army.ipynb

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   "source": [
    "# Fictional Army - Filtering and Sorting"
   ]
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   "source": [
    "### Introduction:\n",
    "\n",
    "This exercise was inspired by this [page](http://chrisalbon.com/python/)\n",
    "\n",
    "Special thanks to: https://github.com/chrisalbon for sharing the dataset and materials.\n",
    "\n",
    "### Step 1. Import the necessary libraries"
   ]
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   "source": [
    "import pandas as pd"
   ]
  },
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   "source": [
    "### Step 2. This is the data given as a dictionary"
   ]
  },
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   "source": [
    "# Create an example dataframe about a fictional army\n",
    "raw_data = {'regiment': ['Nighthawks', 'Nighthawks', 'Nighthawks', 'Nighthawks', 'Dragoons', 'Dragoons', 'Dragoons', 'Dragoons', 'Scouts', 'Scouts', 'Scouts', 'Scouts'],\n",
    "            'company': ['1st', '1st', '2nd', '2nd', '1st', '1st', '2nd', '2nd','1st', '1st', '2nd', '2nd'],\n",
    "            'deaths': [523, 52, 25, 616, 43, 234, 523, 62, 62, 73, 37, 35],\n",
    "            'battles': [5, 42, 2, 2, 4, 7, 8, 3, 4, 7, 8, 9],\n",
    "            'size': [1045, 957, 1099, 1400, 1592, 1006, 987, 849, 973, 1005, 1099, 1523],\n",
    "            'veterans': [1, 5, 62, 26, 73, 37, 949, 48, 48, 435, 63, 345],\n",
    "            'readiness': [1, 2, 3, 3, 2, 1, 2, 3, 2, 1, 2, 3],\n",
    "            'armored': [1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1],\n",
    "            'deserters': [4, 24, 31, 2, 3, 4, 24, 31, 2, 3, 2, 3],\n",
    "            'origin': ['Arizona', 'California', 'Texas', 'Florida', 'Maine', 'Iowa', 'Alaska', 'Washington', 'Oregon', 'Wyoming', 'Louisana', 'Georgia']}"
   ]
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   "source": [
    "### Step 3. Create a dataframe and assign it to a variable called army. \n",
    "\n",
    "#### Don't forget to include the columns names"
   ]
  },
  {
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   "execution_count": null,
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   "source": [
    "### Step 4. Set the 'origin' colum as the index of the dataframe"
   ]
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   "source": [
    "### Step 5. Print only the column veterans"
   ]
  },
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   "source": [
    "### Step 6. Print the columns 'veterans' and 'deaths'"
   ]
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   "source": [
    "### Step 7. Print the name of all the columns."
   ]
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   "source": [
    "### Step 8. Select the 'deaths', 'size' and 'deserters' columns from Maine and Alaska"
   ]
  },
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   "execution_count": null,
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   "source": [
    "### Step 9. Select the rows 3 to 7 and the columns 3 to 6"
   ]
  },
  {
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   "source": [
    "### Step 10. Select every row after the fourth row"
   ]
  },
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   "source": [
    "### Step 11. Select every row up to the 4th row"
   ]
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   "source": [
    "### Step 12. Select the 3rd column up to the 7th column"
   ]
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   "source": [
    "### Step 13. Select rows where df.deaths is greater than 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "### Step 14. Select rows where df.deaths is greater than 500 or less than 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
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   "source": [
    "### Step 15. Select all the regiments not named \"Dragoons\""
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "### Step 16. Select the rows called Texas and Arizona"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "### Step 17. Select the third cell in the row named Arizona"
   ]
  },
  {
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   "source": [
    "### Step 18. Select the third cell down in the column named deaths"
   ]
  },
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   "execution_count": null,
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