Utilize Postgresql for AI development

We utilize the relational database which is the master data storage for AI development.

Inside a company, to disseminate useful information to those who aim AI development, we leave it as a reminder log for technical points that we noticed and release it for utilization.

Keyword is PostgreSQL, pyhon, SQL, AI development, psycopg2.

Overview of the notebook

Effectively utilize the relational database which is the storage place of master data in company, and activate AI development.

General AI development

· According to the task, extract data from the relational database which is the storage destination of the master data, convert it to a CSV file.

· Place the created CSV file on the AI development environment.

· AI development need using Python know-how.

Conventional issue

· When multiple people do AI development within a company, use Docker for efficiency, but data management is complicated.

· Extract data, place CSV files, etc.

· You need to study Python and preprogram data preprocessing.

Solution of the conventional problem

Data on the relational database is directly IO from the program on Python to solve the problem.

· Easy IO from Python program of Docker container.

· Eliminate the complexity of Docker containers by doing IO via network.

· Records extraction, item selection, missing data storage processing, etc. can be performed using knowledge of conventional SQL statements.

Precondition

· As a relational database, it is OSS, I have used records around AI development around me, Postgresql Usage introduction.

· The version of each software is decided with reference to "ReNom" of AI software GRID company which he normally uses.

· For drivers, decide on their own terms from the usage trends when searching the Web.

environment

· Information on the survivor of the titanic of kaggle is used as a sample. Download "train.csv" from the data tag where the data below is obtained.

[Data source] ( https://www.kaggle.com/c/titanic )

· Creating a table named "titanic_train" in a Postgresql server that access via a network outside the container.

· Import the downloaded data into the database.

Container environment

・ubunts 16.04 LTS ・Python:3.6 ・pandas:0.20.3 ・psycopg2:2.7.3.2( https://qiita.com/takahi/items/c9b7fc01cdccd4b7f661 )

sample

In [1]:
# Install "psycopg2" which is common as a driver for Postgresql.
In [2]:
#!/usr/bin/env python
# encoding:utf-8

# Import a library "psycopg 2".
import psycopg2

# Pandas library for relational database prepare.
import pandas.io.sql as psql
In [3]:
# Connect to the Postgresql server via the network.
# (when using Docker, there is no need to be aware of the folder of the source data.)

con = psycopg2.connect("host=(IP) port=(Port) dbname=(DB Name) user=(User) password=(Pass)")
con.get_backend_pid()
cur = con.cursor()
In [4]:
# Of the survivor data of Kaggle's Titanic, download training data in advance and insert it
# into the Postgresql database with the table titled "titanic_train".
In [5]:
# Select in the data frame of Pandas with no prepation.
sql = """select * from titanic_train;"""
df_train_org = psql.read_sql(sql, con)

df_train_org
Out[5]:
passengerid survived pclass name sex age sibsp parch ticket fare cabin embarked
0 1.0 0.0 3.0 Braund, Mr. Owen Harris male 22.0 1.0 0.0 A/5 21171 7.2500 None S
1 2.0 1.0 1.0 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1.0 0.0 PC 17599 71.2833 C85 C
2 3.0 1.0 3.0 Heikkinen, Miss. Laina female 26.0 0.0 0.0 STON/O2. 3101282 7.9250 None S
3 4.0 1.0 1.0 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1.0 0.0 113803 53.1000 C123 S
4 5.0 0.0 3.0 Allen, Mr. William Henry male 35.0 0.0 0.0 373450 8.0500 None S
5 6.0 0.0 3.0 Moran, Mr. James male NaN 0.0 0.0 330877 8.4583 None Q
6 7.0 0.0 1.0 McCarthy, Mr. Timothy J male 54.0 0.0 0.0 17463 51.8625 E46 S
7 8.0 0.0 3.0 Palsson, Master. Gosta Leonard male 2.0 3.0 1.0 349909 21.0750 None S
8 9.0 1.0 3.0 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27.0 0.0 2.0 347742 11.1333 None S
9 10.0 1.0 2.0 Nasser, Mrs. Nicholas (Adele Achem) female 14.0 1.0 0.0 237736 30.0708 None C
10 11.0 1.0 3.0 Sandstrom, Miss. Marguerite Rut female 4.0 1.0 1.0 PP 9549 16.7000 G6 S
11 12.0 1.0 1.0 Bonnell, Miss. Elizabeth female 58.0 0.0 0.0 113783 26.5500 C103 S
12 13.0 0.0 3.0 Saundercock, Mr. William Henry male 20.0 0.0 0.0 A/5. 2151 8.0500 None S
13 14.0 0.0 3.0 Andersson, Mr. Anders Johan male 39.0 1.0 5.0 347082 31.2750 None S
14 15.0 0.0 3.0 Vestrom, Miss. Hulda Amanda Adolfina female 14.0 0.0 0.0 350406 7.8542 None S
15 16.0 1.0 2.0 Hewlett, Mrs. (Mary D Kingcome) female 55.0 0.0 0.0 248706 16.0000 None S
16 17.0 0.0 3.0 Rice, Master. Eugene male 2.0 4.0 1.0 382652 29.1250 None Q
17 18.0 1.0 2.0 Williams, Mr. Charles Eugene male NaN 0.0 0.0 244373 13.0000 None S
18 19.0 0.0 3.0 Vander Planke, Mrs. Julius (Emelia Maria Vande... female 31.0 1.0 0.0 345763 18.0000 None S
19 20.0 1.0 3.0 Masselmani, Mrs. Fatima female NaN 0.0 0.0 2649 7.2250 None C
20 21.0 0.0 2.0 Fynney, Mr. Joseph J male 35.0 0.0 0.0 239865 26.0000 None S
21 22.0 1.0 2.0 Beesley, Mr. Lawrence male 34.0 0.0 0.0 248698 13.0000 D56 S
22 23.0 1.0 3.0 McGowan, Miss. Anna "Annie" female 15.0 0.0 0.0 330923 8.0292 None Q
23 24.0 1.0 1.0 Sloper, Mr. William Thompson male 28.0 0.0 0.0 113788 35.5000 A6 S
24 25.0 0.0 3.0 Palsson, Miss. Torborg Danira female 8.0 3.0 1.0 349909 21.0750 None S
25 26.0 1.0 3.0 Asplund, Mrs. Carl Oscar (Selma Augusta Emilia... female 38.0 1.0 5.0 347077 31.3875 None S
26 27.0 0.0 3.0 Emir, Mr. Farred Chehab male NaN 0.0 0.0 2631 7.2250 None C
27 28.0 0.0 1.0 Fortune, Mr. Charles Alexander male 19.0 3.0 2.0 19950 263.0000 C23 C25 C27 S
28 29.0 1.0 3.0 O'Dwyer, Miss. Ellen "Nellie" female NaN 0.0 0.0 330959 7.8792 None Q
29 30.0 0.0 3.0 Todoroff, Mr. Lalio male NaN 0.0 0.0 349216 7.8958 None S
... ... ... ... ... ... ... ... ... ... ... ... ...
861 862.0 0.0 2.0 Giles, Mr. Frederick Edward male 21.0 1.0 0.0 28134 11.5000 None S
862 863.0 1.0 1.0 Swift, Mrs. Frederick Joel (Margaret Welles Ba... female 48.0 0.0 0.0 17466 25.9292 D17 S
863 864.0 0.0 3.0 Sage, Miss. Dorothy Edith "Dolly" female NaN 8.0 2.0 CA. 2343 69.5500 None S
864 865.0 0.0 2.0 Gill, Mr. John William male 24.0 0.0 0.0 233866 13.0000 None S
865 866.0 1.0 2.0 Bystrom, Mrs. (Karolina) female 42.0 0.0 0.0 236852 13.0000 None S
866 867.0 1.0 2.0 Duran y More, Miss. Asuncion female 27.0 1.0 0.0 SC/PARIS 2149 13.8583 None C
867 868.0 0.0 1.0 Roebling, Mr. Washington Augustus II male 31.0 0.0 0.0 PC 17590 50.4958 A24 S
868 869.0 0.0 3.0 van Melkebeke, Mr. Philemon male NaN 0.0 0.0 345777 9.5000 None S
869 870.0 1.0 3.0 Johnson, Master. Harold Theodor male 4.0 1.0 1.0 347742 11.1333 None S
870 871.0 0.0 3.0 Balkic, Mr. Cerin male 26.0 0.0 0.0 349248 7.8958 None S
871 872.0 1.0 1.0 Beckwith, Mrs. Richard Leonard (Sallie Monypeny) female 47.0 1.0 1.0 11751 52.5542 D35 S
872 873.0 0.0 1.0 Carlsson, Mr. Frans Olof male 33.0 0.0 0.0 695 5.0000 B51 B53 B55 S
873 874.0 0.0 3.0 Vander Cruyssen, Mr. Victor male 47.0 0.0 0.0 345765 9.0000 None S
874 875.0 1.0 2.0 Abelson, Mrs. Samuel (Hannah Wizosky) female 28.0 1.0 0.0 P/PP 3381 24.0000 None C
875 876.0 1.0 3.0 Najib, Miss. Adele Kiamie "Jane" female 15.0 0.0 0.0 2667 7.2250 None C
876 877.0 0.0 3.0 Gustafsson, Mr. Alfred Ossian male 20.0 0.0 0.0 7534 9.8458 None S
877 878.0 0.0 3.0 Petroff, Mr. Nedelio male 19.0 0.0 0.0 349212 7.8958 None S
878 879.0 0.0 3.0 Laleff, Mr. Kristo male NaN 0.0 0.0 349217 7.8958 None S
879 880.0 1.0 1.0 Potter, Mrs. Thomas Jr (Lily Alexenia Wilson) female 56.0 0.0 1.0 11767 83.1583 C50 C
880 881.0 1.0 2.0 Shelley, Mrs. William (Imanita Parrish Hall) female 25.0 0.0 1.0 230433 26.0000 None S
881 882.0 0.0 3.0 Markun, Mr. Johann male 33.0 0.0 0.0 349257 7.8958 None S
882 883.0 0.0 3.0 Dahlberg, Miss. Gerda Ulrika female 22.0 0.0 0.0 7552 10.5167 None S
883 884.0 0.0 2.0 Banfield, Mr. Frederick James male 28.0 0.0 0.0 C.A./SOTON 34068 10.5000 None S
884 885.0 0.0 3.0 Sutehall, Mr. Henry Jr male 25.0 0.0 0.0 SOTON/OQ 392076 7.0500 None S
885 886.0 0.0 3.0 Rice, Mrs. William (Margaret Norton) female 39.0 0.0 5.0 382652 29.1250 None Q
886 887.0 0.0 2.0 Montvila, Rev. Juozas male 27.0 0.0 0.0 211536 13.0000 None S
887 888.0 1.0 1.0 Graham, Miss. Margaret Edith female 19.0 0.0 0.0 112053 30.0000 B42 S
888 889.0 0.0 3.0 Johnston, Miss. Catherine Helen "Carrie" female NaN 1.0 2.0 W./C. 6607 23.4500 None S
889 890.0 1.0 1.0 Behr, Mr. Karl Howell male 26.0 0.0 0.0 111369 30.0000 C148 C
890 891.0 0.0 3.0 Dooley, Mr. Patrick male 32.0 0.0 0.0 370376 7.7500 None Q

891 rows × 12 columns

In [6]:
# Change the SQL statement only for items necessary for analysis and read.
sql = """select passengerid, pclass, sex, age, sibsp, parch, ticket, fare, cabin, embarked from titanic_train;"""
df_train_01 = psql.read_sql(sql, con)

df_train_01
Out[6]:
passengerid pclass sex age sibsp parch ticket fare cabin embarked
0 1.0 3.0 male 22.0 1.0 0.0 A/5 21171 7.2500 None S
1 2.0 1.0 female 38.0 1.0 0.0 PC 17599 71.2833 C85 C
2 3.0 3.0 female 26.0 0.0 0.0 STON/O2. 3101282 7.9250 None S
3 4.0 1.0 female 35.0 1.0 0.0 113803 53.1000 C123 S
4 5.0 3.0 male 35.0 0.0 0.0 373450 8.0500 None S
5 6.0 3.0 male NaN 0.0 0.0 330877 8.4583 None Q
6 7.0 1.0 male 54.0 0.0 0.0 17463 51.8625 E46 S
7 8.0 3.0 male 2.0 3.0 1.0 349909 21.0750 None S
8 9.0 3.0 female 27.0 0.0 2.0 347742 11.1333 None S
9 10.0 2.0 female 14.0 1.0 0.0 237736 30.0708 None C
10 11.0 3.0 female 4.0 1.0 1.0 PP 9549 16.7000 G6 S
11 12.0 1.0 female 58.0 0.0 0.0 113783 26.5500 C103 S
12 13.0 3.0 male 20.0 0.0 0.0 A/5. 2151 8.0500 None S
13 14.0 3.0 male 39.0 1.0 5.0 347082 31.2750 None S
14 15.0 3.0 female 14.0 0.0 0.0 350406 7.8542 None S
15 16.0 2.0 female 55.0 0.0 0.0 248706 16.0000 None S
16 17.0 3.0 male 2.0 4.0 1.0 382652 29.1250 None Q
17 18.0 2.0 male NaN 0.0 0.0 244373 13.0000 None S
18 19.0 3.0 female 31.0 1.0 0.0 345763 18.0000 None S
19 20.0 3.0 female NaN 0.0 0.0 2649 7.2250 None C
20 21.0 2.0 male 35.0 0.0 0.0 239865 26.0000 None S
21 22.0 2.0 male 34.0 0.0 0.0 248698 13.0000 D56 S
22 23.0 3.0 female 15.0 0.0 0.0 330923 8.0292 None Q
23 24.0 1.0 male 28.0 0.0 0.0 113788 35.5000 A6 S
24 25.0 3.0 female 8.0 3.0 1.0 349909 21.0750 None S
25 26.0 3.0 female 38.0 1.0 5.0 347077 31.3875 None S
26 27.0 3.0 male NaN 0.0 0.0 2631 7.2250 None C
27 28.0 1.0 male 19.0 3.0 2.0 19950 263.0000 C23 C25 C27 S
28 29.0 3.0 female NaN 0.0 0.0 330959 7.8792 None Q
29 30.0 3.0 male NaN 0.0 0.0 349216 7.8958 None S
... ... ... ... ... ... ... ... ... ... ...
861 862.0 2.0 male 21.0 1.0 0.0 28134 11.5000 None S
862 863.0 1.0 female 48.0 0.0 0.0 17466 25.9292 D17 S
863 864.0 3.0 female NaN 8.0 2.0 CA. 2343 69.5500 None S
864 865.0 2.0 male 24.0 0.0 0.0 233866 13.0000 None S
865 866.0 2.0 female 42.0 0.0 0.0 236852 13.0000 None S
866 867.0 2.0 female 27.0 1.0 0.0 SC/PARIS 2149 13.8583 None C
867 868.0 1.0 male 31.0 0.0 0.0 PC 17590 50.4958 A24 S
868 869.0 3.0 male NaN 0.0 0.0 345777 9.5000 None S
869 870.0 3.0 male 4.0 1.0 1.0 347742 11.1333 None S
870 871.0 3.0 male 26.0 0.0 0.0 349248 7.8958 None S
871 872.0 1.0 female 47.0 1.0 1.0 11751 52.5542 D35 S
872 873.0 1.0 male 33.0 0.0 0.0 695 5.0000 B51 B53 B55 S
873 874.0 3.0 male 47.0 0.0 0.0 345765 9.0000 None S
874 875.0 2.0 female 28.0 1.0 0.0 P/PP 3381 24.0000 None C
875 876.0 3.0 female 15.0 0.0 0.0 2667 7.2250 None C
876 877.0 3.0 male 20.0 0.0 0.0 7534 9.8458 None S
877 878.0 3.0 male 19.0 0.0 0.0 349212 7.8958 None S
878 879.0 3.0 male NaN 0.0 0.0 349217 7.8958 None S
879 880.0 1.0 female 56.0 0.0 1.0 11767 83.1583 C50 C
880 881.0 2.0 female 25.0 0.0 1.0 230433 26.0000 None S
881 882.0 3.0 male 33.0 0.0 0.0 349257 7.8958 None S
882 883.0 3.0 female 22.0 0.0 0.0 7552 10.5167 None S
883 884.0 2.0 male 28.0 0.0 0.0 C.A./SOTON 34068 10.5000 None S
884 885.0 3.0 male 25.0 0.0 0.0 SOTON/OQ 392076 7.0500 None S
885 886.0 3.0 female 39.0 0.0 5.0 382652 29.1250 None Q
886 887.0 2.0 male 27.0 0.0 0.0 211536 13.0000 None S
887 888.0 1.0 female 19.0 0.0 0.0 112053 30.0000 B42 S
888 889.0 3.0 female NaN 1.0 2.0 W./C. 6607 23.4500 None S
889 890.0 1.0 male 26.0 0.0 0.0 111369 30.0000 C148 C
890 891.0 3.0 male 32.0 0.0 0.0 370376 7.7500 None Q

891 rows × 10 columns

In [7]:
# If there is an important missing item and you want to skip that item, correct the SQL sentence and skip it.

sql = """select * from titanic_train where age <> 'NaN';"""
df_train_02 = psql.read_sql(sql, con)

df_train_02
Out[7]:
passengerid survived pclass name sex age sibsp parch ticket fare cabin embarked
0 1.0 0.0 3.0 Braund, Mr. Owen Harris male 22.0 1.0 0.0 A/5 21171 7.2500 None S
1 2.0 1.0 1.0 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1.0 0.0 PC 17599 71.2833 C85 C
2 3.0 1.0 3.0 Heikkinen, Miss. Laina female 26.0 0.0 0.0 STON/O2. 3101282 7.9250 None S
3 4.0 1.0 1.0 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1.0 0.0 113803 53.1000 C123 S
4 5.0 0.0 3.0 Allen, Mr. William Henry male 35.0 0.0 0.0 373450 8.0500 None S
5 7.0 0.0 1.0 McCarthy, Mr. Timothy J male 54.0 0.0 0.0 17463 51.8625 E46 S
6 8.0 0.0 3.0 Palsson, Master. Gosta Leonard male 2.0 3.0 1.0 349909 21.0750 None S
7 9.0 1.0 3.0 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27.0 0.0 2.0 347742 11.1333 None S
8 10.0 1.0 2.0 Nasser, Mrs. Nicholas (Adele Achem) female 14.0 1.0 0.0 237736 30.0708 None C
9 11.0 1.0 3.0 Sandstrom, Miss. Marguerite Rut female 4.0 1.0 1.0 PP 9549 16.7000 G6 S
10 12.0 1.0 1.0 Bonnell, Miss. Elizabeth female 58.0 0.0 0.0 113783 26.5500 C103 S
11 13.0 0.0 3.0 Saundercock, Mr. William Henry male 20.0 0.0 0.0 A/5. 2151 8.0500 None S
12 14.0 0.0 3.0 Andersson, Mr. Anders Johan male 39.0 1.0 5.0 347082 31.2750 None S
13 15.0 0.0 3.0 Vestrom, Miss. Hulda Amanda Adolfina female 14.0 0.0 0.0 350406 7.8542 None S
14 16.0 1.0 2.0 Hewlett, Mrs. (Mary D Kingcome) female 55.0 0.0 0.0 248706 16.0000 None S
15 17.0 0.0 3.0 Rice, Master. Eugene male 2.0 4.0 1.0 382652 29.1250 None Q
16 19.0 0.0 3.0 Vander Planke, Mrs. Julius (Emelia Maria Vande... female 31.0 1.0 0.0 345763 18.0000 None S
17 21.0 0.0 2.0 Fynney, Mr. Joseph J male 35.0 0.0 0.0 239865 26.0000 None S
18 22.0 1.0 2.0 Beesley, Mr. Lawrence male 34.0 0.0 0.0 248698 13.0000 D56 S
19 23.0 1.0 3.0 McGowan, Miss. Anna "Annie" female 15.0 0.0 0.0 330923 8.0292 None Q
20 24.0 1.0 1.0 Sloper, Mr. William Thompson male 28.0 0.0 0.0 113788 35.5000 A6 S
21 25.0 0.0 3.0 Palsson, Miss. Torborg Danira female 8.0 3.0 1.0 349909 21.0750 None S
22 26.0 1.0 3.0 Asplund, Mrs. Carl Oscar (Selma Augusta Emilia... female 38.0 1.0 5.0 347077 31.3875 None S
23 28.0 0.0 1.0 Fortune, Mr. Charles Alexander male 19.0 3.0 2.0 19950 263.0000 C23 C25 C27 S
24 31.0 0.0 1.0 Uruchurtu, Don. Manuel E male 40.0 0.0 0.0 PC 17601 27.7208 None C
25 34.0 0.0 2.0 Wheadon, Mr. Edward H male 66.0 0.0 0.0 C.A. 24579 10.5000 None S
26 35.0 0.0 1.0 Meyer, Mr. Edgar Joseph male 28.0 1.0 0.0 PC 17604 82.1708 None C
27 36.0 0.0 1.0 Holverson, Mr. Alexander Oskar male 42.0 1.0 0.0 113789 52.0000 None S
28 38.0 0.0 3.0 Cann, Mr. Ernest Charles male 21.0 0.0 0.0 A./5. 2152 8.0500 None S
29 39.0 0.0 3.0 Vander Planke, Miss. Augusta Maria female 18.0 2.0 0.0 345764 18.0000 None S
... ... ... ... ... ... ... ... ... ... ... ... ...
684 857.0 1.0 1.0 Wick, Mrs. George Dennick (Mary Hitchcock) female 45.0 1.0 1.0 36928 164.8667 None S
685 858.0 1.0 1.0 Daly, Mr. Peter Denis male 51.0 0.0 0.0 113055 26.5500 E17 S
686 859.0 1.0 3.0 Baclini, Mrs. Solomon (Latifa Qurban) female 24.0 0.0 3.0 2666 19.2583 None C
687 861.0 0.0 3.0 Hansen, Mr. Claus Peter male 41.0 2.0 0.0 350026 14.1083 None S
688 862.0 0.0 2.0 Giles, Mr. Frederick Edward male 21.0 1.0 0.0 28134 11.5000 None S
689 863.0 1.0 1.0 Swift, Mrs. Frederick Joel (Margaret Welles Ba... female 48.0 0.0 0.0 17466 25.9292 D17 S
690 865.0 0.0 2.0 Gill, Mr. John William male 24.0 0.0 0.0 233866 13.0000 None S
691 866.0 1.0 2.0 Bystrom, Mrs. (Karolina) female 42.0 0.0 0.0 236852 13.0000 None S
692 867.0 1.0 2.0 Duran y More, Miss. Asuncion female 27.0 1.0 0.0 SC/PARIS 2149 13.8583 None C
693 868.0 0.0 1.0 Roebling, Mr. Washington Augustus II male 31.0 0.0 0.0 PC 17590 50.4958 A24 S
694 870.0 1.0 3.0 Johnson, Master. Harold Theodor male 4.0 1.0 1.0 347742 11.1333 None S
695 871.0 0.0 3.0 Balkic, Mr. Cerin male 26.0 0.0 0.0 349248 7.8958 None S
696 872.0 1.0 1.0 Beckwith, Mrs. Richard Leonard (Sallie Monypeny) female 47.0 1.0 1.0 11751 52.5542 D35 S
697 873.0 0.0 1.0 Carlsson, Mr. Frans Olof male 33.0 0.0 0.0 695 5.0000 B51 B53 B55 S
698 874.0 0.0 3.0 Vander Cruyssen, Mr. Victor male 47.0 0.0 0.0 345765 9.0000 None S
699 875.0 1.0 2.0 Abelson, Mrs. Samuel (Hannah Wizosky) female 28.0 1.0 0.0 P/PP 3381 24.0000 None C
700 876.0 1.0 3.0 Najib, Miss. Adele Kiamie "Jane" female 15.0 0.0 0.0 2667 7.2250 None C
701 877.0 0.0 3.0 Gustafsson, Mr. Alfred Ossian male 20.0 0.0 0.0 7534 9.8458 None S
702 878.0 0.0 3.0 Petroff, Mr. Nedelio male 19.0 0.0 0.0 349212 7.8958 None S
703 880.0 1.0 1.0 Potter, Mrs. Thomas Jr (Lily Alexenia Wilson) female 56.0 0.0 1.0 11767 83.1583 C50 C
704 881.0 1.0 2.0 Shelley, Mrs. William (Imanita Parrish Hall) female 25.0 0.0 1.0 230433 26.0000 None S
705 882.0 0.0 3.0 Markun, Mr. Johann male 33.0 0.0 0.0 349257 7.8958 None S
706 883.0 0.0 3.0 Dahlberg, Miss. Gerda Ulrika female 22.0 0.0 0.0 7552 10.5167 None S
707 884.0 0.0 2.0 Banfield, Mr. Frederick James male 28.0 0.0 0.0 C.A./SOTON 34068 10.5000 None S
708 885.0 0.0 3.0 Sutehall, Mr. Henry Jr male 25.0 0.0 0.0 SOTON/OQ 392076 7.0500 None S
709 886.0 0.0 3.0 Rice, Mrs. William (Margaret Norton) female 39.0 0.0 5.0 382652 29.1250 None Q
710 887.0 0.0 2.0 Montvila, Rev. Juozas male 27.0 0.0 0.0 211536 13.0000 None S
711 888.0 1.0 1.0 Graham, Miss. Margaret Edith female 19.0 0.0 0.0 112053 30.0000 B42 S
712 890.0 1.0 1.0 Behr, Mr. Karl Howell male 26.0 0.0 0.0 111369 30.0000 C148 C
713 891.0 0.0 3.0 Dooley, Mr. Patrick male 32.0 0.0 0.0 370376 7.7500 None Q

714 rows × 12 columns

In [8]:
# Easy to read the superviser item into another data frame.

sql = """select survived from titanic_train order by passengerid;"""
df_train_03_y =  psql.read_sql(sql, con)

df_train_03_y
Out[8]:
survived
0 0.0
1 1.0
2 1.0
3 1.0
4 0.0
5 0.0
6 0.0
7 0.0
8 1.0
9 1.0
10 1.0
11 1.0
12 0.0
13 0.0
14 0.0
15 1.0
16 0.0
17 1.0
18 0.0
19 1.0
20 0.0
21 1.0
22 1.0
23 1.0
24 0.0
25 1.0
26 0.0
27 0.0
28 1.0
29 0.0
... ...
861 0.0
862 1.0
863 0.0
864 0.0
865 1.0
866 1.0
867 0.0
868 0.0
869 1.0
870 0.0
871 1.0
872 0.0
873 0.0
874 1.0
875 1.0
876 0.0
877 0.0
878 0.0
879 1.0
880 1.0
881 0.0
882 0.0
883 0.0
884 0.0
885 0.0
886 0.0
887 1.0
888 0.0
889 1.0
890 0.0

891 rows × 1 columns