Cleaning_Data_in_Python/Exploring_Data.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring Data\n",
    "\n",
    "## Common Data Problems\n",
    "\n",
    "* Inconsistent Column Names\n",
    "* Missing Data\n",
    "* Outliers\n",
    "* Duplicate Rows\n",
    "* Untidy\n",
    "* Need to process columns\n",
    "* Column types can signal unexpected data values\n",
    "\n",
    "## Visually Inspect the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Date/Time</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lon</th>\n",
       "      <th>Base</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>4/1/2014 0:11:00</td>\n",
       "      <td>40.7690</td>\n",
       "      <td>-73.9549</td>\n",
       "      <td>B02512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>4/1/2014 0:17:00</td>\n",
       "      <td>40.7267</td>\n",
       "      <td>-74.0345</td>\n",
       "      <td>B02512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>4/1/2014 0:21:00</td>\n",
       "      <td>40.7316</td>\n",
       "      <td>-73.9873</td>\n",
       "      <td>B02512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>4/1/2014 0:28:00</td>\n",
       "      <td>40.7588</td>\n",
       "      <td>-73.9776</td>\n",
       "      <td>B02512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4/1/2014 0:33:00</td>\n",
       "      <td>40.7594</td>\n",
       "      <td>-73.9722</td>\n",
       "      <td>B02512</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0         Date/Time      Lat      Lon    Base\n",
       "0           0  4/1/2014 0:11:00  40.7690 -73.9549  B02512\n",
       "1           1  4/1/2014 0:17:00  40.7267 -74.0345  B02512\n",
       "2           2  4/1/2014 0:21:00  40.7316 -73.9873  B02512\n",
       "3           3  4/1/2014 0:28:00  40.7588 -73.9776  B02512\n",
       "4           4  4/1/2014 0:33:00  40.7594 -73.9722  B02512"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Read the file into DataFrame\n",
    "df = pd.read_csv('https://assets.datacamp.com/production/repositories/666/datasets/c202eb5e7ae1ebf87036a30dcea577096f02c861/nyc_uber_2014.csv')\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Date/Time</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lon</th>\n",
       "      <th>Base</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>94</td>\n",
       "      <td>6/1/2014 6:27:00</td>\n",
       "      <td>40.7554</td>\n",
       "      <td>-73.9738</td>\n",
       "      <td>B02512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>95</td>\n",
       "      <td>6/1/2014 6:35:00</td>\n",
       "      <td>40.7543</td>\n",
       "      <td>-73.9817</td>\n",
       "      <td>B02512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>96</td>\n",
       "      <td>6/1/2014 6:37:00</td>\n",
       "      <td>40.7751</td>\n",
       "      <td>-73.9633</td>\n",
       "      <td>B02512</td>\n",
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       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>97</td>\n",
       "      <td>6/1/2014 6:46:00</td>\n",
       "      <td>40.6952</td>\n",
       "      <td>-74.1784</td>\n",
       "      <td>B02512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>98</td>\n",
       "      <td>6/1/2014 6:51:00</td>\n",
       "      <td>40.7621</td>\n",
       "      <td>-73.9817</td>\n",
       "      <td>B02512</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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       "     Unnamed: 0         Date/Time      Lat      Lon    Base\n",
       "292          94  6/1/2014 6:27:00  40.7554 -73.9738  B02512\n",
       "293          95  6/1/2014 6:35:00  40.7543 -73.9817  B02512\n",
       "294          96  6/1/2014 6:37:00  40.7751 -73.9633  B02512\n",
       "295          97  6/1/2014 6:46:00  40.6952 -74.1784  B02512\n",
       "296          98  6/1/2014 6:51:00  40.7621 -73.9817  B02512"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(297, 5)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'Date/Time', 'Lat', 'Lon', 'Base'], dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 297 entries, 0 to 296\n",
      "Data columns (total 5 columns):\n",
      "Unnamed: 0    297 non-null int64\n",
      "Date/Time     297 non-null object\n",
      "Lat           297 non-null float64\n",
      "Lon           297 non-null float64\n",
      "Base          297 non-null object\n",
      "dtypes: float64(2), int64(1), object(2)\n",
      "memory usage: 11.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis\n",
    "\n",
    "### Frequency Count\n",
    "\n",
    "**Count the number of unique values in our data**\n",
    "\n",
    "Frequency Count of unique Date/Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6/1/2014 0:08:00    3\n",
       "5/1/2014 5:38:00    3\n",
       "5/1/2014 6:07:00    3\n",
       "5/1/2014 6:03:00    2\n",
       "4/1/2014 6:15:00    2\n",
       "Name: Date/Time, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Date/Time'].value_counts(dropna=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summary Statistics\n",
    "\n",
    "Another type of EDA we can do is calculate summary statistics on Numeric Columns. This can help with ...\n",
    "* Spot Outliers\n",
    "    * Value considerably higher or lower than expected\n",
    "    * Observations of interest that we should further investigate\n",
    "    \n",
    "**Numeric Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>297.000000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>297.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>49.000000</td>\n",
       "      <td>40.740521</td>\n",
       "      <td>-73.986724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>28.625612</td>\n",
       "      <td>0.058701</td>\n",
       "      <td>0.099509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.299000</td>\n",
       "      <td>-74.661000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>24.000000</td>\n",
       "      <td>40.723600</td>\n",
       "      <td>-74.003700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>49.000000</td>\n",
       "      <td>40.743000</td>\n",
       "      <td>-73.987700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>74.000000</td>\n",
       "      <td>40.762100</td>\n",
       "      <td>-73.972800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>98.000000</td>\n",
       "      <td>41.016900</td>\n",
       "      <td>-73.462000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0         Lat         Lon\n",
       "count  297.000000  297.000000  297.000000\n",
       "mean    49.000000   40.740521  -73.986724\n",
       "std     28.625612    0.058701    0.099509\n",
       "min      0.000000   40.299000  -74.661000\n",
       "25%     24.000000   40.723600  -74.003700\n",
       "50%     49.000000   40.743000  -73.987700\n",
       "75%     74.000000   40.762100  -73.972800\n",
       "max     98.000000   41.016900  -73.462000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    297.000000\n",
       "mean      40.740521\n",
       "std        0.058701\n",
       "min       40.299000\n",
       "25%       40.723600\n",
       "50%       40.743000\n",
       "75%       40.762100\n",
       "max       41.016900\n",
       "Name: Lat, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Lat'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Visualization\n",
    "\n",
    "* Great way to spot outliers and obvious errors\n",
    "* Plan data cleaning steps\n",
    "\n",
    "### pandas Plot Function\n",
    "* **kind**\n",
    "    * bar, barh, pie, scatter, kde\n",
    "* **color**\n",
    "    * Array of hex codes corresponding sequential to each data series / column.\n",
    "* **linestyle**\n",
    "    * solid, dotted, dashed (applies to line graphs only)\n",
    "* **xlim, ylim**\n",
    "    * Specify a tuple (lower limit, upper limit) for which the plot will be drawn\n",
    "* **legend**\n",
    "    * Boolean value to display or hide the legend\n",
    "* **labels**\n",
    "    * List corresponding to the number of columns in the DataFrame, a descriptive name can be provided here for the legend\n",
    "* **title**\n",
    "    * Title of the Plot\n",
    "\n",
    "**Note: pd.DataFrame.plot() uses matplotlib.pyplot.plot(). pandas's plot is only a convenient shortcut**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip     sex smoker  day    time  size\n",
       "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
       "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
       "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
       "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
       "4       24.59  3.61  Female     No  Sun  Dinner     4"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('https://assets.datacamp.com/production/repositories/666/datasets/b064fa9e0684a38ac15b0a19845367c29fde978d/tips.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histograms & Bar Plots\n",
    "\n",
    "* Histogram for continuous data counts\n",
    "* Bar plots for discrete data counts\n",
    "* Allows us to look at frequencies\n",
    "\n",
    "### Histogram\n",
    "\n",
    "We take the DataFrame (df) and column of interest and call the ***.plot()*** method, we pass ***'hist'*** into the method to have **pandas** create a histogram. \n",
    "\n",
    "The x-axis shows the range of values that are counted\n",
    "The y-axis shows how many observations in our data are in a particular range of values.\n",
    "\n",
    "*Note we have to make sure that matplotlib is loaded before we can show the plot*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2ace68c7da0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.xlabel('Tips ($)')\n",
    "df['tip'].plot('hist')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Box Plots\n",
    "\n",
    "* Visualize basic summary statistics\n",
    "    * Outliers\n",
    "    * Min / Max\n",
    "    * 25th, 50th, 75th percentiles\n",
    "    \n",
    "To create a Box Plot, call the ***.boxplot()*** method on the DataFrame (df)\n",
    "\n",
    "To plot a specific column, we pass it into the ***'column'*** parameter. We pass the ***'by'*** parameter with the name of the column we want to compare Box Plots across.\n",
    "\n",
    "Most of the data is represented by the box. the lines that extend from the box are called *whiskers*. The ends of the *whiskers* show the **maximum** and **minimum** of our data *(Excluding Outliers, Outliers values shown beyond the whiskers)*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2ace6aa7048>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Box Plot to show the Tips between Females and Males\n",
    "df.boxplot(column='tip', by='sex')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scatter Plots\n",
    "\n",
    "When you want to visualize 2 numeric columns, scatter plots are ideal.\n",
    "\n",
    "In the scatter plot below, you can see that there is a positive correlation between *'total_bill'* and *'tip'* amount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2ace6b267f0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='scatter', x='tip', y='total_bill')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
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}