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June 26, 2020 20:53
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{ | |
"cells": [ | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"<h3 align=center>Create a Tuple</h3>" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"create the Tuple <code> (0,1,2,3) </code> and assign it to the variable <code> A</code>:" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 9, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"A = (0,1,2,3)" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"<h3 align=center>Find the elements of a Tuple</h3> " | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Find the first two elements of the Tuple <code> A </code>:" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 8, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"(0, 1)" | |
] | |
}, | |
"execution_count": 8, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"A[0:2]" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"<h3 align=center>Lists </h3> " | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"For the next few questions, you will need the following list:" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 11, | |
"metadata": { | |
"collapsed": false, | |
"jupyter": { | |
"outputs_hidden": false | |
} | |
}, | |
"outputs": [], | |
"source": [ | |
"B=[\"a\",\"b\",\"c\"]" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Find the first two elements of the list <code> B</code>:" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 12, | |
"metadata": { | |
"collapsed": false, | |
"jupyter": { | |
"outputs_hidden": false | |
} | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"['a', 'b']" | |
] | |
}, | |
"execution_count": 12, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"B[0:2]" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Change the first element of the list to an uppercase <code> \"A\" </code> " | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 17, | |
"metadata": { | |
"collapsed": false, | |
"jupyter": { | |
"outputs_hidden": false | |
} | |
}, | |
"outputs": [], | |
"source": [ | |
"B[0]= \"A\"" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"<hr>\n", | |
"<small>Copyright © 2018 IBM Cognitive Class. This notebook and its source code are released under the terms of the [MIT License](https://cognitiveclass.ai/mit-license/).</small>" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": {}, | |
"outputs": [], | |
"source": [] | |
} | |
], | |
"metadata": { | |
"kernelspec": { | |
"display_name": "Python", | |
"language": "python", | |
"name": "conda-env-python-py" | |
}, | |
"language_info": { | |
"codemirror_mode": { | |
"name": "ipython", | |
"version": 3 | |
}, | |
"file_extension": ".py", | |
"mimetype": "text/x-python", | |
"name": "python", | |
"nbconvert_exporter": "python", | |
"pygments_lexer": "ipython3", | |
"version": "3.6.10" | |
} | |
}, | |
"nbformat": 4, | |
"nbformat_minor": 4 | |
} |
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