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{
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"# United States - Crime Rates - 1960 - 2014"
]
},
{
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"source": [
"### Introduction:\n",
"\n",
"This time you will create a data \n",
"\n",
"Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.\n",
"\n",
"### Step 1. Import the necessary libraries"
]
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"### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/04_Apply/US_Crime_Rates/US_Crime_Rates_1960_2014.csv). "
]
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{
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"source": [
"### Step 3. Assign it to a variable called crime."
]
},
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{
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"### Step 4. What is the type of the columns?"
]
},
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"##### Have you noticed that the type of Year is int64. But pandas has a different type to work with Time Series. Let's see it now.\n",
"\n",
"### Step 5. Convert the type of the column Year to datetime64"
]
},
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"### Step 6. Set the Year column as the index of the dataframe"
]
},
{
"cell_type": "code",
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"### Step 7. Delete the Total column"
]
},
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"cell_type": "markdown",
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"source": [
"### Step 8. Group the year by decades and sum the values\n",
"\n",
"#### Pay attention to the Population column number, summing this column is a mistake"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 9. What is the mos dangerous decade to live in the US?"
]
},
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"execution_count": null,
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