/x-python",
1343+
"name": "python",
1344+
"nbconvert_exporter": "python",
1345+
"pygments_lexer": "ipython3",
1346+
"version": "3.8.8"
1347+
}
1348+
},
1349+
"nbformat": 4,
1350+
"nbformat_minor": 1
1351+
}

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