Pandas Week 10.ipynb

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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#37. Seleksi baris dengan banyak kriteria"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Persiapan Data Frame\n",
    "df = pd.read_csv('titanicfull.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Seleksi baris dengan banyak kriteria\n",
    "df[(df['sex']=='female') & (df['age']>=60) & (df['embarked']=='S') & (df['survived']==1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\n",
    "    (df['sex']=='female') & \n",
    "    (df['age']>=60) & \n",
    "    (df['embarked']=='S') & \n",
    "    (df['survived']==1)\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "kr1 = df['sex']=='female'\n",
    "kr2 = df['age']>=60\n",
    "kr3 = df['embarked']=='S'\n",
    "kr4 = df['survived']==1\n",
    "\n",
    "df[kr1 & kr2 & kr3 & kr4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#38. Mengenal parameter header dan skiprows\n",
    "\n",
    "df = pd.read_csv('iris_error.csv')\n",
    "df.head(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('iris_error.csv', header=2, skiprows=[5,6])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#39. Mengacak urutan baris pada DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "   A  B  C  D  E\n",
       "0  2  4  3  4  3\n",
       "1  1  3  2  3  1\n",
       "2  1  2  1  2  4\n",
       "3  2  1  1  1  1\n",
       "4  2  4  1  2  1\n",
       "5  2  3  2  3  4"
      ]
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Persiapan Data Frame\n",
    "\n",
    "n_rows = 6\n",
    "n_cols = 5\n",
    "cols = tuple('ABCDE')\n",
    "\n",
    "df = pd.DataFrame(np.random.randint(1, 5, size=(n_rows, n_cols)), \n",
    "                  columns=cols)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mengacak urutan baris pada DataFrame\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "   A  B  C  D  E\n",
       "2  1  2  1  2  4\n",
       "1  1  3  2  3  1\n",
       "4  2  4  1  2  1\n",
       "0  2  4  3  4  3\n",
       "3  2  1  1  1  1\n",
       "5  2  3  2  3  4"
      ]
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "\n",
    "df.sample(frac=1.0, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "   A  B  C  D  E\n",
       "0  1  2  1  2  4\n",
       "1  1  3  2  3  1\n",
       "2  2  4  1  2  1\n",
       "3  2  4  3  4  3\n",
       "4  2  1  1  1  1\n",
       "5  2  3  2  3  4"
      ]
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df.sample(frac=1.0, random_state=1).reset_index(drop=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#40. Mengakses sekelompok data dengan get_group()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mengakses sekelompok data yang sudah terkelompok dengan get_group()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "grouped_df = df.groupby('sex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped_df.get_group('female').head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped_df = df.groupby('survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "grouped_df.get_group(1).head(10)"
   ]
  }
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