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   "source": [
    "# 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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   "source": [
    "### Step 3. Assign it to a variable called crime."
   ]
  },
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   "execution_count": null,
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   "cell_type": "markdown",
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   "source": [
    "### Step 4. What is the type of the columns?"
   ]
  },
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   "execution_count": null,
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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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   "execution_count": null,
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    "### Step 6. Set the Year column as the index of the dataframe"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "### Step 7. Delete the Total column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
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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
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9. What is the mos dangerous decade to live in the US?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
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