D04S. Dataframes - Refining and Indexing Solutions.ipynb

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    "# D04S: Dataframes - Refining and Indexing Solutions"
   ]
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    "#### Exercise 1: Import the file below and then:\n",
    "\n",
    "1) Drop the Unnamed:0 column<br/>\n",
    "2) Return the columsn list as a new variable<br/>\n",
    "3) Keep only the price, ram, cd and trend columns\n",
    "4) Create new datasets for each of the following where statements:\n",
    "\n",
    "* Where trend greater than 12 and less that or equal to 24\n",
    "* Where ram is either 16 or 32\n",
    "* Where price is greater than 2500 and cd is yes"
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   "source": [
    "path = \"https://vincentarelbundock.github.io/Rdatasets/csv/Ecdat/Computers.csv\""
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    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(path)\n",
    "\n",
    "df = df.drop('Unnamed: 0',axis=1)\n",
    "cols = df.columns\n",
    "df = df[['price','ram','cd','trend']]\n",
    "\n",
    "df1 = df[(df['trend'] > 12) & (df['trend'] <= 24)]\n",
    "df2 = df[df['ram'].isin([16,32])]\n",
    "df3 = df[(df['price'] > 2500) & (df['cd'] == 'yes')]"
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    "#### Exercise 2: Retrieve the records at the following item locations from the base dataset:\n",
    "\n",
    "* 300\n",
    "* 1000-1010\n",
    "* Last 10 records\n",
    "* The middle record"
   ]
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    "df.iloc[300]\n",
    "df.iloc[1000:1010]\n",
    "df.iloc[-10:-1]\n",
    "df.iloc[int((len(df)/2))]"
   ]
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    "#### Exercise 3: From the base dataset you imported:\n",
    "* Set the index as the trend column\n",
    "* Drop the name of the index\n",
    "* Create a new a new dataframe for the trend values 1-6\n",
    "* reset the index of this new dataframe\n",
    "* drop any unwanted variables"
   ]
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    "df = pd.read_csv(path)\n",
    "\n",
    "df = df.set_index('trend')\n",
    "df.index.name = None\n",
    "new = df.ix[[1,2,3,4,5,6]].reset_index().drop(['index','Unnamed: 0'], axis=1)\n",
    "new.head(5)"
   ]
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