├── python_scripts ├── requirements.txt ├── create_df_and_modify.py ├── write_csv_to_postgres.py └── write_df_to_postgres.py ├── csv_files ├── df_creditscore.csv ├── df_exited_age_correlation.csv └── df_exited_salary_correlation.csv ├── dags └── airflow_dag.py ├── README.md └── .gitignore /python_scripts/requirements.txt: -------------------------------------------------------------------------------- 1 | psycopg 2 | pandas 3 | apache-airflow 4 | datetime 5 | requests 6 | pandasql 7 | urllib3 8 | traceback2 9 | apache-airflow 10 | -------------------------------------------------------------------------------- /csv_files/df_creditscore.csv: -------------------------------------------------------------------------------- 1 | ,geography,gender,avg_credit_score,total_exited 2 | 0,France,Female,649.2033524481694,462 3 | 3,Germany,Male,649.8911719939117,365 4 | 1,France,Male,649.9963794351919,351 5 | 5,Spain,Male,651.0448950036206,182 6 | 4,Spain,Female,651.7154696132596,230 7 | 2,Germany,Female,653.0554621848739,447 8 | -------------------------------------------------------------------------------- /csv_files/df_exited_age_correlation.csv: -------------------------------------------------------------------------------- 1 | ,geography,gender,exited,avg_age,avg_salary,number_of_exited_or_not 2 | 0,Spain,Male,1,44.82967032967033,98661.1,182 3 | 1,Spain,Female,1,43.552173913043475,107721.9,230 4 | 2,France,Male,1,44.89173789173789,103018.7,351 5 | 3,Germany,Male,1,44.95616438356164,96741.9,365 6 | 4,Germany,Female,1,44.83892617449664,99841.1,447 7 | 5,France,Female,1,45.34632034632035,103579.8,462 8 | 6,Germany,Female,0,37.351278600269175,104068.8,743 9 | 7,Spain,Female,0,38.00817757009346,98849.8,856 10 | 8,Germany,Male,0,37.28240252897787,101079.5,949 11 | 9,Spain,Male,0,37.71142618849041,98185.5,1199 12 | 10,France,Female,0,37.098614958448756,98523.8,1805 13 | 11,France,Male,0,37.343840729987555,99873.1,2411 14 | -------------------------------------------------------------------------------- /dags/airflow_dag.py: -------------------------------------------------------------------------------- 1 | import os 2 | import sys 3 | from airflow import DAG 4 | from datetime import datetime, timedelta 5 | from airflow.operators.python_operator import PythonOperator 6 | 7 | parent_folder = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) 8 | sys.path.append(parent_folder) 9 | 10 | from write_csv_to_postgres import write_csv_to_postgres_main 11 | from write_df_to_postgres import write_df_to_postgres_main 12 | 13 | start_date = datetime(2023, 1, 1, 12, 10) 14 | 15 | default_args = { 16 | 'owner': 'dogukanulu', 17 | 'start_date': start_date, 18 | 'retries': 1, 19 | 'retry_delay': timedelta(seconds=5) 20 | } 21 | 22 | with DAG('csv_extract_airflow_docker', default_args=default_args, schedule_interval='@daily', catchup=False) as dag: 23 | 24 | write_csv_to_postgres = PythonOperator( 25 | task_id='write_csv_to_postgres', 26 | python_callable=write_csv_to_postgres_main, 27 | retries=1, 28 | retry_delay=timedelta(seconds=15)) 29 | 30 | write_df_to_postgres = PythonOperator( 31 | task_id='write_df_to_postgres', 32 | python_callable=write_df_to_postgres_main, 33 | retries=1, 34 | retry_delay=timedelta(seconds=15)) 35 | 36 | write_csv_to_postgres >> write_df_to_postgres -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ## Information 2 | 3 | ![image](https://github.com/dogukannulu/csv_extract_airflow_docker/assets/91257958/d0c01724-d2c6-4646-8f22-dac0ea3b5931) 4 | 5 | 6 | ```bash 7 | pip install -r requirements.txt 8 | 9 | ``` 10 | 11 | ## Steps 12 | 13 | `write_csv_to_postgres.py`-> gets a csv file from a URL. Saves it into the local working directory as [churn_modelling.csv](https://raw.githubusercontent.com/dogukannulu/datasets/master/Churn_Modelling.csv). After reading the csv file, it writes it to a local PostgreSQL table 14 | 15 | `create_df_and_modify.py` -> reads the same Postgres table and creates a pandas dataframe out of it, modifies it. Then, creates 3 separate dataframes. 16 | 17 | `write_df_to_postgres.py` -> writes these 3 dataframes to 3 separate tables located in Postgres server. 18 | 19 | 20 | 21 | ## Apache Airflow: 22 | 23 | Run the following command to clone the necessary repo on your local 24 | 25 | ```bash 26 | git clone https://github.com/dogukannulu/docker-airflow.git 27 | ``` 28 | After cloning the repo, run the following command only once: 29 | 30 | ```bash 31 | docker build --rm --build-arg AIRFLOW_DEPS="datadog,dask" --build-arg PYTHON_DEPS="flask_oauthlib>=0.9" -t puckel/docker-airflow . 32 | ``` 33 | 34 | Then run the following command: 35 | 36 | ```bash 37 | docker-compose -f docker-compose-LocalExecutor.yml up -d 38 | ``` 39 | 40 | Now you have a running Airflow container and you can reach out to that on `https://localhost:8080`. If there is `No module: ...` error, you can access to bash with the following command: 41 | 42 | ```bash 43 | docker exec -it /bin/bash 44 | ``` 45 | 46 | Then: 47 | ```bash 48 | pip install 49 | ``` 50 | 51 | After all these, we can move all .py files under dags folder in docker-airflow repo. 52 | 53 | ## PostgreSQL 54 | 55 | I am using a local Postgres Server and installed `PgAdmin` to control the tables instead of using `psql`. I defined the necessary info to access to Postgres server in .zshrc (if MacOS). 56 | -------------------------------------------------------------------------------- /python_scripts/create_df_and_modify.py: -------------------------------------------------------------------------------- 1 | """ 2 | Creates a base dataframe out of churn_modelling table and creates 3 separate dataframes out of it. 3 | """ 4 | import os 5 | import logging 6 | import psycopg2 7 | import traceback 8 | import numpy as np 9 | import pandas as pd 10 | 11 | postgres_host = os.environ.get('postgres_host') 12 | postgres_database = os.environ.get('postgres_database') 13 | postgres_user = os.environ.get('postgres_user') 14 | postgres_password = os.environ.get('postgres_password') 15 | postgres_port = os.environ.get('postgres_port') 16 | 17 | logging.basicConfig(level=logging.INFO, 18 | format='%(asctime)s:%(funcName)s:%(levelname)s:%(message)s') 19 | 20 | try: 21 | conn = psycopg2.connect( 22 | host=postgres_host, 23 | database=postgres_database, 24 | user=postgres_user, 25 | password=postgres_password, 26 | port=postgres_port 27 | ) 28 | cur = conn.cursor() 29 | logging.info('Postgres server connection is successful') 30 | except Exception as e: 31 | traceback.print_exc() 32 | logging.error("Couldn't create the Postgres connection") 33 | 34 | 35 | def create_base_df(cur): 36 | """ 37 | Base dataframe of churn_modelling table 38 | """ 39 | cur.execute("""SELECT * FROM churn_modelling""") 40 | rows = cur.fetchall() 41 | 42 | col_names = [desc[0] for desc in cur.description] 43 | df = pd.DataFrame(rows, columns=col_names) 44 | 45 | df.drop('rownumber', axis=1, inplace=True) 46 | index_to_be_null = np.random.randint(10000, size=30) 47 | df.loc[index_to_be_null, ['balance','creditscore','geography']] = np.nan 48 | 49 | most_occured_country = df['geography'].value_counts().index[0] 50 | df['geography'].fillna(value=most_occured_country, inplace=True) 51 | 52 | avg_balance = df['balance'].mean() 53 | df['balance'].fillna(value=avg_balance, inplace=True) 54 | 55 | median_creditscore = df['creditscore'].median() 56 | df['creditscore'].fillna(value=median_creditscore, inplace=True) 57 | 58 | return df 59 | 60 | 61 | def create_creditscore_df(df): 62 | df_creditscore = df[['geography', 'gender', 'exited', 'creditscore']].groupby(['geography','gender']).agg({'creditscore':'mean', 'exited':'sum'}) 63 | df_creditscore.rename(columns={'exited':'total_exited', 'creditscore':'avg_credit_score'}, inplace=True) 64 | df_creditscore.reset_index(inplace=True) 65 | 66 | df_creditscore.sort_values('avg_credit_score', inplace=True) 67 | 68 | return df_creditscore 69 | 70 | 71 | def create_exited_age_correlation(df): 72 | df_exited_age_correlation = df.groupby(['geography', 'gender', 'exited']).agg({ 73 | 'age': 'mean', 74 | 'estimatedsalary': 'mean', 75 | 'exited': 'count' 76 | }).rename(columns={ 77 | 'age': 'avg_age', 78 | 'estimatedsalary': 'avg_salary', 79 | 'exited': 'number_of_exited_or_not' 80 | }).reset_index().sort_values('number_of_exited_or_not') 81 | 82 | return df_exited_age_correlation 83 | 84 | 85 | def create_exited_salary_correlation(df): 86 | df_salary = df[['geography','gender','exited','estimatedsalary']].groupby(['geography','gender']).agg({'estimatedsalary':'mean'}).sort_values('estimatedsalary') 87 | df_salary.reset_index(inplace=True) 88 | 89 | min_salary = round(df_salary['estimatedsalary'].min(),0) 90 | 91 | df['is_greater'] = df['estimatedsalary'].apply(lambda x: 1 if x>min_salary else 0) 92 | 93 | df_exited_salary_correlation = pd.DataFrame({ 94 | 'exited': df['exited'], 95 | 'is_greater': df['estimatedsalary'] > df['estimatedsalary'].min(), 96 | 'correlation': np.where(df['exited'] == (df['estimatedsalary'] > df['estimatedsalary'].min()), 1, 0) 97 | }) 98 | 99 | return df_exited_salary_correlation 100 | 101 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | share/python-wheels/ 24 | *.egg-info/ 25 | .installed.cfg 26 | *.egg 27 | MANIFEST 28 | 29 | # PyInstaller 30 | # Usually these files are written by a python script from a template 31 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 32 | *.manifest 33 | *.spec 34 | 35 | # Installer logs 36 | pip-log.txt 37 | pip-delete-this-directory.txt 38 | 39 | # Unit test / coverage reports 40 | htmlcov/ 41 | .tox/ 42 | .nox/ 43 | .coverage 44 | .coverage.* 45 | .cache 46 | nosetests.xml 47 | coverage.xml 48 | *.cover 49 | *.py,cover 50 | .hypothesis/ 51 | .pytest_cache/ 52 | cover/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | .pybuilder/ 76 | target/ 77 | 78 | # Jupyter Notebook 79 | .ipynb_checkpoints 80 | 81 | # IPython 82 | profile_default/ 83 | ipython_config.py 84 | 85 | # pyenv 86 | # For a library or package, you might want to ignore these files since the code is 87 | # intended to run in multiple environments; otherwise, check them in: 88 | # .python-version 89 | 90 | # pipenv 91 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 92 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 93 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 94 | # install all needed dependencies. 95 | #Pipfile.lock 96 | 97 | # poetry 98 | # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. 99 | # This is especially recommended for binary packages to ensure reproducibility, and is more 100 | # commonly ignored for libraries. 101 | # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control 102 | #poetry.lock 103 | 104 | # pdm 105 | # Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. 106 | #pdm.lock 107 | # pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it 108 | # in version control. 109 | # https://pdm.fming.dev/#use-with-ide 110 | .pdm.toml 111 | 112 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm 113 | __pypackages__/ 114 | 115 | # Celery stuff 116 | celerybeat-schedule 117 | celerybeat.pid 118 | 119 | # SageMath parsed files 120 | *.sage.py 121 | 122 | # Environments 123 | .env 124 | .venv 125 | env/ 126 | venv/ 127 | ENV/ 128 | env.bak/ 129 | venv.bak/ 130 | 131 | # Spyder project settings 132 | .spyderproject 133 | .spyproject 134 | 135 | # Rope project settings 136 | .ropeproject 137 | 138 | # mkdocs documentation 139 | /site 140 | 141 | # mypy 142 | .mypy_cache/ 143 | .dmypy.json 144 | dmypy.json 145 | 146 | # Pyre type checker 147 | .pyre/ 148 | 149 | # pytype static type analyzer 150 | .pytype/ 151 | 152 | # Cython debug symbols 153 | cython_debug/ 154 | 155 | # PyCharm 156 | # JetBrains specific template is maintained in a separate JetBrains.gitignore that can 157 | # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore 158 | # and can be added to the global gitignore or merged into this file. For a more nuclear 159 | # option (not recommended) you can uncomment the following to ignore the entire idea folder. 160 | #.idea/ 161 | .DS_Store 162 | -------------------------------------------------------------------------------- /python_scripts/write_csv_to_postgres.py: -------------------------------------------------------------------------------- 1 | """ 2 | Downloads the csv file from the URL. Creates a new table in the Postgres server. 3 | Reads the file as a dataframe and inserts each record to the Postgres table. 4 | """ 5 | import psycopg2 6 | import os 7 | import traceback 8 | import logging 9 | import pandas as pd 10 | import urllib.request 11 | 12 | logging.basicConfig(level=logging.INFO, 13 | format='%(asctime)s:%(funcName)s:%(levelname)s:%(message)s') 14 | 15 | 16 | postgres_host = os.environ.get('postgres_host') 17 | postgres_database = os.environ.get('postgres_database') 18 | postgres_user = os.environ.get('postgres_user') 19 | postgres_password = os.environ.get('postgres_password') 20 | postgres_port = os.environ.get('postgres_port') 21 | dest_folder = os.environ.get('dest_folder') 22 | 23 | url = "https://raw.githubusercontent.com/dogukannulu/datasets/master/Churn_Modelling.csv" 24 | destination_path = f'{dest_folder}/churn_modelling.csv' 25 | 26 | try: 27 | conn = psycopg2.connect( 28 | host=postgres_host, 29 | database=postgres_database, 30 | user=postgres_user, 31 | password=postgres_password, 32 | port=postgres_port 33 | ) 34 | cur = conn.cursor() 35 | logging.info('Postgres server connection is successful') 36 | except Exception as e: 37 | traceback.print_exc() 38 | logging.error("Couldn't create the Postgres connection") 39 | 40 | 41 | def download_file_from_url(url: str, dest_folder: str): 42 | """ 43 | Download a file from a specific URL and download to the local direcory 44 | """ 45 | if not os.path.exists(str(dest_folder)): 46 | os.makedirs(str(dest_folder)) # create folder if it does not exist 47 | 48 | try: 49 | urllib.request.urlretrieve(url, destination_path) 50 | logging.info('csv file downloaded successfully to the working directory') 51 | except Exception as e: 52 | logging.error(f'Error while downloading the csv file due to: {e}') 53 | traceback.print_exc() 54 | 55 | 56 | def create_postgres_table(): 57 | """ 58 | Create the Postgres table with a desired schema 59 | """ 60 | try: 61 | cur.execute("""CREATE TABLE IF NOT EXISTS churn_modelling (RowNumber INTEGER PRIMARY KEY, CustomerId INTEGER, 62 | Surname VARCHAR(50), CreditScore INTEGER, Geography VARCHAR(50), Gender VARCHAR(20), Age INTEGER, 63 | Tenure INTEGER, Balance FLOAT, NumOfProducts INTEGER, HasCrCard INTEGER, IsActiveMember INTEGER, EstimatedSalary FLOAT, Exited INTEGER)""") 64 | 65 | logging.info(' New table churn_modelling created successfully to postgres server') 66 | except: 67 | logging.warning(' Check if the table churn_modelling exists') 68 | 69 | 70 | def write_to_postgres(): 71 | """ 72 | Create the dataframe and write to Postgres table if it doesn't already exist 73 | """ 74 | df = pd.read_csv(f'{dest_folder}/churn_modelling.csv') 75 | inserted_row_count = 0 76 | 77 | for _, row in df.iterrows(): 78 | count_query = f"""SELECT COUNT(*) FROM churn_modelling WHERE RowNumber = {row['RowNumber']}""" 79 | cur.execute(count_query) 80 | result = cur.fetchone() 81 | 82 | if result[0] == 0: 83 | inserted_row_count += 1 84 | cur.execute("""INSERT INTO churn_modelling (RowNumber, CustomerId, Surname, CreditScore, Geography, Gender, Age, 85 | Tenure, Balance, NumOfProducts, HasCrCard, IsActiveMember, EstimatedSalary, Exited) VALUES (%s, %s, %s,%s, %s, %s,%s, %s, %s,%s, %s, %s,%s, %s)""", 86 | (int(row[0]), int(row[1]), str(row[2]), int(row[3]), str(row[4]), str(row[5]), int(row[6]), int(row[7]), float(row[8]), int(row[9]), int(row[10]), int(row[11]), float(row[12]), int(row[13]))) 87 | 88 | logging.info(f' {inserted_row_count} rows from csv file inserted into churn_modelling table successfully') 89 | 90 | def write_csv_to_postgres_main(): 91 | download_file_from_url(url, dest_folder) 92 | create_postgres_table() 93 | write_to_postgres() 94 | conn.commit() 95 | cur.close() 96 | conn.close() 97 | 98 | 99 | if __name__ == '__main__': 100 | download_file_from_url(url, dest_folder) 101 | create_postgres_table() 102 | write_to_postgres() 103 | conn.commit() 104 | cur.close() 105 | conn.close() 106 | -------------------------------------------------------------------------------- /python_scripts/write_df_to_postgres.py: -------------------------------------------------------------------------------- 1 | import os 2 | import logging 3 | import psycopg2 4 | import traceback 5 | from create_df_and_modify import create_base_df, create_creditscore_df, create_exited_age_correlation, create_exited_salary_correlation 6 | 7 | postgres_host = os.environ.get('postgres_host') 8 | postgres_database = os.environ.get('postgres_database') 9 | postgres_user = os.environ.get('postgres_user') 10 | postgres_password = os.environ.get('postgres_password') 11 | postgres_port = os.environ.get('postgres_port') 12 | 13 | logging.basicConfig(level=logging.INFO, 14 | format='%(asctime)s:%(funcName)s:%(levelname)s:%(message)s') 15 | 16 | try: 17 | conn = psycopg2.connect( 18 | host=postgres_host, 19 | database=postgres_database, 20 | user=postgres_user, 21 | password=postgres_password, 22 | port=postgres_port 23 | ) 24 | cur = conn.cursor() 25 | logging.info('Postgres server connection is successful') 26 | except Exception as e: 27 | traceback.print_exc() 28 | logging.error("Couldn't create the Postgres connection") 29 | 30 | 31 | def create_new_tables_in_postgres(): 32 | try: 33 | cur.execute("""CREATE TABLE IF NOT EXISTS churn_modelling_creditscore (geography VARCHAR(50), gender VARCHAR(20), avg_credit_score FLOAT, total_exited INTEGER)""") 34 | cur.execute("""CREATE TABLE IF NOT EXISTS churn_modelling_exited_age_correlation (geography VARCHAR(50), gender VARCHAR(20), exited INTEGER, avg_age FLOAT, avg_salary FLOAT,number_of_exited_or_not INTEGER)""") 35 | cur.execute("""CREATE TABLE IF NOT EXISTS churn_modelling_exited_salary_correlation (exited INTEGER, is_greater INTEGER, correlation INTEGER)""") 36 | logging.info("3 tables created successfully in Postgres server") 37 | except Exception as e: 38 | traceback.print_exc() 39 | logging.error(f'Tables cannot be created due to: {e}') 40 | 41 | 42 | def insert_creditscore_table(df_creditscore): 43 | query = "INSERT INTO churn_modelling_creditscore (geography, gender, avg_credit_score, total_exited) VALUES (%s,%s,%s,%s)" 44 | row_count = 0 45 | for _, row in df_creditscore.iterrows(): 46 | values = (row['geography'],row['gender'],row['avg_credit_score'],row['total_exited']) 47 | cur.execute(query,values) 48 | row_count += 1 49 | 50 | logging.info(f"{row_count} rows inserted into table churn_modelling_creditscore") 51 | 52 | 53 | def insert_exited_age_correlation_table(df_exited_age_correlation): 54 | query = """INSERT INTO churn_modelling_exited_age_correlation (Geography, Gender, exited, avg_age, avg_salary, number_of_exited_or_not) VALUES (%s,%s,%s,%s,%s,%s)""" 55 | row_count = 0 56 | for _, row in df_exited_age_correlation.iterrows(): 57 | values = (row['geography'],row['gender'],row['exited'],row['avg_age'],row['avg_salary'],row['number_of_exited_or_not']) 58 | cur.execute(query,values) 59 | row_count += 1 60 | 61 | logging.info(f"{row_count} rows inserted into table churn_modelling_exited_age_correlation") 62 | 63 | 64 | def insert_exited_salary_correlation_table(df_exited_salary_correlation): 65 | query = """INSERT INTO churn_modelling_exited_salary_correlation (exited, is_greater, correlation) VALUES (%s,%s,%s)""" 66 | row_count = 0 67 | for _, row in df_exited_salary_correlation.iterrows(): 68 | values = (int(row['exited']),int(row['is_greater']),int(row['correlation'])) 69 | cur.execute(query,values) 70 | row_count += 1 71 | 72 | logging.info(f"{row_count} rows inserted into table churn_modelling_exited_salary_correlation") 73 | 74 | 75 | def write_df_to_postgres_main(): 76 | main_df = create_base_df(cur) 77 | df_creditscore = create_creditscore_df(main_df) 78 | df_exited_age_correlation = create_exited_age_correlation(main_df) 79 | df_exited_salary_correlation = create_exited_salary_correlation(main_df) 80 | 81 | create_new_tables_in_postgres() 82 | insert_creditscore_table(df_creditscore) 83 | insert_exited_age_correlation_table(df_exited_age_correlation) 84 | insert_exited_salary_correlation_table(df_exited_salary_correlation) 85 | 86 | conn.commit() 87 | cur.close() 88 | conn.close() 89 | 90 | 91 | if __name__ == '__main__': 92 | main_df = create_base_df(cur) 93 | df_creditscore = create_creditscore_df(main_df) 94 | df_exited_age_correlation = create_exited_age_correlation(main_df) 95 | df_exited_salary_correlation = create_exited_salary_correlation(main_df) 96 | 97 | create_new_tables_in_postgres() 98 | insert_creditscore_table(df_creditscore) 99 | insert_exited_age_correlation_table(df_exited_age_correlation) 100 | insert_exited_salary_correlation_table(df_exited_salary_correlation) 101 | 102 | conn.commit() 103 | cur.close() 104 | conn.close() -------------------------------------------------------------------------------- /csv_files/df_exited_salary_correlation.csv: -------------------------------------------------------------------------------- 1 | ,exited,is_greater,correlation 2 | 0,1,1,1 3 | 1,0,1,0 4 | 2,1,1,1 5 | 3,0,0,1 6 | 4,0,0,1 7 | 5,1,1,1 8 | 6,0,0,1 9 | 7,1,1,1 10 | 8,0,0,1 11 | 9,0,0,1 12 | 10,0,0,1 13 | 11,0,0,1 14 | 12,0,0,1 15 | 13,0,1,0 16 | 14,0,0,1 17 | 15,0,0,1 18 | 16,1,0,0 19 | 17,0,0,1 20 | 18,0,1,0 21 | 19,0,0,1 22 | 20,0,1,0 23 | 21,0,1,0 24 | 22,1,1,1 25 | 23,0,0,1 26 | 24,0,1,0 27 | 25,0,1,0 28 | 26,0,1,0 29 | 27,0,0,1 30 | 28,0,1,0 31 | 29,0,0,1 32 | 30,1,1,1 33 | 31,0,1,0 34 | 32,0,0,1 35 | 33,0,0,1 36 | 34,0,1,0 37 | 35,1,0,0 38 | 36,0,1,0 39 | 37,0,1,0 40 | 38,0,0,1 41 | 39,0,1,0 42 | 40,0,0,1 43 | 41,1,1,1 44 | 42,0,0,1 45 | 43,1,1,1 46 | 44,0,1,0 47 | 45,0,1,0 48 | 46,1,1,1 49 | 47,1,1,1 50 | 48,0,0,1 51 | 49,0,1,0 52 | 50,0,1,0 53 | 51,0,0,1 54 | 52,0,1,0 55 | 53,1,1,1 56 | 54,1,0,0 57 | 55,0,1,0 58 | 56,0,0,1 59 | 57,0,0,1 60 | 58,1,0,0 61 | 59,0,0,1 62 | 60,0,0,1 63 | 61,0,1,0 64 | 62,0,1,0 65 | 63,0,1,0 66 | 64,0,0,1 67 | 65,0,0,1 68 | 66,0,1,0 69 | 67,0,1,0 70 | 68,0,1,0 71 | 69,0,0,1 72 | 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1894 | 1892,0,1,0 1895 | 1893,0,1,0 1896 | 1894,0,1,0 1897 | 1895,0,1,0 1898 | 1896,0,1,0 1899 | 1897,0,0,1 1900 | 1898,1,1,1 1901 | 1899,0,1,0 1902 | 1900,0,1,0 1903 | 1901,0,0,1 1904 | 1902,0,1,0 1905 | 1903,0,0,1 1906 | 1904,0,1,0 1907 | 1905,0,1,0 1908 | 1906,0,1,0 1909 | 1907,0,0,1 1910 | 1908,0,0,1 1911 | 1909,0,0,1 1912 | 1910,0,1,0 1913 | 1911,0,0,1 1914 | 1912,1,1,1 1915 | 1913,0,0,1 1916 | 1914,0,1,0 1917 | 1915,0,1,0 1918 | 1916,1,0,0 1919 | 1917,1,0,0 1920 | 1918,0,0,1 1921 | 1919,1,1,1 1922 | 1920,0,0,1 1923 | 1921,1,1,1 1924 | 1922,1,1,1 1925 | 1923,1,1,1 1926 | 1924,0,0,1 1927 | 1925,0,1,0 1928 | 1926,0,1,0 1929 | 1927,0,1,0 1930 | 1928,1,0,0 1931 | 1929,0,1,0 1932 | 1930,0,1,0 1933 | 1931,1,1,1 1934 | 1932,0,0,1 1935 | 1933,0,0,1 1936 | 1934,0,0,1 1937 | 1935,0,0,1 1938 | 1936,0,1,0 1939 | 1937,0,0,1 1940 | 1938,0,1,0 1941 | 1939,0,0,1 1942 | 1940,1,1,1 1943 | 1941,0,0,1 1944 | 1942,0,1,0 1945 | 1943,0,1,0 1946 | 1944,0,1,0 1947 | 1945,1,1,1 1948 | 1946,0,1,0 1949 | 1947,0,1,0 1950 | 1948,0,1,0 1951 | 1949,0,1,0 1952 | 1950,1,0,0 1953 | 1951,1,1,1 1954 | 1952,0,0,1 1955 | 1953,0,1,0 1956 | 1954,0,1,0 1957 | 1955,1,1,1 1958 | 1956,1,1,1 1959 | 1957,1,1,1 1960 | 1958,0,1,0 1961 | 1959,0,0,1 1962 | 1960,0,0,1 1963 | 1961,0,0,1 1964 | 1962,1,1,1 1965 | 1963,0,0,1 1966 | 1964,1,1,1 1967 | 1965,1,1,1 1968 | 1966,0,1,0 1969 | 1967,0,1,0 1970 | 1968,0,0,1 1971 | 1969,0,1,0 1972 | 1970,0,0,1 1973 | 1971,0,0,1 1974 | 1972,0,0,1 1975 | 1973,0,1,0 1976 | 1974,0,1,0 1977 | 1975,0,0,1 1978 | 1976,0,1,0 1979 | 1977,1,1,1 1980 | 1978,0,1,0 1981 | 1979,0,1,0 1982 | 1980,0,0,1 1983 | 1981,0,1,0 1984 | 1982,0,1,0 1985 | 1983,0,1,0 1986 | 1984,0,1,0 1987 | 1985,1,0,0 1988 | 1986,0,1,0 1989 | 1987,1,0,0 1990 | 1988,1,1,1 1991 | 1989,0,0,1 1992 | 1990,0,1,0 1993 | 1991,0,0,1 1994 | 1992,0,0,1 1995 | 1993,1,1,1 1996 | 1994,1,0,0 1997 | 1995,0,1,0 1998 | 1996,1,0,0 1999 | 1997,0,1,0 2000 | 1998,0,0,1 2001 | 1999,0,1,0 2002 | 2000,0,0,1 2003 | 2001,0,0,1 2004 | 2002,0,0,1 2005 | 2003,0,1,0 2006 | 2004,0,0,1 2007 | 2005,1,0,0 2008 | 2006,0,0,1 2009 | 2007,1,0,0 2010 | 2008,0,1,0 2011 | 2009,1,1,1 2012 | 2010,0,1,0 2013 | 2011,0,0,1 2014 | 2012,0,0,1 2015 | 2013,0,1,0 2016 | 2014,0,0,1 2017 | 2015,0,0,1 2018 | 2016,0,1,0 2019 | 2017,0,0,1 2020 | 2018,0,1,0 2021 | 2019,0,1,0 2022 | 2020,0,1,0 2023 | 2021,0,1,0 2024 | 2022,0,0,1 2025 | 2023,0,1,0 2026 | 2024,1,0,0 2027 | 2025,0,0,1 2028 | 2026,0,0,1 2029 | 2027,1,0,0 2030 | 2028,0,1,0 2031 | 2029,0,1,0 2032 | 2030,0,1,0 2033 | 2031,0,1,0 2034 | 2032,1,1,1 2035 | 2033,0,1,0 2036 | 2034,0,0,1 2037 | 2035,0,0,1 2038 | 2036,0,0,1 2039 | 2037,1,1,1 2040 | 2038,1,1,1 2041 | 2039,0,1,0 2042 | 2040,0,1,0 2043 | 2041,0,0,1 2044 | 2042,0,0,1 2045 | 2043,0,0,1 2046 | 2044,0,0,1 2047 | 2045,0,0,1 2048 | 2046,0,0,1 2049 | 2047,0,0,1 2050 | 2048,0,1,0 2051 | 2049,1,0,0 2052 | 2050,1,1,1 2053 | 2051,0,1,0 2054 | 2052,0,1,0 2055 | 2053,1,1,1 2056 | 2054,0,0,1 2057 | 2055,0,0,1 2058 | 2056,1,1,1 2059 | 2057,0,1,0 2060 | 2058,0,1,0 2061 | 2059,0,0,1 2062 | 2060,0,1,0 2063 | 2061,0,1,0 2064 | 2062,0,0,1 2065 | 2063,1,1,1 2066 | 2064,0,1,0 2067 | 2065,0,1,0 2068 | 2066,0,1,0 2069 | 2067,0,0,1 2070 | 2068,1,1,1 2071 | 2069,0,1,0 2072 | 2070,1,0,0 2073 | 2071,0,1,0 2074 | 2072,0,1,0 2075 | 2073,0,0,1 2076 | 2074,1,1,1 2077 | 2075,0,1,0 2078 | 2076,1,1,1 2079 | 2077,0,1,0 2080 | 2078,1,1,1 2081 | 2079,0,0,1 2082 | 2080,1,0,0 2083 | 2081,0,1,0 2084 | 2082,0,1,0 2085 | 2083,0,1,0 2086 | 2084,1,1,1 2087 | 2085,0,0,1 2088 | 2086,0,1,0 2089 | 2087,1,0,0 2090 | 2088,0,0,1 2091 | 2089,0,0,1 2092 | 2090,0,0,1 2093 | 2091,0,0,1 2094 | 2092,1,0,0 2095 | 2093,0,0,1 2096 | 2094,0,1,0 2097 | 2095,0,0,1 2098 | 2096,0,0,1 2099 | 2097,0,0,1 2100 | 2098,1,0,0 2101 | 2099,0,1,0 2102 | 2100,0,1,0 2103 | 2101,0,0,1 2104 | 2102,0,0,1 2105 | 2103,0,1,0 2106 | 2104,0,0,1 2107 | 2105,0,0,1 2108 | 2106,1,0,0 2109 | 2107,0,0,1 2110 | 2108,0,0,1 2111 | 2109,0,1,0 2112 | 2110,1,0,0 2113 | 2111,0,1,0 2114 | 2112,0,0,1 2115 | 2113,0,0,1 2116 | 2114,0,1,0 2117 | 2115,1,0,0 2118 | 2116,0,0,1 2119 | 2117,0,0,1 2120 | 2118,1,1,1 2121 | 2119,0,1,0 2122 | 2120,1,1,1 2123 | 2121,0,1,0 2124 | 2122,0,0,1 2125 | 2123,0,1,0 2126 | 2124,1,1,1 2127 | 2125,0,1,0 2128 | 2126,0,0,1 2129 | 2127,0,1,0 2130 | 2128,0,1,0 2131 | 2129,0,1,0 2132 | 2130,0,1,0 2133 | 2131,0,1,0 2134 | 2132,1,1,1 2135 | 2133,0,1,0 2136 | 2134,0,0,1 2137 | 2135,1,0,0 2138 | 2136,0,0,1 2139 | 2137,0,0,1 2140 | 2138,0,1,0 2141 | 2139,1,0,0 2142 | 2140,0,0,1 2143 | 2141,1,0,0 2144 | 2142,0,1,0 2145 | 2143,0,1,0 2146 | 2144,0,0,1 2147 | 2145,0,1,0 2148 | 2146,0,0,1 2149 | 2147,0,1,0 2150 | 2148,0,0,1 2151 | 2149,1,0,0 2152 | 2150,0,0,1 2153 | 2151,1,1,1 2154 | 2152,1,0,0 2155 | 2153,1,0,0 2156 | 2154,1,0,0 2157 | 2155,0,0,1 2158 | 2156,0,1,0 2159 | 2157,0,1,0 2160 | 2158,0,1,0 2161 | 2159,0,0,1 2162 | 2160,0,0,1 2163 | 2161,0,1,0 2164 | 2162,0,1,0 2165 | 2163,0,0,1 2166 | 2164,0,1,0 2167 | 2165,0,1,0 2168 | 2166,0,0,1 2169 | 2167,0,0,1 2170 | 2168,1,0,0 2171 | 2169,0,0,1 2172 | 2170,0,1,0 2173 | 2171,0,1,0 2174 | 2172,0,0,1 2175 | 2173,0,1,0 2176 | 2174,0,1,0 2177 | 2175,0,1,0 2178 | 2176,0,0,1 2179 | 2177,0,0,1 2180 | 2178,0,0,1 2181 | 2179,0,1,0 2182 | 2180,0,0,1 2183 | 2181,1,0,0 2184 | 2182,0,0,1 2185 | 2183,0,0,1 2186 | 2184,0,1,0 2187 | 2185,0,1,0 2188 | 2186,1,0,0 2189 | 2187,0,0,1 2190 | 2188,0,1,0 2191 | 2189,0,1,0 2192 | 2190,1,1,1 2193 | 2191,1,0,0 2194 | 2192,0,1,0 2195 | 2193,0,1,0 2196 | 2194,0,1,0 2197 | 2195,1,1,1 2198 | 2196,1,1,1 2199 | 2197,0,0,1 2200 | 2198,0,0,1 2201 | 2199,0,0,1 2202 | 2200,0,1,0 2203 | 2201,0,1,0 2204 | 2202,0,1,0 2205 | 2203,0,1,0 2206 | 2204,0,0,1 2207 | 2205,0,1,0 2208 | 2206,0,1,0 2209 | 2207,0,0,1 2210 | 2208,1,0,0 2211 | 2209,0,0,1 2212 | 2210,0,0,1 2213 | 2211,0,1,0 2214 | 2212,0,1,0 2215 | 2213,1,0,0 2216 | 2214,0,1,0 2217 | 2215,0,0,1 2218 | 2216,0,1,0 2219 | 2217,0,1,0 2220 | 2218,0,1,0 2221 | 2219,0,1,0 2222 | 2220,0,1,0 2223 | 2221,1,1,1 2224 | 2222,0,1,0 2225 | 2223,0,1,0 2226 | 2224,0,1,0 2227 | 2225,0,0,1 2228 | 2226,0,1,0 2229 | 2227,0,1,0 2230 | 2228,0,0,1 2231 | 2229,0,1,0 2232 | 2230,0,1,0 2233 | 2231,0,0,1 2234 | 2232,0,1,0 2235 | 2233,0,0,1 2236 | 2234,1,0,0 2237 | 2235,1,1,1 2238 | 2236,1,1,1 2239 | 2237,1,1,1 2240 | 2238,0,1,0 2241 | 2239,1,1,1 2242 | 2240,0,1,0 2243 | 2241,1,0,0 2244 | 2242,0,1,0 2245 | 2243,1,1,1 2246 | 2244,0,1,0 2247 | 2245,1,1,1 2248 | 2246,0,0,1 2249 | 2247,0,1,0 2250 | 2248,0,0,1 2251 | 2249,0,0,1 2252 | 2250,0,0,1 2253 | 2251,0,0,1 2254 | 2252,0,1,0 2255 | 2253,0,1,0 2256 | 2254,0,1,0 2257 | 2255,0,1,0 2258 | 2256,0,0,1 2259 | 2257,0,1,0 2260 | 2258,0,0,1 2261 | 2259,0,1,0 2262 | 2260,0,0,1 2263 | 2261,0,1,0 2264 | 2262,1,0,0 2265 | 2263,0,1,0 2266 | 2264,0,0,1 2267 | 2265,0,1,0 2268 | 2266,1,1,1 2269 | 2267,0,1,0 2270 | 2268,1,1,1 2271 | 2269,0,0,1 2272 | 2270,1,0,0 2273 | 2271,0,0,1 2274 | 2272,1,0,0 2275 | 2273,0,0,1 2276 | 2274,1,1,1 2277 | 2275,0,1,0 2278 | 2276,1,0,0 2279 | 2277,0,0,1 2280 | 2278,0,1,0 2281 | 2279,0,0,1 2282 | 2280,0,0,1 2283 | 2281,0,1,0 2284 | 2282,0,1,0 2285 | 2283,0,1,0 2286 | 2284,0,1,0 2287 | 2285,1,0,0 2288 | 2286,0,1,0 2289 | 2287,0,1,0 2290 | 2288,0,1,0 2291 | 2289,1,0,0 2292 | 2290,0,0,1 2293 | 2291,0,1,0 2294 | 2292,0,0,1 2295 | 2293,1,1,1 2296 | 2294,0,1,0 2297 | 2295,1,1,1 2298 | 2296,1,1,1 2299 | 2297,0,1,0 2300 | 2298,0,0,1 2301 | 2299,0,0,1 2302 | 2300,0,0,1 2303 | 2301,0,1,0 2304 | 2302,0,0,1 2305 | 2303,0,1,0 2306 | 2304,0,1,0 2307 | 2305,0,1,0 2308 | 2306,0,0,1 2309 | 2307,0,1,0 2310 | 2308,0,1,0 2311 | 2309,1,0,0 2312 | 2310,0,0,1 2313 | 2311,0,1,0 2314 | 2312,1,0,0 2315 | 2313,0,1,0 2316 | 2314,0,1,0 2317 | 2315,0,0,1 2318 | 2316,1,0,0 2319 | 2317,0,0,1 2320 | 2318,0,1,0 2321 | 2319,0,1,0 2322 | 2320,0,1,0 2323 | 2321,0,1,0 2324 | 2322,0,0,1 2325 | 2323,0,1,0 2326 | 2324,0,0,1 2327 | 2325,0,1,0 2328 | 2326,0,1,0 2329 | 2327,0,0,1 2330 | 2328,0,0,1 2331 | 2329,0,1,0 2332 | 2330,0,1,0 2333 | 2331,0,1,0 2334 | 2332,0,1,0 2335 | 2333,0,1,0 2336 | 2334,0,0,1 2337 | 2335,1,0,0 2338 | 2336,1,0,0 2339 | 2337,0,1,0 2340 | 2338,0,0,1 2341 | 2339,0,1,0 2342 | 2340,1,1,1 2343 | 2341,0,1,0 2344 | 2342,0,1,0 2345 | 2343,0,1,0 2346 | 2344,1,0,0 2347 | 2345,0,0,1 2348 | 2346,1,0,0 2349 | 2347,1,0,0 2350 | 2348,0,0,1 2351 | 2349,1,1,1 2352 | 2350,1,0,0 2353 | 2351,1,1,1 2354 | 2352,1,1,1 2355 | 2353,0,1,0 2356 | 2354,0,1,0 2357 | 2355,1,0,0 2358 | 2356,0,0,1 2359 | 2357,0,0,1 2360 | 2358,0,0,1 2361 | 2359,0,1,0 2362 | 2360,0,0,1 2363 | 2361,0,1,0 2364 | 2362,1,0,0 2365 | 2363,0,0,1 2366 | 2364,0,0,1 2367 | 2365,0,1,0 2368 | 2366,0,1,0 2369 | 2367,1,0,0 2370 | 2368,0,1,0 2371 | 2369,1,1,1 2372 | 2370,0,1,0 2373 | 2371,0,1,0 2374 | 2372,1,1,1 2375 | 2373,0,1,0 2376 | 2374,0,0,1 2377 | 2375,0,0,1 2378 | 2376,0,0,1 2379 | 2377,0,0,1 2380 | 2378,0,1,0 2381 | 2379,0,0,1 2382 | 2380,0,1,0 2383 | 2381,1,0,0 2384 | 2382,0,0,1 2385 | 2383,0,1,0 2386 | 2384,0,1,0 2387 | 2385,0,1,0 2388 | 2386,0,0,1 2389 | 2387,0,1,0 2390 | 2388,1,0,0 2391 | 2389,0,0,1 2392 | 2390,0,1,0 2393 | 2391,0,1,0 2394 | 2392,0,1,0 2395 | 2393,1,0,0 2396 | 2394,0,0,1 2397 | 2395,1,0,0 2398 | 2396,0,1,0 2399 | 2397,0,0,1 2400 | 2398,0,0,1 2401 | 2399,0,1,0 2402 | 2400,0,0,1 2403 | 2401,0,0,1 2404 | 2402,0,0,1 2405 | 2403,0,0,1 2406 | 2404,1,1,1 2407 | 2405,0,1,0 2408 | 2406,0,1,0 2409 | 2407,1,1,1 2410 | 2408,0,0,1 2411 | 2409,0,1,0 2412 | 2410,0,0,1 2413 | 2411,0,0,1 2414 | 2412,0,1,0 2415 | 2413,0,1,0 2416 | 2414,1,1,1 2417 | 2415,0,1,0 2418 | 2416,0,0,1 2419 | 2417,0,0,1 2420 | 2418,1,0,0 2421 | 2419,0,1,0 2422 | 2420,0,1,0 2423 | 2421,0,1,0 2424 | 2422,0,0,1 2425 | 2423,0,1,0 2426 | 2424,0,0,1 2427 | 2425,0,1,0 2428 | 2426,0,1,0 2429 | 2427,1,0,0 2430 | 2428,0,1,0 2431 | 2429,0,0,1 2432 | 2430,1,0,0 2433 | 2431,1,1,1 2434 | 2432,1,0,0 2435 | 2433,0,0,1 2436 | 2434,1,1,1 2437 | 2435,0,0,1 2438 | 2436,0,0,1 2439 | 2437,0,0,1 2440 | 2438,1,1,1 2441 | 2439,1,1,1 2442 | 2440,0,0,1 2443 | 2441,0,0,1 2444 | 2442,0,1,0 2445 | 2443,0,1,0 2446 | 2444,0,0,1 2447 | 2445,0,0,1 2448 | 2446,0,1,0 2449 | 2447,0,0,1 2450 | 2448,0,1,0 2451 | 2449,1,1,1 2452 | 2450,0,0,1 2453 | 2451,0,0,1 2454 | 2452,0,0,1 2455 | 2453,0,0,1 2456 | 2454,1,1,1 2457 | 2455,1,1,1 2458 | 2456,0,1,0 2459 | 2457,0,0,1 2460 | 2458,0,0,1 2461 | 2459,1,0,0 2462 | 2460,1,0,0 2463 | 2461,0,1,0 2464 | 2462,1,1,1 2465 | 2463,0,0,1 2466 | 2464,0,0,1 2467 | 2465,0,0,1 2468 | 2466,0,1,0 2469 | 2467,0,1,0 2470 | 2468,0,1,0 2471 | 2469,1,1,1 2472 | 2470,0,1,0 2473 | 2471,0,1,0 2474 | 2472,0,0,1 2475 | 2473,1,1,1 2476 | 2474,1,1,1 2477 | 2475,0,1,0 2478 | 2476,0,1,0 2479 | 2477,1,0,0 2480 | 2478,0,1,0 2481 | 2479,0,1,0 2482 | 2480,0,0,1 2483 | 2481,0,0,1 2484 | 2482,0,0,1 2485 | 2483,0,0,1 2486 | 2484,0,1,0 2487 | 2485,0,0,1 2488 | 2486,0,1,0 2489 | 2487,0,0,1 2490 | 2488,0,0,1 2491 | 2489,0,0,1 2492 | 2490,0,0,1 2493 | 2491,0,1,0 2494 | 2492,1,1,1 2495 | 2493,0,1,0 2496 | 2494,0,1,0 2497 | 2495,1,1,1 2498 | 2496,0,1,0 2499 | 2497,0,1,0 2500 | 2498,0,0,1 2501 | 2499,1,1,1 2502 | 2500,0,0,1 2503 | 2501,0,1,0 2504 | 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