diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..e72a96d
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,166 @@
+# Created by https://www.toptal.com/developers/gitignore/api/python
+# Edit at https://www.toptal.com/developers/gitignore?templates=python
+
+### Python ###
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+
+# End of https://www.toptal.com/developers/gitignore/api/python
diff --git a/README.md b/README.md
index 91b2957..93f10e0 100644
--- a/README.md
+++ b/README.md
@@ -1,5 +1,40 @@
 # hackathon-season2
 
+## USAGE
+To generate CSV from XML:
+> python xml2csv.py
+
+```text
+usage: xml2csv.py [-h] [--xml XML] [--csv CSV] [--convert-default]
+
+optional arguments:
+  -h, --help         show this help message and exit
+  --xml XML          Specify the name of a xml file to read from.
+  --csv CSV          Specify the name of a csv file to write to.
+  --convert-default  Convert active 3 years experience+ Airhostess, Pilot, Steward to csv file without duplicate ID and passport number.
+```
+
+To execute DAL and generate JSON:
+> python csv2sqlite.py
+```text
+usage: csv2sqlite.py [-h] [--csv CSV] [--json JSON] [--database DATABASE] [--table-name TABLE_NAME] [--region-param REGION_PARAM] [--dept-param DEPT_PARAM] [--nation-param NATION_PARAM]
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --csv CSV             Specify a csv file.
+  --json JSON           Specify a json outfile.
+  --database DATABASE   Specify the name of a database file to write to.
+  --table-name TABLE_NAME
+                        Specify the name of a table
+  --region-param REGION_PARAM
+                        Specify parameter for SQLite View region-based query
+  --dept-param DEPT_PARAM
+                        Specify parameter for SQLite View department-based query
+  --nation-param NATION_PARAM
+                        Specify parameter for SQLite View nationality-based query
+
+```
+
 ## เลข Status จะมีตามนี้ครับ
 - 1 = Active
 - 2 = Resigned
@@ -28,15 +63,29 @@
 - Creative ตอนทำ Data visualization 
 - ผลลัพธ์จาก SQLlite ต้องได้เป็น **JSON** format
 
-**ตัวอย่าง Data visualization**
+[**Data visualization**](data-visualization.ipynb)
+
+*Gender2 is data after database migration*
+
+![alt text](images/1.png)
+![alt text](images/2.png)
+![alt text](images/3.png)
+![alt text](images/4.png)
+![alt text](images/5.png)
+![alt text](images/6.png)
+![alt text](images/7.png)
+![alt text](images/8.png)
+![alt text](images/9.png)
+![alt text](images/10.png)
+
 
-[dev-mountain-visualization](https://dev-moutain-dataviz.netlify.app/)
 
 
-## ตัวอย่างการสร้าง PR
-ทีม Dev mountain
+## การสร้าง PR
+ทีม นอนต่อเลยได้ไหม
 สมาชิก
-- [annibuliful](https://github.com/annibuliful)
-- [lordbenz](https://github.com/lordbenz)
+- [N.SX](https://github.com/rsxss)
+- [Sathipp](https://github.com/Sathipp)
+- [Jiruschai](https://github.com/JirusAnun)
 
-Repo: [hackathon](https://github.com/devmountaintechfest/hackathon-season2)
+Repo: [hackathon](https://github.com/rsxss/hackathon-season2)
diff --git a/csv2sqlite.py b/csv2sqlite.py
new file mode 100644
index 0000000..fd9af05
--- /dev/null
+++ b/csv2sqlite.py
@@ -0,0 +1,146 @@
+import argparse
+import csv
+import collections
+import json
+import sqlite3
+
+from contextlib import closing
+
+
+def _json_dumps(objects):
+    output = {}
+    for i, obj in enumerate(objects):
+        output[i] = []
+        for emp in map(EMPLOYEE_RECORD._make, iter(obj)):
+            output[i].append(emp._asdict())
+    return json.dumps(output, indent=2)
+
+
+def main():
+    with closing(sqlite3.connect(DATABASE)) as conn, open(
+        CSV_FILE, "r"
+    ) as infile, open(JSON_FILE, "w") as outfile:
+        csv_dict_reader = csv.DictReader(infile)
+        payload = (
+            list(map(lambda struct: record[struct], TABLE_SCHEMA))
+            for record in csv_dict_reader
+        )
+
+        with conn as cur:
+            cur.execute(
+                f"CREATE TABLE IF NOT EXISTS {TABLE_NAME}({JOINED_TABLE_SCHEMA})"
+            )
+            cur.execute(
+                f"CREATE VIEW IF NOT EXISTS `VIEW_EMP_REGION` AS SELECT * FROM {TABLE_NAME} ORDER BY REGION"
+            )
+            cur.execute(
+                f"CREATE VIEW IF NOT EXISTS `VIEW_EMP_DEPARTMENT` AS SELECT * FROM {TABLE_NAME} ORDER BY DEPT"
+            )
+            cur.execute(
+                f"CREATE VIEW IF NOT EXISTS `VIEW_EMP_NATIONALITY` AS SELECT * FROM {TABLE_NAME} ORDER BY NATIONALITY"
+            )
+
+            try:
+                cur.executemany(
+                    f'INSERT INTO {TABLE_NAME} VALUES (?{", ?" * (len(TABLE_SCHEMA) - 1)})',
+                    payload,
+                )
+            except sqlite3.IntegrityError:
+                pass
+
+            result_sets = [
+                cur.execute(
+                    "SELECT * FROM `VIEW_EMP_REGION` WHERE REGION = ?",
+                    (args.region_param,),
+                ),
+                cur.execute(
+                    "SELECT * FROM `VIEW_EMP_DEPARTMENT` WHERE DEPT = ?",
+                    (args.dept_param,),
+                ),
+                cur.execute(
+                    "SELECT * FROM `VIEW_EMP_NATIONALITY` WHERE NATIONALITY = ?",
+                    (args.nation_param,),
+                ),
+            ]
+            outfile.write(_json_dumps(result_sets))
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        "--csv",
+        default="data-devclub-1.csv",
+        help="Specify a csv file."
+    )
+    parser.add_argument(
+        "--json",
+        default="data-devclub-1.json",
+        help="Specify a json outfile."
+    )
+    parser.add_argument(
+        "--database",
+        default="data-devclub-1.db",
+        help="Specify the name of a database file to write to.",
+    )
+    parser.add_argument(
+        "--table-name",
+        default="TBL_DEVCLUB",
+        help="Specify the name of a table"
+    )
+    parser.add_argument(
+        "--region-param",
+        default="Europe",
+        help="Specify parameter for SQLite View region-based query",
+    )
+    parser.add_argument(
+        "--dept-param",
+        default="Flight Planning",
+        help="Specify parameter for SQLite View department-based query",
+    )
+    parser.add_argument(
+        "--nation-param",
+        default="Ukraine",
+        help="Specify parameter for SQLite View nationality-based query",
+    )
+
+    args = parser.parse_args()
+
+    if not args.csv:
+        print("[-] Please specify a csv file.")
+        exit(1)
+
+    if not args.database:
+        print("[-] Please specify the name of a database file to write to.")
+        exit(1)
+
+    if not args.table_name:
+        print("[-] Please specify the name of a table.")
+        exit(1)
+
+    CSV_FILE = args.csv
+    JSON_FILE = args.json
+    DATABASE = args.database
+    TABLE_NAME = args.table_name
+    TABLE_SCHEMA = {
+        "EMPID": "INTEGER PRIMARY KEY",
+        "PASSPORT": "CHAR(11) NOT NULL UNIQUE",
+        "FIRSTNAME": "VARCHAR(255)",
+        "LASTNAME": "VARCHAR(255)",
+        "GENDER": "INT2",
+        "BIRTHDAY": "CHAR(10)",
+        "NATIONALITY": "VARCHAR(56)",
+        "HIRED": "CHAR(10)",
+        "DEPT": "VARCHAR(56)",
+        "POSITION": "VARCHAR(56)",
+        "STATUS": "TINYINT",
+        "REGION": "VARCHAR(85)",
+    }
+
+    JOINED_TABLE_SCHEMA = ",\n".join(
+        (f"{struct} {domain}" for struct, domain in TABLE_SCHEMA.items())
+    )
+
+    EMPLOYEE_RECORD = collections.namedtuple("EmployeeRecord", ",".join(TABLE_SCHEMA))
+
+    main()
diff --git a/data-devclub-1-France.csv b/data-devclub-1-France.csv
new file mode 100644
index 0000000..bb24c19
--- /dev/null
+++ b/data-devclub-1-France.csv
@@ -0,0 +1,2 @@
+EMPID,PASSPORT,FIRSTNAME,LASTNAME,GENDER,BIRTHDAY,NATIONALITY,HIRED,DEPT,POSITION,STATUS,REGION
+93,UXL43IOW6OV,Honorato,Maxwell,1,09-03-1982,France,04-02-2017,Aircraft Maintenance,Airhostess,1,Europe
diff --git a/data-devclub-1-Germany.csv b/data-devclub-1-Germany.csv
new file mode 100644
index 0000000..be0788f
--- /dev/null
+++ b/data-devclub-1-Germany.csv
@@ -0,0 +1,2 @@
+EMPID,PASSPORT,FIRSTNAME,LASTNAME,GENDER,BIRTHDAY,NATIONALITY,HIRED,DEPT,POSITION,STATUS,REGION
+29,CMK62UAD3VK,Rowan,Leonard,1,15-07-1974,Germany,27-03-2004,Aircraft Maintenance,Pilot,1,Ocenia
diff --git a/data-devclub-1-Indonesia.csv b/data-devclub-1-Indonesia.csv
new file mode 100644
index 0000000..ab4b28f
--- /dev/null
+++ b/data-devclub-1-Indonesia.csv
@@ -0,0 +1,2 @@
+EMPID,PASSPORT,FIRSTNAME,LASTNAME,GENDER,BIRTHDAY,NATIONALITY,HIRED,DEPT,POSITION,STATUS,REGION
+33,EWD45RJW5YK,Carter,Velasquez,0,23-11-1967,Indonesia,27-02-2005,Flight Planning,Pilot,1,APAC
diff --git a/data-devclub-1-Italy.csv b/data-devclub-1-Italy.csv
new file mode 100644
index 0000000..ebd7278
--- /dev/null
+++ b/data-devclub-1-Italy.csv
@@ -0,0 +1,3 @@
+EMPID,PASSPORT,FIRSTNAME,LASTNAME,GENDER,BIRTHDAY,NATIONALITY,HIRED,DEPT,POSITION,STATUS,REGION
+34,BFS82MEY3CX,Selma,Bush,0,26-03-1972,Italy,10-10-2008,Flight Attendance,Airhostess,1,USA
+80,EUC74ENE9ZK,Ryan,Rush,0,13-06-1998,Italy,31-07-2019,Aircraft Maintenance,Pilot,1,Middle East
diff --git a/data-devclub-1-Mexico.csv b/data-devclub-1-Mexico.csv
new file mode 100644
index 0000000..63a47af
--- /dev/null
+++ b/data-devclub-1-Mexico.csv
@@ -0,0 +1,2 @@
+EMPID,PASSPORT,FIRSTNAME,LASTNAME,GENDER,BIRTHDAY,NATIONALITY,HIRED,DEPT,POSITION,STATUS,REGION
+50,MRC33GHJ2KW,Calvin,Roach,1,16-04-1999,Mexico,18-03-2011,Flight Attendance,Steward,1,Europe
diff --git a/data-devclub-1-Netherlands.csv b/data-devclub-1-Netherlands.csv
new file mode 100644
index 0000000..6f0bbcd
--- /dev/null
+++ b/data-devclub-1-Netherlands.csv
@@ -0,0 +1,2 @@
+EMPID,PASSPORT,FIRSTNAME,LASTNAME,GENDER,BIRTHDAY,NATIONALITY,HIRED,DEPT,POSITION,STATUS,REGION
+23,NFH65BYM0VB,Armand,Horn,0,24-05-1987,Netherlands,19-06-2007,Aircraft Maintenance,Airhostess,1,Ocenia
diff --git a/data-devclub-1-Pakistan.csv b/data-devclub-1-Pakistan.csv
new file mode 100644
index 0000000..621bd1f
--- /dev/null
+++ b/data-devclub-1-Pakistan.csv
@@ -0,0 +1,2 @@
+EMPID,PASSPORT,FIRSTNAME,LASTNAME,GENDER,BIRTHDAY,NATIONALITY,HIRED,DEPT,POSITION,STATUS,REGION
+3,JUI65YBK7AF,Jada,Bender,0,28-05-1963,Pakistan,11-02-2001,Pilot,Pilot,1,Canada
diff --git a/data-devclub-1-Peru.csv b/data-devclub-1-Peru.csv
new file mode 100644
index 0000000..af44a54
--- /dev/null
+++ b/data-devclub-1-Peru.csv
@@ -0,0 +1,2 @@
+EMPID,PASSPORT,FIRSTNAME,LASTNAME,GENDER,BIRTHDAY,NATIONALITY,HIRED,DEPT,POSITION,STATUS,REGION
+97,SUF73DKV4QE,Dante,Hart,0,21-12-1999,Peru,22-02-2016,Pilot,Pilot,1,Europe
diff --git a/data-devclub-1-Philippines.csv b/data-devclub-1-Philippines.csv
new file mode 100644
index 0000000..5353e4b
--- /dev/null
+++ b/data-devclub-1-Philippines.csv
@@ -0,0 +1,2 @@
+EMPID,PASSPORT,FIRSTNAME,LASTNAME,GENDER,BIRTHDAY,NATIONALITY,HIRED,DEPT,POSITION,STATUS,REGION
+66,WKV12UQC6QF,Zachery,Valentine,0,04-06-1971,Philippines,25-08-2011,Flight Attendance,Steward,1,Middle East
diff --git a/data-devclub-1-Singapore.csv b/data-devclub-1-Singapore.csv
new file mode 100644
index 0000000..9e0d5b6
--- /dev/null
+++ b/data-devclub-1-Singapore.csv
@@ -0,0 +1,2 @@
+EMPID,PASSPORT,FIRSTNAME,LASTNAME,GENDER,BIRTHDAY,NATIONALITY,HIRED,DEPT,POSITION,STATUS,REGION
+95,OUP31WOE2IE,Dara,Wilcox,1,29-06-1996,Singapore,18-05-2011,Flight Attendance,Airhostess,1,Canada
diff --git a/data-devclub-1-Ukraine.csv b/data-devclub-1-Ukraine.csv
new file mode 100644
index 0000000..bd5e2e1
--- /dev/null
+++ b/data-devclub-1-Ukraine.csv
@@ -0,0 +1,2 @@
+EMPID,PASSPORT,FIRSTNAME,LASTNAME,GENDER,BIRTHDAY,NATIONALITY,HIRED,DEPT,POSITION,STATUS,REGION
+5,AZE20CSG4MU,Lillian,Reese,0,03-12-1982,Ukraine,19-05-2002,Flight Planning,Steward,1,Canada
diff --git a/data-devclub-1.csv b/data-devclub-1.csv
new file mode 100644
index 0000000..6770f52
--- /dev/null
+++ b/data-devclub-1.csv
@@ -0,0 +1,13 @@
+EMPID,PASSPORT,FIRSTNAME,LASTNAME,GENDER,BIRTHDAY,NATIONALITY,HIRED,DEPT,POSITION,STATUS,REGION
+3,JUI65YBK7AF,Jada,Bender,0,28-05-1963,Pakistan,11-02-2001,Pilot,Pilot,1,Canada
+5,AZE20CSG4MU,Lillian,Reese,0,03-12-1982,Ukraine,19-05-2002,Flight Planning,Steward,1,Canada
+23,NFH65BYM0VB,Armand,Horn,0,24-05-1987,Netherlands,19-06-2007,Aircraft Maintenance,Airhostess,1,Ocenia
+29,CMK62UAD3VK,Rowan,Leonard,1,15-07-1974,Germany,27-03-2004,Aircraft Maintenance,Pilot,1,Ocenia
+33,EWD45RJW5YK,Carter,Velasquez,0,23-11-1967,Indonesia,27-02-2005,Flight Planning,Pilot,1,APAC
+34,BFS82MEY3CX,Selma,Bush,0,26-03-1972,Italy,10-10-2008,Flight Attendance,Airhostess,1,USA
+50,MRC33GHJ2KW,Calvin,Roach,1,16-04-1999,Mexico,18-03-2011,Flight Attendance,Steward,1,Europe
+66,WKV12UQC6QF,Zachery,Valentine,0,04-06-1971,Philippines,25-08-2011,Flight Attendance,Steward,1,Middle East
+80,EUC74ENE9ZK,Ryan,Rush,0,13-06-1998,Italy,31-07-2019,Aircraft Maintenance,Pilot,1,Middle East
+93,UXL43IOW6OV,Honorato,Maxwell,1,09-03-1982,France,04-02-2017,Aircraft Maintenance,Airhostess,1,Europe
+95,OUP31WOE2IE,Dara,Wilcox,1,29-06-1996,Singapore,18-05-2011,Flight Attendance,Airhostess,1,Canada
+97,SUF73DKV4QE,Dante,Hart,0,21-12-1999,Peru,22-02-2016,Pilot,Pilot,1,Europe
diff --git a/data-devclub-1.db b/data-devclub-1.db
new file mode 100644
index 0000000..568a7a6
Binary files /dev/null and b/data-devclub-1.db differ
diff --git a/data-devclub-1.json b/data-devclub-1.json
new file mode 100644
index 0000000..4e3f8b7
--- /dev/null
+++ b/data-devclub-1.json
@@ -0,0 +1,92 @@
+{
+  "0": [
+    {
+      "EMPID": 50,
+      "PASSPORT": "MRC33GHJ2KW",
+      "FIRSTNAME": "Calvin",
+      "LASTNAME": "Roach",
+      "GENDER": 1,
+      "BIRTHDAY": "16-04-1999",
+      "NATIONALITY": "Mexico",
+      "HIRED": "18-03-2011",
+      "DEPT": "Flight Attendance",
+      "POSITION": "Steward",
+      "STATUS": 1,
+      "REGION": "Europe"
+    },
+    {
+      "EMPID": 93,
+      "PASSPORT": "UXL43IOW6OV",
+      "FIRSTNAME": "Honorato",
+      "LASTNAME": "Maxwell",
+      "GENDER": 1,
+      "BIRTHDAY": "09-03-1982",
+      "NATIONALITY": "France",
+      "HIRED": "04-02-2017",
+      "DEPT": "Aircraft Maintenance",
+      "POSITION": "Airhostess",
+      "STATUS": 1,
+      "REGION": "Europe"
+    },
+    {
+      "EMPID": 97,
+      "PASSPORT": "SUF73DKV4QE",
+      "FIRSTNAME": "Dante",
+      "LASTNAME": "Hart",
+      "GENDER": 0,
+      "BIRTHDAY": "21-12-1999",
+      "NATIONALITY": "Peru",
+      "HIRED": "22-02-2016",
+      "DEPT": "Pilot",
+      "POSITION": "Pilot",
+      "STATUS": 1,
+      "REGION": "Europe"
+    }
+  ],
+  "1": [
+    {
+      "EMPID": 5,
+      "PASSPORT": "AZE20CSG4MU",
+      "FIRSTNAME": "Lillian",
+      "LASTNAME": "Reese",
+      "GENDER": 0,
+      "BIRTHDAY": "03-12-1982",
+      "NATIONALITY": "Ukraine",
+      "HIRED": "19-05-2002",
+      "DEPT": "Flight Planning",
+      "POSITION": "Steward",
+      "STATUS": 1,
+      "REGION": "Canada"
+    },
+    {
+      "EMPID": 33,
+      "PASSPORT": "EWD45RJW5YK",
+      "FIRSTNAME": "Carter",
+      "LASTNAME": "Velasquez",
+      "GENDER": 0,
+      "BIRTHDAY": "23-11-1967",
+      "NATIONALITY": "Indonesia",
+      "HIRED": "27-02-2005",
+      "DEPT": "Flight Planning",
+      "POSITION": "Pilot",
+      "STATUS": 1,
+      "REGION": "APAC"
+    }
+  ],
+  "2": [
+    {
+      "EMPID": 5,
+      "PASSPORT": "AZE20CSG4MU",
+      "FIRSTNAME": "Lillian",
+      "LASTNAME": "Reese",
+      "GENDER": 0,
+      "BIRTHDAY": "03-12-1982",
+      "NATIONALITY": "Ukraine",
+      "HIRED": "19-05-2002",
+      "DEPT": "Flight Planning",
+      "POSITION": "Steward",
+      "STATUS": 1,
+      "REGION": "Canada"
+    }
+  ]
+}
\ No newline at end of file
diff --git a/data-visualization.ipynb b/data-visualization.ipynb
new file mode 100644
index 0000000..fde2455
--- /dev/null
+++ b/data-visualization.ipynb
@@ -0,0 +1,1457 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/rsxss/hackathon-season2/blob/main/data-visualization.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Dev Mountain Tech Festival Season 2**\n"
+      ],
+      "metadata": {
+        "id": "fGI0SGI8iKLU"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Befor migration"
+      ],
+      "metadata": {
+        "id": "ZXzAqLkVjhhF"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 59,
+      "metadata": {
+        "id": "j1crNZld6bnW"
+      },
+      "outputs": [],
+      "source": [
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "import collections\n",
+        "import plotly.express as px\n",
+        "from datetime import datetime"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Chart"
+      ],
+      "metadata": {
+        "id": "DUdfWDyaneH_"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 60,
+      "metadata": {
+        "id": "ypPBntDw7KvQ"
+      },
+      "outputs": [],
+      "source": [
+        "data = pd.read_csv(\"/content/data-devclub-1.csv\")\n",
+        "df = pd.DataFrame(data)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 61,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 468
+        },
+        "id": "dV1zXU1G7Xo8",
+        "outputId": "5ce429b3-a1fa-49a3-d19b-8ddb6b3b31e2"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "    EMPID     PASSPORT    FIRSTNAME   LASTNAME  GENDER    BIRTHDAY  \\\n",
+              "0       1  OQI75XIE4UX  Christopher    Kennedy       1  05-02-1978   \n",
+              "1       2  CDC87ETW8EQ       Burton   Gallegos       0  22-09-1960   \n",
+              "2       3  JUI65YBK7AF         Jada     Bender       0  28-05-1963   \n",
+              "3       4  CYB72WYU6UR       Winter      Blair       0  14-02-1993   \n",
+              "4       5  AZE20CSG4MU      Lillian      Reese       0  03-12-1982   \n",
+              "..    ...          ...          ...        ...     ...         ...   \n",
+              "95     96  LJB26BQM6PS        Laith  Whitfield       0  14-01-1987   \n",
+              "96     97  SUF73DKV4QE        Dante       Hart       0  21-12-1999   \n",
+              "97     98  DOB31QXV4GZ        Moana      Walsh       1  03-05-1995   \n",
+              "98     99  OUB92IKA7AU        Jonah    Solomon       1  25-05-1985   \n",
+              "99    100  WIN16ELU8GN      Orlando      Mayer       0  11-01-1961   \n",
+              "\n",
+              "           NATIONALITY       HIRED                  DEPT    POSITION  STATUS  \\\n",
+              "0          South Korea  20-02-2005       Flight Planning     Steward       2   \n",
+              "1              Germany  29-10-2021  Aircraft Maintenance       Pilot       1   \n",
+              "2             Pakistan  11-02-2001                 Pilot       Pilot       1   \n",
+              "3              Austria  24-03-2022                 Pilot     Steward       4   \n",
+              "4              Ukraine  19-05-2002       Flight Planning     Steward       1   \n",
+              "..                 ...         ...                   ...         ...     ...   \n",
+              "95  Russian Federation  09-02-2001                 Pilot  Airhostess       0   \n",
+              "96                Peru  22-02-2016                 Pilot       Pilot       1   \n",
+              "97              Brazil  21-04-2002                 Pilot  Airhostess       3   \n",
+              "98            Pakistan  30-04-2023     Flight Attendance       Pilot       3   \n",
+              "99             Austria  07-04-2003                 Pilot       Pilot       0   \n",
+              "\n",
+              "    REGION  \n",
+              "0   Europe  \n",
+              "1     APAC  \n",
+              "2   Canada  \n",
+              "3   Ocenia  \n",
+              "4   Canada  \n",
+              "..     ...  \n",
+              "95    APAC  \n",
+              "96  Europe  \n",
+              "97  Ocenia  \n",
+              "98  Europe  \n",
+              "99    APAC  \n",
+              "\n",
+              "[100 rows x 12 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-f492f102-2821-4565-9efe-f47b863c41f5\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <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>EMPID</th>\n",
+              "      <th>PASSPORT</th>\n",
+              "      <th>FIRSTNAME</th>\n",
+              "      <th>LASTNAME</th>\n",
+              "      <th>GENDER</th>\n",
+              "      <th>BIRTHDAY</th>\n",
+              "      <th>NATIONALITY</th>\n",
+              "      <th>HIRED</th>\n",
+              "      <th>DEPT</th>\n",
+              "      <th>POSITION</th>\n",
+              "      <th>STATUS</th>\n",
+              "      <th>REGION</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1</td>\n",
+              "      <td>OQI75XIE4UX</td>\n",
+              "      <td>Christopher</td>\n",
+              "      <td>Kennedy</td>\n",
+              "      <td>1</td>\n",
+              "      <td>05-02-1978</td>\n",
+              "      <td>South Korea</td>\n",
+              "      <td>20-02-2005</td>\n",
+              "      <td>Flight Planning</td>\n",
+              "      <td>Steward</td>\n",
+              "      <td>2</td>\n",
+              "      <td>Europe</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2</td>\n",
+              "      <td>CDC87ETW8EQ</td>\n",
+              "      <td>Burton</td>\n",
+              "      <td>Gallegos</td>\n",
+              "      <td>0</td>\n",
+              "      <td>22-09-1960</td>\n",
+              "      <td>Germany</td>\n",
+              "      <td>29-10-2021</td>\n",
+              "      <td>Aircraft Maintenance</td>\n",
+              "      <td>Pilot</td>\n",
+              "      <td>1</td>\n",
+              "      <td>APAC</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>3</td>\n",
+              "      <td>JUI65YBK7AF</td>\n",
+              "      <td>Jada</td>\n",
+              "      <td>Bender</td>\n",
+              "      <td>0</td>\n",
+              "      <td>28-05-1963</td>\n",
+              "      <td>Pakistan</td>\n",
+              "      <td>11-02-2001</td>\n",
+              "      <td>Pilot</td>\n",
+              "      <td>Pilot</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Canada</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>4</td>\n",
+              "      <td>CYB72WYU6UR</td>\n",
+              "      <td>Winter</td>\n",
+              "      <td>Blair</td>\n",
+              "      <td>0</td>\n",
+              "      <td>14-02-1993</td>\n",
+              "      <td>Austria</td>\n",
+              "      <td>24-03-2022</td>\n",
+              "      <td>Pilot</td>\n",
+              "      <td>Steward</td>\n",
+              "      <td>4</td>\n",
+              "      <td>Ocenia</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>5</td>\n",
+              "      <td>AZE20CSG4MU</td>\n",
+              "      <td>Lillian</td>\n",
+              "      <td>Reese</td>\n",
+              "      <td>0</td>\n",
+              "      <td>03-12-1982</td>\n",
+              "      <td>Ukraine</td>\n",
+              "      <td>19-05-2002</td>\n",
+              "      <td>Flight Planning</td>\n",
+              "      <td>Steward</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Canada</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>95</th>\n",
+              "      <td>96</td>\n",
+              "      <td>LJB26BQM6PS</td>\n",
+              "      <td>Laith</td>\n",
+              "      <td>Whitfield</td>\n",
+              "      <td>0</td>\n",
+              "      <td>14-01-1987</td>\n",
+              "      <td>Russian Federation</td>\n",
+              "      <td>09-02-2001</td>\n",
+              "      <td>Pilot</td>\n",
+              "      <td>Airhostess</td>\n",
+              "      <td>0</td>\n",
+              "      <td>APAC</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>96</th>\n",
+              "      <td>97</td>\n",
+              "      <td>SUF73DKV4QE</td>\n",
+              "      <td>Dante</td>\n",
+              "      <td>Hart</td>\n",
+              "      <td>0</td>\n",
+              "      <td>21-12-1999</td>\n",
+              "      <td>Peru</td>\n",
+              "      <td>22-02-2016</td>\n",
+              "      <td>Pilot</td>\n",
+              "      <td>Pilot</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Europe</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>97</th>\n",
+              "      <td>98</td>\n",
+              "      <td>DOB31QXV4GZ</td>\n",
+              "      <td>Moana</td>\n",
+              "      <td>Walsh</td>\n",
+              "      <td>1</td>\n",
+              "      <td>03-05-1995</td>\n",
+              "      <td>Brazil</td>\n",
+              "      <td>21-04-2002</td>\n",
+              "      <td>Pilot</td>\n",
+              "      <td>Airhostess</td>\n",
+              "      <td>3</td>\n",
+              "      <td>Ocenia</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>98</th>\n",
+              "      <td>99</td>\n",
+              "      <td>OUB92IKA7AU</td>\n",
+              "      <td>Jonah</td>\n",
+              "      <td>Solomon</td>\n",
+              "      <td>1</td>\n",
+              "      <td>25-05-1985</td>\n",
+              "      <td>Pakistan</td>\n",
+              "      <td>30-04-2023</td>\n",
+              "      <td>Flight Attendance</td>\n",
+              "      <td>Pilot</td>\n",
+              "      <td>3</td>\n",
+              "      <td>Europe</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>99</th>\n",
+              "      <td>100</td>\n",
+              "      <td>WIN16ELU8GN</td>\n",
+              "      <td>Orlando</td>\n",
+              "      <td>Mayer</td>\n",
+              "      <td>0</td>\n",
+              "      <td>11-01-1961</td>\n",
+              "      <td>Austria</td>\n",
+              "      <td>07-04-2003</td>\n",
+              "      <td>Pilot</td>\n",
+              "      <td>Pilot</td>\n",
+              "      <td>0</td>\n",
+              "      <td>APAC</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>100 rows × 12 columns</p>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f492f102-2821-4565-9efe-f47b863c41f5')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-f492f102-2821-4565-9efe-f47b863c41f5 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-f492f102-2821-4565-9efe-f47b863c41f5');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "display(df)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 62,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 261
+        },
+        "id": "WyIMOVPqJN8r",
+        "outputId": "6b8561c8-13d2-4247-bc1a-9dc5d7237f89"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 252x252 with 1 Axes>"
+            ],
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPQAAAD0CAYAAACsLwv+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAPWElEQVR4nO3dfZBddX3H8ffHPAAaaoRsY57IomAwYo2wIBArDGihECB/ZNSINLG0GVsQaK0Cajt0SmeIOtii7TgZsWQAAwjWQPApDQkPIUU2EiABkRAJSUjMRokSRSDh2z/OL9PLziZ79+7dvezXz2smc8/5nafvOXc/93fOuWc3igjMLIc3tLoAM2seB9osEQfaLBEH2iwRB9osEQfaLBEH2pD0OUnf2M/08yT9aDBrssbI30MPTZKeAcYCe4DfAt8HLoqIXf1cbzvwc2BEROzuX5U22NxDD21nR8Qo4BigA/hCi+uxFnOgE4iILVQ99NGSzpG0TtJOSSskvXPvfJIuk7RF0guSnpR0Wmm/UtKNZbZ7y+tOSbsknShprqT7a9ZzkqSHJP26vJ5UM22FpH+RtLJs50eSxgz8UTBwoFOQNAk4E3gBWARcCrQB3wPulDRS0hTgIuC4iDgYOB14pofVfaC8jo6IURGxqtu2DgHuAq4FDgWuAe6SdGjNbB8DPgH8MTAS+Idm7Kf1zoEe2r4raSdwP3AP8DhwV0QsjYhXgC8DBwEnUV1rHwBMlTQiIp6JiKcb2OZZwFMRcUNE7I6IRcBPgbNr5vmviPhZRLwI3ApMa3gPrU8c6KFtZkSMjojJEfG3wHhg496JEfEqsAmYEBHrqXruK4Htkm6WNL6Bbb5mG8VGYELN+Laa4d8BoxrYjjXAgc7lOWDy3hFJAiYBWwAi4lsR8f4yTwDze1hHb197vGYbxWF7t2Gt5UDncitwlqTTJI0APg28BDwgaYqkUyUdAPweeBF4tYd1dJX2t+1jG98D3iHpY5KGS/oIMBVY0uydsb5zoBOJiCeBjwNfBXZQXdeeHREvU10/X13at1HdsLqih3X8DvhXYGW5U35Ct+m/BGZQfVj8EvgsMCMidgzUfln9/GCJWSLuoc0ScaDNEnGgzRJxoM0SGT6YGxszZky0t7cP5ibNUlq9evWOiGjr3j6ogW5vb6ezs3MwN2mWkqTuT+sBPuU2S8WBNkvEgTZLxIE2S8SBNkvEgTZLxIE2S8SBNktkUB8sqce4iYexbcumVpfRMm+dMImtm59tdRk2RL3uAr1tyyYmX/aH+8cvNs6f0eoSbAjzKbdZIg60WSIOtFkiDrRZIg60WSIOtFkiDrRZIg60WSIOtFkiDrRZIg60WSIOtFkiDrRZIg60WSIOtFkiDrRZIg60WSIOtFkiDrRZInUHWtIwSQ9LWlLGD5f0oKT1km6RNHLgyjSzevSlh74EeKJmfD7wlYg4AngeuKCZhZlZ39UVaEkTgbOAb5RxAacCt5VZFgIzB6JAM6tfvT30vwGfBV4t44cCOyNidxnfDEzoaUFJ8yR1Surs6urqV7Fmtn+9BlrSDGB7RKxuZAMRsSAiOiKio62trZFVmFmd6vlD+9OBcySdCRwI/BHw78BoScNLLz0R2DJwZZpZPXrtoSPiioiYGBHtwEeBuyPiPGA5MKvMNgdYPGBVmlld+vM99GXA30taT3VNfV1zSjKzRvXp/7aKiBXAijK8ATi++SWZWaP8pJhZIg60WSIOtFkiDrRZIg60WSIOtFkiDrRZIg60WSIOtFkiDrRZIg60WSIOtFkiDrRZIg60WSIOtFkiDrRZIg60WSIOtFkiDrRZIg60WSIOtFkiDrRZIg60WSIOtFkiDrRZIg60WSIOtFkiDrRZIg60WSIOtFkiDrRZIg60WSK9BlrSgZJ+LOkRSesk/XNpP1zSg5LWS7pF0siBL9fM9qeeHvol4NSIeA8wDThD0gnAfOArEXEE8DxwwcCVaWb16DXQUdlVRkeUfwGcCtxW2hcCMwekQjOrW13X0JKGSVoDbAeWAk8DOyNid5llMzBhH8vOk9QpqbOrq6sZNZvZPtQV6IjYExHTgInA8cBR9W4gIhZEREdEdLS1tTVYppnVo093uSNiJ7AcOBEYLWl4mTQR2NLk2sysj+q5y90maXQZPgj4EPAEVbBnldnmAIsHqkgzq8/w3mdhHLBQ0jCqD4BbI2KJpMeBmyVdBTwMXDeAddofiHETD2Pblk2tLqNl3jphEls3P9vw8r0GOiIeBd7bQ/sGqutps6bZtmUTky9b0uoyWmbj/Bn9Wt5Pipkl4kCbJeJAmyXiQJsl4kCbJeJAmyXiQJsl4kCbJeJAmyXiQJsl4kCbJeJAmyXiQJsl4kCbJeJAmyVSzx84sME0bASSWl2FDVEO9OvNnlf8C/7WMJ9ymyXiQJsl4kCbJeJAmyXiQJsl4kCbJeJAmyXiQJsl4kCbJeJAmyXiQJsl4kCbJeJAmyXiQJsl4kCbJdJroCVNkrRc0uOS1km6pLQfImmppKfK61sGvlwz2596eujdwKcjYipwAnChpKnA5cCyiDgSWFbGzayFeg10RGyNiJ+U4ReAJ4AJwLnAwjLbQmDmQBVpZvXp0zW0pHbgvcCDwNiI2FombQPG7mOZeZI6JXV2dXX1o1Qz603dgZY0CrgduDQiflM7LSICiJ6Wi4gFEdERER1tbW39KtbM9q+uQEsaQRXmmyLiO6X5F5LGlenjgO0DU6KZ1aueu9wCrgOeiIhraibdAcwpw3OAxc0vz8z6op4/4zsdOB94TNKa0vY54GrgVkkXABuBDw9MiWZWr14DHRH3A/v6y++nNbccM+sPPylmlogDbZaIA22WiANtlogDbZaIA22WiANtlogDbZaIA22WiANtlogDbZaIA22WiANtlogDbZaIA22WiANtlogDbZaIA22WiANtlogDbZaIA22WiANtlogDbZaIA22WiANtlogDbZaIA22WiANtlogDbZaIA22WiANtlogDbZZIr4GW9E1J2yWtrWk7RNJSSU+V17cMbJlmVo96eujrgTO6tV0OLIuII4FlZdzMWqzXQEfEvcCvujWfCywswwuBmU2uy8wa0Og19NiI2FqGtwFj9zWjpHmSOiV1dnV1Nbg5M6tHv2+KRUQAsZ/pCyKiIyI62tra+rs5M9uPRgP9C0njAMrr9uaVZGaNajTQdwBzyvAcYHFzyjGz/qjna6tFwCpgiqTNki4ArgY+JOkp4INl3MxabHhvM0TE7H1MOq3JtZhZP/lJMbNEHGizRBxos0QcaLNEHGizRBxos0QcaLNEHGizRBxos0QcaLNEHGizRBxos0QcaLNEHGizRBxos0QcaLNEHGizRBxos0QcaLNEHGizRBxos0QcaLNEHGizRBxos0QcaLNEHGizRBxos0QcaLNEHGizRBxos0QcaLNEHGizRPoVaElnSHpS0npJlzerKDNrTMOBljQM+A/gz4GpwGxJU5tVmJn1XX966OOB9RGxISJeBm4Gzm1OWWbWCEVEYwtKs4AzIuKvyvj5wPsi4qJu880D5pXRKcCTjZc7KMYAO1pdRAt5/4fG/k+OiLbujcMHeqsRsQBYMNDbaRZJnRHR0eo6WsX7P7T3vz+n3FuASTXjE0ubmbVIfwL9EHCkpMMljQQ+CtzRnLLMrBENn3JHxG5JFwE/BIYB34yIdU2rrHWGzOXBAPH+D2EN3xQzs9cfPylmlogDbZZImkBL+rykdZIelbRG0vskXSrpjS2saVertt0TSXvKsVkr6duS3iipQ9K1ZfpcSV/rZR0zW/lEYNl+SDqqjI+XdNs+5u11f+rc5lxJ4/u7nsGQItCSTgRmAMdExJ8AHwQ2AZcCgxJoSQP+nX4TvBgR0yLiaOBl4JMR0RkRF/dhHTOpHvVtldnA/eWViHguImZ1n6nJ78dcwIEeROOAHRHxEkBE7ABmUb0JyyUtB5D0Z5JWSfpJ6aFGSTpO0nfK9HMlvShppKQDJW0o7X8t6SFJj0i6fW+vL+l6SV+X9CDwxfIV3ipJj0m6qgXHoS/uA46QdIqkJd0nSmqXdHc541km6TBJJwHnAF8qPf3bB7NgSaOA9wMXUH1NurfOtWV4rqQ7JN0NLCuLjZf0A0lPSfpizbpml/dpraT5pW1YeU/Xlml/V56I7ABuKvt8kKRjJd0jabWkH0oaV5a/WNLj5ZjdXNpOLsutkfSwpIMH9CBFxJD/B4wC1gA/A/4TOLm0PwOMKcNjgHuBN5Xxy4B/ovrqbkNp+zLV9+vTgZOBRaX90JptXQV8qgxfDywBhpXxO4C/KMMXArtafWy6Hadd5XU4sBj4G+AUYElpnwt8rQzfCcwpw38JfLdmn2e1qP7zgOvK8APAsUA7sLam/s3AITXjG4A3AwcCG6kehhoPPAu0lWNxN9WZx7HA0prtjS6vK4COMjyibLutjH+E6itbgOeAA7oteycwvebndPhAHqMUPXRE7KJ6M+YBXcAtkuZ2m+0EqlPFlZLWAHOonofdDTwt6Z1Uv3ByDfAB4E+pejGAoyXdJ+kxqh+qd9Ws99sRsacMTwcWleEbmriLzXJQ2fdOqh/o6/Yz74nAt8rwDVQ9Y6vNpvolIMrr7B7mWRoRv6oZXxYRv46I3wOPA5OB44AVEdFV3v+bqN7zDcDbJH1V0hnAb3pY/xTgaGBpOZZfoHpKEuBRqp7848Du0rYSuEbSxVQh3919hc00FK776lJCtQJYUYI3p9ssonqze/ohuJfq10BfAf6HqhcaBnymTL8emBkRj5QPilNqlv1t91Ia3YdB8GJETKttkNSqWvpE0iHAqcC7JQXV+xNUv8Jbq/v78VLN8B728zMfEc9Leg9wOvBJ4MNUZyevKQVYFxEn9rCKs6g+GM4GPi/p3RFxtaS7gDOpOpPTI+Kn+9nVfknRQ0uaIunImqZpVKdXLwB7r1n+F5gu6YiyzJskvaNMu4/qBtqqiOgCDqX6JF5bph8MbJU0gqqH3peVlGu7XuYbCh7gtfuy92yl9pgOplnADRExOSLaI2IS8HNe+/sE9foxcLKkMap+r382cI+kMcAbIuJ2qp73mDJ/7T4/CbSVG7FIGiHpXZLeAEyKiOVUl3NvBkZJentEPBYR86ku545qZOfrlSLQVNcmC/fekKA6tb6S6jG+H0haXoI6F1hU5lnF/x/cB4GxVD01VKdOj0W58AH+scyzEtjfp+slwIXlDGFCk/atVT4FfKIcq/Op9g2qU93PlBs8g3lTbDbw393abgeu6OuKImIrcDmwHHgEWB0Ri6nesxXlVPrGmnVfD3y9tA+j+nCZL+kRqns3J5X2G8t7/zBwbUTsBC4tN9kepToD/H5f6+0LP/pplkiWHtrMcKDNUnGgzRJxoM0ScaDNEnGgzRJxoM0S+T+LFLi5B9oIbwAAAABJRU5ErkJggg==\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ],
+      "source": [
+        "plt.rcParams[\"figure.figsize\"] = [3.50, 3.50]\n",
+        "plt.rcParams[\"figure.autolayout\"] = True\n",
+        "\n",
+        "n, bins, patches = plt.hist(df['POSITION'], bins=3, edgecolor='black')\n",
+        "ticks = [(patch._x0 + patch._x1)/2 for patch in patches]\n",
+        "plt.xticks(ticks)\n",
+        "plt.title(\"Position\")\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 63,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 263
+        },
+        "id": "h8bn13soHBsp",
+        "outputId": "1e7ada69-fa9c-4f2e-dd3e-61929c3a4a19"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "OrderedDict([(0, 52), (1, 48)])\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 252x252 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "count = collections.Counter(df['GENDER'])\n",
+        "count = collections.OrderedDict(sorted(count.items()))\n",
+        "Value = count.values()\n",
+        "print(count)\n",
+        "Key = [\"Female\",\"Male\"]\n",
+        "\n",
+        "fig, ax = plt.subplots()\n",
+        "ax.pie(Value,labels = Key, autopct='%.2f%%', startangle=90)\n",
+        "plt.title(\"Gender\")\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 64,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 198
+        },
+        "id": "4gjlxN3_27v6",
+        "outputId": "0a0a65fb-913d-49cb-ad14-c2a4e4d1ef0a"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Counter({'Aircraft Maintenance': 34, 'Flight Planning': 24, 'Flight Attendance': 22, 'Pilot': 20})\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 252x252 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "count_dept = collections.Counter(df['DEPT'])\n",
+        "Value_dept = count_dept.values()\n",
+        "Key_dept = count_dept.keys();\n",
+        "print(count_dept)\n",
+        "\n",
+        "fig1, ax1 = plt.subplots()\n",
+        "ax1.pie(Value_dept,labels = Key_dept, autopct='%.2f%%', startangle=90)\n",
+        "plt.title(\"Department\")\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 65,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 191
+        },
+        "id": "8m3YYxQJ41L4",
+        "outputId": "f5a710e2-f498-43ab-ddc2-597f0c5394f3"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "dict_values([30, 13, 19, 38])\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 252x252 with 1 Axes>"
+            ],
+            "image/png": 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efQRRzMzJ5aZh5nnO3b2bjU1NnJ2ZyY/yzFHUIwf2U5CcwnlZrW59LyEBxMFyGOpoLkJDYs85XEiEsG/Bg9WEGk0P2VBzE41bV5E0bByp4z0c9H0AQN26RaRPPq1dg2kFp1JvLEYFmmmu2kOgcjfJo6aQPGoKgcrdNFftQQWbqTcWk1Zw6qF6Khig5pN/k33qN1CBpsNmqRAEu3o61juSRfj7uPG87JzIS86JfFBfz5qGBu7eu4ffjhrNy86JXJyVzaMH9reru6mpiTdqa3jVOZHHxo7ll3v3ElSKoFLcs3cvj44dy6sTj+L12ho2NTWxvrGRVJvwysSJrGtsoDYYpDwQYG1DQ1thADhVr7cYGCTyPzHieD1YV8H+0vvNH6YKke46i/SCL5I0fDz7F/yGqiVPkjzyKDKPmwHAwY3L8e/ZSO5Z3yY5bwIZrrPY/fgcsNkZev6cQ/MUQ8+fzb7n7wIVItNzPsl5hy86tStLyTz2XGxJqSTlTUQFmtj9+K2kTToZW2ps3C9EhAzLrydgjR4ABKEuFDLtCoXIc7R/XPtOXR0XZmWTbLMxNjmZ8UnJeBsbARiflMw4a6RxYVY279TVcW5mJo0hRcjqxybCg+X7uG14xOj+6Zj3u70J8KPpRySkODiLSu10sK9l8oiJjL7ugXbpSbn5jLr2/nbp6ZNPJX3y4VFAzunfJOf0b7YrlzbpFMZMOiWiPdmnHPa6FRHyLv1Zl+cQDYJKMXPbVrb7/XxryBCOT0vj7vx8Zu/cQarNRobNxrPj24+c9wWaOS71cOS8kUkO9gZM94T8pMNfiXyHg7WNDUxKSWGow843tm3l0uxstvv9hICjUzu8XToDLQ4JT0KKA6arbMz2t0gU7CK87JxITTDI93ftYmNTE09UVvDI2HEcn5bG4xUH+E35Pn6ZP6rPfd0+4rCD4Xd37qA4P59HDuxnfVMTp6dncEVuq3/HGcCf+typJq4k6pzDoHoE0BXZdjtfTE9ncX0d65uaOD7NHBVcmJXNqoaGduVHOJLYEzg8F7K3OcBIRxIjHUnsaT6cvicQYESb25JFtbUcnZrKwZBih7+Z+0eP4e3aWhqsWxmLM6J6gpq4kKji0Jl33aCgIhCgJhgEoDEU4qOD9UxKTqE2FGKr3w/A0vp6JkV4UnFOZiZv1NbgD4XY6fezrdmPJzWVY1NT2dbsZ6ffj18p3qit4ZzMw3MmzUrxz8pKbhg6jMZQiJalTEEUza0jio0yXO6jYnXumiNDot5WDPrdl8oDAW7fU0ZIQQjFV7KymZ6Zyd0j8/nBrl3YBLJtNu6xbineqavl08ZGvjc8j8kpKVyQlcVXt27BjvCLESOxW7/0O0aM5KadOwgBX8vJYXJKyqE+n6ms5LKcbNJsNr6QkkJjKMRlW7YwLTODbHu77SxOBjYfkQ9DExMSLoaks6h0HGaw2oFAoseQ7IwfuH1G+5lhTcKQiLcV7ngboOkWw+NtgKZvJKI4DNqFCwmGFocEJxHFYVLXRTT9AC0OCU4iikNn4cg0/QctDglOIoqD3pMiMdDikOAkojikx9sATbfQ4pDgaHHoX5RjhhnTaOKOFod+hNtn+DHjIayMty1RoKLrIpr+jBaHI8xIKvbd7nhq8YvpP1iS4yq6qW2+22fUYK4diRj3MIHQ4pDgJKI4xCZ6SgwZK+W75znmv7865aY1y1JuG36Lo3Ta3/MIhoSfeEo8d7ct7/YZ5cD5mHseJCpaHBKcRFxb0T60UT/kKNm97Rb7q1susv83L5OGo0UY3ZJXY5OaxWmpJ1qHd3pKPFXeQm+r0Gpun7HTcLnPw5yDSMTHt1ocEhwtDlHELds+n+14dcf5thWj0qXpC3QQo/A3Q4esRGR6WNIfPCWeGm+h92+t2vMZmwyXewbmvgNDYmZ4bNDikOBocegjJ8jGDbMdr+6eblszLkWaJ9GFB2eT0PhqZkakLc0etQTi+fBEt8/wGi73hZjbuiXSln9aHBIcLQ694DTbp5/eYn+t/EzbuolJEpzC4b0Qu+Sh3Nz/KpFpEbJswJOeEk+tt9D7RniG22csN1zuy4DXgZQIdfsj5V0X0fRnElEcjviXTgiFptvWrLvZ/lrlKbb1kx0S6moz04gEIfhkTtbETookAS96SjwXeAu9rfwd3D7jHcPlvhJzt6hE+L+tjbcBmr6RCF+ytnzedZG+YyMUnGH7eM1Njtfrpsoml11UnwPMPJGTtSwg0lUItTTgNU+J58veQu+K8Ay3z1hguNyzgH8SISx/PyIArIm3EZq+kYjBXkYDu2LRdhIB/yW2pWtvcLzRcLRsO8Ymamg02z95wtiNTTbb5G4W3w9M8xZ6jbYZhss9B3NPxv6K1+0zBn20rkQn4cQBwFlUWkaUHu+l4G/8un3Jmln2twJTZOexIuREo922LMjM+PiOvGGRY9t3zC7gTG+hd2vbDMPlvh24Lxq2xYDH3T7jxngboekbiXhbAbACcxPdXpFBQ92V9ve819oX4pQ9x4lwate1+sZvhub2ZiJxDPAfT4nnLG+htyw8w+0zfmW43DnAz6NiYHTp1Zbvmv5FoorDSnooDtnUVX/bvmjdtxyLHGPYf7xI5F2YY8GStFRvjd3e22H2JOBtT4nnbG+ht9XjQbfPKLIEYnafjYwu78XbAE3fSVRxWNF1ERhK9YFCx9ufXWV/N20EVceLxGc/hXnDhzb2sYljgTc8JZ5zvYXeujZ5twLZwLf62Ee02Oz2GTvibYSm7ySqOCzGnBFvZ/9IKvbd4HjD9w374qyh1B4nEt89LtYlJ2/c63D0dK4hEl8EXvWUeC70FnoPiY3bZ4QMl7sQyAK+GoV++srL8TZAEx0SckISwFlU+i4wHWCc7Nt1k71006X2j4bkUH+sSP9ZUHbZmFEfbk5OiuaI5VXg695Cb6sFaIbLnYrpJHVOFPvqDW63z0j0FaUaElgcLrr9z3Nm2d/65kX25XmZ0nh0vO2JxDaHY8clY0eNRqTdji995GngO95Cb6s96AyXOxNYhDnKiAcfuX2G3gpvgNBvrrA95fWU/ym90vH+tI6E4fp/NzDid7Uc+5fDt+h3vtPIcQ/XMfWROmb8s57dtaFIVSlZ7Wfyg3VMfrCOktX+Q+krdgfxPFxHwQO1fP+NRlqE9ecLzXavffnwvpRPrvVzyVL8MRAGMOcX/tw20e0z6oALgXUx6LM7PB6nfjUxIGHFgeLq7cDSjrJnTU3izW+3jgsz94wU1s7JZPXsTC6Z4uDu95va1atoUMx7v4nlN2bw3xszmPd+E5UNpgjMKW3gr19NZeP3MtlYEeTNTQGqGxUr9wRZOyeTZDt49wZpaFb8ZWVzs/pa/pjonnQrZntKPL9um+j2GRXADI6QJ2kYtcBzR7hPTQxJXHEwKekoY9oEB0PTWnsYZ6ccPq73R/Y/fmtTgPOPMusOSRPOP8rBm5sClNWGqGmC08Y6EBGuPS6ZV3wBbALNQVBKcbBZkWSH33/kJ3Xa0PWSZEuN2plG5ueeEs/tbRPdPqMMOI8YeZJ2wHNun1F/BPvTxJhEF4d/Agd6UuGORY2Mu7+Wp7zN3H1Oe7+kXbUhxuUc/ljGZtvYVRtiV61ibLaEpQu7ahVZKcJFkx2c8Gg9ozJt5KQIi3cGA+VfHjG+96fVI+7zlHjmtE10+4ytmNGkjtQqVn1LMcBIbHEorm4AHu1JlXvPTWXHj7K4xpPEQ//1d12hG/zsjBRWz87kDxekcue7TeRfNHx1xeLK7O1/3s6+Bfui0kcX/NlT4rmmbaLbZxiY8ShrYtz/YrfPWBbjPjRHmMQWB5OHgB7/yq85LokXjfbhKMdk2dhRfXiicmdNiDFZNsZkCTtrVFi6YkxW6xuTVWVB/IrgSnfuhJqPaxh/63j8+/w07Wk/txFlBJjvKfFc2jbD7TNWYEa0bmhXKzqEgB/FqG1NHEl8cSiuLqObE2EbDwQPvf+3L4BrePvTv6DAwdubA1Q2KCobFG9vDnBBgYNRWTayU2DZzgBKKZ5Y6+cyV2sfrDvfbWLsV/M+USHJUyFLSARC/shPRaKMA3jeU+L5ctsMt89YAnwDaI5Bv0+4fcZACKWvaUPC+jm0ojhnCubju6SWpKtfPMh7W4PsP6gYmSHMm57C65sCrN8fwiYwIdfGIxenMibbxie7gzzyiZ+/XZoGwN9X+blviXm1v+OsFK47IRmAT3YHmfVKAw0BxYUFDh68MBURc/Twiq+ZlXuC6tVZk3cERMaXPVtG3bo6UsemMm72uCP5adQB53kLvcvbZljBYp4heheFOmCKNQGqGWAMDHEAKM75E/CDeJrwj5ysj/44dMjp8bTBohI421vo9bbNMFzuG4C/ta/SK37h9hn3RqktTT8j8W8rDjOPHj65iDZ/yc3pL/tDDsFcyVnQNsPtMx4HfhKFPrYBf4hCO5p+ysAZOQAU59wGPBiPrs94yb9/+dbm4Y5sB5PvNYM9NWxvYHfJbkJNIZKHJTN29ljsae0dJmvX1lL2dBmEYMi0IeRdkgeAv9zPjod3EKwLkupMZezNY7E5bBxYeICK9ypIGpbE+O+Px+awUb+hnppPahj1rVHhTW/DDBazs22fhst9N3BnH075CrfP+Fcf6mv6OQNp5ADwCHEKbFp5bl618yfOVmm7/7Gb/CvymXzPZLJPymb/6+1dDlRIsfufu3H+2EnBfQVUL6+mcZe56HLP83sYNmMYU347BXu6ncrFlQBULa2i4JcFpBekU+etQylF+YJy8i7Na9v8BGChp8TTLsPtM+4CHujl6T6qhWHgM7DEobg6AHwb6Gv8hB6xJC3Va5+aM8me0XpU0LSnifQvmC7cGcdkULOivbtBw+YGUkamkDwiGZvDRs6pOdSuqkUpRb1RT84pZtS6IWcOoXZl7aF6KqgI+UOIXaj6qIpMTyaOzIgr8F3AW54ST6Twdz8E5vfwdJcB3+9hHU0CMrDEAaC42ssRDp3WUTCXlDEph37QNR/X0FzR/klic2UzSUMPPWTBMcRBc2Uzwbog9nQ7YpdW6QBDzx3K5l9upvlAM+mT06n6oIph5w7rzMQTMCNap4Unun2GAm4EXurmqe4DZlq7gWsGOANPHACKqx8A3uiyXBToLJjL2OvHcuCdA2z6302EGkOHfuh9ZcgZQyi4u4Bxt4zjwNsHGHbeMGq9tWx/aDtlT5dxyMeiNWcCL3lKPMnhiW6fEQSuBt7uotsAcKXbZxzJ9RqaODIwxcHkOmBvrDu5I29Yh/7RKaNTmDh3IgXzCsg5LYfkEcntyiQNSWo1oghUBkgakoQ9007wYBAVVK3Sw2mubKZhc4M5n/HmfsZ9dxz2dDv1n3W4/ukrwFOeEk+r+x9rJPA14KNOTvVnbp+hA8cOIgauOBRX78X8wsds/mGbw7Fjc5LjtI7yAzWme7YKmROGQ89pvw1G2sQ0mvY24S/3EwqEqF5eTdYJWYgIGa4Mqj+uBqDyg0qyTshqVXfvS3sZ8bURZh/+bntkzgQe85R4Wg1j3D7jIHARsDpCnSfdPuP+zhrVDDy6FAcRCYrIahFZJyKvikhuTzsRkZNFpLcz4z3pp3Xw1eLqpcAsICbPa+/IG7alJZjLjod3sPmezTTtacL3Ix8V71dQtayKDT/fwMbbN+LIdZB7lvnRNVc2s/WPW02b7cLob49m6++3svH2jWSfkk3qGHOld/6V+Rx46wAbfraBYF2QIdMOb7TdsM1cKpHmNKcRck7LYdMvNnFw40EyPV3ut3s98Me2iW6fUQ1cAKwPS34ecxSmGWR06ecgInVKqUzrfQmwQSnVL73iwm1tRXHOj4jwY+gL5XZb+ZfHjclCJNYxG2JJsbfQO69touFyjwM+AD4EvmPNS2gGGT29rViKudEKIjJJRN4UkRUiskREXFb6FdYoY42ILLbSpovIa9b7PBFZKCKfisjfRGSbiAwXEaeIGCLyVyvvbRFJ66KviSKyVES8InJPh1YXV99PlHeHKh4+7LMEFwaAYk+Jp53LuRVa/nS0MAxqui0OYg6fzwUWWEmPAd9TSp0E/JTDezfeBVyglDoeaLeEGPhf4B2l1DHAv4DwoCiTgT9beVWYKwk76+v/gIeVUh6g88U/xbOulCsAAATRSURBVNV3AL/o3tl2Tq1IzeK01KnRaKsfcL+nxNPutsHtM3ZpYRjcdEcc0kRkNbAHGAksFJFMzCvLC1beo0CL3+6HwHwRuQmIFFz1TOBZAKXUm5iLhFrYopRqmRBbATi76OsMzFWGYEaF6pzi6nuBOZgxCHrNb4YNWYVITPbUjAMCfD3eRmj6H90Rhwal1FRMV1zB3GHJBlQppaaG/bkBlFKzMa/Q44AVItKpd04bwqOiBDFjFHTYl0XPJhuLqx/BjN7cq9gGTULjgsyMfhkKv5f8A/OpjkbTim7fViilDmK6zf4EOAhsEZErAMTkeOv9JKXUcqXUXUA5pkiE8yFwpVV2BuYKws76remoL6utq6z37cKkdUhx9XOYs/I99oN4KDf3YyXSbq1CAqKAe7yF3uvbbpCj0UAPJySVUqswFzZdjfljvEFE1gCfApdZxX5nTRCuw3SqWdOmmXnADCv/CszblVo6p6O+fgDcKiJerInSblNc/S4wFXi3u1WCEHwyJ2tCj/rpn+wELvAWevuyKlMzwDniS7ZFJAUIKqUCIvIlzAnF+E3uFefYMCdJf0EXYtmPgrn0haeBW72F3qp4G6Lp38RDHCZjOtbYMAPDflcp9fERNSISxTnnYC75ntJRkVMmjN3QaLN1mN/PqQDmeAu9z8fbEE1iMLCCvfSV4pxkYC7wP0Cr7bIWZGZ8fEfesGjslh0P3gSu9xZ6daxHTbfR4hCJ4pwJwJ+Ay1uSzhg/Zm2N3X5c/IzqFauAX3oLvS/H2xBN4qHFoTOKc6YBdy1JS837bv6IRBKG/2KKwmvxNkSTuGhx6AYzH/vCyetTkudiemzGYtfsaPEhpii8FW9DNImPFoce4CnxjAduwwxFN6qL4keKSuA14B/eQm+3H8tqNF2hxaEXWLEQTsLcZu4S4EQib9odK3YD/8YM7/aedmLSxAItDlHAU+IZDVwMfBWYDmR1WqHnNGE6f/0HeBlY7i306n+cJqZocYgBnhJPPlCAuco0/HUSkIa58CvSXwOwFdhs/a3H9DBdr0cHmiONFocEQESCgBdzIdoW4DtKqQ49HEXkcsygPJ9Zx3cDi5VS/+ll/9OBnyqlLulNfU1iMnBjSA4sGqzVqMdiejre2kX5y4FDK0eVUndFEgYrRodGExEtDolHp9G4ROR0zCA7v7Nif04SkfkiMtOqs1VEfiMiK4ErRGSGFU1rpYi8YMXPQES+IiI+q5yO9zAI0eKQQHQnGpdS6iMrf6412vg8QlMHlFInYk5w/gI4zzr+BPixmOHv/oo5wXoSkB/L89L0TyLun6bpd7RE4xoDGLSPxtVSLqWb7T1nvZ6GefvxodVGMubIxIUZlWsjgIg8CdwchfPQJBBaHBKDBqXUVBFJB97CnHOYjxUhqxfttex6I8BCpdTV4ZkiMlDiY2r6gL6tSCC6G40LM3hOd3wtlgFniEiB1UaGiEwBfJjxOydZ5a7uqAHNwEWLQ4LRzWhczwJzRWRV2A88UlvlmJv+PCMia7FuKZRSjZi3EaXWhGSHW/5pBi7az0Gj0UREjxw0Gk1EtDhoNJqIaHHQaDQR0eKg0WgiosVBo9FERIuDRqOJiBYHjUYTES0OGo0mIlocNBpNRLQ4aDSaiGhx0Gg0Efl/6NWla3pdLWgAAAAASUVORK5CYII=\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "count_status = collections.Counter(df['STATUS'])\n",
+        "count_status = collections.OrderedDict(sorted(count_status.items()))\n",
+        "dict_status = {'0': count_status[0], '1': count_status[1], '2': count_status[2], '3': count_status[3]+count_status[4]}\n",
+        "Value_status = dict_status.values()\n",
+        "\n",
+        "Key_status = [\"Active\",\"Resigned\",\"Retired\",\"Anomaly Information\"]\n",
+        "print(Value_status)\n",
+        "explode = (0, 0,0,0.1)\n",
+        "\n",
+        "fig2, ax2 = plt.subplots()\n",
+        "ax2.pie(Value_status,explode=explode,labels = Key_status, autopct='%.2f%%', startangle=90)\n",
+        "plt.title(\"Status\")\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 66,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 278
+        },
+        "id": "dKO21KiiZIu2",
+        "outputId": "13ca9e2c-c93e-4627-cf04-8ee081c06e86"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[<matplotlib.lines.Line2D at 0x7fec2bf5a5d0>]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 66
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 252x252 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ],
+      "source": [
+        "df_year = pd.DatetimeIndex(df['HIRED']).year\n",
+        "dict_year = collections.Counter(df_year)\n",
+        "dict_year = collections.OrderedDict(sorted(dict_year.items()))\n",
+        "Value_year = list(dict_year.values())\n",
+        "Keys_year = list(dict_year.keys())\n",
+        "\n",
+        "plt.title(\"The hiring rate for every years\")\n",
+        "plt.xlabel('Year')\n",
+        "plt.ylabel('Frequency')\n",
+        "plt.grid(color='silver', linestyle=':', linewidth=1)\n",
+        "plt.plot(Keys_year, Value_year)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 67,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 278
+        },
+        "id": "sclqyTvMpSXh",
+        "outputId": "ef61537e-0e7a-467d-ea78-6f2e70a437ed"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[<matplotlib.lines.Line2D at 0x7fec2bec5fd0>]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 67
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 252x252 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ],
+      "source": [
+        "bd = pd.DatetimeIndex(df['BIRTHDAY']).year\n",
+        "dict_bd = collections.Counter(bd)\n",
+        "dict_bd = collections.OrderedDict(sorted(dict_bd.items()))\n",
+        "Value_bd = np.array(list(dict_bd.values()))\n",
+        "Keys_bd = np.array(list(dict_bd.keys()))\n",
+        "Keys_bd =[2022-x for x in Keys_bd]\n",
+        "\n",
+        "plt.xlim([22, 62])\n",
+        "plt.title(\"Age of employee\")\n",
+        "plt.xlabel('Age')\n",
+        "plt.ylabel('Frequency')\n",
+        "plt.grid(color='silver', linestyle=':', linewidth=1)\n",
+        "plt.plot(Keys_bd, Value_bd)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Map"
+      ],
+      "metadata": {
+        "id": "OvRUS5AtnQ0c"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "***Please use colab to see map***"
+      ],
+      "metadata": {
+        "id": "S2CFE08mri7Q"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 68,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "L94m2DNNuKfM",
+        "outputId": "8287105b-d488-4233-b2ec-1657b5da50e5"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "               country  count\n",
+            "0          South Korea      2\n",
+            "1              Germany      4\n",
+            "2             Pakistan      4\n",
+            "3              Austria      5\n",
+            "4              Ukraine      2\n",
+            "5               Norway      2\n",
+            "6               Mexico      5\n",
+            "7        United States      4\n",
+            "8               Sweden      5\n",
+            "9          Philippines      5\n",
+            "10             Vietnam      6\n",
+            "11         Netherlands      4\n",
+            "12          Costa Rica      5\n",
+            "13               India      3\n",
+            "14            Colombia      2\n",
+            "15        South Africa      3\n",
+            "16  Russian Federation      3\n",
+            "17      United Kingdom      3\n",
+            "18               China      2\n",
+            "19           Indonesia      3\n",
+            "20               Italy      4\n",
+            "21                Peru      6\n",
+            "22             Ireland      2\n",
+            "23              Turkey      3\n",
+            "24              Brazil      3\n",
+            "25               Spain      1\n",
+            "26           Australia      2\n",
+            "27           Singapore      3\n",
+            "28             Belgium      1\n",
+            "29         New Zealand      1\n",
+            "30             Nigeria      1\n",
+            "31              France      1\n"
+          ]
+        }
+      ],
+      "source": [
+        "dict_na = pd.DataFrame(collections.Counter(df['NATIONALITY']), index=[0])\n",
+        "\n",
+        "data = pd.DataFrame(dict_na).T.reset_index()\n",
+        "data.columns=['country', 'count']\n",
+        "\n",
+        "print(data)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 69,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 542
+        },
+        "id": "P-lUFc_IzPYI",
+        "outputId": "180f1177-4b1d-46ea-8275-5d1a01800a84"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<html>\n",
+              "<head><meta charset=\"utf-8\" /></head>\n",
+              "<body>\n",
+              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
+              "        <script src=\"https://cdn.plot.ly/plotly-2.8.3.min.js\"></script>                <div id=\"fb3467dc-2cbc-4a97-9b80-19aa2a3d3a4e\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"fb3467dc-2cbc-4a97-9b80-19aa2a3d3a4e\")) {                    Plotly.newPlot(                        \"fb3467dc-2cbc-4a97-9b80-19aa2a3d3a4e\",                        [{\"coloraxis\":\"coloraxis\",\"geo\":\"geo\",\"hovertemplate\":\"country=%{location}<br>count=%{z}<extra></extra>\",\"locationmode\":\"country names\",\"locations\":[\"Australia\",\"Austria\",\"Belgium\",\"Brazil\",\"China\",\"Colombia\",\"Costa Rica\",\"France\",\"Germany\",\"India\",\"Indonesia\",\"Ireland\",\"Italy\",\"Mexico\",\"Netherlands\",\"New 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:{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"white\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2}}},\"geo\":{\"domain\":{\"x\":[0.0,1.0],\"y\":[0.0,1.0]},\"projection\":{\"type\":\"natural earth\"},\"center\":{},\"showcoastlines\":false},\"coloraxis\":{\"colorbar\":{\"title\":{\"text\":\"count\"}},\"colorscale\":[[0.0,\"rgb(243, 224, 247)\"],[0.16666666666666666,\"rgb(228, 199, 241)\"],[0.3333333333333333,\"rgb(209, 175, 232)\"],[0.5,\"rgb(185, 152, 221)\"],[0.6666666666666666,\"rgb(159, 130, 206)\"],[0.8333333333333334,\"rgb(130, 109, 186)\"],[1.0,\"rgb(99, 88, 159)\"]]},\"legend\":{\"tracegroupgap\":0},\"margin\":{\"t\":60},\"title\":{\"text\":\"Employee Nationality\"}},                        {\"responsive\": true}                    ).then(function(){\n",
+              "                            \n",
+              "var gd = document.getElementById('fb3467dc-2cbc-4a97-9b80-19aa2a3d3a4e');\n",
+              "var x = new MutationObserver(function (mutations, observer) {{\n",
+              "        var display = window.getComputedStyle(gd).display;\n",
+              "        if (!display || display === 'none') {{\n",
+              "            console.log([gd, 'removed!']);\n",
+              "            Plotly.purge(gd);\n",
+              "            observer.disconnect();\n",
+              "        }}\n",
+              "}});\n",
+              "\n",
+              "// Listen for the removal of the full notebook cells\n",
+              "var notebookContainer = gd.closest('#notebook-container');\n",
+              "if (notebookContainer) {{\n",
+              "    x.observe(notebookContainer, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "// Listen for the clearing of the current output cell\n",
+              "var outputEl = gd.closest('.output');\n",
+              "if (outputEl) {{\n",
+              "    x.observe(outputEl, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "                        })                };                            </script>        </div>\n",
+              "</body>\n",
+              "</html>"
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "database = px.data.gapminder().query('year == 2007')\n",
+        "\n",
+        "df_na = pd.merge(database, data, how='inner', on='country')\n",
+        "\n",
+        "\n",
+        "fig = px.choropleth(df_na,\n",
+        "                    locations=\"country\",\n",
+        "                    locationmode=\"country names\",\n",
+        "                    color=\"count\",\n",
+        "                    color_continuous_scale=px.colors.sequential.Purp,\n",
+        "                    projection=\"natural earth\"\n",
+        ")\n",
+        "\n",
+        "fig.update_layout(\n",
+        "    title_text = 'Employee Nationality',\n",
+        "    geo=dict(\n",
+        "        showcoastlines=False,\n",
+        "        )\n",
+        ")\n",
+        "\n",
+        "fig.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## After migration"
+      ],
+      "metadata": {
+        "id": "jlFu8Rf2muF-"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Chart"
+      ],
+      "metadata": {
+        "id": "56bjc31Qnljd"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 70,
+      "metadata": {
+        "id": "MEEYzmSb1u9B"
+      },
+      "outputs": [],
+      "source": [
+        "data_result = pd.read_csv(\"/content/data-devclub-1_result.csv\")\n",
+        "df_result = pd.DataFrame(data_result)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 71,
+      "metadata": {
+        "id": "IazK_5dj2URC",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 426
+        },
+        "outputId": "125a3a92-e112-4e79-8647-c63986911df8"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "    EMPID     PASSPORT FIRSTNAME   LASTNAME  GENDER    BIRTHDAY  NATIONALITY  \\\n",
+              "0       3  JUI65YBK7AF      Jada     Bender       0  28-05-1963     Pakistan   \n",
+              "1       5  AZE20CSG4MU   Lillian      Reese       0  03-12-1982      Ukraine   \n",
+              "2      23  NFH65BYM0VB    Armand       Horn       0  24-05-1987  Netherlands   \n",
+              "3      29  CMK62UAD3VK     Rowan    Leonard       1  15-07-1974      Germany   \n",
+              "4      33  EWD45RJW5YK    Carter  Velasquez       0  23-11-1967    Indonesia   \n",
+              "5      34  BFS82MEY3CX     Selma       Bush       0  26-03-1972        Italy   \n",
+              "6      50  MRC33GHJ2KW    Calvin      Roach       1  16-04-1999       Mexico   \n",
+              "7      66  WKV12UQC6QF   Zachery  Valentine       0  04-06-1971  Philippines   \n",
+              "8      80  EUC74ENE9ZK      Ryan       Rush       0  13-06-1998        Italy   \n",
+              "9      93  UXL43IOW6OV  Honorato    Maxwell       1  09-03-1982       France   \n",
+              "10     95  OUP31WOE2IE      Dara     Wilcox       1  29-06-1996    Singapore   \n",
+              "11     97  SUF73DKV4QE     Dante       Hart       0  21-12-1999         Peru   \n",
+              "\n",
+              "         HIRED                  DEPT    POSITION  STATUS       REGION  \n",
+              "0   11-02-2001                 Pilot       Pilot       1       Canada  \n",
+              "1   19-05-2002       Flight Planning     Steward       1       Canada  \n",
+              "2   19-06-2007  Aircraft Maintenance  Airhostess       1       Ocenia  \n",
+              "3   27-03-2004  Aircraft Maintenance       Pilot       1       Ocenia  \n",
+              "4   27-02-2005       Flight Planning       Pilot       1         APAC  \n",
+              "5   10-10-2008     Flight Attendance  Airhostess       1          USA  \n",
+              "6   18-03-2011     Flight Attendance     Steward       1       Europe  \n",
+              "7   25-08-2011     Flight Attendance     Steward       1  Middle East  \n",
+              "8   31-07-2019  Aircraft Maintenance       Pilot       1  Middle East  \n",
+              "9   04-02-2017  Aircraft Maintenance  Airhostess       1       Europe  \n",
+              "10  18-05-2011     Flight Attendance  Airhostess       1       Canada  \n",
+              "11  22-02-2016                 Pilot       Pilot       1       Europe  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-17a497db-0a73-4c17-b0d2-9e5165ff1aa5\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <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>EMPID</th>\n",
+              "      <th>PASSPORT</th>\n",
+              "      <th>FIRSTNAME</th>\n",
+              "      <th>LASTNAME</th>\n",
+              "      <th>GENDER</th>\n",
+              "      <th>BIRTHDAY</th>\n",
+              "      <th>NATIONALITY</th>\n",
+              "      <th>HIRED</th>\n",
+              "      <th>DEPT</th>\n",
+              "      <th>POSITION</th>\n",
+              "      <th>STATUS</th>\n",
+              "      <th>REGION</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>3</td>\n",
+              "      <td>JUI65YBK7AF</td>\n",
+              "      <td>Jada</td>\n",
+              "      <td>Bender</td>\n",
+              "      <td>0</td>\n",
+              "      <td>28-05-1963</td>\n",
+              "      <td>Pakistan</td>\n",
+              "      <td>11-02-2001</td>\n",
+              "      <td>Pilot</td>\n",
+              "      <td>Pilot</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Canada</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>5</td>\n",
+              "      <td>AZE20CSG4MU</td>\n",
+              "      <td>Lillian</td>\n",
+              "      <td>Reese</td>\n",
+              "      <td>0</td>\n",
+              "      <td>03-12-1982</td>\n",
+              "      <td>Ukraine</td>\n",
+              "      <td>19-05-2002</td>\n",
+              "      <td>Flight Planning</td>\n",
+              "      <td>Steward</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Canada</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>23</td>\n",
+              "      <td>NFH65BYM0VB</td>\n",
+              "      <td>Armand</td>\n",
+              "      <td>Horn</td>\n",
+              "      <td>0</td>\n",
+              "      <td>24-05-1987</td>\n",
+              "      <td>Netherlands</td>\n",
+              "      <td>19-06-2007</td>\n",
+              "      <td>Aircraft Maintenance</td>\n",
+              "      <td>Airhostess</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Ocenia</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>29</td>\n",
+              "      <td>CMK62UAD3VK</td>\n",
+              "      <td>Rowan</td>\n",
+              "      <td>Leonard</td>\n",
+              "      <td>1</td>\n",
+              "      <td>15-07-1974</td>\n",
+              "      <td>Germany</td>\n",
+              "      <td>27-03-2004</td>\n",
+              "      <td>Aircraft Maintenance</td>\n",
+              "      <td>Pilot</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Ocenia</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>33</td>\n",
+              "      <td>EWD45RJW5YK</td>\n",
+              "      <td>Carter</td>\n",
+              "      <td>Velasquez</td>\n",
+              "      <td>0</td>\n",
+              "      <td>23-11-1967</td>\n",
+              "      <td>Indonesia</td>\n",
+              "      <td>27-02-2005</td>\n",
+              "      <td>Flight Planning</td>\n",
+              "      <td>Pilot</td>\n",
+              "      <td>1</td>\n",
+              "      <td>APAC</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>34</td>\n",
+              "      <td>BFS82MEY3CX</td>\n",
+              "      <td>Selma</td>\n",
+              "      <td>Bush</td>\n",
+              "      <td>0</td>\n",
+              "      <td>26-03-1972</td>\n",
+              "      <td>Italy</td>\n",
+              "      <td>10-10-2008</td>\n",
+              "      <td>Flight Attendance</td>\n",
+              "      <td>Airhostess</td>\n",
+              "      <td>1</td>\n",
+              "      <td>USA</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>50</td>\n",
+              "      <td>MRC33GHJ2KW</td>\n",
+              "      <td>Calvin</td>\n",
+              "      <td>Roach</td>\n",
+              "      <td>1</td>\n",
+              "      <td>16-04-1999</td>\n",
+              "      <td>Mexico</td>\n",
+              "      <td>18-03-2011</td>\n",
+              "      <td>Flight Attendance</td>\n",
+              "      <td>Steward</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Europe</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>66</td>\n",
+              "      <td>WKV12UQC6QF</td>\n",
+              "      <td>Zachery</td>\n",
+              "      <td>Valentine</td>\n",
+              "      <td>0</td>\n",
+              "      <td>04-06-1971</td>\n",
+              "      <td>Philippines</td>\n",
+              "      <td>25-08-2011</td>\n",
+              "      <td>Flight Attendance</td>\n",
+              "      <td>Steward</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Middle East</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>80</td>\n",
+              "      <td>EUC74ENE9ZK</td>\n",
+              "      <td>Ryan</td>\n",
+              "      <td>Rush</td>\n",
+              "      <td>0</td>\n",
+              "      <td>13-06-1998</td>\n",
+              "      <td>Italy</td>\n",
+              "      <td>31-07-2019</td>\n",
+              "      <td>Aircraft Maintenance</td>\n",
+              "      <td>Pilot</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Middle East</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>93</td>\n",
+              "      <td>UXL43IOW6OV</td>\n",
+              "      <td>Honorato</td>\n",
+              "      <td>Maxwell</td>\n",
+              "      <td>1</td>\n",
+              "      <td>09-03-1982</td>\n",
+              "      <td>France</td>\n",
+              "      <td>04-02-2017</td>\n",
+              "      <td>Aircraft Maintenance</td>\n",
+              "      <td>Airhostess</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Europe</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10</th>\n",
+              "      <td>95</td>\n",
+              "      <td>OUP31WOE2IE</td>\n",
+              "      <td>Dara</td>\n",
+              "      <td>Wilcox</td>\n",
+              "      <td>1</td>\n",
+              "      <td>29-06-1996</td>\n",
+              "      <td>Singapore</td>\n",
+              "      <td>18-05-2011</td>\n",
+              "      <td>Flight Attendance</td>\n",
+              "      <td>Airhostess</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Canada</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11</th>\n",
+              "      <td>97</td>\n",
+              "      <td>SUF73DKV4QE</td>\n",
+              "      <td>Dante</td>\n",
+              "      <td>Hart</td>\n",
+              "      <td>0</td>\n",
+              "      <td>21-12-1999</td>\n",
+              "      <td>Peru</td>\n",
+              "      <td>22-02-2016</td>\n",
+              "      <td>Pilot</td>\n",
+              "      <td>Pilot</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Europe</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-17a497db-0a73-4c17-b0d2-9e5165ff1aa5')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-17a497db-0a73-4c17-b0d2-9e5165ff1aa5 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-17a497db-0a73-4c17-b0d2-9e5165ff1aa5');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "display(df_result)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 72,
+      "metadata": {
+        "id": "IxEwec_r3KE7"
+      },
+      "outputs": [],
+      "source": [
+        "data = pd.read_csv(\"/content/data-devclub-1.csv\")\n",
+        "df = pd.DataFrame(data)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 73,
+      "metadata": {
+        "id": "44QO0nnw2nSI",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 261
+        },
+        "outputId": "b0cfb55c-755d-4e46-c410-4ce2db4f99f3"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 252x252 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "x = [len(df_result),len(df)]\n",
+        "y = [\"Remaining\",\"Out\"]\n",
+        "\n",
+        "fig, ax = plt.subplots()\n",
+        "explode = (0, 0.1)\n",
+        "ax.pie(x,labels = y,explode=explode, autopct='%.2f%%', startangle=90)\n",
+        "\n",
+        "plt.title(\"Transfer\")\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "count = collections.Counter(df_result['GENDER'])\n",
+        "count = collections.OrderedDict(sorted(count.items()))\n",
+        "\n",
+        "Value = count.values()\n",
+        "\n",
+        "print(count)\n",
+        "Key = [\"Male\",\"Female\"]\n",
+        "\n",
+        "fig, ax = plt.subplots()\n",
+        "ax.pie(Value,labels = Key, autopct='%.2f%%', startangle=90)\n",
+        "plt.title(\"Gender\")\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 278
+        },
+        "id": "FKWGKQa-jqcx",
+        "outputId": "f901879c-ad94-46bc-d600-52fdf81ee038"
+      },
+      "execution_count": 74,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "OrderedDict([(0, 8), (1, 4)])\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 252x252 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Map"
+      ],
+      "metadata": {
+        "id": "Rk_C4oWonXlO"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "***Please use colab to see map***"
+      ],
+      "metadata": {
+        "id": "os5OCEUprSBD"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 75,
+      "metadata": {
+        "id": "gglCO2Rl5llG",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "10007e6d-689f-40d2-d90e-501f70f8ca5d"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "        country  count\n",
+            "0      Pakistan      1\n",
+            "1       Ukraine      1\n",
+            "2   Netherlands      1\n",
+            "3       Germany      1\n",
+            "4     Indonesia      1\n",
+            "5         Italy      2\n",
+            "6        Mexico      1\n",
+            "7   Philippines      1\n",
+            "8        France      1\n",
+            "9     Singapore      1\n",
+            "10         Peru      1\n"
+          ]
+        }
+      ],
+      "source": [
+        "dict_na_result = pd.DataFrame(collections.Counter(df_result['NATIONALITY']), index=[0])\n",
+        "\n",
+        "data_result = pd.DataFrame(dict_na_result).T.reset_index()\n",
+        "data_result.columns=['country', 'count']\n",
+        "\n",
+        "print(data_result)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 76,
+      "metadata": {
+        "id": "6NSU1R0t563t",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 542
+        },
+        "outputId": "f598775e-5b19-41c9-8161-618539306b7f"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
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+              "                            \n",
+              "var gd = document.getElementById('f880986c-8cc1-4ecb-9daa-3b0399abcd13');\n",
+              "var x = new MutationObserver(function (mutations, observer) {{\n",
+              "        var display = window.getComputedStyle(gd).display;\n",
+              "        if (!display || display === 'none') {{\n",
+              "            console.log([gd, 'removed!']);\n",
+              "            Plotly.purge(gd);\n",
+              "            observer.disconnect();\n",
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+              "}});\n",
+              "\n",
+              "// Listen for the removal of the full notebook cells\n",
+              "var notebookContainer = gd.closest('#notebook-container');\n",
+              "if (notebookContainer) {{\n",
+              "    x.observe(notebookContainer, {childList: true});\n",
+              "}}\n",
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+              "    x.observe(outputEl, {childList: true});\n",
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+              "\n",
+              "                        })                };                            </script>        </div>\n",
+              "</body>\n",
+              "</html>"
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "database = px.data.gapminder().query('year == 2007')\n",
+        "\n",
+        "df_na_result = pd.merge(database, data_result, how='inner', on='country')\n",
+        "\n",
+        "\n",
+        "fig = px.choropleth(df_na_result,\n",
+        "                    locations=\"country\",\n",
+        "                    locationmode=\"country names\",\n",
+        "                    color=\"count\",\n",
+        "                    color_continuous_scale=px.colors.sequential.Purp,\n",
+        "                    projection=\"natural earth\"\n",
+        ")\n",
+        "\n",
+        "fig.update_layout(\n",
+        "    title_text = 'Remaining Employee Nationality',\n",
+        "    geo=dict( showcoastlines=False )\n",
+        ")\n",
+        "\n",
+        "fig.show()"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "collapsed_sections": [],
+      "authorship_tag": "ABX9TyMDF8S3sb/9SLZsbcV1jI2a",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
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diff --git a/xml2csv.py b/xml2csv.py
new file mode 100644
index 0000000..0cb64d9
--- /dev/null
+++ b/xml2csv.py
@@ -0,0 +1,115 @@
+import argparse
+import csv
+import xml.etree.ElementTree as ET
+
+from datetime import datetime
+
+attribute_list = (
+    "EMPID",
+    "PASSPORT",
+    "FIRSTNAME",
+    "LASTNAME",
+    "GENDER",
+    "BIRTHDAY",
+    "NATIONALITY",
+    "HIRED",
+    "DEPT",
+    "POSITION",
+    "STATUS",
+    "REGION",
+)
+
+current_datetime = datetime.now()
+
+
+def ensure_years_experiences(date_str, years=3):
+    leap_days = 0
+    target_date = datetime(int(date_str[6:]), int(date_str[3:5]), int(date_str[:2]))
+    for year in range(target_date.year, current_datetime.year + 2):
+        leap_days += not (year & 0b11)
+    return (current_datetime - target_date).days > years * 365 + leap_days
+
+
+def xml2csv_converter_helper(records, convert_default):
+    existed_EMPID_set = set()
+    existed_PASSPORT = set()
+    for record in records:
+        if convert_default and (
+            record[10].text != "1"
+            or record[9].text not in ("Airhostess", "Pilot", "Steward")
+            or record[0].text == record[1].text
+            or record[0].text in existed_EMPID_set
+            or record[1].text in existed_PASSPORT
+            or not ensure_years_experiences(record[7].text, years=3)
+        ):
+            continue
+        existed_EMPID_set.add(record[0].text)
+        existed_PASSPORT.add(record[1].text)
+        yield list(map(lambda attribute: record.find(attribute).text, attribute_list))
+
+
+def xml2csv_converter(xml_file, csv_file, convert_default):
+    tree = ET.parse(xml_file)
+    records = tree.getroot()
+    csv_by_nationality = dict()
+    with open(csv_file, "w", newline="") as output_file:
+        csv_writer = csv.writer(output_file, delimiter=",")
+        csv_writer.writerow(attribute_list)
+        for record in xml2csv_converter_helper(records, convert_default):
+            nationality = record[6]
+            if nationality not in csv_by_nationality:
+                csv_by_nationality_file = (
+                    f"{csv_file[:-4]}-{nationality}.csv"
+                    if csv_file[-4:] == ".csv"
+                    else csv_file
+                )
+                csv_by_nationality[nationality] = csv.writer(
+                    open(csv_by_nationality_file, "w", newline=""), delimiter=","
+                )
+                csv_by_nationality[nationality].writerow(attribute_list)
+            csv_writer.writerow(record)
+            csv_by_nationality[nationality].writerow(record)
+
+
+def main():
+    try:
+        xml_file = args.xml
+        csv_file = args.csv
+        convert_default = args.convert_default
+        xml2csv_converter(
+            xml_file=xml_file, csv_file=csv_file, convert_default=convert_default
+        )
+    except Exception as e:
+        print(e)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        "--xml",
+        default="data-devclub-1.xml",
+        help="Specify the name of a xml file to read from."
+    )
+    parser.add_argument(
+        "--csv",
+        default="data-devclub-1.csv",
+        help="Specify the name of a csv file to write to.",
+    )
+    parser.add_argument(
+        "--convert-default",
+        action="store_false",
+        help="Convert actives 3 years experiences+ Airhostess, Pilot, Steward to csv file without duplicate ID and passport number.",
+    )
+
+    args = parser.parse_args()
+
+    if not args.xml:
+        print("[-] Please specify a xml file.")
+        exit(1)
+
+    if not args.csv:
+        print("[-] Please specify the name of a csv file to write to.")
+        exit(1)
+
+    main()