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david  committed 8c5dfe3

updated to 0.14

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  • Parent commits 6ee2a97

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Files changed (11)

File lessons/01 - Lesson.ipynb

 {
  "metadata": {
-  "name": ""
+  "name": "",
+  "signature": "sha256:8b62480a926966d4d31e90040b023667b450385778a0b54e8caf084800b77821"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
       "##import (library) as (give the library a nickname/alias)\n",
       "import matplotlib.pyplot as plt\n",
       "import pandas as pd #only needed to determine version number\n",
+      "import sys #only needed to determine Python version number\n",
       "\n",
       "# Enable inline plotting\n",
       "%matplotlib inline"
      "cell_type": "code",
      "collapsed": false,
      "input": [
+      "print 'Python version ' + sys.version\n",
       "print 'Pandas version ' + pd.__version__"
      ],
      "language": "python",
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "Pandas version 0.13.0\n"
+        "Python version 2.7.5 |Anaconda 1.8.0 (64-bit)| (default, Jul  1 2013, 12:37:52) [MSC v.1500 64 bit (AMD64)]\n",
+        "Pandas version 0.14.0\n"
        ]
       }
      ],
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
-        "<p>5 rows \u00d7 2 columns</p>\n",
         "</div>"
        ],
        "metadata": {},
         "1  Jessica     155\n",
         "2     Mary      77\n",
         "3     John     578\n",
-        "4      Mel     973\n",
-        "\n",
-        "[5 rows x 2 columns]"
+        "4      Mel     973"
        ]
       }
      ],
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "* Export the dataframe to a ***csv*** file. We can name the file ***births1880.csv***. The function ***to_csv*** will be used to export the file. The file will be saved in the same location of the notebook unless specified otherwise."
+      "Export the dataframe to a ***csv*** file. We can name the file ***births1880.csv***. The function ***to_csv*** will be used to export the file. The file will be saved in the same location of the notebook unless specified otherwise."
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "Location = r'C:\\Users\\david\\births1880.csv'\n",
+      "Location = r'C:\\Users\\david\\notebooks\\pandas\\births1880.csv'\n",
       "df = read_csv(Location)"
      ],
      "language": "python",
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
-        "<p>4 rows \u00d7 2 columns</p>\n",
         "</div>"
        ],
        "metadata": {},
         "0  Jessica  155\n",
         "1     Mary   77\n",
         "2     John  578\n",
-        "3      Mel  973\n",
-        "\n",
-        "[4 rows x 2 columns]"
+        "3      Mel  973"
        ]
       }
      ],
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
-        "<p>5 rows \u00d7 2 columns</p>\n",
         "</div>"
        ],
        "metadata": {},
         "1  Jessica  155\n",
         "2     Mary   77\n",
         "3     John  578\n",
-        "4      Mel  973\n",
-        "\n",
-        "[5 rows x 2 columns]"
+        "4      Mel  973"
        ]
       }
      ],
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
-        "<p>5 rows \u00d7 2 columns</p>\n",
         "</div>"
        ],
        "metadata": {},
         "1  Jessica     155\n",
         "2     Mary      77\n",
         "3     John     578\n",
-        "4      Mel     973\n",
-        "\n",
-        "[5 rows x 2 columns]"
+        "4      Mel     973"
        ]
       }
      ],
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Delete the csv file now that we are done using it."
+      "#### Delete the csv file now that we are done using it."
      ]
     },
     {
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
-        "<p>1 rows \u00d7 2 columns</p>\n",
         "</div>"
        ],
        "metadata": {},
        "prompt_number": 17,
        "text": [
         "  Names  Births\n",
-        "4   Mel     973\n",
-        "\n",
-        "[1 rows x 2 columns]"
+        "4   Mel     973"
        ]
       }
      ],
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
-        "<p>1 rows \u00d7 2 columns</p>\n",
         "</div>"
        ],
        "metadata": {},
        "prompt_number": 19,
        "text": [
         "  Names  Births\n",
-        "4   Mel     973\n",
-        "\n",
-        "[1 rows x 2 columns]"
+        "4   Mel     973"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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weLHtwCJSnUQC/vhHaNJE/14Kmdbw4vFtgFTwgC+/tCPs5s6FnXd2nUZ8NmyY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\n",
        "text": [
-        "<matplotlib.figure.Figure at 0xaa97fd0>"
+        "<matplotlib.figure.Figure at 0xc043908>"
        ]
       }
      ],

File lessons/02 - Lesson.ipynb

 {
  "metadata": {
-  "name": ""
+  "name": "",
+  "signature": "sha256:a35dda3242a1c415daf3c38d37ceb8b12ddd61d2e2d0d969ffea53ac39040c03"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
       "##import (library) as (give the library a nickname/alias)\n",
       "import matplotlib.pyplot as plt\n",
       "import pandas as pd\n",
+      "import sys #only needed to determine Python version number\n",
       "\n",
       "# Enable inline plotting\n",
       "%matplotlib inline"
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 1
+     "prompt_number": 22
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
+      "print 'Python version ' + sys.version\n",
       "print 'Pandas version ' + pd.__version__"
      ],
      "language": "python",
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "Pandas version 0.13.0\n"
+        "Python version 2.7.5 |Anaconda 1.8.0 (64-bit)| (default, Jul  1 2013, 12:37:52) [MSC v.1500 64 bit (AMD64)]\n",
+        "Pandas version 0.14.0\n"
        ]
       }
      ],
-     "prompt_number": 2
+     "prompt_number": 23
     },
     {
      "cell_type": "markdown",
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 3
+     "prompt_number": 24
     },
     {
      "cell_type": "markdown",
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 4
+     "prompt_number": 25
     },
     {
      "cell_type": "code",
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 5
+     "prompt_number": 26
     },
     {
      "cell_type": "code",
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 6
+     "prompt_number": 27
     },
     {
      "cell_type": "code",
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 7
+     "prompt_number": 28
     },
     {
      "cell_type": "code",
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 8
+     "prompt_number": 29
     },
     {
      "cell_type": "markdown",
        ]
       }
      ],
-     "prompt_number": 9
+     "prompt_number": 30
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "* Generate a random numbers between 0 and 1000    "
+      "Generate a random numbers between 0 and 1000    "
      ]
     },
     {
        ]
       }
      ],
-     "prompt_number": 10
+     "prompt_number": 31
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "* Merge the ***names*** and the ***births*** data set using the ***zip*** function."
+      "Merge the ***names*** and the ***births*** data set using the ***zip*** function."
      ]
     },
     {
        ]
       }
      ],
-     "prompt_number": 11
+     "prompt_number": 32
     },
     {
      "cell_type": "markdown",
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
-        "<p>10 rows \u00d7 2 columns</p>\n",
         "</div>"
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 12,
+       "prompt_number": 33,
        "text": [
         "     Names  Births\n",
         "0     Mary     968\n",
         "6  Jessica     155\n",
         "7     Mary     403\n",
         "8     Mary     199\n",
-        "9     Mary     191\n",
-        "\n",
-        "[10 rows x 2 columns]"
+        "9     Mary     191"
        ]
       }
      ],
-     "prompt_number": 12
+     "prompt_number": 33
     },
     {
      "cell_type": "markdown",
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 13
+     "prompt_number": 34
     },
     {
      "cell_type": "markdown",
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 14
+     "prompt_number": 35
     },
     {
      "cell_type": "markdown",
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 15
+     "prompt_number": 36
     },
     {
      "cell_type": "markdown",
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "Location = r'C:\\Users\\david\\births1880.txt'\n",
+      "Location = r'C:\\Users\\david\\notebooks\\pandas\\births1880.txt'\n",
       "df = read_csv(Location)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 16
+     "prompt_number": 37
     },
     {
      "cell_type": "markdown",
         "<class 'pandas.core.frame.DataFrame'>\n",
         "Int64Index: 999 entries, 0 to 998\n",
         "Data columns (total 2 columns):\n",
-        "Mary    999  non-null values\n",
-        "968     999  non-null values\n",
+        "Mary    999 non-null object\n",
+        "968     999 non-null int64\n",
         "dtypes: int64(1), object(1)"
        ]
       }
      ],
-     "prompt_number": 17
+     "prompt_number": 38
     },
     {
      "cell_type": "markdown",
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
-        "<p>5 rows \u00d7 2 columns</p>\n",
         "</div>"
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 18,
+       "prompt_number": 39,
        "text": [
         "      Mary  968\n",
         "0  Jessica  155\n",
         "1  Jessica   77\n",
         "2      Bob  578\n",
         "3  Jessica  973\n",
-        "4  Jessica  124\n",
-        "\n",
-        "[5 rows x 2 columns]"
+        "4  Jessica  124"
        ]
       }
      ],
-     "prompt_number": 18
+     "prompt_number": 39
     },
     {
      "cell_type": "markdown",
         "<class 'pandas.core.frame.DataFrame'>\n",
         "Int64Index: 1000 entries, 0 to 999\n",
         "Data columns (total 2 columns):\n",
-        "0    1000  non-null values\n",
-        "1    1000  non-null values\n",
+        "0    1000 non-null object\n",
+        "1    1000 non-null int64\n",
         "dtypes: int64(1), object(1)"
        ]
       }
      ],
-     "prompt_number": 19
+     "prompt_number": 40
     },
     {
      "cell_type": "markdown",
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
-        "<p>5 rows \u00d7 2 columns</p>\n",
         "</div>"
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 20,
+       "prompt_number": 41,
        "text": [
         "           0    1\n",
         "995     John  151\n",
         "996  Jessica  511\n",
         "997     John  756\n",
         "998  Jessica  294\n",
-        "999     John  152\n",
-        "\n",
-        "[5 rows x 2 columns]"
+        "999     John  152"
        ]
       }
      ],
-     "prompt_number": 20
+     "prompt_number": 41
     },
     {
      "cell_type": "markdown",
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
-        "<p>5 rows \u00d7 2 columns</p>\n",
         "</div>"
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 21,
+       "prompt_number": 42,
        "text": [
         "     Names  Births\n",
         "0     Mary     968\n",
         "1  Jessica     155\n",
         "2  Jessica      77\n",
         "3      Bob     578\n",
-        "4  Jessica     973\n",
-        "\n",
-        "[5 rows x 2 columns]"
+        "4  Jessica     973"
        ]
       }
      ],
-     "prompt_number": 21
+     "prompt_number": 42
     },
     {
      "cell_type": "markdown",
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 22
+     "prompt_number": 43
     },
     {
      "cell_type": "markdown",
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 23,
+       "prompt_number": 44,
        "text": [
         "array(['Mary', 'Jessica', 'Bob', 'John', 'Mel'], dtype=object)"
        ]
       }
      ],
-     "prompt_number": 23
+     "prompt_number": 44
     },
     {
      "cell_type": "code",
        ]
       }
      ],
-     "prompt_number": 24
+     "prompt_number": 45
     },
     {
      "cell_type": "code",
         "unique       5\n",
         "top        Bob\n",
         "freq       206\n",
-        "Name: Names, dtype: object\n"
+        "dtype: object\n"
        ]
       }
      ],
-     "prompt_number": 25
+     "prompt_number": 46
     },
     {
      "cell_type": "markdown",
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 26
+     "prompt_number": 47
     },
     {
      "cell_type": "code",
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
-        "<p>5 rows \u00d7 1 columns</p>\n",
         "</div>"
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 27,
+       "prompt_number": 48,
        "text": [
         "         Births\n",
         "Names          \n",
         "Jessica   97826\n",
         "John      90705\n",
         "Mary      99438\n",
-        "Mel      102319\n",
-        "\n",
-        "[5 rows x 1 columns]"
+        "Mel      102319"
        ]
       }
      ],
-     "prompt_number": 27
+     "prompt_number": 48
     },
     {
      "cell_type": "markdown",
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
-        "<p>1 rows \u00d7 1 columns</p>\n",
         "</div>"
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 28,
+       "prompt_number": 49,
        "text": [
         "       Births\n",
         "Names        \n",
-        "Bob    106817\n",
-        "\n",
-        "[1 rows x 1 columns]"
+        "Bob    106817"
        ]
       }
      ],
-     "prompt_number": 28
+     "prompt_number": 49
     },
     {
      "cell_type": "code",
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 29,
+       "prompt_number": 50,
        "text": [
         "106817"
        ]
       }
      ],
-     "prompt_number": 29
+     "prompt_number": 50
     },
     {
      "cell_type": "markdown",
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
-        "<p>5 rows \u00d7 1 columns</p>\n",
         "</div>"
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 30,
+       "prompt_number": 51,
        "text": [
         "         Births\n",
         "Names          \n",
         "Mel      102319\n",
         "Mary      99438\n",
         "Jessica   97826\n",
-        "John      90705\n",
-        "\n",
-        "[5 rows x 1 columns]"
+        "John      90705"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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pJzXts3+kStlT0KB7Bdhc7u8JjkWSJEmSJEmSJElqBBvN0vReAeaT3wsB8Nvk\n90h8IywiaQb4MRfS9H4MvJ/8ndDgF8SrISwK0vReAv4c+Og0j72P/Ia5+8jvqH5jGT+f/LEbdwBj\n5O9++CPyh/Z9lfyeCoCl5K8TvQe4GTiwjJ9Nfr/FOmDNrktFktSvF8nTR/+X/M1evwWcVx4b7tju\nQ+QXfshF4Q7ytOxbgH8Efr48dgNwGrA78HdMnoGcCVxeljeVx8FvE1OQoe6bSI31InAV+S/4f+oY\nPxS4jvwX/h7Ad8r4OPmM4CfAt8hn4l8rjz0EjABHkz/Q7+tlfDfgqbL8IHAN8KVyk2ac00fSjv0x\nsIJtP6b7T4BPkc8G/hswr+OxH5f7V8hTUHSsD5E/hPJh8ndMH1v2saxs8x7gT4HjgLvxQhAFsChI\nO/Z98lnBCiabzfsw+df9aMe2O/Opw98G9gdOLOu7A4vLcw8j9xrOBRbg90UogEVBml7n1UafBPbr\nWD8f+Dy5Ufy9jm3Hpzxv6hVL4+Szh18ELgQeIH+L3NvIZwVXk6eQ7gMuAV7oPw1JkiRJkiRJkiRJ\nkiRJkiRJkiRJkqRg/x+iAOjMo0p88gAAAABJRU5ErkJggg==\n",
        "text": [
-        "<matplotlib.figure.Figure at 0xab884a8>"
+        "<matplotlib.figure.Figure at 0xc786518>"
        ]
       }
      ],
-     "prompt_number": 30
+     "prompt_number": 51
     },
     {
      "cell_type": "markdown",

File lessons/03 - Lesson.ipynb

 {
  "metadata": {
-  "name": ""
+  "name": "",
+  "signature": "sha256:48ce4a6fdeaef06d6866aaff2c37a5760d039bb0c5bc9cb220a14dddb2f70fb1"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
       "import pandas as pd\n",
       "import matplotlib.pyplot as plt\n",
       "import numpy.random as np\n",
+      "import sys\n",
       "\n",
       "# Turn on inline plotting\n",
       "%matplotlib inline"
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 1
+     "prompt_number": 39
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
+      "print 'Python version ' + sys.version\n",
       "print 'Pandas version: ' + pd.__version__"
      ],
      "language": "python",
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "Pandas version: 0.13.0\n"
+        "Python version 2.7.5 |Anaconda 1.8.0 (64-bit)| (default, Jul  1 2013, 12:37:52) [MSC v.1500 64 bit (AMD64)]\n",
+        "Pandas version: 0.14.0\n"
        ]
       }
      ],
-     "prompt_number": 2
+     "prompt_number": 40
     },
     {
      "cell_type": "markdown",
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 3
+     "prompt_number": 41
     },
     {
      "cell_type": "markdown",
         "<class 'pandas.core.frame.DataFrame'>\n",
         "Int64Index: 836 entries, 0 to 835\n",
         "Data columns (total 4 columns):\n",
-        "State            836  non-null values\n",
-        "Status           836  non-null values\n",
-        "CustomerCount    836  non-null values\n",
-        "StatusDate       836  non-null values\n",
+        "State            836 non-null object\n",
+        "Status           836 non-null int64\n",
+        "CustomerCount    836 non-null int64\n",
+        "StatusDate       836 non-null datetime64[ns]\n",
         "dtypes: datetime64[ns](1), int64(2), object(1)"
        ]
       }
      ],
-     "prompt_number": 4
+     "prompt_number": 42
     },
     {
      "cell_type": "code",
         "      <td> GA</td>\n",
         "      <td> 1</td>\n",
         "      <td> 877</td>\n",
-        "      <td>2009-01-05 00:00:00</td>\n",
+        "      <td>2009-01-05</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>1</th>\n",
         "      <td> FL</td>\n",
         "      <td> 1</td>\n",
         "      <td> 901</td>\n",
-        "      <td>2009-01-12 00:00:00</td>\n",
+        "      <td>2009-01-12</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>2</th>\n",
         "      <td> fl</td>\n",
         "      <td> 3</td>\n",
         "      <td> 749</td>\n",
-        "      <td>2009-01-19 00:00:00</td>\n",
+        "      <td>2009-01-19</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>3</th>\n",
         "      <td> FL</td>\n",
         "      <td> 3</td>\n",
         "      <td> 111</td>\n",
-        "      <td>2009-01-26 00:00:00</td>\n",
+        "      <td>2009-01-26</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>4</th>\n",
         "      <td> GA</td>\n",
         "      <td> 1</td>\n",
         "      <td> 300</td>\n",
-        "      <td>2009-02-02 00:00:00</td>\n",
+        "      <td>2009-02-02</td>\n",
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
-        "<p>5 rows \u00d7 4 columns</p>\n",
         "</div>"
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 5,
+       "prompt_number": 43,
        "text": [
-        "  State  Status  CustomerCount          StatusDate\n",
-        "0    GA       1            877 2009-01-05 00:00:00\n",
-        "1    FL       1            901 2009-01-12 00:00:00\n",
-        "2    fl       3            749 2009-01-19 00:00:00\n",
-        "3    FL       3            111 2009-01-26 00:00:00\n",
-        "4    GA       1            300 2009-02-02 00:00:00\n",
-        "\n",
-        "[5 rows x 4 columns]"
+        "  State  Status  CustomerCount StatusDate\n",
+        "0    GA       1            877 2009-01-05\n",
+        "1    FL       1            901 2009-01-12\n",
+        "2    fl       3            749 2009-01-19\n",
+        "3    FL       3            111 2009-01-26\n",
+        "4    GA       1            300 2009-02-02"
        ]
       }
      ],
-     "prompt_number": 5
+     "prompt_number": 43
     },
     {
      "cell_type": "markdown",
        ]
       }
      ],
-     "prompt_number": 6
+     "prompt_number": 44
     },
     {
      "cell_type": "markdown",
      "source": [
       "# Grab Data from Excel  \n",
       "\n",
-      "We will be using the ***read_excel*** function to read in data from an excel file. The function allows you to read in specfic tabs by name or location.  \n",
-      "\n",
-      "**read_excel:**  \n",
-      "Parameters    \n",
-      "<u>io</u> : string, file-like object or xlrd workbook  \n",
-      "&nbsp;`If a string, expected to be a path to xls or xlsx file`  \n",
-      "<u>sheetname</u> : string  \n",
-      "&nbsp;`Name of Excel sheet`  \n",
-      "<u>index_col</u> : int, default None  \n",
-      "&nbsp;`Column to use as the row labels of the DataFrame. Pass None if\n",
-      "there is no such column`  "
+      "We will be using the ***read_excel*** function to read in data from an excel file. The function allows you to read in specfic tabs by name or location."
      ]
     },
     {
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 7
+     "prompt_number": 45
     },
     {
      "cell_type": "markdown",
      "collapsed": false,
      "input": [
       "# Location of file\n",
-      "Location = r'C:\\Users\\David\\Lesson3.xlsx'\n",
+      "Location = r'C:\\Users\\david\\notebooks\\pandas\\Lesson3.xlsx'\n",
       "\n",
       "# Parse a specific sheet\n",
       "df = read_excel(Location, 0, index_col='StatusDate')\n",
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 8,
+       "prompt_number": 46,
        "text": [
         "State            object\n",
         "Status            int64\n",
        ]
       }
      ],
-     "prompt_number": 8
+     "prompt_number": 46
     },
     {
      "cell_type": "code",
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 9,
+       "prompt_number": 47,
        "text": [
         "<class 'pandas.tseries.index.DatetimeIndex'>\n",
-        "[2009-01-05 00:00:00, ..., 2012-12-31 00:00:00]\n",
+        "[2009-01-05, ..., 2012-12-31]\n",
         "Length: 836, Freq: None, Timezone: None"
        ]
       }
      ],
-     "prompt_number": 9
+     "prompt_number": 47
     },
     {
      "cell_type": "code",
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
-        "<p>5 rows \u00d7 3 columns</p>\n",
         "</div>"
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 10,
+       "prompt_number": 48,
        "text": [
         "           State  Status  CustomerCount\n",
         "StatusDate                             \n",
         "2009-01-12    FL       1            901\n",
         "2009-01-19    fl       3            749\n",
         "2009-01-26    FL       3            111\n",
-        "2009-02-02    GA       1            300\n",
-        "\n",
-        "[5 rows x 3 columns]"
+        "2009-02-02    GA       1            300"
        ]
       }
      ],
-     "prompt_number": 10
+     "prompt_number": 48
     },
     {
      "cell_type": "markdown",
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 11,
+       "prompt_number": 49,
        "text": [
         "array([u'GA', u'FL', u'fl', u'TX', u'NY', u'NJ'], dtype=object)"
        ]
       }
      ],
-     "prompt_number": 11
+     "prompt_number": 49
     },
     {
      "cell_type": "markdown",
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 12
+     "prompt_number": 50
     },
     {
      "cell_type": "code",
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 13,
+       "prompt_number": 51,
        "text": [
         "array([u'GA', u'FL', u'TX', u'NY', u'NJ'], dtype=object)"
        ]
       }
      ],
-     "prompt_number": 13
+     "prompt_number": 51
     },
     {
      "cell_type": "code",
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 14
+     "prompt_number": 52
     },
     {
      "cell_type": "markdown",
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stderr",
-       "text": [
-        "C:\\Users\\david\\Anaconda\\lib\\site-packages\\pandas\\core\\series.py:628: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame\n",
-        "  self.where(~key, value, inplace=True)\n"
-       ]
-      }
-     ],
-     "prompt_number": 15
+     "outputs": [],
+     "prompt_number": 53
     },
     {
      "cell_type": "markdown",
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 16,
+       "prompt_number": 54,
        "text": [
         "array([u'GA', u'FL', u'NY', u'TX'], dtype=object)"
        ]
       }
      ],
-     "prompt_number": 16
+     "prompt_number": 54
     },
     {
      "cell_type": "markdown",
       {
        "metadata": {},
        "output_type": "display_data",
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WQhyC568taQ+eUnv86EfA4EygSuRrLpo4BH9CKXg+RBIwt2iosdoSvCzssrOT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A06er9+Utt2NawdsSvOkcAwNMEajC/AYG2PHy+TCXS60UvCpMkkehUH8En4aC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\nB/8Hrb/Ipf4Qh6wAAAAASUVORK5CYII=\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x4fbe7f0>"
+        "<matplotlib.figure.Figure at 0xd645f28>"
        ]
       }
      ],
-     "prompt_number": 17
+     "prompt_number": 55
     },
     {
      "cell_type": "markdown",
         "      <th>2009-04-27</th>\n",
         "      <td> NY</td>\n",
         "      <td> 1</td>\n",
-        "      <td> 753</td>\n",
+        "      <td> 447</td>\n",
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
-        "<p>10 rows \u00d7 3 columns</p>\n",
         "</div>"
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 18,
+       "prompt_number": 56,
        "text": [
         "           State  Status  CustomerCount\n",
         "StatusDate                             \n",
         "2009-04-13    NY       1            520\n",
         "2009-04-20    NY       1            820\n",
         "2009-04-20    NY       1            937\n",
-        "2009-04-27    NY       1            753\n",
-        "\n",
-        "[10 rows x 3 columns]"
+        "2009-04-27    NY       1            447"
        ]
       }
      ],
-     "prompt_number": 18
+     "prompt_number": 56
     },
     {
      "cell_type": "markdown",
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
-        "<p>5 rows \u00d7 2 columns</p>\n",
         "</div>"
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 19,
+       "prompt_number": 57,
        "text": [
         "                  Status  CustomerCount\n",
         "State StatusDate                       \n",
         "      2009-02-02       1            653\n",
         "      2009-03-23       1            752\n",
         "      2009-04-06       2           1086\n",
-        "      2009-06-08       1            649\n",
-        "\n",
-        "[5 rows x 2 columns]"
+        "      2009-06-08       1            649"
        ]
       }
      ],
-     "prompt_number": 19
+     "prompt_number": 57
     },
     {
      "cell_type": "markdown",
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
-        "<p>5 rows \u00d7 1 columns</p>\n",
         "</div>"
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 20,
+       "prompt_number": 58,
        "text": [
         "                  CustomerCount\n",
         "State StatusDate               \n",
         "      2009-02-02            653\n",
         "      2009-03-23            752\n",
         "      2009-04-06           1086\n",
-        "      2009-06-08            649\n",
-        "\n",
-        "[5 rows x 1 columns]"
+        "      2009-06-08            649"
        ]
       }
      ],
-     "prompt_number": 20
+     "prompt_number": 58
     },
     {
      "cell_type": "code",
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 21,
+       "prompt_number": 59,
        "text": [
         "MultiIndex(levels=[[u'FL', u'GA', u'NY', u'TX'], [2009-01-05 00:00:00, 2009-01-12 00:00:00, 2009-01-19 00:00:00, 2009-02-02 00:00:00, 2009-02-23 00:00:00, 2009-03-09 00:00:00, 2009-03-16 00:00:00, 2009-03-23 00:00:00, 2009-03-30 00:00:00, 2009-04-06 00:00:00, 2009-04-13 00:00:00, 2009-04-20 00:00:00, 2009-04-27 00:00:00, 2009-05-04 00:00:00, 2009-05-11 00:00:00, 2009-05-18 00:00:00, 2009-05-25 00:00:00, 2009-06-08 00:00:00, 2009-06-22 00:00:00, 2009-07-06 00:00:00, 2009-07-13 00:00:00, 2009-07-20 00:00:00, 2009-07-27 00:00:00, 2009-08-10 00:00:00, 2009-08-17 00:00:00, 2009-08-24 00:00:00, 2009-08-31 00:00:00, 2009-09-07 00:00:00, 2009-09-14 00:00:00, 2009-09-21 00:00:00, 2009-09-28 00:00:00, 2009-10-05 00:00:00, 2009-10-12 00:00:00, 2009-10-19 00:00:00, 2009-10-26 00:00:00, 2009-11-02 00:00:00, 2009-11-23 00:00:00, 2009-11-30 00:00:00, 2009-12-07 00:00:00, 2009-12-14 00:00:00, 2010-01-04 00:00:00, 2010-01-11 00:00:00, 2010-01-18 00:00:00, 2010-01-25 00:00:00, 2010-02-08 00:00:00, 2010-02-15 00:00:00, 2010-02-22 00:00:00, 2010-03-01 00:00:00, 2010-03-08 00:00:00, 2010-03-15 00:00:00, 2010-04-05 00:00:00, 2010-04-12 00:00:00, 2010-04-26 00:00:00, 2010-05-03 00:00:00, 2010-05-10 00:00:00, 2010-05-17 00:00:00, 2010-05-24 00:00:00, 2010-05-31 00:00:00, 2010-06-14 00:00:00, 2010-06-28 00:00:00, 2010-07-05 00:00:00, 2010-07-19 00:00:00, 2010-07-26 00:00:00, 2010-08-02 00:00:00, 2010-08-09 00:00:00, 2010-08-16 00:00:00, 2010-08-30 00:00:00, 2010-09-06 00:00:00, 2010-09-13 00:00:00, 2010-09-20 00:00:00, 2010-09-27 00:00:00, 2010-10-04 00:00:00, 2010-10-11 00:00:00, 2010-10-18 00:00:00, 2010-10-25 00:00:00, 2010-11-01 00:00:00, 2010-11-08 00:00:00, 2010-11-15 00:00:00, 2010-11-29 00:00:00, 2010-12-20 00:00:00, 2011-01-03 00:00:00, 2011-01-10 00:00:00, 2011-01-17 00:00:00, 2011-02-07 00:00:00, 2011-02-14 00:00:00, 2011-02-21 00:00:00, 2011-02-28 00:00:00, 2011-03-07 00:00:00, 2011-03-14 00:00:00, 2011-03-21 00:00:00, 2011-03-28 00:00:00, 2011-04-04 00:00:00, 2011-04-18 00:00:00, 2011-04-25 00:00:00, 2011-05-02 00:00:00, 2011-05-09 00:00:00, 2011-05-16 00:00:00, 2011-05-23 00:00:00, 2011-05-30 00:00:00, 2011-06-06 00:00:00, ...]],\n",
         "           labels=[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...], [1, 3, 7, 9, 17, 19, 20, 21, 23, 25, 27, 28, 29, 30, 31, 35, 38, 40, 41, 44, 45, 46, 47, 48, 49, 52, 54, 56, 57, 59, 60, 62, 66, 68, 69, 70, 71, 72, 75, 76, 77, 78, 79, 85, 88, 89, 92, 96, 97, 99, 100, 101, 103, 104, 105, 108, 109, 110, 112, 114, 115, 117, 118, 119, 125, 126, 127, 128, 129, 131, 133, 134, 135, 136, 137, 140, 146, 150, 151, 152, 153, 157, 0, 3, 7, 22, 23, 24, 27, 28, 34, 37, 42, 47, 50, 55, 58, 66, 67, 69, ...]],\n",
        ]
       }
      ],
-     "prompt_number": 21
+     "prompt_number": 59
     },
     {
      "cell_type": "code",
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 22,
+       "prompt_number": 60,
        "text": [
         "Index([u'FL', u'GA', u'NY', u'TX'], dtype='object')"
        ]
       }
      ],
-     "prompt_number": 22
+     "prompt_number": 60
     },
     {
      "cell_type": "code",
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 23,
+       "prompt_number": 61,
        "text": [
         "<class 'pandas.tseries.index.DatetimeIndex'>\n",
-        "[2009-01-05 00:00:00, ..., 2012-12-10 00:00:00]\n",
+        "[2009-01-05, ..., 2012-12-10]\n",
         "Length: 161, Freq: None, Timezone: None"
        ]
       }
      ],
-     "prompt_number": 23
+     "prompt_number": 61
     },
     {
      "cell_type": "markdown",
       {
        "metadata": {},
        "output_type": "display_data",
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LI08oVvt4K/WUK/MUwnz/Pn20x8HEPzpaWbnn2297fz0kR/gKPTMhrMxTSJs2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\nkkGDBpHnn3+edOrUiWRmZgY03p07d5K9e/e6Dv7nnnuOjBk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QIvHfvDm359sN8LLi94Av61z+4Fzn39Iif36OAVAZzU47pff916yBIUNgwgSY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\nGo1Go9FoNBqNRqPRaDQajUajCSD/H/CDSgNxn5IdAAAAAElFTkSuQmCC\n",
        "text": [
-        "<matplotlib.figure.Figure at 0xb9f0400>"
+        "<matplotlib.figure.Figure at 0xcbab518>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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9E3ij+IVy4tyxA7ixpremaNGmjhYNwLJPqcPsjfLdA+rPRcOhhA9vpHYFZAp8QUEBPv74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\nPPsbMKDxdiJ2F11QwM6V/v3FH9sUeBM7rlMGV1e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FquNUgw8ygtejdyuNP44SjRkffiiP4fnayszBt7bKxe+mI/vII/3V4ffsESdU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\n",
        "text": [
-        "<matplotlib.figure.Figure at 0xb67cd68>"
+        "<matplotlib.figure.Figure at 0xd5be320>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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bb2LWrFkYPXo0MjIykJCQgGXLlgEAkpKSMHr0aCQlJSEoKAgLFiywupkWLFiA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\nbbF06VLcdNNNALSfQOZqu167dg0hISG4ePEi0tPT8dprr3m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3V7zK6eRGUvgVgC46xeDEggUwfHjhg89KMeRrMQQdfIaUgtAUhpYW537S0ZE+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\niTgAAAAASUVORK5CYII=\n",
        "text": [
-        "<matplotlib.figure.Figure at 0xb9f1470>"
+        "<matplotlib.figure.Figure at 0xcc7ad30>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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2rfz9/RsbHU/1F4PStfBKtSrgA5H4FtKcRKO3X9tKoJsAL1c/eEfExgI//CA+\nsKmhu/3115UAL6V9sQHe0gc55RmO0FD2+TduvNq+lFRWMhnDw0mZghD7cs1mdaTB26uiadOGDapL\nMaubzzFoaGD3uJjFU9zc2BhGfb1rPsiJocE7QekMHmD66uTJTX/ya5mGBuDgQdZFUmqk0OClbFHg\nCD5thF1Fav0dUD6DdyTRuLmxckU5Bn1t4WqvKT4DrS0R3QR4OfvBOyIhgQV46/pqpewLISeHLaLB\n6cJa0+CVyOABYMIE1nI2I0O+c8C3TYEQ+3Jl8LZ8qKlhCUHr1vbfJ5VMw+cYiOkWa4mzgVZDg9c4\namTwACs3DA0Fvv5aedtCkWINVntIocGXlAAWLYlkw8MDeOQRebN4KWvgOZTM4Dn93dF4iJK18K4G\neKMW3ja6CfBqaPAcYpf0U1p3sx5g1ZoGX1MjrM+3K8yaBaxbB3z/fZrTbcXAN4MXcg7kCvC2fOAz\nQCyVP3yOgdwSjaHBaxy1MngAmDiRLUSQkaGOfb5YDrBKTbduLGt1JUuqrVUuwAcFsXVbd+yQZ/9y\naPBytgy2xtEAK4eSlTRSZPBGLfzV6CbAq6XBA8xuXJzwLF5J3Y3o6gxeSvtubkzfFzqb1dKHmhpl\nv6QfewzYuTNKlvI5OTR4uTJ4Wz44GmDlkEqiUUqDd5R8GBq8hiFy/QJwlbg4IDmZZbFapKCAHR/W\nvlYeXNXhlczgASA6mgViqRdTB+TR4JXO4JWSaPgghURjZPBXo4sAX1/P6lyFNmGSWoMeM4ZNolHD\nvjNsDbBKbV9MgLfW4JWerPaPf6TJMtiqdw2ebwYvhUTD5xjIPchqywclJ3IZGrwD5F6PlS/czFY1\n1v90hlwzWC1xtRZeyUFWjvHjWQXUuXPS7lcuDV7pKhpHKJnByy3RWHPiBOs+qsV7WUp0EeDFDrBK\nrXvdcgurG+Y7cKek7mZrgFVq+2IyeEsf1PiinjgxCuPGAZ98Iu1+5dLglaqDV3KQlc8xkFuisfbh\nzz+BU6dYD3olMDR4ByjdSdIeJpN2+9MokcG7osETKS/RcHAzW6UcbNV7HbySg6x8ULoO/sAB9q8c\n4zNaQhcBXmwGL4fuNW0asHs3cPy4OvZtceYMu2F79ZLXvisafEPDlZ4hSpKWloYRI5jtn36Sbr9y\naPBK9qJRcpBVi3Xw6enAddcB+/aJtykEQ4N3gFYyeIBlNTEx8vc6EQKXvcu9ElD37uIzeDX0dw6T\nSfr+NHrI3pWBAAAgAElEQVTvRcM3wDfXOvgDB9hkOCOD1wBa0eA54uOB1audt6RVSnezJ8/IocEL\nHWTlfFBroJyzP3066wxaWirNfvXei6Yl18GXlwOnTwOTJrFMXoqOmc4wNHgHaCmDB4CwMBZQLy85\nqzpyzmC1xNf3yqIdQlEzgwdYML73XmDNGtf3RcQ0eD1n8FqrolGyDv7gQdZjyseHJS1Hjoi3q3V0\nEeC1pMFz8OlPo5TuZi+Dl9q+hwfQpQurPuAL54NaGbzlMZg9m3UGdTVjq6piCQefLywh56B1a/bl\nKaZzqVAf+FTRNNc6+AMHrrTUvv56ZXR4Q4N3gNYyeIDVV5eWqq/hXbjAZrEq0WcdEK/Dq53BA8DQ\noewLKiXFtf3Iob8DbKxAqaxZazNZlayDT08HIiPZ/4cOVf8elhPdBHgtafAAqwaJj3ecxSuhux04\nAAwYYPvmkMO+UB1eKxo8x2OPAatWubZPvvq7LfvOkCOoitXgpfJFiTp4Z4Oslj4cOHAlwN90E5vX\nIvdyf4YG7wA1O0k6YuZMYMsWVqaoFkrUv1sithZeCxk8AEyZwhZSP3FC/D7kqIHnUKofjZKtCvig\nVAZfX88W9x44kD0eNoxdl67+qtMqugjwYiUauXWvrl2Bu+5iFTVq2AccD7DKYV9ogNeSBg+woDVt\nGvDhh+L3KSSDF3oO5MjgrX0gYjb4zGRVqg5e7hWdOB+OHmWtpLm1X00m4PHHgbffFm+bD4YG7wCt\nZvAAG2xdtUqZUitbKJ3B61mD55g9mwV4sYOZcmnwgDL9aC5eZAO6ziad6amKhu9MVkt5hmPqVPZ8\nZqZ4+1pFFwFebAavhO41dCjg5wds26a8/dpalpFwPzeVsK93DR4A+vdnyzBu3Spun3Jr8FJLNNY+\n8JFnAKBNGzbXw9XkRUt18OnpVy9K7+XFxmaWLhVv3xmGBu8ALWfwAOtPs2KF8nYPH2Yd8dq2Vc6m\n3jV4jsceEz+zVU4NXomsmW+Ad3Nj11ZVlbz+APJLNBy2MniA/ar78kt1x9PkQBcBXqsaPMekSUwq\nyc5W1r6zCU6GBm//GNxzD7vZc3KE71NODV6OQVZrH/gGeECagVYletHwrYO3lcEDbBLfvfcC770n\n3gdHGBq8A7SewbduDTz8MLBypbJ2ldbfAcDfn010EtpHW2sZvJcX8NBD4m5oOTV4LWXwSvkDKFNF\nU1LCqmgCA22/PncuK3t2Zd1hraGLAK9lDZ5j9mzg00+bZjty23cW4OWw36oVC26nT/PbnvNBrQDv\n6Bg8+ihboctZTyFr5NTg5cjgrX2orHReQcMhRYDnq8HLXQfPyTMmk+1tBgxg4zPJyeL9cGRfDXQR\n4LWewQNAz57AiBHA558rY6+hgfXUsPVzU27E6PBaWZXLkt69WcvYL78U9r6WosEDytXCuzpbnU8G\nb0+esYQrmZR74pNSuBTgGxoacO2112LChAkAgLKyMkRHR6NPnz4YPXo0ysvLzdsmJiYiLCwM4eHh\nSBE4q0DrGjwHN9jKXRxy2s/NBbp1Azp3tr+NXPaFBHjOB7UyeGfHQEwbYb3XwSst0WilDt7eAKsl\nY8eyQeWffxbviz37auBSgF+2bBkiIiJguvybJykpCdHR0cjOzsaoUaOQlJQEAMjMzERycjIyMzOx\nfft2xMfHo1GAiKvF7M8Wo0axi+yXX+S3pYb+ziGmFl6r5/COO9jiLQcP8n+P3HXwcs9k1aIGr0Qd\nPJ8M3s2NafFyT3xSCtEBvrCwENu2bcOsWbNAl1PWrVu3IjY2FgAQGxuLzZs3AwC2bNmCqVOnwtPT\nEyEhIQgNDcXevXt529JaP3h7uLk1XdJPTvsFBVev4GSNXPaFZPBa1uAB1iHzkUeEZfF670WjtESj\nhTr44cOjcOwY0K+f833FxAC//sp+JUuF7jT4xx9/HK+//jrcLJYRKi0thZ+fHwDAz88PpZdXVzh5\n8iSCgoLM2wUFBaGoqIi3LS12k7RHbCxbWELsykd8KStj/azVQMzCH1rN4AG2ss/69fwzZ733ouHT\nC55DL1U0zgZZDx8G+vThdw22bcu+9N95R7w/WkFUgP/222/h6+uLa6+91py9W2MymczSjb3X+aLF\nfvD26NSJNbR6/3157fMJ8IYGz+8YBAYCUVHAunXO98fN7GzTRjr7liilwStZRaNEHbyzDD45OU1Q\nQUJCAvDZZ+zXmhSopcF7iHnTb7/9hq1bt2Lbtm2orq5GRUUFpk+fDj8/P5SUlMDf3x/FxcXw9fUF\nAAQGBqKgoMD8/sLCQgTaKUadMWMGQkJCAADe3t4YPHgwamuj4Ol55SBxP3ecPU5PTxe0vVSPExKi\nMGYM0L69fPbLyoDi4jSkpSn/+QMColBczG/79PR0REVFobYWOHHCsb9yPObsO9t+9mwgPj4NYWHA\nbbfZ376sDOjUKQomk7T2ucfHjwOVldIeDw7ucUVFFDp25Pf+06eBtm2ltW9r+7o6ICtL/PXRqhVw\n7pz99+fmAgEB/PcfGAgMGZKG554DVq1y7fPL8TgtLQ0ff/wxAJjjpU3IRdLS0uiOO+4gIqKnn36a\nkpKSiIgoMTGRnn32WSIiOnz4MEVGRlJNTQ39/fff1Lt3b2psbLxqX/bciY8nWrHCVU+V5dZbiZKT\n5dv/qFFEKSny7d8Rf/9N1LOnsPckJBAtXy6LO5LQ0EB0zTVEe/Y43i4riyg0VD4/TpwgCgyUb/9E\nRCNHEu3cyW/b118neuIJWd0hIqLoaKLt28W/Py+PqFcv+6/fcgvRjh3C9vnHH0Q9ehDV1Yn3Syns\nxU5J6uA5uWXevHn48ccf0adPH+zcuRPz5s0DAERERGDSpEmIiIjAuHHjsHLlSkESjZ40eA4+S/q5\ngtoafHGxsFphLWvwABsgf/RR54OtcurvgDbr4PVQRdPKgURDxKqknJVIWjN0KNCjB/D11+L9UhuX\nA/zIkSOx9XJbPh8fH6SmpiI7OxspKSnwtrgT5s+fj9zcXGRlZWHMmDGCbOhJg+eYOBE4fDgNGRny\n7F9NDb51azYQVVbmfFvOBy1r8BwzZrCb2dHnElJBI9Q+cKWbpJQTbax9EDrIqkQvGjkHWfPzgVat\n0tCli/D9StUrXq1YpIuZrHrM4D09WY21XFm8mhk8IHw2q9YzeIBNHPvnP4FPPrG/jZw18AC7bjw9\nhbdPEILSg6x8kLNMMj2dtYcWw113sR42v/8u3jc10UWA10sdvDWJiVFITpZuJJ6jrg64dMl5Fibn\n5+c72YnzQat18NZwbYTtZdBCM3gx50DqUklrH7RYBy+FRGMvgz9wALj9duc+2MLdHfjXv1zP4tWK\nRboI8HrI/mwREACMGcMaWkkJl0UKGMaQHKG18Ho5hzffzCY/2ftFLbcGD8ibNdfWsgSBb5mn3jJ4\nW1/M6enC9XdLZs5ka7a6so6vWugmwOuhF40t+wkJrI2w0Pa6juArz8j5+flKNHrS4AH2pemoP43c\nGjwgfVC19IHT3/kmB0r2onElAXB3ZwPltlafOnAAqKlx7oM9OnZkExhdkVsNDd4BeugmaY9bbmGD\nkjt2SLdPtfV3oHlq8BzTp7OM7fJE7CbIrcED8s5mFSLPAPrpJgnYlmnKy1lr6+7dXdv3v/4FrF6t\n3Bq1UqGLAK+HfvD27JtM0i/pd+4cvwBvaPDijkGnTsB997Eb2holNHipM3hLH4RU0EjlC59j4KpE\nA9huOHbwIFuzeNQo5z44olcvYORI8XKrocE7QM8ZPABMm8Y6TB4/Ls3+ysoctwlWAqEafE2Nvs7h\n7Nms3YT1T34lNHi5M3i+FTScLxUV8vdHl+IXnq1KGj4tgvny+OPAsmXSyq1yo4sAr2cNHmA/c2Ni\nxC/ybI0eNfjaWn1o8BzXXcfW6fzhh6bP612DFyrRtG3LPq/Q5nL27NtDqgzeWqLhBliluBduvpn9\nuvvuO+HvNTR4B+g9gweA+Hj2k1+K+mYtafB8MzutrcnKh9mzgVWrmj6nhAbPTXaSA6EBHgD69gWy\nsuTxh0OKAG8vg5dq1TOTSbqJT0qhiwCvZw2eIywMGDIE2LDB9f3yDfByfv727Vk5YUUFPx/UGmR1\n5RhMngz89ltTaU2pOni5NHgxAT48HDh6VBr79pBKorHM4OvrgcxMpsFLdS/cdx+Qnc1+GQjB0OAd\n0BwyeKDpYiCucO6c+ho8IEyH12MG364d8OCDwIcfXnlO73XwQgdZARbg5czgiVgwlnqQ9ehRICiI\nHU+paNWK3cdLl0q3TznRRYDXuwbPMX48K7374w/X9qsFDR7gp8NbavBqfEm7egwefZQF+Lo6FoSq\nqoQFDDH2pR5ktdbghQyyAq4HeGfHoL6e1bG7OnHPWqKxHGCV8l549FFgyxbWwoAvhgbvAD3VUDvC\n3Z1p8a5m8VrQ4AFhtfB6zOABICKCrQS0ZQvL3jt2ZBNq5ETODF6LGrxU97f1IKurM1jt4ePDFvWx\nHp/RIroI8HrtRWPL/syZLFicOSN+v1rQ4AF+tfB61uA5uP40QvV3sfbl7EUjJsD37MkmC4md8OTs\nGEgxwArYzuC5AVap74W5c9k1wbdowtDgHaDHbpL26NqVdaizNYmGL1rS4Plk8A0NTGf1ELV+mPrc\nfTeQkcGkNbn1d0B7Gby7OysSyM6WxycpA7xlBi9lDbw1ffuyfvGffy7P/qVCFwFej/3gHdmfM4f9\nvLPVN8MZRPwDvBIavLNB1rS0NFUlNimOgZcX8NBDwJIlwgO83uvgOVzR4Z0dAyklGi6DLylh/w8K\n4ueDGObOZSWTfEqFDQ3eDkTNK4MH2De/n5+4CROVlay3jRbGJPhm8HrV3y2Ji2MZodw18IC8M1kr\nK4UPsgLy6vBySDScPCNnx9V//IPtPzVVPhuuovkA39BwpVOcULSowXOILZkUMsCqFQ1ezQxeqmPQ\nuzdr/ayEBi9nLxo1Mng+GrwU14elRGM9wCrHvWAyXcninWFo8HZobtk7x6RJwP79wnVNrejvQMvK\n4AFg0SI2+UlutNRNksPVyU6OkOoet5RopJzB6ohp04A//5R/pq9YNB/gXfl216oGDzCZ5eGHWa94\nIQjJ4OX+/B07shpmR9lmc9DgOa6/Hhg3Tn77WtTg+/ZlyYiYRlvOjoFcEo1lBi/XvdC6NWtpsWyZ\n4+0MDd4OzTWDB9iF8emnwsrPtFIDD7CfqHyy+OaSwStF27ZsSUYxg/COIBKvwbdvz647OVY1kroO\n/tIl4O+/gX79XN8nH+LjgeRk4OxZZewJQfMB3pUMXssaPMDqi0eMEFZqpSUNHnCuwzcXDV5J+25u\n0i60wflQXMyW6hNbripWh1e6Dv7wYTY5zTKpkPM68PNjpc/vv29/G0ODt0NzmcVqD24xEL5dGfku\n9qEURgYvD1LLNERswlZCgvh9yKXDS10HL9cMVkfMncvuY+tulmqjiwAv9uRrWYPnGDWKfcZffuG3\nTyGLfSjx+Z3VwjcnDV5J+1IOtKalpWH1atYVc9Ei8fsRm8ErXQdva4BV7usgMpKNU3z5pe3XDQ3+\nMtaZbHPpJGkPNzdh/Wm0pMEDRgYvF1Jm8EVFwLx5TAp05TzIVQsvtUQj5wxWR3C94uVe/UoImgvw\n1j9xXMng9aK/xsaylYP4lBzqUYNXc7k+vVwD1kiVwdfXAytWROGFF4D+/V3bl9Y1eC8v1hvGVoBX\n4jr45z9ZlZKtX+PW9uvr2Z/caC7AWy+51dwzeIDNjpwyxfEgDYceNXi1luvTM1Jl8ImJbF//93+u\n7yswkH3pnD/v+r4skUqiadWKlXJ26MB6PimNmxvw73/z6xX/yivsT3af5DchDOsA39w1eI6EBOC9\n99gXmiP0qMGrKdHo6RqwRIoAv3cvsHw5EBeXJkmLYzc3JtMIHWhVsg5+717b8oxS10FsLPDTT8Cx\nY47tf/+9sH7yYtF8gG8JGTwADBjAOvZ9/bXj7fSowTf3Sig5cFWiuXiRrUa1YgXQrZt0fsmhw0sp\n0Zw7p8wMVnu0b88mML7zjv1tysuBffvYv3Kj+QDfEjR4jjlznA+2ak2D9/FhE0suXbLvg5oZvN6u\nAQ5XM/inngKGD2ctMaQ8BmJ0eGf2pZRoANsZvJLXwZw5wNq1TdcrtrSflsZ8NQI8Wk4GDwATJwK5\nuaz3uC1qatjxaNdOWb8cYTIB/v6Of24aGbxwXMngv/uOSQArVkjrEyBPLbyUGTygTgWNJcHBwOjR\n9td82LGDtbzQbIAvKCjAbbfdhv79+2PAgAF45/LvkbKyMkRHR6NPnz4YPXo0yi0+QWJiIsLCwhAe\nHo6UlBS7+26pGjzAPmdcnP0snms0xrcFqlKf35FMY2jw4uyLzeBPnwYeeQT45JMrrY2lPAZiMng+\nGrxUGXzbtkBoqHAfpObxx5lMw7WbsLSfmgrce6+GA7ynpyfefvttHD58GHv27MG7776LI0eOICkp\nCdHR0cjOzsaoUaOQlJQEAMjMzERycjIyMzOxfft2xMfHo9FO1yLrJbBaUgYPsACfnGz75GtNf+dw\nNtBqZPDCad9eeAZPxIL79OnArbfK41dYGJCXJ22Jn1T9pjp2BIYMYe3F1Wb4cHZfbN7c9PmiIvYl\nHBWl4QDv7++PwZdHMtq3b49+/fqhqKgIW7duRWxsLAAgNjYWmy9/ui1btmDq1Knw9PRESEgIQkND\nsXfvXpv7tpXBN9deNLYICGB9x9euvfo1oQFeqc/vqBbe0ODF2e/QQXgGv3o1kJ9/9WxVKY9Bmzbs\nGrWuEnGEUnXwN90EbN8uzgc54CY+WdrfsQO47TZ2H2s2wFuSn5+P/fv3Y/jw4SgtLYWfnx8AwM/P\nD6WlpQCAkydPIohbOwtAUFAQioqKbO5PSolGryQksDbC1j9ytFYDz+GsksbI4IUjVKLJzZVmtiof\npNbhpfqVbjJpa3zq7ruBggK2li/Hjh2sPUnr1uwXF99Fu8XiUoC/cOEC7r33XixbtgwdrHqQmkwm\nmByIxfZek3KQVa/66y23sAtgx46mzwupgXfFvlAMDV56+0IGWevrmSxjb7aq1MdAqA7PpxeN3Emc\nGteBhwebYLZ0KbNPxPR3bqk/b2/pJ41d5YPYN9bV1eHee+/F9OnTMXHiRAAsay8pKYG/vz+Ki4vh\n6+sLAAgMDERBQYH5vYWFhQgMDLS537femoG//goBAHh7eyMvbzA8PaMAXDlJ3M8dZ4/T09MFbS/1\nY1fsJyQACxemwdPzyut796ahqgoAtPX5AwKicPKk7dfT09NRWxuFVq2UP/6cfbXOvyv227cHTp5M\nQ1qa8+1372bbDxxoe3sOqT5f375R2LeP//bO7NfVRcHTU53zI/fj8HBg8eIoTJwIfPppGurrgWuu\nYa+3apWGH34AYmKE7z8tLQ0ff/wxACAkJAR2IRE0NjbS9OnTae7cuU2ef/rppykpKYmIiBITE+nZ\nZ58lIqLDhw9TZGQk1dTU0N9//029e/emxsbGq/YLgD79tOlzb7xB9MQTYrzUNxcuEPn4EOXnX3nu\n+eeJXnpJPZ/skZ5ONHCg/dfj4oj++1/l/GkOZGQQRUQ4327vXiJfX6LCQvl94ti1i+jmm6Xb37/+\nRfT229LtT2v83/8RzZtHtHw50cyZV54fNoxozx5pbNgL5aIy+F9//RWfffYZBg0ahGuvvRYAK4Oc\nN28eJk2ahNWrVyMkJAQbNmwAAERERGDSpEmIiIiAh4cHVq5cyVuiaYkaPMC0xJgY4L//Zf1EACbR\nKLVKjRAMDV56+Gjw3GzV5ctZnxilkEODb873+L//DdxwA3DttcCMGVee9/aWZqDV0XUiSoO/5ZZb\n0NjYiPT0dOzfvx/79+/H2LFj4ePjg9TUVGRnZyMlJQXeFkvQz58/H7m5ucjKysKYMWPs7tvQ4K8Q\nH88qI7iBGKGDrEp9/q5dmZZoa7GDtLQ0ZGYCPXoo4opN+2oi1j6fAP/UU8CwYWy2qhw+2MPPj92X\nZ87w296ZfSUCvJrXwTXXAH37puHHH9kAK4cUAb6w0HHSp7mZrNajyi01gwdYzfGQIcDlH0KCB1mV\nws0N8PW1PZu1rIx1+JOrLru54myQVc7Zqs4wmcQ1HbPm4EF2v7eEX3hTpgDR0ezLkcPVAF9TA9x3\nH0sE7aG5AC9lBs8NTqiFFPa5Jf0A7dbBA/Zr4cvKojB2rHpf0nq9Blq1YmV0tn4V2ZqtKocPjhBS\nSWPLfkUFqwf/8ktlMni1r4M5c6Lwww9Nn3M1wM+dy+TRefPsb6P5AN+SM3gAGD8eOHWK1dJqdSYr\nYF+H37oVmDBBeX/0jslkO4tXYrYqH1zV4ZcuZQ3qMjKavwbPYT3s6EqA//hjYOdONiHSUesSXQT4\nlqrBA2zaNbekn1Y1eMB2gK+qAlJT0zBunGJuXIWerwFbOvyaNbZnq8rlgz2EZPDW9s+dY31aXn2V\nBXglJBotXgdiA/xffwFPP81ai3fs6HhbzQf4ltaLxhYzZ7KeFufPs4tCi9jqR5OayrRaLY4b6AHr\nfjR5ecrNVnWGK33h33yTdU69+26mw7eUDN4aMQH+7FnWqGzlSiAiwvn2mg/wLakfvD26dmU3RPv2\nwhopKfn5bWXwW7cCsbHK+WALPV8Dlv1o6utZSeTzzwtfW1WOY3DNNcCJE1ffr87snz4NrFrFZt32\n7Mm+wEpLm78Gb8u+0ADf0AA88AAL8Pffz+89mg/wRgbP+Ne/gF691PbCPtaDrI2NwDffGPq7K1hK\nNFKurSoFXl6s9DUvT9j7XnuNVZT07Mm044EDmUzTEu9xoQH+xRdZwnu5SS8vNB/gW1I/eEcMGcK0\nN7XsO8M6g9+7ly0VV1CgnA+20PM1wA2y/vEHq6T6+GOIWltVrmPAd6CVs19SwsYQ5s+/8trAgc2/\nDt6efSEBfssWVjWVnMx63PBFdC8aubDVD37WLB9MnnxOHYcMBGE9on/bbfLb7Ny5M8rKyuQ3pDDt\n2zP5Yt485Wer8kGoDp+YyGZnW36OQYPYv0YGb5/sbFY5tXUrm28iBM0FeFsZfGXlObB2CwYGV+Oo\na6kWtVe+dOgALFzIJsg4m60qlw+OCA8Hfv6Zn/2CAuDTT4EjR5q+NnAg+9fQ4G1z4QJwzz2sauqG\nG4Tb1bxEU1enjh8GBmrTvj3TutWYrcoHIaWSr77KViuznMkJKBfgtUjr1mzg1F5PeCJg1izg+uuB\nRx8VZ0PzAd7WTD4DA75oUXvly8MPs3YEfGaryuWDIzgN3tmP63Xr0rBxI6vdtsbbm0k9rn5GZ2jx\nOnDWE37pUiAnh5VE8l2H2RrNSzRGBm/QUgkPV9sDx3Tpwgb8SksBf3/7233yCZus16WL7dczMlpm\nBg9ckWmsf9n89BOrlvn9d7ZMolhMpCFx22Qy4dprqUm1yNChwJ9/mgwN3sAuJpNxfajFLbcAr7zC\nFpG2RXY2cPPNLBPV6iQ9NRk2jA2gDx9+5bmiIibLfPwxMHo0v/3YuwcMiUYCvvjiCwwdOhQdOnRA\n9+7dMX78ePz666+i97dw4UJMnz5dQg9dp7i4GA8//DC6d++Ojh07ol+/fli4cCGq2BJTsqHFY2Fw\nBWc6/EsvsX7oRnC3jfVAa20t6xCZkMA/uDtCcwHeVpmklnnrrbfw+OOP4/nnn8epU6dQUFCAhIQE\nbN26VW3XJKGhoQFlZWW48cYbUVNTgz179qCiogI//vgjzp8/jzyhM10URovaa3PywVEt/OHDrF3F\nkCHy2eeL2ufBnn3rAP/446wU8rnnJDIszYJR0gCAAgObPte7t/3lqNSmvLyc2rdvTxs3brT5emxs\nLD3//PPmx7t27aKgoCDz46SkJAoMDKQOHTpQ3759aceOHfT9999Tq1atyNPTk9q3b0+DBw8mIqKi\noiKaMGEC+fj4UGhoKH3wwQfm/bz44ot033330YMPPkgdOnSggQMHUnZ2Ni1evJh8fX2pR48elJKS\n0sTvmTNnUkBAAAUGBtLzzz9PDQ0NRET00Ucf0U033USPP/44denShZ5//nn6z3/+Q4MGDXJ4LH79\n9VcaOnQoderUia6//nr67bffzK/17NmTUlNTm/j74IMPEhHRsWPHyGQy0dq1a6lHjx7UtWtXevXV\nV4mI7B4LaxxdH7t27XLot9yobV9uH7ZuJRo71vZr991H9Nprzf8YuGI/KYkoKoqopoZo7VqisDCi\n8nLh+7d3D2gqcgKgrl2bPhccrN0A//3335OHh4c5OFozY8YMeuGFF8yPLQN8VlYWBQcHU3FxMRER\nHT9+nPLy8oiIaOHChTR9+vQm+xoxYgQlJCRQTU0NpaenU7du3Wjnzp1ExAJm69atKSUlherr6ykm\nJoZ69uxJixcvpvr6evrggw+oV69e5n1NnDiRZs+eTVVVVXTq1CkaNmwYvffee0TEAryHhwetWLGC\nGhoa6NKlSzR8+HBauHCh3eNw9uxZ8vb2ps8++4waGhpo3bp11LlzZyorKyMiopCQENqxY4d5+4UL\nF14V4OPi4qi6upoOHDhAXl5elJWVZfdYWKPV66MlkJ1NFBJy9fP79xMFBBBdvKi8T3qivp7ojjuI\nJkwg6tqVrcUrBnv3gOYkGjEavMkkzZ9Qzp49i65du8LNwfxxsjP45+7ujpqaGhw+fBh1dXXo0aMH\nevfubX6P5fsKCgrw22+/YcmSJWjVqhUiIyMxa9YsfPLJJ+Ztbr31VkRHR8Pd3R333Xcfzp49i3nz\n5sHd3R2TJ09Gfn4+KioqUFpaiu+//x5vv/022rRpg27dumHu3LlYv369eV/du3dHQkIC3Nzc0Lp1\na5SVlSEgIMDuZ/zuu+/Qt29fTJs2DW5ubpgyZQrCw8PxzTff8D4mL774Iry8vDBo0CBERkbiwIED\nNo+Fgbbo1Yu1qLh0qenzL74IPPss0LatOn7pBXd34IsvmEzz7rvAgAHS7l/zAZ6PBk8kzZ9QunTp\ngqhiyXYAACAASURBVDNnzqCxsVHwe0NDQ7F06VIsXLgQfn5+mDp1KortrFx98uRJ+Pj4oF27dubn\nevTogaKiIvNjX4s5zG3atEHXrl3NMzzbXK6zunDhAo4fP466ujoEBASgc+fO6Ny5M2bPno3Tp0+b\n3x8cHHzV5zxp3QvYyr8eVouu9uzZs4l/zvC3qLNr27YtLjhbkJQnWtVem4sPHh6ss2ROzpXn9u5l\nfZO4yTnN/Ri4ar9DBzYj2JXZyvbQXICvrW0abLVcRXPjjTfCy8sLX3/9tc3X27Vr16TKpMRq0dKp\nU6di9+7dOH78OEwmE5599lkAV0+97969O8rKypoEvRMnTiAoKEiwz8HBwfDy8sLZs2dx7tw5nDt3\nDufPn0dGRoZ5G2v7//jHP/D111/bzaQDAwNx/PjxJs8dP34cgZebjrRr1w4XL140v2Z9HBzhqA2B\ngTaw7kmzYAHwn/+wmZoG6qK5AO/p2TSoa7mKplOnTli0aBESEhKwZcsWVFVVoa6uDt9//z2effZZ\nDB48GNu2bcO5c+dQUlKCpUuXmt+bnZ2NnTt3oqamBl5eXmjdujXcLzd79/f3R35+vjmgBgcH46ab\nbsJzzz2HmpoaHDx4EGvWrMGDDz4o2OeAgACMHj0aTzzxBCorK9HY2Ii8vDz87KCpyBNPPIGKigrE\nxsbixIkTAICioiI8+eSTyMjIwPjx45GdnY1169ahvr4eycnJyMrKwh133AEAGDx4MNavX4/6+nrs\n27cPmzZt4h24rY+FULTYg6S5+WBZKvnrr6yqZuZM5ezzQW0f1LKvuQDv5dVUptFyBg+w4PfWW2/h\nlVdega+vL3r06IGVK1fi7rvvxvTp0xEZGYmQkBCMHTsWU6ZMMQe2mpoaPPfcc+jWrRsCAgJw5swZ\nJCYmAgDuv9zNv0uXLhg6dCgAYN26dcjPz0f37t1xzz33YNGiRbj99tsBsCzXOmA6evzJJ5+gtrYW\nERER8PHxwf3332/Oqm3tq3Pnzvjtt9/g6emJ4cOHo2PHjvjHP/4Bb29vhIaGwsfHB99++y3efPNN\ndO3aFW+88Qa+/fZb+FxeX/Dll19GXl4eOnfujIULF2LatGkOfbXE1rEw0BaWAf6FF9hfS+wOqUnE\njdnKAwDq0oWotJQ9rq8ncnMzqiQMHOPo+tBqeVxz8uH334mGDCHasYMoNJSork5Z+3xQ2we57du7\nBzSdwSuxGK+BgYFr9O3LZJkXXmDVM0IWpDCQF831ounVi5CSAoSGsi5rwcFAZaXRa8TAPkYvGvUJ\nCGCLq2dkCFs32EAa7N0Dmvuubd2aLU/Vrx+rpjEyeAMD7RMVxdZaNYK7ttCcRBMXx+poly8HFi8G\nLo8jGhiIQsv1z83Jhy++AO66Sz37zlDbB7Xsay6DnzuX/VlilEIbGGgb4x7VJprT4G25Y2isBo4w\nrg+Dlo5uNHhbdO7c2ZjRaGCXzp07q+2CgYEm0ZwGb4uysjJz0ykhf7t27RL1Pqn+Wrp9pXwoKyuz\ne+20VO1VSz6obV8LPqhlX9EAv337doSHhyMsLAxLliyR3V56errsNgz72vahpdvXgg9q29eCD2rZ\nVyzANzQ0YM6cOdi+fTsyMzOxbt06HDlyRFab5ZZLpahAS7evBR9aun0t+KC2fS34oJZ9xQL83r17\nERoaipCQEHh6emLKlCnYsmWLUuYNDAwMWhyKBfiioqImfcaDgoIE9QsXQ35+vqz7N+xr34eWbl8L\nPqhtXws+qGafFGLjxo00a9Ys8+NPP/2U5syZ02SbyMhIAmD8GX/Gn/Fn/An4i4yMtBl3FSuTDAwM\nREFBgflxQUHBVQtWqD0QYmBgYNCcUEyiGTp0KHJycpCfn4/a2lokJyfjzjvvVMq8gYGBQYtDsQze\nw8MDK1aswJgxY9DQ0ICHH34Y/fr1U8q8gYGBQYtDU60KDAwMDAykw33hwoUL1XZCLKWlpUhJSYHJ\nZELXrl0Vt79x40bs3LkTHh4eCAgIUNw+AOzevRvdu3c3r+eqNKWlpdi2bRsaGxvh5+enuP3169cj\nNTUVJpNJ1CLkUlBRUQEvLy9VbAMwr/nbuXNnNDQ0wM1N2Qnq//vf/1BdXY0uXbqAiFRrK7Jnzx74\n+vqqci+cOnUKP/74I4gI3bp1U9y+PXTRqsAWGzZswI033oi0tDTExsYiNTUVlZWVitiurq5GXFwc\nli9fjjZt2mDOnDn47rvvFLHNsXv3bgwePBjz589HXFwcNm/eDABQ8gfZzp07MXDgQKSmpuLuu+/G\n999/j4qKCkVs19fXY8GCBXjjjTfQ2NiImTNnYuvWrYrY5vjqq6/Qq1cvrF27FhcvXlTUNseSJUvQ\np08fzL3cgtXd3V2xa+Ds2bMYM2YMxowZgw0bNqC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J5udMJhMyMjLg7e0NPz8/3Hvvvbh0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\nFwjLAAAAAElFTkSuQmCC\n",
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/zEdQM1jNeM2FD3OQ1eC00yQDavNmf/cblAYfdhZNLsKM4IOWaDJZuVKqj8bx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\n3yBraWlppBKbH8egYUP41a/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