Tutorial 10- Python Pandas Tutorials- Part 2.ipynb

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  {
   "cell_type": "markdown",
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   "source": [
    "### Tutorial 10- Python Pandas Tutorials- Part 2\n",
    "\n",
    "In Part we are going to learn about\n",
    "1. StringIO\n",
    "2. Pandas read_csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "085ab65c",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Reading Different Data sources with the help of pandas\n",
    "from io import StringIO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9fc71488",
   "metadata": {},
   "outputs": [
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>80.62</td>\n",
       "      <td>az</td>\n",
       "      <td>t</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>d</td>\n",
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       "<p>5 rows × 378 columns</p>\n",
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       "   ID       y  X0 X1  X2 X3 X4 X5 X6 X8  ...  X375  X376  X377  X378  X379  \\\n",
       "0   0  130.81   k  v  at  a  d  u  j  o  ...     0     0     1     0     0   \n",
       "1   6   88.53   k  t  av  e  d  y  l  o  ...     1     0     0     0     0   \n",
       "2   7   76.26  az  w   n  c  d  x  j  x  ...     0     0     0     0     0   \n",
       "3   9   80.62  az  t   n  f  d  x  l  e  ...     0     0     0     0     0   \n",
       "4  13   78.02  az  v   n  f  d  h  d  n  ...     0     0     0     0     0   \n",
       "\n",
       "   X380  X382  X383  X384  X385  \n",
       "0     0     0     0     0     0  \n",
       "1     0     0     0     0     0  \n",
       "2     0     1     0     0     0  \n",
       "3     0     0     0     0     0  \n",
       "4     0     0     0     0     0  \n",
       "\n",
       "[5 rows x 378 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df=pd.read_csv('mercedesbenz.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b63ae7c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d91def8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4168ad21",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=('col1,col2,col3\\n'\n",
    "     'x,y,1\\n'\n",
    "     'a,b,2\\n'\n",
    "     'c,d,3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "98e70f96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "type(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f311f255",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<_io.StringIO at 0x1a1eb0b5e50>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##in memeory file format object\n",
    "StringIO(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2282cb7c",
   "metadata": {},
   "outputs": [
    {
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       "      <th>2</th>\n",
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       "      <td>3</td>\n",
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      "text/plain": [
       "  col1 col2  col3\n",
       "0    x    y     1\n",
       "1    a    b     2\n",
       "2    c    d     3"
      ]
     },
     "execution_count": 18,
     "metadata": {},
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   ],
   "source": [
    "pd.read_csv(StringIO(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "80ea6cce",
   "metadata": {},
   "outputs": [
    {
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       "      <th>col2</th>\n",
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       "      <th>0</th>\n",
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       "      <th>1</th>\n",
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       "  col1 col2\n",
       "0    x    y\n",
       "1    a    b\n",
       "2    c    d"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "pd.read_csv(StringIO(data),usecols=['col1','col2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "498a0c64",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X0</th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>X3</th>\n",
       "      <th>X4</th>\n",
       "      <th>X5</th>\n",
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       "      <th>0</th>\n",
       "      <td>k</td>\n",
       "      <td>v</td>\n",
       "      <td>at</td>\n",
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       "      <td>u</td>\n",
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       "      <th>1</th>\n",
       "      <td>k</td>\n",
       "      <td>t</td>\n",
       "      <td>av</td>\n",
       "      <td>e</td>\n",
       "      <td>d</td>\n",
       "      <td>y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>az</td>\n",
       "      <td>w</td>\n",
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       "      <td>d</td>\n",
       "      <td>x</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>az</td>\n",
       "      <td>t</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>d</td>\n",
       "      <td>x</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>az</td>\n",
       "      <td>v</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>d</td>\n",
       "      <td>h</td>\n",
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      "text/plain": [
       "   X0 X1  X2 X3 X4 X5\n",
       "0   k  v  at  a  d  u\n",
       "1   k  t  av  e  d  y\n",
       "2  az  w   n  c  d  x\n",
       "3  az  t   n  f  d  x\n",
       "4  az  v   n  f  d  h"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df=pd.read_csv('mercedesbenz.csv',usecols=['X0','X1','X2','X3','X4','X5'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b09a7e08",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('test.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c46f62b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "##datatypes in csv\n",
    "data = ('a,b,c,d\\n'\n",
    "            '1,2,3,4\\n'\n",
    "            '5,6,7,8\\n'\n",
    "            '9,10,11')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9d1c4355",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(StringIO(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e8772a92",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3 entries, 0 to 2\n",
      "Data columns (total 4 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   a       3 non-null      int64  \n",
      " 1   b       3 non-null      int64  \n",
      " 2   c       3 non-null      int64  \n",
      " 3   d       2 non-null      float64\n",
      "dtypes: float64(1), int64(3)\n",
      "memory usage: 224.0 bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a7a7f4bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(StringIO(data),dtype='object')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9d046c34",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3 entries, 0 to 2\n",
      "Data columns (total 4 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   a       3 non-null      object\n",
      " 1   b       3 non-null      object\n",
      " 2   c       3 non-null      object\n",
      " 3   d       2 non-null      object\n",
      "dtypes: object(4)\n",
      "memory usage: 224.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "7a03db92",
   "metadata": {},
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       "      <th>0</th>\n",
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       "      <td>4.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>5</td>\n",
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       "      <td>8.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
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      ],
      "text/plain": [
       "   a   b   c    d\n",
       "0  1   2   3  4.0\n",
       "1  5   6   7  8.0\n",
       "2  9  10  11  NaN"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "07f10ae9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0\n",
       "b    0\n",
       "c    0\n",
       "d    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "acadfb59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['a'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "f4cd6262",
   "metadata": {},
   "outputs": [],
   "source": [
    "##datatypes in csv\n",
    "data = ('a,b,c,d\\n'\n",
    "            '1,2,3,4\\n'\n",
    "            '5,6,7,8\\n'\n",
    "            '9,10,11')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "05aec552",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(StringIO(data),dtype={'a':int,'b':float,'c':int})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "154d5990",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\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>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>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a     b   c    d\n",
       "0  1   2.0   3  4.0\n",
       "1  5   6.0   7  8.0\n",
       "2  9  10.0  11  NaN"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "afb54a36",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3 entries, 0 to 2\n",
      "Data columns (total 4 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   a       3 non-null      int32  \n",
      " 1   b       3 non-null      float64\n",
      " 2   c       3 non-null      int32  \n",
      " 3   d       2 non-null      float64\n",
      "dtypes: float64(2), int32(2)\n",
      "memory usage: 200.0 bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f4012fa9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a      int32\n",
       "b    float64\n",
       "c      int32\n",
       "d    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "536aeb25",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = ('index,a,b,c\\n'\n",
    "           '4,apple,bat,5.7\\n'\n",
    "            '8,orange,cow,10')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "6c02b4b0",
   "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",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>apple</td>\n",
       "      <td>bat</td>\n",
       "      <td>5.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>orange</td>\n",
       "      <td>cow</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            a    b     c\n",
       "index                   \n",
       "4       apple  bat   5.7\n",
       "8      orange  cow  10.0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv(StringIO(data),index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "bb874233",
   "metadata": {},
   "outputs": [],
   "source": [
    "# use index cols and usecols\n",
    "data = ('a,b,c\\n'\n",
    "           '4,apple,bat\\n'\n",
    "            '8,orange,cow')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "69a9c135",
   "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>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>apple</td>\n",
       "      <td>bat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>orange</td>\n",
       "      <td>cow</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        b    c\n",
       "a             \n",
       "4   apple  bat\n",
       "8  orange  cow"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv(StringIO(data),usecols=['a','b','c'],index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "44147186",
   "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>item_code</th>\n",
       "      <th>item_name</th>\n",
       "      <th>display_level</th>\n",
       "      <th>selectable</th>\n",
       "      <th>sort_sequence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AA0</td>\n",
       "      <td>All items - old base</td>\n",
       "      <td>0</td>\n",
       "      <td>T</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AA0R</td>\n",
       "      <td>Purchasing power of the consumer dollar - old ...</td>\n",
       "      <td>0</td>\n",
       "      <td>T</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SA0</td>\n",
       "      <td>All items</td>\n",
       "      <td>0</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SA0E</td>\n",
       "      <td>Energy</td>\n",
       "      <td>1</td>\n",
       "      <td>T</td>\n",
       "      <td>375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SA0L1</td>\n",
       "      <td>All items less food</td>\n",
       "      <td>1</td>\n",
       "      <td>T</td>\n",
       "      <td>359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>SSEA011</td>\n",
       "      <td>College textbooks</td>\n",
       "      <td>3</td>\n",
       "      <td>T</td>\n",
       "      <td>314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>SSEE041</td>\n",
       "      <td>Smartphones</td>\n",
       "      <td>4</td>\n",
       "      <td>T</td>\n",
       "      <td>335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>SSFV031A</td>\n",
       "      <td>Food at elementary and secondary schools</td>\n",
       "      <td>3</td>\n",
       "      <td>T</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>SSGE013</td>\n",
       "      <td>Infants' equipment</td>\n",
       "      <td>3</td>\n",
       "      <td>T</td>\n",
       "      <td>356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>SSHJ031</td>\n",
       "      <td>Infants' furniture</td>\n",
       "      <td>3</td>\n",
       "      <td>T</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>400 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    item_code                                          item_name  \\\n",
       "0         AA0                               All items - old base   \n",
       "1        AA0R  Purchasing power of the consumer dollar - old ...   \n",
       "2         SA0                                          All items   \n",
       "3        SA0E                                             Energy   \n",
       "4       SA0L1                                All items less food   \n",
       "..        ...                                                ...   \n",
       "395   SSEA011                                  College textbooks   \n",
       "396   SSEE041                                        Smartphones   \n",
       "397  SSFV031A           Food at elementary and secondary schools   \n",
       "398   SSGE013                                 Infants' equipment   \n",
       "399   SSHJ031                                 Infants' furniture   \n",
       "\n",
       "     display_level selectable  sort_sequence  \n",
       "0                0          T              2  \n",
       "1                0          T            400  \n",
       "2                0          T              1  \n",
       "3                1          T            375  \n",
       "4                1          T            359  \n",
       "..             ...        ...            ...  \n",
       "395              3          T            314  \n",
       "396              4          T            335  \n",
       "397              3          T            122  \n",
       "398              3          T            356  \n",
       "399              3          T            165  \n",
       "\n",
       "[400 rows x 5 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('https://download.bls.gov/pub/time.series/cu/cu.item',sep='\\t')           "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4233d884",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0480dced",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "96df1a01",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "89b64f78",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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