samples/snowpark-pandas/api-examples/api_examples.ipynb

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   "cell_type": "markdown",
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   "metadata": {},
   "source": [
    "# Snowpark Pandas API Examples"
   ]
  },
  {
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   "execution_count": 1,
   "id": "184ccba4-4afc-4fe4-bef8-6227d0a1422c",
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     "text": [
      "UserWarning: Snowpark pandas has been in Public Preview since 1.17.0. See https://docs.snowflake.com/LIMITEDACCESS/snowpark-pandas for details.\n"
     ]
    }
   ],
   "source": [
    "import modin.pandas as pd\n",
    "# Import the Snowpark pandas plugin for modin\n",
    "import snowflake.snowpark.modin.plugin\n",
    "\n",
    "# Create a Snowpark session with a default connection\n",
    "from snowflake.snowpark.session import Session\n",
    "session = Session.builder.create()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5af3a5cc-c834-4852-bb57-2fa42eb4e3bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a Snowpark Pandas DataFrame with sample data\n",
    "df = pd.DataFrame([['a', 2.1, 1],['b', None, 2],['c', 6.3, 3]], columns=[\"COL_STR\", \"COL_FLOAT\", \"COL_INT\"])"
   ]
  },
  {
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   "execution_count": 3,
   "id": "2ca156a8-f54a-439f-9d64-3b0bac0c7d6c",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>COL_STR</th>\n",
       "      <th>COL_FLOAT</th>\n",
       "      <th>COL_INT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>2.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>c</td>\n",
       "      <td>6.3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "  COL_STR  COL_FLOAT  COL_INT\n",
       "0       a        2.1        1\n",
       "1       b        NaN        2\n",
       "2       c        6.3        3"
      ]
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    "df"
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   "execution_count": 4,
   "id": "9640d276-39b6-48d8-939f-27e4f627c7af",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_snowflake(\"pandas_test\", if_exists='replace',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "59f04776-ea5f-434c-9068-25cd36b8a636",
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   "outputs": [],
   "source": [
    "# Create a dataframe out of a Snowflake table.\n",
    "df = pd.read_snowflake('pandas_test')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3a1e9e6e-6a50-4b81-a460-8acced1cd256",
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    {
     "data": {
      "text/plain": [
       "(3, 3)"
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     "execution_count": 6,
     "metadata": {},
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   "source": [
    "df.shape"
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   "execution_count": 7,
   "id": "c9b81946-1db2-445b-9c2f-1a84071e786a",
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       "  COL_STR  COL_FLOAT  COL_INT\n",
       "0       a        2.1        1\n",
       "1       b        NaN        2"
      ]
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     "metadata": {},
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   "source": [
    "df.head(2)"
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   "id": "a72b51c6-1866-43f5-9f5c-bed65394bb16",
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   "source": [
    "df.dropna(subset=[\"COL_FLOAT\"], inplace=True)"
   ]
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   "execution_count": 9,
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       "      <td>c</td>\n",
       "      <td>6.3</td>\n",
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       "  COL_STR  COL_FLOAT  COL_INT\n",
       "0       a        2.1        1\n",
       "2       c        6.3        3"
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   "id": "76af2a69-b4d4-4e88-ac25-ad77a7891c97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 3)"
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     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
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  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0d1dc138-2885-40f4-b44a-a00f36a32cc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "COL_STR       object\n",
       "COL_FLOAT    float64\n",
       "COL_INT        int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "51f88148-2617-4f1c-9d31-05e1ec9093bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.reset_index(drop=True).to_snowflake('pandas_test2', if_exists='replace',index=True, index_label=['row_pos'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b2756d9-571a-4123-a104-1893642283b0",
   "metadata": {},
   "source": [
    "## IO (Read and Write)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "80eb4472-735b-4556-93b6-33d2e4d59998",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reading and writing to Snowflake\n",
    "df = pd.DataFrame({\"fruit\": [\"apple\", \"orange\"], \"size\": [3.4, 5.4], \"weight\": [1.4, 3.2]})\n",
    "df.to_snowflake(\"test_table\", if_exists=\"replace\", index=False )"
   ]
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   "cell_type": "code",
   "execution_count": 15,
   "id": "89d1c4a7-a03c-4b2d-956d-8a82ddbe46a1",
   "metadata": {},
   "outputs": [
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       "      <td>apple</td>\n",
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       "      <td>1.4</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>orange</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.2</td>\n",
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       "    fruit  size  weight\n",
       "0   apple   3.4     1.4\n",
       "1  orange   5.4     3.2"
      ]
     },
     "execution_count": 15,
     "metadata": {},
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   "source": [
    "df_read = pd.read_snowflake(\"test_table\")\n",
    "df_read"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "44f477d5-c257-450c-b73e-63ee2a400b5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate sample CSV file\n",
    "with open(\"data.csv\", \"w\") as f: \n",
    "    f.write('fruit,size,weight\\napple,3.4,1.4\\norange,5.4,3.2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a50d0121-29ce-4cd0-b8f9-5abdf89ee40a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read from local CSV file\n",
    "df_csv = pd.read_csv(\"data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bf936dd2-363d-4e31-9722-e197e330933e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate sample JSON file\n",
    "with open(\"data.json\", \"w\") as f: \n",
    "    f.write('{\"fruit\":\"apple\", \"size\":3.4, \"weight\":1.4},{\"fruit\":\"orange\", \"size\":5.4, \"weight\":3.2}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3a6edbf7-3eda-41b6-bb5d-48571edb2b39",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read from local JSON file\n",
    "df_json = pd.read_json('data.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4b6d044c-eadc-4eb5-a223-7cca96e4984c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Upload data.json and data.csv to Snowflake stage named @TEST_STAGE\n",
    "# Read CSV and JSON file from stage\n",
    "df_csv = pd.read_csv('@TEST_STAGE/data.csv')\n",
    "df_json = pd.read_json('@TEST_STAGE/data.json')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cca5a411-d26c-40be-8ff3-aa67f24788ea",
   "metadata": {},
   "source": [
    "## Indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "9e0a18b6-2464-410c-bb24-1674521b60e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({\"a\": [1,2,3], \"b\": [\"x\", \"y\", \"z\"]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "47e97ef0-2eb2-4907-b35f-b1c312880d39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['a', 'b'], dtype='object')"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.columns"
   ]
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  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "b63eeeff-b087-4d04-a39e-01a182eb9c26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([0, 1, 2], dtype='int64')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.index"
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   "cell_type": "code",
   "execution_count": 61,
   "id": "5eaac998-1e41-457f-8f8b-1e5ddc0169dc",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    3\n",
       "Name: a, dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df[\"a\"]"
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  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "4865232b-d38a-4a16-a18d-eb53993d5dd1",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    x\n",
       "1    y\n",
       "2    z\n",
       "Name: b, dtype: object"
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     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df[\"b\"]"
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   "cell_type": "code",
   "execution_count": 63,
   "id": "ac5fbe44-c118-4fa0-8b69-4b90d5951cf7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'x'"
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     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.iloc[0,1]"
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   "execution_count": 64,
   "id": "3507d7d7-484c-4d30-84f5-513483a45641",
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    "df.columns = [\"c\", \"d\"]\n",
    "df"
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>z</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   d\n",
       "c   \n",
       "1  x\n",
       "2  y\n",
       "3  z"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.set_index(\"c\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "fe57976d-2487-4ce7-b94f-0dda43f4498f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>renamed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>z</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  renamed\n",
       "c        \n",
       "1       x\n",
       "2       y\n",
       "3       z"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rename(columns={\"d\": \"renamed\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec299aab-6ff5-4032-83ad-2a5b2142c442",
   "metadata": {},
   "source": [
    "## Missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "902abd0f-fe77-4c6e-aaaf-c4eec0b2c3e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A    B   C    D\n",
       "0  NaN  2.0 NaN  0.0\n",
       "1  3.0  4.0 NaN  1.0\n",
       "2  NaN  NaN NaN  NaN\n",
       "3  NaN  3.0 NaN  4.0"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "df = pd.DataFrame([[np.nan, 2, np.nan, 0],\n",
    "                [3, 4, np.nan, 1],\n",
    "                [np.nan, np.nan, np.nan, np.nan],\n",
    "                [np.nan, 3, np.nan, 4]],\n",
    "                columns=list(\"ABCD\"))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "c5566bc2-afca-4334-bd13-6dcafaf38c04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       A      B     C      D\n",
       "0   True  False  True  False\n",
       "1  False  False  True  False\n",
       "2   True   True  True   True\n",
       "3   True  False  True  False"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "a0ba81eb-5884-4b0c-96e5-83ceefdb089c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A    B    C    D\n",
       "0  0.0  2.0  0.0  0.0\n",
       "1  3.0  4.0  0.0  1.0\n",
       "2  0.0  0.0  0.0  0.0\n",
       "3  0.0  3.0  0.0  4.0"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "06201331-df6d-490a-a937-6072b367f58b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A    B   C    D\n",
       "0  NaN  2.0 NaN  0.0\n",
       "1  3.0  4.0 NaN  1.0\n",
       "3  NaN  3.0 NaN  4.0"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(how=\"all\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5896b703-663c-45a8-9e08-20242f41cbcd",
   "metadata": {},
   "source": [
    "## Type conversion\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "325db1d9-2dff-4c0f-9c68-9c0bd3cb780f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>int</th>\n",
       "      <th>str</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   int str\n",
       "0    1   4\n",
       "1    2   5\n",
       "2    3   6"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\"int\": [1,2,3], \"str\": [\"4\", \"5\", \"6\"]})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "73ac4d5b-f432-4001-a949-e91328344823",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>int</th>\n",
       "      <th>str</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   int  str\n",
       "0  1.0  4.0\n",
       "1  2.0  5.0\n",
       "2  3.0  6.0"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_float = df.astype(float)\n",
    "df_float"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "ef487a94-a614-4d0f-970f-0fa8a4fbe00d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "int    float64\n",
       "str    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_float.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "0c601897-c5cd-41b9-9921-a0445045b1bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4.0\n",
       "1    5.0\n",
       "2    6.0\n",
       "Name: str, dtype: float64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_numeric(df.str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "3e47a950-bae6-4caa-90d4-2f2e9f68ccc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2015-02-04\n",
       "1   2016-03-05\n",
       "dtype: datetime64[ns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'year': [2015, 2016],\n",
    "                'month': [2, 3],\n",
    "                'day': [4, 5]})\n",
    "pd.to_datetime(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fc9a2b6-502f-4f37-86e1-5baac641f969",
   "metadata": {},
   "source": [
    "## Binary Operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "4d06caa3-1e6c-4704-8150-4e9e91f49129",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0    1     2\n",
       "0  7.0  9.0  11.0\n",
       "1  NaN  NaN   NaN"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_1 = pd.DataFrame([[1,2,3],[4,5,6]])\n",
    "df_2 = pd.DataFrame([[6,7,8]])\n",
    "df_1.add(df_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "9ceda722-a4e4-4131-a1d8-27e38901429f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3\n",
       "1    4\n",
       "2    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 = pd.Series([1, 2, 3])\n",
    "s2 = pd.Series([2, 2, 2])\n",
    "s1 + s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "d4aa9da9-df28-4407-93b3-3f16f37f1d6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>A+B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A  B  A+B\n",
       "0  1  4    5\n",
       "1  2  5    7\n",
       "2  3  6    9"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\"A\": [1,2,3], \"B\": [4,5,6]})\n",
    "df[\"A+B\"] = df[\"A\"] + df[\"B\"]\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3f00290-a305-4577-9e4a-497395a49a35",
   "metadata": {},
   "source": [
    "## Aggregations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "1c702f4d-d50b-40df-8c3c-c136f163e66a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sum</th>\n",
       "      <td>12.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        A     B     C\n",
       "sum  12.0  15.0  18.0\n",
       "min   1.0   2.0   3.0"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame([[1, 2, 3],\n",
    "                [4, 5, 6],\n",
    "                [7, 8, 9],\n",
    "                [np.nan, np.nan, np.nan]],\n",
    "                columns=['A', 'B', 'C'])\n",
    "df.agg(['sum', 'min'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "dacbcfa2-9d5d-4469-879a-120b8fe119b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    4.0\n",
       "B    5.0\n",
       "C    6.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.median()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ccf3f16-15b1-4259-9bf6-153ded3296ec",
   "metadata": {},
   "source": [
    "## Merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "d04ab95d-049a-4ccd-8af9-41add16affff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lkey</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bar</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>baz</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>foo</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  lkey  value\n",
       "0  foo      1\n",
       "1  bar      2\n",
       "2  baz      3\n",
       "3  foo      5"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.DataFrame({'lkey': ['foo', 'bar', 'baz', 'foo'],\n",
    "                    'value': [1, 2, 3, 5]})\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "9cd9a13f-09fa-42cf-a7e9-82654db252f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rkey</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bar</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>baz</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>foo</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  rkey  value\n",
       "0  foo      5\n",
       "1  bar      6\n",
       "2  baz      7\n",
       "3  foo      8"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.DataFrame({'rkey': ['foo', 'bar', 'baz', 'foo'],\n",
    "                    'value': [5, 6, 7, 8]})\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "73bc8890-762b-4f14-a30d-a995ccc3c623",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lkey</th>\n",
       "      <th>value_x</th>\n",
       "      <th>rkey</th>\n",
       "      <th>value_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>1</td>\n",
       "      <td>foo</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>foo</td>\n",
       "      <td>1</td>\n",
       "      <td>foo</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bar</td>\n",
       "      <td>2</td>\n",
       "      <td>bar</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>baz</td>\n",
       "      <td>3</td>\n",
       "      <td>baz</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>foo</td>\n",
       "      <td>5</td>\n",
       "      <td>foo</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>foo</td>\n",
       "      <td>5</td>\n",
       "      <td>foo</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  lkey  value_x rkey  value_y\n",
       "0  foo        1  foo        5\n",
       "1  foo        1  foo        8\n",
       "2  bar        2  bar        6\n",
       "3  baz        3  baz        7\n",
       "4  foo        5  foo        5\n",
       "5  foo        5  foo        8"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.merge(df2, left_on='lkey', right_on='rkey')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "2e9dabd6-bec1-4c3e-82c7-a38ed6813811",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>A</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>K0</td>\n",
       "      <td>A0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>K1</td>\n",
       "      <td>A1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>K2</td>\n",
       "      <td>A2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>K3</td>\n",
       "      <td>A3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>K4</td>\n",
       "      <td>A4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>K5</td>\n",
       "      <td>A5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key   A\n",
       "0  K0  A0\n",
       "1  K1  A1\n",
       "2  K2  A2\n",
       "3  K3  A3\n",
       "4  K4  A4\n",
       "5  K5  A5"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3', 'K4', 'K5'],\n",
    "                'A': ['A0', 'A1', 'A2', 'A3', 'A4', 'A5']})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "1c8743e3-a62a-4327-bc69-3d4c5239126d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key_caller</th>\n",
       "      <th>A</th>\n",
       "      <th>key_other</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>K0</td>\n",
       "      <td>A0</td>\n",
       "      <td>K0</td>\n",
       "      <td>B0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>K1</td>\n",
       "      <td>A1</td>\n",
       "      <td>K1</td>\n",
       "      <td>B1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>K2</td>\n",
       "      <td>A2</td>\n",
       "      <td>K2</td>\n",
       "      <td>B2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>K3</td>\n",
       "      <td>A3</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>K4</td>\n",
       "      <td>A4</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>K5</td>\n",
       "      <td>A5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key_caller   A key_other     B\n",
       "0         K0  A0        K0    B0\n",
       "1         K1  A1        K1    B1\n",
       "2         K2  A2        K2    B2\n",
       "3         K3  A3      None  None\n",
       "4         K4  A4      None  None\n",
       "5         K5  A5      None  None"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "other = pd.DataFrame({'key': ['K0', 'K1', 'K2'],\n",
    "                    'B': ['B0', 'B1', 'B2']})\n",
    "df.join(other, lsuffix='_caller', rsuffix='_other')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb2c7b09-1841-490b-9cf0-94701ec3810a",
   "metadata": {},
   "source": [
    "## Groupby"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "eac8e6c3-9df6-4ee2-a6a6-a5f7268bece8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Animal</th>\n",
       "      <th>Max Speed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Falcon</td>\n",
       "      <td>380.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Falcon</td>\n",
       "      <td>370.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Parrot</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Parrot</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Animal  Max Speed\n",
       "0  Falcon      380.0\n",
       "1  Falcon      370.0\n",
       "2  Parrot       24.0\n",
       "3  Parrot       26.0"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'Animal': ['Falcon', 'Falcon','Parrot', 'Parrot'],\n",
    "                   'Max Speed': [380., 370., 24., 26.]})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "45f3c072-230a-4399-879c-ca462b1cac46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Max Speed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Animal</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Falcon</th>\n",
       "      <td>375.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parrot</th>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Max Speed\n",
       "Animal           \n",
       "Falcon      375.0\n",
       "Parrot       25.0"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Animal']).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83559599-4340-4907-afa3-294dadbe4e91",
   "metadata": {},
   "source": [
    "## Pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "ca3c7d10-0570-4238-ab51-8199ffd3337a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>small</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>large</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>large</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>foo</td>\n",
       "      <td>two</td>\n",
       "      <td>small</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>foo</td>\n",
       "      <td>two</td>\n",
       "      <td>small</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>bar</td>\n",
       "      <td>one</td>\n",
       "      <td>large</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>bar</td>\n",
       "      <td>one</td>\n",
       "      <td>small</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>bar</td>\n",
       "      <td>two</td>\n",
       "      <td>small</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>bar</td>\n",
       "      <td>two</td>\n",
       "      <td>large</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A    B      C  D  E\n",
       "0  foo  one  small  1  2\n",
       "1  foo  one  large  2  4\n",
       "2  foo  one  large  2  5\n",
       "3  foo  two  small  3  5\n",
       "4  foo  two  small  3  6\n",
       "5  bar  one  large  4  6\n",
       "6  bar  one  small  5  8\n",
       "7  bar  two  small  6  9\n",
       "8  bar  two  large  7  9"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\"A\": [\"foo\", \"foo\", \"foo\", \"foo\", \"foo\",\n",
    "                        \"bar\", \"bar\", \"bar\", \"bar\"],\n",
    "                \"B\": [\"one\", \"one\", \"one\", \"two\", \"two\",\n",
    "                        \"one\", \"one\", \"two\", \"two\"],\n",
    "                \"C\": [\"small\", \"large\", \"large\", \"small\",\n",
    "                        \"small\", \"large\", \"small\", \"small\",\n",
    "                        \"large\"],\n",
    "                \"D\": [1, 2, 2, 3, 3, 4, 5, 6, 7],\n",
    "                \"E\": [2, 4, 5, 5, 6, 6, 8, 9, 9]})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "66373055-bd93-4643-b4ac-602ef4017bf8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>C</th>\n",
       "      <th>large</th>\n",
       "      <th>small</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">bar</th>\n",
       "      <th>one</th>\n",
       "      <td>4.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>7.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">foo</th>\n",
       "      <th>one</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "C        large  small\n",
       "A   B                \n",
       "bar one    4.0      5\n",
       "    two    7.0      6\n",
       "foo one    4.0      1\n",
       "    two    NaN      6"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(df, values='D', index=['A', 'B'],\n",
    "                   columns=['C'], aggfunc=\"sum\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "e614babb-a7ea-4272-a6e8-1c6348489c58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>foo</th>\n",
       "      <th>bar</th>\n",
       "      <th>baz</th>\n",
       "      <th>zoo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>one</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>one</td>\n",
       "      <td>B</td>\n",
       "      <td>2</td>\n",
       "      <td>y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>one</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>two</td>\n",
       "      <td>A</td>\n",
       "      <td>4</td>\n",
       "      <td>q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>two</td>\n",
       "      <td>B</td>\n",
       "      <td>5</td>\n",
       "      <td>w</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>two</td>\n",
       "      <td>C</td>\n",
       "      <td>6</td>\n",
       "      <td>t</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   foo bar  baz zoo\n",
       "0  one   A    1   x\n",
       "1  one   B    2   y\n",
       "2  one   C    3   z\n",
       "3  two   A    4   q\n",
       "4  two   B    5   w\n",
       "5  two   C    6   t"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'foo': ['one', 'one', 'one', 'two', 'two','two'],\n",
    "                'bar': ['A', 'B', 'C', 'A', 'B', 'C'],\n",
    "                'baz': [1, 2, 3, 4, 5, 6],\n",
    "                'zoo': ['x', 'y', 'z', 'q', 'w', 't']})\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04d57af9-bd91-44b6-86bf-10f61e172b8f",
   "metadata": {},
   "source": [
    "## MultiIndex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "04c12a03-27cf-4145-842a-175973d14c18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex([('bar', 'one'),\n",
       "            ('bar', 'two'),\n",
       "            ('baz', 'one'),\n",
       "            ('baz', 'two'),\n",
       "            ('foo', 'one'),\n",
       "            ('foo', 'two'),\n",
       "            ('qux', 'one'),\n",
       "            ('qux', 'two')],\n",
       "           names=['first', 'second'])"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arrays = [\n",
    "    [\"bar\", \"bar\", \"baz\", \"baz\", \"foo\", \"foo\", \"qux\", \"qux\"],\n",
    "    [\"one\", \"two\", \"one\", \"two\", \"one\", \"two\", \"one\", \"two\"],\n",
    "]\n",
    "\n",
    "tuples = list(zip(*arrays))\n",
    "\n",
    "index = pd.MultiIndex.from_tuples(tuples, names=[\"first\", \"second\"])\n",
    "\n",
    "index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "737b7035-a81a-4fdc-bb5d-9f5fdc14fd1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "first  second\n",
       "bar    one      -1.881666\n",
       "       two       0.595870\n",
       "baz    one      -0.283088\n",
       "       two      -1.223938\n",
       "foo    one      -0.102025\n",
       "       two       0.629615\n",
       "qux    one      -0.973516\n",
       "       two       0.750192\n",
       "dtype: float64"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "pd.Series(np.random.randn(8), index=index)"
   ]
  }
 ],
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