8.1 Reading Data In Text Format.ipynb

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    "![title](img/cover4.png)"
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   "source": [
    "<center><em>Copyright! This material is protected, please do not copy or distribute.    by:Taher Assaf</em></center>"
   ]
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  {
   "cell_type": "markdown",
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   "source": [
    "***\n",
    "<h1 align=\"center\">Udemy course : Python Bootcamp for Data Science 2021 Numpy Pandas & Seaborn</h1> \n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8.1 Reading Data In Text Format"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we import pandas library:"
   ]
  },
  {
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   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can read a dataframe from a csv file using the function **pd.read_csv()**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "hide_input": false
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   "outputs": [],
   "source": [
    "df1 = pd.read_csv('data/auto1.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "W e can display the head of this dataframe using th function **head()**:"
   ]
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
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       "      <td>chevrolet chevelle malibu</td>\n",
       "      <td>18</td>\n",
       "      <td>8</td>\n",
       "      <td>307.0</td>\n",
       "      <td>130</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>buick skylark 320</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>350.0</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>plymouth satellite</td>\n",
       "      <td>18</td>\n",
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       "      <td>318.0</td>\n",
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       "      <td>304.0</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ford torino</td>\n",
       "      <td>17</td>\n",
       "      <td>8</td>\n",
       "      <td>302.0</td>\n",
       "      <td>140</td>\n",
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      "text/plain": [
       "                        name  mpg  cylinders  displacement  horsepower\n",
       "0  chevrolet chevelle malibu   18          8         307.0         130\n",
       "1          buick skylark 320   15          8         350.0         165\n",
       "2         plymouth satellite   18          8         318.0         150\n",
       "3              amc rebel sst   16          8         304.0         150\n",
       "4                ford torino   17          8         302.0         140"
      ]
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     "execution_count": 3,
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   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can read a dataframe from a file where the values are separated by other symbols using the function **pd.read_table()**:"
   ]
  },
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   "cell_type": "code",
   "execution_count": 4,
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   "outputs": [],
   "source": [
    "df2 = pd.read_table('data/auto1.csv', sep = ',')"
   ]
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   "execution_count": 5,
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       "      <th>name</th>\n",
       "      <th>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
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       "      <td>chevrolet chevelle malibu</td>\n",
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       "      <td>307.0</td>\n",
       "      <td>130</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>buick skylark 320</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>350.0</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>plymouth satellite</td>\n",
       "      <td>18</td>\n",
       "      <td>8</td>\n",
       "      <td>318.0</td>\n",
       "      <td>150</td>\n",
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       "      <th>3</th>\n",
       "      <td>amc rebel sst</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>304.0</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ford torino</td>\n",
       "      <td>17</td>\n",
       "      <td>8</td>\n",
       "      <td>302.0</td>\n",
       "      <td>140</td>\n",
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       "                        name  mpg  cylinders  displacement  horsepower\n",
       "0  chevrolet chevelle malibu   18          8         307.0         130\n",
       "1          buick skylark 320   15          8         350.0         165\n",
       "2         plymouth satellite   18          8         318.0         150\n",
       "3              amc rebel sst   16          8         304.0         150\n",
       "4                ford torino   17          8         302.0         140"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the data has no column labels (header), we need to use an argument called **header = None**:"
   ]
  },
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   "cell_type": "code",
   "execution_count": 6,
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       "      <td>130</td>\n",
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       "      <th>1</th>\n",
       "      <td>buick skylark 320</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>350.0</td>\n",
       "      <td>165</td>\n",
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       "      <th>2</th>\n",
       "      <td>plymouth satellite</td>\n",
       "      <td>18</td>\n",
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       "      <td>318.0</td>\n",
       "      <td>150</td>\n",
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       "      <td>amc rebel sst</td>\n",
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       "      <td>304.0</td>\n",
       "      <td>150</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ford torino</td>\n",
       "      <td>17</td>\n",
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       "                           0   1  2      3    4\n",
       "0  chevrolet chevelle malibu  18  8  307.0  130\n",
       "1          buick skylark 320  15  8  350.0  165\n",
       "2         plymouth satellite  18  8  318.0  150\n",
       "3              amc rebel sst  16  8  304.0  150\n",
       "4                ford torino  17  8  302.0  140"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = pd.read_csv('data/auto2.csv', header = None)\n",
    "df3.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the data has no column labels, we can assign labels for the columns using the argument **names**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
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       "                        name  mpg  cylinders  displacement  horsepower\n",
       "0  chevrolet chevelle malibu   18          8         307.0         130\n",
       "1          buick skylark 320   15          8         350.0         165\n",
       "2         plymouth satellite   18          8         318.0         150\n",
       "3              amc rebel sst   16          8         304.0         150\n",
       "4                ford torino   17          8         302.0         140"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = pd.read_csv('data/auto2.csv', names = ['name', 'mpg', 'cylinders', 'displacement',\n",
    "                                             'horsepower'])\n",
    "df3.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "<h1 align=\"center\">Thank You</h1> \n",
    "\n",
    "***"
   ]
  }
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