Lecture07_Sub_Pandas_Problem01.ipynb

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   "source": [
    "# Quiz 1"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "### 1. &#45936;&#51060;&#53552; &#49483;&#51012; &#50500;&#47000;&#51032; &#51452;&#49548;&#50640;&#49436; &#51069;&#50612;&#50728;&#45796;.\n",
    "https://raw.githubusercontent.com/justmarkham/DAT8/master/data/u.user"
   ]
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   "source": [
    "import pandas as pd\n",
    "users = pd.read_table('https://raw.githubusercontent.com/justmarkham/DAT8/master/data/u.user', \n",
    "    sep='|', index_col='user_id')"
   ]
  },
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   "source": [
    "### 2. 25 entries &#44620;&#51648; &#51069;&#50612;&#50728;&#45796;."
   ]
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   "source": [
    "### 3. column &#51060;&#47492;&#51008; &#47924;&#50631;&#51064;&#44032;?"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. users dataframe&#51032; &#47784;&#46304; column&#51012; &#53685;&#44228;&#51201;&#51004;&#47196; &#50836;&#50557;&#54616;&#50668;&#46972;."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. occupation(&#51649;&#50629;)&#48324; &#54217;&#44512; &#45208;&#51060;(age)&#45716;? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. occupation(&#51649;&#50629;)&#48324;&#47196;, &#45208;&#51060;(age)&#51032; &#52572;&#49548;&#44050;, &#52572;&#45824;&#44050;&#51012; &#44396;&#54616;&#50668;&#46972;."
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7. &#51649;&#50629;(occupation)&#44284; &#49457;&#48324;(gender)&#50640; &#46384;&#47480; &#54217;&#44512; &#45208;&#51060;(age)&#47484; &#44396;&#54616;&#50668;&#46972;. "
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quiz 2"
   ]
  },
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   "source": [
    "### 1. &#50500;&#47000;&#51032; Series 3&#44060;&#47484; &#49373;&#49457;&#54616;&#46972;.\n",
    " - 1 ~ 4(4&#54252;&#54632;)&#50640;&#49436; &#51076;&#51032;&#51032; &#51221;&#49688;&#47484; 100&#44060;&#47484; data&#47196; &#44032;&#51648;&#44256; &#51080;&#45716; s1\n",
    " - 1 ~ 3(3&#54252;&#54632;)&#50640;&#49436; &#51076;&#51032;&#51032; &#51221;&#49688;&#47484; 100&#44060;&#47484; data&#47196; &#44032;&#51648;&#44256; &#51080;&#45716; s2\n",
    " - 10000 ~ 30000(30000&#54252;&#54632;)&#50640;&#49436; &#51076;&#51032;&#51032; &#51221;&#49688;&#47484; 100&#44060;&#47484; data&#47196; &#44032;&#51648;&#44256; &#51080;&#45716; s3"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. 3&#44060;&#51032; Series&#47484; &#52972;&#47100;&#51004;&#47196; &#44032;&#51648;&#45716; DataFrame&#51012; &#49373;&#49457;&#54616;&#46972;.\n",
    " - column&#51032; &#51060;&#47492;&#51008; &#44033;&#44033;\n",
    "  - beds\n",
    "  - baths\n",
    "  - price_per_meter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. 3&#44060;&#51032; column&#51012; &#54633;&#52828; &#54980;, 1&#44060;&#51032; column&#51012; &#44032;&#51652; DataFrame&#51012; &#49373;&#49457;&#54616;&#46972;. (Series&#44032; &#50500;&#45772; DataFrame)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. &#50948;&#50640;&#49436; &#49373;&#49457;&#54620; dataframe&#51032; index&#45716; 0~99 &#44032; 3&#48264; &#46321;&#51109;&#54620;&#45796;. 0~299&#47196; &#48320;&#44221;&#54616;&#50668;&#46972;."
   ]
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