From 724394167acb085b353eaf3d48cc3a58305a64c7 Mon Sep 17 00:00:00 2001 From: Paul Harnagel Date: Wed, 5 Aug 2020 18:24:29 -0500 Subject: [PATCH] Added in video exercises These exercises already have hundreds of views. We hope they are helpful to the other learners. --- .../Exercises_with_solutions.ipynb | 66 ++++++++-------- .../Chipotle/Exercises_with_solutions.ipynb | 19 ++++- .../Euro12/Exercises_with_Solutions.ipynb | 77 ++++++++----------- .../Exercise_with_solutions.ipynb | 16 +++- .../Exercise_with_solutions.ipynb | 29 +++++-- .../Occupation/Exercises_with_solutions.ipynb | 57 +++++++------- .../Regiment/Exercises_solutions.ipynb | 69 ++++++++--------- .../Exercises_with_solutions.ipynb | 58 +++++++------- .../Exercises_with_solutions.ipynb | 61 ++++++++------- .../Exercises_with_solutions.ipynb | 77 ++++++++----------- .../Wind_Stats/Exercises_with_solutions.ipynb | 17 +++- .../Chipotle/Exercise_with_Solutions.ipynb | 20 ++++- .../Exercises_with_code_and_solutions.ipynb | 19 ++++- .../Exercises_code_with_solutions.ipynb | 27 +++++-- .../Exercises-with-solutions-code.ipynb | 73 ++++++++---------- .../Exercises_with_code_and_solutions.ipynb | 65 ++++++++-------- .../Exercises_with_solutions_and_code.ipynb | 69 ++++++++--------- 17 files changed, 438 insertions(+), 381 deletions(-) diff --git a/01_Getting_&_Knowing_Your_Data/World Food Facts/Exercises_with_solutions.ipynb b/01_Getting_&_Knowing_Your_Data/World Food Facts/Exercises_with_solutions.ipynb index 71299d66b..213c4e859 100644 --- a/01_Getting_&_Knowing_Your_Data/World Food Facts/Exercises_with_solutions.ipynb +++ b/01_Getting_&_Knowing_Your_Data/World Food Facts/Exercises_with_solutions.ipynb @@ -4,7 +4,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Ex1 - Getting and knowing your Data" + "# Ex1 - Getting and knowing your Data\n", + "Check out [World Food Facts Exercises Video Tutorial](https://youtu.be/_jCSK4cMcVw) to watch a data scientist go through the exercises" ] }, { @@ -43,9 +44,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stderr", @@ -70,9 +69,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -303,9 +300,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -325,9 +320,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -354,9 +347,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -391,9 +382,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -429,9 +418,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -458,9 +445,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -487,9 +472,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -516,9 +499,7 @@ { "cell_type": "code", "execution_count": 13, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -539,21 +520,34 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python [conda root]", + "display_name": "Python 3", "language": "python", - "name": "conda-root-py" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.12" + "pygments_lexer": "ipython3", + "version": "3.7.3" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false } }, "nbformat": 4, diff --git a/02_Filtering_&_Sorting/Chipotle/Exercises_with_solutions.ipynb b/02_Filtering_&_Sorting/Chipotle/Exercises_with_solutions.ipynb index ef10f6672..773dd80df 100644 --- a/02_Filtering_&_Sorting/Chipotle/Exercises_with_solutions.ipynb +++ b/02_Filtering_&_Sorting/Chipotle/Exercises_with_solutions.ipynb @@ -4,7 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Ex1 - Filtering and Sorting Data" + "# Ex1 - Filtering and Sorting Data\n", + "\n", + "Check out [Chipotle Exercises Video Tutorial](https://youtu.be/ZZPiWZpdekA) to watch a data scientist go through the exercises" ] }, { @@ -2212,7 +2214,20 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.4" + "version": "3.7.3" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false } }, "nbformat": 4, diff --git a/02_Filtering_&_Sorting/Euro12/Exercises_with_Solutions.ipynb b/02_Filtering_&_Sorting/Euro12/Exercises_with_Solutions.ipynb index 1fae2c983..29a9be096 100644 --- a/02_Filtering_&_Sorting/Euro12/Exercises_with_Solutions.ipynb +++ b/02_Filtering_&_Sorting/Euro12/Exercises_with_Solutions.ipynb @@ -4,7 +4,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Ex2 - Filtering and Sorting Data" + "# Ex2 - Filtering and Sorting Data\n", + "Check out [Euro 12 Exercises Video Tutorial](https://youtu.be/iqk5d48Qisg) to watch a data scientist go through the exercises" ] }, { @@ -19,9 +20,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import pandas as pd" @@ -44,9 +43,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -583,9 +580,7 @@ { "cell_type": "code", "execution_count": 37, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -628,9 +623,7 @@ { "cell_type": "code", "execution_count": 43, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -657,9 +650,7 @@ { "cell_type": "code", "execution_count": 44, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -722,9 +713,7 @@ { "cell_type": "code", "execution_count": 82, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -883,7 +872,6 @@ "cell_type": "code", "execution_count": 56, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -1040,9 +1028,7 @@ { "cell_type": "code", "execution_count": 55, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1069,9 +1055,7 @@ { "cell_type": "code", "execution_count": 57, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1201,9 +1185,7 @@ { "cell_type": "code", "execution_count": 66, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1333,9 +1315,7 @@ { "cell_type": "code", "execution_count": 84, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1579,9 +1559,7 @@ { "cell_type": "code", "execution_count": 86, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -2119,9 +2097,7 @@ { "cell_type": "code", "execution_count": 89, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -2177,23 +2153,36 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.12" + "pygments_lexer": "ipython3", + "version": "3.7.3" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/02_Filtering_&_Sorting/Fictional Army/Exercise_with_solutions.ipynb b/02_Filtering_&_Sorting/Fictional Army/Exercise_with_solutions.ipynb index 9d98ce4c0..a5ebfde1e 100644 --- a/02_Filtering_&_Sorting/Fictional Army/Exercise_with_solutions.ipynb +++ b/02_Filtering_&_Sorting/Fictional Army/Exercise_with_solutions.ipynb @@ -4,7 +4,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Fictional Army - Filtering and Sorting" + "# Fictional Army - Filtering and Sorting\n", + "Check out [Fictional Army Exercises Video Tutorial](https://youtu.be/42LGuRea7DE) to watch a data scientist go through the exercises" ] }, { @@ -1939,6 +1940,19 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false } }, "nbformat": 4, diff --git a/03_Grouping/Alcohol_Consumption/Exercise_with_solutions.ipynb b/03_Grouping/Alcohol_Consumption/Exercise_with_solutions.ipynb index bf2ed5124..841a5af59 100644 --- a/03_Grouping/Alcohol_Consumption/Exercise_with_solutions.ipynb +++ b/03_Grouping/Alcohol_Consumption/Exercise_with_solutions.ipynb @@ -4,7 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Ex - GroupBy" + "# Ex - GroupBy\n", + "\n", + "Check out [Alcohol Consumption Exercises Video Tutorial](https://youtu.be/az67CMdmS6s) to watch a data scientist go through the exercises" ] }, { @@ -531,23 +533,36 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.16" + "pygments_lexer": "ipython3", + "version": "3.7.3" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/03_Grouping/Occupation/Exercises_with_solutions.ipynb b/03_Grouping/Occupation/Exercises_with_solutions.ipynb index 9fb68d237..1f8e419c5 100644 --- a/03_Grouping/Occupation/Exercises_with_solutions.ipynb +++ b/03_Grouping/Occupation/Exercises_with_solutions.ipynb @@ -4,7 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Occupation" + "# Occupation\n", + "\n", + "Check out [Occupation Exercises Video Tutorial](https://youtu.be/jL3EVCoYIJQ) to watch a data scientist go through the exercises" ] }, { @@ -21,9 +23,7 @@ { "cell_type": "code", "execution_count": 64, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import pandas as pd" @@ -46,9 +46,7 @@ { "cell_type": "code", "execution_count": 65, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -142,9 +140,7 @@ { "cell_type": "code", "execution_count": 66, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -193,9 +189,7 @@ { "cell_type": "code", "execution_count": 150, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -257,9 +251,7 @@ { "cell_type": "code", "execution_count": 151, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -433,9 +425,7 @@ { "cell_type": "code", "execution_count": 152, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -504,9 +494,7 @@ { "cell_type": "code", "execution_count": 154, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -578,23 +566,36 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.11" + "pygments_lexer": "ipython3", + "version": "3.7.3" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/03_Grouping/Regiment/Exercises_solutions.ipynb b/03_Grouping/Regiment/Exercises_solutions.ipynb index f71a8519d..00e825065 100644 --- a/03_Grouping/Regiment/Exercises_solutions.ipynb +++ b/03_Grouping/Regiment/Exercises_solutions.ipynb @@ -4,7 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Regiment" + "# Regiment\n", + "\n", + "Check out [Regiment Exercises Video Tutorial](https://youtu.be/MFZ3uakwAEk) to watch a data scientist go through the exercises" ] }, { @@ -21,9 +23,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import pandas as pd" @@ -62,9 +62,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -218,9 +216,7 @@ { "cell_type": "code", "execution_count": 26, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -286,9 +282,7 @@ { "cell_type": "code", "execution_count": 29, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -436,9 +430,7 @@ { "cell_type": "code", "execution_count": 33, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -468,9 +460,7 @@ { "cell_type": "code", "execution_count": 35, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -504,9 +494,7 @@ { "cell_type": "code", "execution_count": 36, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -572,9 +560,7 @@ { "cell_type": "code", "execution_count": 37, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -663,9 +649,7 @@ { "cell_type": "code", "execution_count": 41, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -699,9 +683,7 @@ { "cell_type": "code", "execution_count": 50, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -740,23 +722,36 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.11" + "pygments_lexer": "ipython3", + "version": "3.7.3" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/04_Apply/US_Crime_Rates/Exercises_with_solutions.ipynb b/04_Apply/US_Crime_Rates/Exercises_with_solutions.ipynb index 8e807b326..6687d0e8d 100644 --- a/04_Apply/US_Crime_Rates/Exercises_with_solutions.ipynb +++ b/04_Apply/US_Crime_Rates/Exercises_with_solutions.ipynb @@ -4,7 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# United States - Crime Rates - 1960 - 2014" + "# United States - Crime Rates - 1960 - 2014\n", + "\n", + "Check out [Crime Rates Exercises Video Tutorial](https://youtu.be/46lmk1JvcWA) to watch a data scientist go through the exercises" ] }, { @@ -23,9 +25,7 @@ { "cell_type": "code", "execution_count": 95, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -49,9 +49,7 @@ { "cell_type": "code", "execution_count": 265, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -192,9 +190,7 @@ { "cell_type": "code", "execution_count": 266, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -236,9 +232,7 @@ { "cell_type": "code", "execution_count": 267, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -280,9 +274,7 @@ { "cell_type": "code", "execution_count": 268, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -440,9 +432,7 @@ { "cell_type": "code", "execution_count": 269, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -588,7 +578,6 @@ "cell_type": "code", "execution_count": 270, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -744,9 +733,7 @@ { "cell_type": "code", "execution_count": 276, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -778,23 +765,36 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.12" + "pygments_lexer": "ipython3", + "version": "3.7.3" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/05_Merge/Fictitous Names/Exercises_with_solutions.ipynb b/05_Merge/Fictitous Names/Exercises_with_solutions.ipynb index 1f89f05b6..a4d5a6c7c 100644 --- a/05_Merge/Fictitous Names/Exercises_with_solutions.ipynb +++ b/05_Merge/Fictitous Names/Exercises_with_solutions.ipynb @@ -4,7 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Fictitious Names" + "# Fictitious Names\n", + "\n", + "Check out [Fictitious Names Exercises Video Tutorial](https://youtu.be/6DbgcHBiOqo) to watch a data scientist go through the exercises" ] }, { @@ -26,9 +28,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import pandas as pd" @@ -74,9 +74,7 @@ { "cell_type": "code", "execution_count": 12, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -182,9 +180,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -298,9 +294,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -397,9 +391,7 @@ { "cell_type": "code", "execution_count": 13, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -501,9 +493,7 @@ { "cell_type": "code", "execution_count": 15, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -619,9 +609,7 @@ { "cell_type": "code", "execution_count": 16, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -684,9 +672,7 @@ { "cell_type": "code", "execution_count": 17, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -796,23 +782,36 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.11" + "pygments_lexer": "ipython3", + "version": "3.7.3" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/06_Stats/US_Baby_Names/Exercises_with_solutions.ipynb b/06_Stats/US_Baby_Names/Exercises_with_solutions.ipynb index 56234726f..f83fecad6 100644 --- a/06_Stats/US_Baby_Names/Exercises_with_solutions.ipynb +++ b/06_Stats/US_Baby_Names/Exercises_with_solutions.ipynb @@ -4,7 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# US - Baby Names" + "# US - Baby Names\n", + "\n", + "Check out [Baby Names Exercises Video Tutorial](https://youtu.be/Daf2QNAy-qA) to watch a data scientist go through the exercises" ] }, { @@ -23,9 +25,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import pandas as pd" @@ -48,9 +48,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -86,9 +84,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -245,9 +241,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -343,9 +337,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -374,9 +366,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -467,9 +457,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -498,9 +486,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -531,9 +517,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -560,9 +544,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -916,9 +898,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -945,9 +925,7 @@ { "cell_type": "code", "execution_count": 12, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1022,23 +1000,36 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.12" + "pygments_lexer": "ipython3", + "version": "3.7.3" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/06_Stats/Wind_Stats/Exercises_with_solutions.ipynb b/06_Stats/Wind_Stats/Exercises_with_solutions.ipynb index 4a7363065..7fab5059f 100644 --- a/06_Stats/Wind_Stats/Exercises_with_solutions.ipynb +++ b/06_Stats/Wind_Stats/Exercises_with_solutions.ipynb @@ -4,7 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Wind Statistics" + "# Wind Statistics\n", + "\n", + "Check out [Wind Statistics Exercises Video Tutorial](https://youtu.be/2x3WsWiNV18) to watch a data scientist go through the exercises" ] }, { @@ -4147,6 +4149,19 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false } }, "nbformat": 4, diff --git a/07_Visualization/Chipotle/Exercise_with_Solutions.ipynb b/07_Visualization/Chipotle/Exercise_with_Solutions.ipynb index 12d9743f6..a020818f1 100644 --- a/07_Visualization/Chipotle/Exercise_with_Solutions.ipynb +++ b/07_Visualization/Chipotle/Exercise_with_Solutions.ipynb @@ -4,7 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Visualizing Chipotle's Data" + "# Visualizing Chipotle's Data\n", + "\n", + "Check out [Chipotle's Visualization Exercises Video Tutorial](https://youtu.be/BLD2mAB3kaw) to watch a data scientist go through the exercises" ] }, { @@ -67,7 +69,6 @@ "cell_type": "code", "execution_count": 134, "metadata": {}, - "outputs": [ { "data": { @@ -352,7 +353,20 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.4" + "version": "3.7.3" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false } }, "nbformat": 4, diff --git a/07_Visualization/Tips/Exercises_with_code_and_solutions.ipynb b/07_Visualization/Tips/Exercises_with_code_and_solutions.ipynb index 3418dc4e7..b78b0f582 100644 --- a/07_Visualization/Tips/Exercises_with_code_and_solutions.ipynb +++ b/07_Visualization/Tips/Exercises_with_code_and_solutions.ipynb @@ -4,7 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Tips" + "# Tips\n", + "\n", + "Check out [Tips Visualization Exercises Video Tutorial](https://youtu.be/oiuKFigW4YM) to watch a data scientist go through the exercises" ] }, { @@ -547,7 +549,20 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.0" + "version": "3.7.3" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false } }, "nbformat": 4, diff --git a/07_Visualization/Titanic_Desaster/Exercises_code_with_solutions.ipynb b/07_Visualization/Titanic_Desaster/Exercises_code_with_solutions.ipynb index 2d438d85a..ee7a4619c 100644 --- a/07_Visualization/Titanic_Desaster/Exercises_code_with_solutions.ipynb +++ b/07_Visualization/Titanic_Desaster/Exercises_code_with_solutions.ipynb @@ -4,7 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Visualizing the Titanic Disaster" + "# Visualizing the Titanic Disaster\n", + "\n", + "Check out [Titanic Visualization Exercises Video Tutorial](https://youtu.be/CBT0buoF_Ns) to watch a data scientist go through the exercises" ] }, { @@ -550,21 +552,34 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.16" + "pygments_lexer": "ipython3", + "version": "3.7.3" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false } }, "nbformat": 4, diff --git a/09_Time_Series/Apple_Stock/Exercises-with-solutions-code.ipynb b/09_Time_Series/Apple_Stock/Exercises-with-solutions-code.ipynb index 6191320fd..e2bbb9b0a 100644 --- a/09_Time_Series/Apple_Stock/Exercises-with-solutions-code.ipynb +++ b/09_Time_Series/Apple_Stock/Exercises-with-solutions-code.ipynb @@ -4,7 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Apple Stock" + "# Apple Stock\n", + "\n", + "Check out [Apple Stock Exercises Video Tutorial](https://youtu.be/wpXkR_IZcug) to watch a data scientist go through the exercises" ] }, { @@ -22,9 +24,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", @@ -53,9 +53,7 @@ { "cell_type": "code", "execution_count": 32, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -160,9 +158,7 @@ { "cell_type": "code", "execution_count": 33, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -196,9 +192,7 @@ { "cell_type": "code", "execution_count": 34, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -232,9 +226,7 @@ { "cell_type": "code", "execution_count": 35, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -342,9 +334,7 @@ { "cell_type": "code", "execution_count": 36, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -372,9 +362,7 @@ { "cell_type": "code", "execution_count": 39, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -480,9 +468,7 @@ { "cell_type": "code", "execution_count": 48, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -590,9 +576,7 @@ { "cell_type": "code", "execution_count": 65, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -619,9 +603,7 @@ { "cell_type": "code", "execution_count": 66, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -650,9 +632,7 @@ { "cell_type": "code", "execution_count": 81, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -694,23 +674,36 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.12" + "pygments_lexer": "ipython3", + "version": "3.7.3" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/09_Time_Series/Investor_Flow_of_Funds_US/Exercises_with_code_and_solutions.ipynb b/09_Time_Series/Investor_Flow_of_Funds_US/Exercises_with_code_and_solutions.ipynb index a60c955f6..85b201bdb 100644 --- a/09_Time_Series/Investor_Flow_of_Funds_US/Exercises_with_code_and_solutions.ipynb +++ b/09_Time_Series/Investor_Flow_of_Funds_US/Exercises_with_code_and_solutions.ipynb @@ -4,7 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Investor - Flow of Funds - US" + "# Investor - Flow of Funds - US\n", + "\n", + "Check out [Investor Flow of Funds Exercises Video Tutorial](https://youtu.be/QG6WbOgC9QE) to watch a data scientist go through the exercises" ] }, { @@ -21,9 +23,7 @@ { "cell_type": "code", "execution_count": 30, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import pandas as pd" @@ -46,9 +46,7 @@ { "cell_type": "code", "execution_count": 31, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -171,9 +169,7 @@ { "cell_type": "code", "execution_count": 32, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# weekly data" @@ -189,9 +185,7 @@ { "cell_type": "code", "execution_count": 33, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -320,9 +314,7 @@ { "cell_type": "code", "execution_count": 34, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -361,9 +353,7 @@ { "cell_type": "code", "execution_count": 35, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -391,9 +381,7 @@ { "cell_type": "code", "execution_count": 36, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -834,9 +822,7 @@ { "cell_type": "code", "execution_count": 37, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1069,9 +1055,7 @@ { "cell_type": "code", "execution_count": 38, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1196,23 +1180,36 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.11" + "pygments_lexer": "ipython3", + "version": "3.7.3" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/10_Deleting/Iris/Exercises_with_solutions_and_code.ipynb b/10_Deleting/Iris/Exercises_with_solutions_and_code.ipynb index b6a1dca98..0414f7fd6 100644 --- a/10_Deleting/Iris/Exercises_with_solutions_and_code.ipynb +++ b/10_Deleting/Iris/Exercises_with_solutions_and_code.ipynb @@ -4,7 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Iris" + "# Iris\n", + "\n", + "Check out [Iris Exercises Video Tutorial](https://youtu.be/yAtzFLCWSZo) to watch a data scientist go through the exercises" ] }, { @@ -21,9 +23,7 @@ { "cell_type": "code", "execution_count": 13, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", @@ -47,9 +47,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -142,9 +140,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -241,9 +237,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -276,9 +270,7 @@ { "cell_type": "code", "execution_count": 36, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -504,9 +496,7 @@ { "cell_type": "code", "execution_count": 39, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1104,9 +1094,7 @@ { "cell_type": "code", "execution_count": 40, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1191,9 +1179,7 @@ { "cell_type": "code", "execution_count": 52, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1278,9 +1264,7 @@ { "cell_type": "code", "execution_count": 53, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1365,9 +1349,7 @@ { "cell_type": "code", "execution_count": 56, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1461,23 +1443,36 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.11" + "pygments_lexer": "ipython3", + "version": "3.7.3" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 }