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index.ipynb

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@@ -20,7 +20,7 @@
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"source": [
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"In the past few lessons, we have learned about working with different types of data in Python: strings, numbers (ints and floats), and booleans. Now let's put that knowledge into action.\n",
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"\n",
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"In this lesson we'll imagine that you just stopped by an Python programming conference and met a few people there. You exchanged some information with a very industrious person who you met, and want to use your programming skills to format this information correctly. "
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"In this lesson we'll imagine that at a nice social gathering you exchanged business cards with a few people. One of the business cards belongs to Art Vandelay, a new travel agent. You want to use your programming skills to format this information correctly. "
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]
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},
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{
@@ -57,9 +57,9 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Yea, he may not be the best person to get to know. But we know that when people fill out forms, they misenter information all of the time. As a celebrity once said, \"I'm really getting into the Internet lately, I just wish it were more organized.\"\n",
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"Yea, he may not be the best person to get to know. But we know that when people fill out forms, they misenter information all of the time.\n",
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"\n",
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"And as data scientists, we also see the need of organizing and cleaning data to then make sense of it. So let's get to work."
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"So as data scientists, we often need to clean and organize data to then make sense of it. Let's get to work. "
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]
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},
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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": 6,
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'art.vandelay@vandelay.cop'"
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"'art.vandelay@vandelay.co'"
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]
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},
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"execution_count": 6,
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"execution_count": 1,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"\"art.vandelay@vandelay.cop\" # False"
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"\"art.vandelay@vandelay.co\" # False"
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]
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},
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{
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.4"
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"version": "3.6.1"
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}
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},
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"nbformat": 4,

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