From 6d6d94acfbf554eb4a6bbf4a4e1901c390755b6d Mon Sep 17 00:00:00 2001 From: Rohith Ravindranath Date: Thu, 19 Sep 2019 12:33:46 -0400 Subject: [PATCH] update --- db2_with_json/README.md | 73 + .../data/home-sales-training-data.csv | 1461 +++++++++++++++++ db2_with_json/docs/source/images/img-1.png | Bin 0 -> 122483 bytes db2_with_json/docs/source/images/img-2.png | Bin 0 -> 284460 bytes db2_with_json/docs/source/images/img-3.png | Bin 0 -> 214396 bytes db2_with_json/docs/source/images/img-4.png | Bin 0 -> 428635 bytes db2_with_json/docs/source/images/img-5.png | Bin 0 -> 256335 bytes db2_with_json/docs/source/images/img-6.png | Bin 0 -> 256634 bytes db2_with_json/docs/source/images/img-7.png | Bin 0 -> 169193 bytes .../notebook/JSON Db2 House Value.ipynb | 745 +++++++++ 10 files changed, 2279 insertions(+) create mode 100644 db2_with_json/README.md create mode 100644 db2_with_json/data/home-sales-training-data.csv create mode 100644 db2_with_json/docs/source/images/img-1.png create mode 100644 db2_with_json/docs/source/images/img-2.png create mode 100644 db2_with_json/docs/source/images/img-3.png create mode 100644 db2_with_json/docs/source/images/img-4.png create mode 100644 db2_with_json/docs/source/images/img-5.png create mode 100644 db2_with_json/docs/source/images/img-6.png create mode 100644 db2_with_json/docs/source/images/img-7.png create mode 100644 db2_with_json/notebook/JSON Db2 House Value.ipynb diff --git a/db2_with_json/README.md b/db2_with_json/README.md new file mode 100644 index 0000000..fd7ff35 --- /dev/null +++ b/db2_with_json/README.md @@ -0,0 +1,73 @@ +# Db2 with JSON Documents + +In this sample, we will be going over the JSON features in Db2. We will be creating a table with some columns represented by JSON documents, and we will also demonstrate how to store and retrieve such information. + +## 1. Setting Up Enviroment +In order to run our code, we will be using `Jupyter Notebook`. If you don't have Jupyter Notebook installed, you can either download it from the Anaconda Distribution or use `pip install`. The links are provided below. + +1. [Anaconda Distribution](https://www.anaconda.com/distribution/) +2. [Installing Using Pip](https://jupyter.org/install) + +After successfully installing Jupyter Notebook, we can open it and create a new notebook. You may also download the demo notebook created for you under the `notebook/` folder. + +## 2. Importing Libraries + +Before we start communicating with our database, we need to import certain libraries that will aid us in communicating. + +![Importing Libraries](docs/source/images/img-1.png) + + +We will be importing two libraries- `ibm_db` and `ibm_db_dbi`. ibm_db is a library with low-level functions that will directly connect to our db2 database. To make things easier for you, we will be using ibm-dbi, which communicates with ibm-db and gives us an easy interface to interact with our data and import our data as a pandas dataframe. + + +## 3. Establishing a Connection + +We now need to establish a connection to our Db2 instance which will be used to store our data for this notebook. For this demo we used Db2 on Cloud, however, any Db2 instance will work. + +In order to create a connection with our Db2 instance, we need to aquire the service credentials. For Db2 on Cloud, this can be found on your IBM Cloud account. + +![Getting Service Credentials](docs/source/images/img-2.png) + +On our IBM Cloud account, if you search your Db2 instance name and head to the `Service Crendtials` tab, you will see the credentials that can be used to connect to your instance. If there isn't one that exits, click the `New Credentials` button to create one. + +Now that we have our credentials, lets head back to our notebook and input that information. + +![Getting Service Credentials](docs/source/images/img-3.png) + +Make sure to input the correct credential values to the corresponding label. If you have successfully connected to your Db2 instance, you will see the success message at the bottom of the picture, if not, and error message will pop up. + +***NOTE: YOU WILL ALSO NEED THE SCHEMA NAME TO CREATE THE TABLE. MAKE SURE YOU ALSO INPUT THAT INFORMATION IN THE `SCHEMA_NAME` LINE*** + +## 4. Creating a Table + +We will now be creating a table to store our data. The data we will be using is the `House_Value` dataset that can be found in the `data` folder. When we create our table using SQL, notice how `BLDGTYPE` and `HOUSESTYLE` have type `BLOB`, while the other columns have normal data types. By stating a specific column to be type `BLOB`, we are telling our DBMS that these columns will hold JSON documents. This is the first step in creating a table that has the capability of storing normal data and JSON data. + +![Creating Table](docs/source/images/img-4.png) + +## 5. Inserting Data + +Now that our table has been created, we can now add data to it. As mentioned before, the data we will be using is the `House_Value` dataset that can be found in the `data` folder. + +![Inserting Data](docs/source/images/img-5.png) + +As you can see in the picture, as we go through each row extracting data from each column, we are creating one big SQL statement that will be executed. Most importantly, `BLDGTYPE` and `HOUSESTYLE` data is being wrapped into a JSON document. This is because we specified in our `INSERT` statement that both of these columns will store JSON data. We will also need to make a system call - `SYSTOOLS.JSON2BSON()` - which will convert our data in [BSON](https://en.wikipedia.org/wiki/BSON). This new BSON is what is going to be stored in our database, thereby increasing efficiency. + + +## 6. Retrieving Data + +When trying to extract JSON data from our database we will be using the system call - `SYSTOOLS.BSON2JSON()`. Since we stored our data as BSON, we need to convert it back to JSON so that we are able to understand what we stored. In the picture below, you can see the system call in action as well as how the JSON object appears in the table when we print the result of our SQL statement. + +![Getting Data_1](docs/source/images/img-6.png) + +However, sometimes you may only want to retrieve a certain attribute within the JSON object instead of the entire object. For this purpose, we will have to use the system call `JSON_VAL()`. This will give us the specific attribute/value that we specify in the JSON object. + +![Getting Data_2](docs/source/images/img-7.png) + +## License + +This code pattern is licensed under the Apache License, Version 2. Separate third-party code objects invoked within this code pattern are licensed by their respective providers pursuant to their own separate licenses. Contributions are subject to the [Developer Certificate of Origin, Version 1.1](https://developercertificate.org/) and the [Apache License, Version 2](https://www.apache.org/licenses/LICENSE-2.0.txt). + +[Apache License FAQ](https://www.apache.org/foundation/license-faq.html#WhatDoesItMEAN) + + + diff --git a/db2_with_json/data/home-sales-training-data.csv b/db2_with_json/data/home-sales-training-data.csv new file mode 100644 index 0000000..bc24404 --- /dev/null +++ b/db2_with_json/data/home-sales-training-data.csv @@ -0,0 +1,1461 @@ +ID,LOTAREA,BLDGTYPE,HOUSESTYLE,OVERALLCOND,YEARBUILT,ROOFSTYLE,EXTERCOND,FOUNDATION,BSMTCOND,HEATING,HEATINGQC,CENTRALAIR,ELECTRICAL,FULLBATH,HALFBATH,BEDROOMABVGR,KITCHENABVGR,KITCHENQUAL,TOTRMSABVGRD,FIREPLACES,FIREPLACEQU,GARAGETYPE,GARAGEFINISH,GARAGECARS,GARAGECOND,POOLAREA,POOLQC,FENCE,MOSOLD,YRSOLD,SALEPRICE +1,8450,1Fam,2Story,5,2003,Gable,TA,PConc,TA,GasA,Ex,Y,SBrkr,2,1,3,1,Gd,8,0,NA,Attchd,RFn,2,TA,0,NA,NA,2,2008,208500 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+874,12144,1Fam,1Story,7,1949,Gable,TA,CBlock,TA,GasA,Gd,Y,SBrkr,1,0,3,1,TA,6,1,Gd,Attchd,Unf,1,TA,0,NA,NA,9,2009,133000 +875,5720,1Fam,1.5Fin,6,1941,Gable,TA,CBlock,TA,GasA,Ex,Y,SBrkr,1,1,3,1,TA,5,0,NA,Detchd,Unf,1,TA,0,NA,NA,8,2009,66500 +876,9000,1Fam,2Story,5,2007,Gable,TA,PConc,TA,GasA,Ex,Y,SBrkr,2,1,4,1,Ex,11,1,Gd,BuiltIn,Fin,2,TA,0,NA,NA,8,2007,303477 +877,25286,1Fam,1Story,5,1963,Gable,TA,PConc,TA,GasA,Gd,Y,SBrkr,1,0,3,1,TA,5,0,NA,Attchd,Unf,2,TA,0,NA,NA,1,2007,132250 +878,8834,1Fam,2Story,5,2004,Hip,TA,PConc,TA,GasA,Ex,Y,SBrkr,2,1,4,1,Ex,10,1,Gd,Attchd,Fin,3,TA,0,NA,NA,6,2009,350000 +879,11782,1Fam,SFoyer,7,1961,Gable,TA,CBlock,TA,GasA,TA,Y,SBrkr,1,0,3,1,Gd,6,0,NA,Detchd,Unf,2,TA,0,NA,MnPrv,6,2010,148000 +880,7000,1Fam,1Story,8,1978,Gable,Gd,CBlock,TA,GasA,Ex,Y,SBrkr,1,0,3,1,TA,6,0,NA,Attchd,Unf,1,TA,0,NA,GdWo,7,2009,136500 +881,7024,1Fam,1Story,5,2005,Gable,TA,PConc,Gd,GasA,Gd,Y,SBrkr,1,1,2,1,TA,5,0,NA,Attchd,Fin,2,TA,0,NA,NA,6,2007,157000 +882,13758,1Fam,1.5Fin,5,1990,Gable,Gd,CBlock,TA,GasA,Ex,Y,SBrkr,2,1,3,1,Gd,7,1,TA,Attchd,RFn,2,TA,0,NA,NA,4,2007,187500 +883,9636,1Fam,2Story,5,1992,Gable,TA,PConc,TA,GasA,Gd,Y,SBrkr,2,1,3,1,TA,7,1,TA,BuiltIn,RFn,2,TA,0,NA,MnPrv,12,2009,178000 +884,6204,1Fam,2.5Fin,5,1912,Gable,TA,PConc,Fa,GasA,TA,N,SBrkr,1,0,5,1,TA,10,0,NA,Detchd,Unf,1,Gd,0,NA,NA,3,2006,118500 +885,7150,1Fam,1Story,5,1967,Gable,TA,CBlock,TA,GasA,TA,Y,SBrkr,1,0,3,1,TA,5,0,NA,Attchd,RFn,1,TA,0,NA,GdWo,7,2009,100000 +886,5119,TwnhsE,1Story,5,1999,Gable,TA,PConc,TA,GasA,Ex,Y,SBrkr,2,0,2,1,Gd,5,1,TA,Attchd,Fin,2,TA,0,NA,NA,1,2008,328900 +887,8393,Duplex,1Story,5,1959,Gable,TA,CBlock,TA,GasA,Ex,Y,SBrkr,2,0,4,2,TA,8,0,NA,Attchd,Fin,2,TA,0,NA,NA,6,2006,145000 +888,16466,1Fam,1.5Fin,7,1955,Gable,Gd,PConc,TA,GasA,TA,Y,SBrkr,1,1,3,1,TA,8,0,NA,Attchd,Unf,1,TA,0,NA,NA,4,2008,135500 +889,15865,1Fam,1Story,6,1970,Flat,Gd,PConc,Gd,GasA,Ex,Y,SBrkr,2,0,4,1,Gd,8,1,TA,Attchd,Unf,2,TA,0,NA,NA,10,2007,268000 +890,12160,1Fam,1Story,4,1953,Hip,TA,CBlock,TA,GasA,Ex,Y,SBrkr,1,0,2,1,TA,6,1,TA,Attchd,RFn,2,TA,0,NA,NA,2,2009,149500 +891,8064,1Fam,1.5Fin,7,1949,Gable,Gd,CBlock,TA,GasA,Ex,Y,SBrkr,1,0,3,1,TA,6,1,Po,Detchd,Unf,2,TA,0,NA,MnPrv,7,2007,122900 +892,11184,1Fam,2Story,5,1978,Hip,TA,CBlock,TA,GasA,Gd,Y,SBrkr,2,1,3,1,TA,7,1,TA,Attchd,RFn,2,TA,0,NA,NA,7,2009,172500 +893,8414,1Fam,1Story,8,1963,Hip,TA,CBlock,TA,GasA,TA,Y,SBrkr,1,0,3,1,TA,6,0,NA,Attchd,RFn,1,TA,0,NA,MnPrv,2,2006,154500 +894,13284,1Fam,1Story,5,1954,Gable,TA,PConc,TA,GasA,TA,Y,SBrkr,1,0,3,1,TA,6,1,Gd,Attchd,Unf,1,TA,0,NA,GdPrv,6,2008,165000 +895,7018,Duplex,1Story,5,1979,Gable,TA,Slab,NA,GasA,TA,Y,SBrkr,2,0,4,2,TA,8,0,NA,Attchd,Unf,2,TA,0,NA,NA,6,2009,118858 +896,7056,1Fam,2Story,5,1963,Hip,TA,CBlock,TA,GasA,TA,Y,SBrkr,1,1,4,1,TA,8,1,TA,Attchd,RFn,2,TA,0,NA,NA,10,2008,140000 +897,8765,1Fam,1Story,6,1936,Gable,TA,BrkTil,TA,GasA,Ex,N,SBrkr,1,0,2,1,TA,6,0,NA,Detchd,Unf,1,TA,0,NA,NA,4,2006,106500 +898,7018,Duplex,2Story,5,1979,Gable,TA,Slab,NA,GasA,TA,Y,SBrkr,2,0,6,2,TA,12,0,NA,Detchd,Unf,2,TA,0,NA,NA,6,2009,142953 +899,12919,1Fam,1Story,5,2009,Hip,TA,PConc,TA,GasA,Ex,Y,SBrkr,2,1,2,1,Ex,11,2,Gd,Attchd,Fin,3,TA,0,NA,NA,3,2010,611657 +900,6993,1Fam,1Story,7,1961,Gable,TA,CBlock,TA,GasA,TA,Y,SBrkr,1,0,3,1,TA,6,1,TA,Attchd,Unf,1,TA,0,NA,NA,6,2006,135000 +901,7340,1Fam,1Story,6,1971,Gable,TA,CBlock,TA,GasA,TA,Y,SBrkr,1,0,2,1,TA,4,0,NA,Detchd,Unf,1,TA,0,NA,NA,6,2007,110000 +902,8712,1Fam,1Story,7,1957,Hip,Gd,CBlock,TA,GasA,TA,Y,SBrkr,1,0,2,1,TA,5,0,NA,Detchd,Unf,1,TA,0,NA,NA,5,2009,153000 +903,7875,1Fam,2Story,5,2003,Gable,TA,PConc,TA,GasA,Ex,Y,SBrkr,2,1,3,1,Gd,8,1,Gd,Attchd,Fin,2,TA,0,NA,NA,7,2006,180000 +904,14859,1Fam,1Story,5,2006,Hip,TA,PConc,TA,GasA,Ex,Y,SBrkr,2,0,3,1,Gd,7,1,Gd,Attchd,RFn,3,TA,0,NA,NA,8,2006,240000 +905,6173,1Fam,1Story,6,1967,Gable,TA,CBlock,TA,GasA,TA,Y,SBrkr,1,0,3,1,TA,6,0,NA,Attchd,Unf,1,TA,0,NA,MnPrv,8,2007,125500 +906,9920,1Fam,1Story,5,1954,Gable,TA,CBlock,TA,GasA,TA,Y,SBrkr,1,0,3,1,TA,6,0,NA,Attchd,RFn,1,TA,0,NA,MnPrv,2,2010,128000 +907,13501,1Fam,1Story,5,2006,Gable,TA,PConc,TA,GasA,Ex,Y,SBrkr,2,0,3,1,Gd,8,1,Gd,Attchd,RFn,3,TA,0,NA,NA,6,2007,255000 +908,11500,1Fam,1.5Fin,7,1936,Gable,TA,CBlock,TA,GasA,Gd,Y,SBrkr,1,1,3,1,Gd,6,1,Gd,Attchd,Fin,1,TA,0,NA,NA,6,2006,250000 +909,8885,1Fam,1Story,5,1983,Gable,TA,CBlock,TA,GasA,TA,Y,SBrkr,1,0,2,1,TA,5,0,NA,Attchd,Unf,2,TA,0,NA,MnPrv,6,2006,131000 +910,12589,1Fam,2Story,5,2005,Gable,TA,PConc,TA,GasA,Ex,Y,SBrkr,2,1,3,1,Gd,8,1,Gd,Attchd,Fin,2,TA,0,NA,NA,6,2009,174000 +911,11600,Duplex,2Story,5,1960,Gable,TA,CBlock,TA,GasA,TA,Y,FuseA,2,0,5,2,TA,12,0,NA,Detchd,Unf,2,TA,0,NA,NA,1,2010,154300 +912,9286,1Fam,1Story,7,1977,Gable,TA,CBlock,Gd,GasA,TA,Y,SBrkr,1,1,3,1,Gd,5,0,NA,Detchd,Unf,1,TA,0,NA,NA,10,2009,143500 +913,6120,1Fam,1Story,7,1925,Gable,TA,BrkTil,TA,GasA,TA,N,SBrkr,1,0,3,1,TA,6,0,NA,Detchd,Unf,1,TA,0,NA,MnPrv,7,2006,88000 +914,6270,Duplex,2Story,6,1949,Gable,TA,CBlock,TA,GasA,TA,N,FuseA,2,0,4,2,TA,8,0,NA,2Types,Unf,3,TA,0,NA,NA,8,2007,145000 +915,3000,TwnhsE,2Story,5,2009,Gable,TA,PConc,TA,GasA,Ex,Y,SBrkr,2,1,2,1,Gd,4,0,NA,Detchd,RFn,2,TA,0,NA,NA,6,2009,173733 +916,2001,Twnhs,2Story,5,1970,Gable,TA,CBlock,TA,GasA,Fa,Y,SBrkr,1,1,3,1,TA,6,0,NA,Attchd,Unf,1,TA,0,NA,NA,1,2007,75000 +917,9000,1Fam,1Story,3,1949,Gable,TA,CBlock,TA,GasA,TA,N,FuseA,0,0,1,1,TA,4,0,NA,Detchd,Unf,1,TA,0,NA,NA,10,2006,35311 +918,17140,1Fam,1Story,6,1956,Gable,TA,CBlock,TA,GasA,Ex,Y,FuseA,1,0,3,1,TA,6,0,NA,Attchd,RFn,1,TA,0,NA,NA,4,2009,135000 +919,13125,1Fam,2Story,5,1991,Gable,TA,PConc,TA,GasA,Ex,Y,SBrkr,2,1,4,1,Gd,8,1,TA,Attchd,RFn,3,TA,0,NA,GdPrv,11,2007,238000 +920,11029,1Fam,1Story,8,1958,Hip,TA,CBlock,TA,GasA,Ex,Y,SBrkr,1,0,3,1,TA,6,1,TA,Attchd,Unf,2,TA,0,NA,NA,5,2008,176500 +921,8462,1Fam,2Story,5,1994,Gable,Gd,PConc,Gd,GasA,Ex,Y,SBrkr,2,1,3,1,Gd,7,0,NA,Attchd,RFn,2,TA,0,NA,NA,7,2007,201000 +922,8777,Duplex,1.5Fin,7,1900,Gable,TA,CBlock,TA,GasA,Gd,Y,SBrkr,2,2,4,2,TA,9,0,NA,NA,NA,0,NA,0,NA,GdPrv,9,2008,145900 +923,10237,1Fam,1Story,5,2005,Gable,TA,PConc,TA,GasA,Ex,Y,SBrkr,2,0,3,1,Gd,6,1,Gd,Attchd,Fin,2,TA,0,NA,NA,10,2006,169990 +924,8012,TwnhsE,1Story,5,1993,Gable,TA,PConc,TA,GasA,Ex,Y,SBrkr,2,0,2,1,Gd,5,1,Fa,Attchd,RFn,2,TA,0,NA,NA,7,2008,193000 +925,10240,1Fam,1Story,6,1980,Gable,Gd,CBlock,TA,GasA,TA,Y,SBrkr,2,0,3,1,TA,7,1,TA,Attchd,Unf,2,TA,0,NA,NA,5,2006,207500 +926,15611,1Fam,1Story,6,1977,Gable,TA,PConc,TA,GasA,TA,Y,SBrkr,2,0,3,1,Ex,6,0,NA,Attchd,RFn,2,TA,0,NA,NA,3,2008,175000 +927,11999,1Fam,2Story,5,2003,Hip,TA,PConc,TA,GasA,Ex,Y,SBrkr,2,1,4,1,Ex,10,1,Gd,BuiltIn,Fin,3,TA,0,NA,NA,5,2007,285000 +928,9900,1Fam,2Story,5,1968,Gable,TA,CBlock,TA,GasA,Gd,Y,SBrkr,2,1,4,1,TA,9,1,Gd,Attchd,RFn,2,TA,0,NA,GdPrv,4,2008,176000 +929,11838,1Fam,1Story,5,2001,Hip,TA,PConc,TA,GasA,Ex,Y,SBrkr,2,0,3,1,Ex,7,1,TA,Attchd,RFn,2,TA,0,NA,NA,6,2009,236500 +930,13006,1Fam,2Story,5,1997,Gable,TA,PConc,TA,GasA,Gd,Y,SBrkr,2,1,4,1,Gd,8,1,TA,BuiltIn,Fin,2,TA,0,NA,NA,11,2006,222000 +931,8925,1Fam,1Story,5,2007,Gable,TA,PConc,TA,GasA,Ex,Y,SBrkr,2,0,3,1,Gd,7,0,NA,Attchd,Fin,3,TA,0,NA,NA,7,2009,201000 +932,9100,1Fam,1Story,6,1965,Gable,TA,CBlock,TA,GasA,Ex,Y,SBrkr,1,0,2,1,TA,5,0,NA,Detchd,Unf,1,TA,0,NA,GdWo,7,2009,117500 +933,11670,1Fam,1Story,5,2006,Hip,TA,PConc,Gd,GasA,Ex,Y,SBrkr,2,0,3,1,Ex,8,1,Gd,Attchd,Fin,3,TA,0,NA,NA,3,2007,320000 +934,8487,1Fam,1Story,5,2004,Gable,TA,PConc,TA,GasA,Ex,Y,SBrkr,2,0,3,1,Gd,6,0,NA,Attchd,RFn,2,TA,0,NA,NA,8,2009,190000 +935,27650,1Fam,1Story,7,1960,Flat,TA,CBlock,TA,GasA,Ex,Y,SBrkr,2,0,4,1,Gd,9,1,Gd,Attchd,RFn,2,TA,0,NA,NA,11,2008,242000 +936,5825,1Fam,1Story,5,1926,Gable,Gd,PConc,TA,GasA,Gd,Y,SBrkr,1,0,1,1,TA,5,0,NA,Detchd,Unf,2,TA,0,NA,NA,6,2006,79900 +937,10083,1Fam,1Story,5,2003,Gable,TA,PConc,TA,GasA,Ex,Y,SBrkr,2,0,2,1,Gd,5,0,NA,Attchd,RFn,2,TA,0,NA,NA,8,2009,184900 +938,9675,1Fam,2Story,5,2005,Gable,TA,PConc,TA,GasA,Ex,Y,SBrkr,2,1,3,1,Gd,8,1,Gd,Attchd,RFn,2,TA,0,NA,NA,2,2009,253000 +939,8760,1Fam,2Story,5,2006,Gable,TA,PConc,Gd,GasA,Ex,Y,SBrkr,2,1,3,1,Gd,7,0,NA,Attchd,RFn,3,TA,0,NA,NA,8,2006,239799 +940,24090,1Fam,2Story,7,1940,Gable,Gd,CBlock,TA,GasA,Ex,Y,SBrkr,2,0,4,1,TA,10,2,TA,Attchd,Unf,1,TA,0,NA,NA,6,2010,244400 +941,12640,Duplex,1Story,5,1976,Gable,TA,CBlock,TA,GasA,TA,Y,SBrkr,2,0,4,2,TA,8,0,NA,Attchd,Unf,2,TA,0,NA,NA,7,2006,150900 +942,8755,1Fam,2Story,5,1999,Gable,TA,PConc,TA,GasA,Ex,Y,SBrkr,2,1,3,1,Gd,8,1,TA,BuiltIn,RFn,2,TA,0,NA,GdPrv,6,2009,214000 +943,7711,Duplex,1Story,3,1977,Gable,TA,PConc,TA,GasA,TA,Y,SBrkr,2,0,4,2,TA,8,0,NA,NA,NA,0,NA,0,NA,NA,8,2007,150000 +944,25000,Duplex,1Story,4,1967,Gable,TA,CBlock,TA,GasA,TA,Y,SBrkr,2,0,4,2,TA,8,0,NA,Attchd,Unf,2,TA,0,NA,NA,6,2007,143000 +945,14375,1Fam,SLvl,6,1958,Gable,TA,CBlock,TA,GasA,Gd,Y,FuseA,1,0,3,1,Gd,7,1,Gd,Basment,RFn,2,TA,0,NA,NA,1,2009,137500 +946,8820,1Fam,1.5Fin,6,1890,Hip,TA,BrkTil,TA,GasA,TA,Y,SBrkr,1,0,2,1,TA,7,0,NA,Detchd,Unf,2,TA,0,NA,MnWw,9,2009,124900 +947,8163,1Fam,SLvl,6,1959,Gable,Gd,CBlock,TA,GasA,TA,Y,SBrkr,1,0,3,1,TA,6,1,TA,Attchd,RFn,1,TA,0,NA,NA,3,2006,143000 +948,14536,1Fam,1Story,5,2002,Hip,TA,PConc,TA,GasA,Ex,Y,SBrkr,2,0,3,1,Gd,9,1,Gd,Attchd,Fin,3,TA,0,NA,NA,11,2007,270000 +949,14006,1Fam,2Story,5,2002,Gable,TA,PConc,TA,GasA,Ex,Y,SBrkr,2,1,3,1,Gd,7,1,TA,Attchd,RFn,2,TA,0,NA,NA,2,2006,192500 +950,9360,1Fam,1Story,7,1972,Hip,TA,CBlock,TA,GasA,TA,Y,SBrkr,1,1,3,1,Gd,5,1,TA,Attchd,RFn,2,TA,0,NA,NA,3,2010,197500 +951,7200,1Fam,1Story,8,1950,Gable,TA,CBlock,TA,GasA,Gd,Y,SBrkr,1,0,3,1,Gd,5,0,NA,Detchd,RFn,2,TA,0,NA,NA,7,2007,129000 +952,7800,1Fam,1Story,5,1965,Gable,TA,CBlock,TA,GasA,Gd,Y,SBrkr,1,0,3,1,TA,6,0,NA,Detchd,Unf,1,TA,0,NA,MnPrv,7,2006,119900 +953,7200,1Fam,SFoyer,8,1972,Gable,Gd,CBlock,TA,GasA,Gd,Y,SBrkr,1,0,2,1,TA,5,0,NA,Detchd,Fin,1,TA,0,NA,MnPrv,4,2009,133900 +954,11075,1Fam,2Story,4,1969,Gable,TA,CBlock,TA,GasA,Ex,Y,SBrkr,2,1,4,1,TA,7,1,Po,Attchd,RFn,2,TA,0,NA,MnPrv,9,2008,172000 +955,9400,Duplex,SFoyer,5,1975,Flat,TA,CBlock,Gd,GasA,TA,Y,SBrkr,2,0,4,0,TA,4,0,NA,NA,NA,0,NA,0,NA,NA,10,2006,127500 +956,7136,Duplex,2Story,6,1946,Gable,TA,CBlock,TA,GasA,TA,N,FuseF,2,0,4,2,TA,8,0,NA,Attchd,Unf,2,TA,0,NA,NA,8,2007,145000 +957,1300,TwnhsE,2Story,6,1980,Gable,TA,CBlock,TA,GasA,TA,Y,SBrkr,1,1,2,1,TA,5,1,TA,Attchd,Fin,2,TA,0,NA,GdPrv,5,2009,124000 +958,7420,1Fam,1Story,5,1962,Hip,TA,CBlock,TA,GasA,TA,Y,SBrkr,1,0,3,1,TA,6,0,NA,Detchd,Fin,2,TA,0,NA,NA,4,2007,132000 +959,8450,1Fam,1Story,5,2003,Gable,TA,PConc,TA,GasA,Ex,Y,SBrkr,2,0,3,1,Gd,6,0,NA,Attchd,RFn,2,TA,0,NA,NA,10,2007,185000 +960,2572,Twnhs,2Story,5,1999,Hip,TA,PConc,TA,GasA,Ex,Y,SBrkr,2,1,3,1,Gd,6,0,NA,Detchd,Unf,2,TA,0,NA,NA,5,2010,155000 +961,7207,1Fam,1Story,7,1958,Gable,Gd,CBlock,TA,GasA,Gd,Y,SBrkr,1,0,2,1,TA,4,0,NA,NA,NA,0,NA,0,NA,NA,2,2010,116500 +962,12227,1Fam,2Story,7,1977,Gable,Gd,CBlock,Gd,GasA,TA,Y,SBrkr,2,1,4,1,TA,11,1,TA,Attchd,Fin,2,TA,0,NA,NA,7,2008,272000 +963,2308,TwnhsE,2Story,6,1976,Gable,TA,CBlock,TA,GasA,TA,Y,SBrkr,2,1,3,1,Gd,7,1,TA,Detchd,Unf,2,TA,0,NA,NA,7,2007,155000 +964,11923,1Fam,1Story,5,2007,Gable,TA,PConc,TA,GasA,Ex,Y,SBrkr,2,0,2,1,Ex,7,0,NA,Attchd,Fin,2,TA,0,NA,NA,5,2009,239000 +965,11316,1Fam,2Story,5,2002,Gable,TA,PConc,TA,GasA,Ex,Y,SBrkr,2,1,4,1,Gd,8,1,Gd,BuiltIn,Fin,2,TA,0,NA,NA,2,2010,214900 +966,10237,1Fam,2Story,5,2005,Gable,TA,PConc,TA,GasA,Ex,Y,SBrkr,2,1,3,1,Gd,8,1,Gd,Attchd,Fin,2,TA,0,NA,NA,7,2007,178900 +967,9600,1Fam,1.5Fin,7,1940,Gable,Gd,BrkTil,Fa,GasA,Ex,Y,SBrkr,1,1,2,1,TA,7,2,TA,Detchd,Unf,1,TA,0,NA,NA,6,2009,160000 +968,7390,1Fam,1Story,7,1955,Hip,TA,CBlock,TA,GasA,TA,Y,SBrkr,1,0,3,1,TA,6,0,NA,Attchd,Unf,1,TA,0,NA,NA,7,2008,135000 +969,5925,1Fam,1.5Fin,6,1910,Gable,TA,BrkTil,TA,Grav,Fa,N,SBrkr,1,0,2,1,TA,6,0,NA,NA,NA,0,NA,0,NA,GdWo,5,2009,37900 +970,10382,2fmCon,SLvl,5,1958,Hip,Fa,CBlock,TA,GasA,TA,Y,SBrkr,1,0,2,1,TA,6,0,NA,Attchd,RFn,1,TA,0,NA,NA,3,2006,140000 +971,10800,1Fam,1.5Fin,4,1949,Gable,TA,CBlock,TA,GasA,TA,N,FuseA,1,1,4,1,TA,6,0,NA,NA,NA,0,NA,0,NA,NA,12,2006,135000 +972,2268,Twnhs,2Story,5,2003,Gable,TA,PConc,TA,GasA,Ex,Y,SBrkr,2,0,2,1,Gd,6,0,NA,BuiltIn,RFn,2,TA,0,NA,NA,7,2009,173000 +973,7892,TwnhsE,1Story,5,1979,Gable,TA,CBlock,TA,GasA,TA,Y,SBrkr,2,0,2,1,TA,5,1,TA,Attchd,Unf,1,TA,0,NA,NA,4,2010,99500 +974,11639,1Fam,1Story,5,2007,Gable,TA,PConc,TA,GasA,Ex,Y,SBrkr,2,0,3,1,Gd,6,0,NA,Attchd,Fin,2,TA,0,NA,NA,12,2008,182000 +975,11414,1Fam,2Story,8,1910,Gable,Gd,BrkTil,TA,GasA,TA,N,SBrkr,1,0,3,1,Gd,8,0,NA,Detchd,Unf,2,TA,0,NA,GdPrv,10,2009,167500 +976,2651,Twnhs,2Story,5,2000,Gable,TA,PConc,TA,GasA,Ex,Y,SBrkr,2,1,3,1,Gd,6,0,NA,Detchd,Unf,2,TA,0,NA,NA,4,2006,165000 +977,5900,1Fam,1Story,7,1923,Gable,TA,PConc,TA,GasA,TA,Y,FuseA,1,0,2,1,Fa,4,0,NA,NA,NA,0,NA,0,NA,NA,8,2006,85500 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+986,10880,2fmCon,1Story,5,1950,Gable,TA,CBlock,TA,GasW,TA,N,SBrkr,1,0,3,1,TA,5,0,NA,Detchd,Unf,1,TA,0,NA,NA,8,2008,125000 +987,5310,1Fam,1.5Fin,8,1910,Hip,Gd,CBlock,Fa,GasA,Gd,Y,SBrkr,1,0,2,1,Gd,5,0,NA,Attchd,Unf,1,TA,0,NA,NA,6,2006,117000 +988,10159,1Fam,1Story,5,2009,Hip,TA,PConc,TA,GasA,Ex,Y,SBrkr,2,1,3,1,Ex,8,1,Gd,Attchd,Fin,3,TA,0,NA,NA,4,2010,395192 +989,12046,1Fam,2Story,6,1976,Gable,TA,CBlock,TA,GasA,TA,Y,SBrkr,2,1,4,1,Gd,8,1,TA,Attchd,Fin,2,TA,0,NA,NA,6,2007,195000 +990,8125,1Fam,2Story,5,2006,Gable,TA,PConc,Gd,GasA,Ex,Y,SBrkr,2,1,3,1,Gd,6,0,NA,Attchd,RFn,2,TA,0,NA,NA,8,2006,197000 +991,9452,1Fam,2Story,5,1997,Gable,TA,PConc,TA,GasA,Ex,Y,SBrkr,2,1,3,1,Gd,7,1,TA,Attchd,Fin,3,TA,0,NA,NA,6,2006,348000 +992,17671,1Fam,2Story,9,1882,Gable,Gd,BrkTil,TA,GasA,Gd,Y,SBrkr,1,1,4,1,Gd,8,1,Gd,Attchd,Unf,2,TA,0,NA,NA,11,2009,168000 +993,9760,1Fam,2Story,8,1964,Hip,TA,CBlock,TA,GasA,Gd,Y,SBrkr,2,1,3,1,Gd,7,1,Fa,Attchd,RFn,2,TA,0,NA,MnWw,7,2007,187000 +994,8846,1Fam,2Story,5,2005,Gable,TA,PConc,TA,GasA,Ex,Y,SBrkr,2,1,3,1,Gd,6,0,NA,Attchd,RFn,2,TA,0,NA,NA,8,2006,173900 +995,12456,1Fam,1Story,5,2006,Hip,TA,PConc,TA,GasA,Ex,Y,SBrkr,2,0,3,1,Ex,7,1,Gd,Attchd,Fin,3,TA,0,NA,NA,7,2009,337500 +996,4712,1Fam,1.5Fin,7,1946,Gable,TA,CBlock,TA,GasA,TA,Y,SBrkr,1,1,3,1,TA,5,0,NA,Detchd,Unf,1,TA,0,NA,MnPrv,8,2006,121600 +997,10659,1Fam,1Story,6,1961,Hip,TA,CBlock,TA,GasA,TA,Y,SBrkr,1,0,3,1,TA,6,0,NA,Attchd,Unf,1,TA,0,NA,NA,1,2006,136500 +998,11717,1Fam,1Story,6,1970,Hip,TA,CBlock,TA,GasA,TA,Y,SBrkr,2,0,2,1,TA,6,1,TA,Attchd,RFn,2,TA,0,NA,NA,2,2009,185000 +999,9786,1Fam,1Story,4,1922,Hip,TA,BrkTil,Fa,GasA,Fa,N,SBrkr,1,0,3,1,TA,6,1,Gd,Detchd,Unf,1,Fa,0,NA,NA,5,2006,91000 +1000,6762,1Fam,1Story,5,2006,Gable,TA,PConc,TA,GasA,Ex,Y,SBrkr,2,0,2,1,Gd,6,0,NA,Attchd,RFn,2,TA,0,NA,NA,2,2010,206000 +1001,10206,1Fam,1Story,3,1952,Flat,TA,Slab,NA,GasW,Fa,N,FuseF,1,0,2,1,Fa,4,0,NA,Detchd,Unf,2,Fa,0,NA,NA,7,2009,82000 +1002,5400,1Fam,1Story,6,1920,Gable,TA,BrkTil,TA,GasA,Ex,Y,FuseA,1,0,2,1,Ex,4,0,NA,Detchd,Unf,1,TA,0,NA,NA,1,2007,86000 +1003,11957,1Fam,1Story,5,2006,Gable,TA,PConc,TA,GasA,Ex,Y,SBrkr,2,0,3,1,Gd,7,1,Gd,Attchd,RFn,3,TA,0,NA,NA,7,2008,232000 +1004,11500,Duplex,1Story,6,1976,Gable,TA,CBlock,TA,GasA,Fa,Y,SBrkr,2,0,4,2,TA,8,0,NA,Detchd,Unf,2,TA,0,NA,NA,6,2007,136905 +1005,3182,TwnhsE,1Story,5,2005,Gable,TA,PConc,TA,GasA,Ex,Y,SBrkr,2,0,1,1,Gd,7,1,Gd,Attchd,Fin,2,TA,0,NA,NA,5,2009,181000 +1006,8385,1Fam,SLvl,8,1977,Gable,TA,CBlock,Gd,GasA,TA,Y,SBrkr,2,0,3,1,TA,6,0,NA,Attchd,Unf,1,TA,0,NA,NA,11,2008,149900 +1007,12155,1Fam,1Story,3,1970,Gable,TA,CBlock,TA,GasA,Gd,Y,SBrkr,2,0,3,1,TA,7,1,TA,Attchd,Unf,2,TA,0,NA,NA,3,2007,163500 +1008,2217,TwnhsE,2Story,4,1970,Gable,TA,CBlock,TA,GasA,TA,Y,SBrkr,1,1,3,1,TA,6,0,NA,Attchd,RFn,1,TA,0,NA,NA,8,2009,88000 +1009,12118,1Fam,1Story,5,2004,Hip,TA,PConc,TA,GasA,Ex,Y,SBrkr,2,0,3,1,Gd,7,1,Gd,Attchd,Fin,2,TA,0,NA,NA,4,2009,240000 +1010,6000,1Fam,1.5Fin,5,1926,Gable,Fa,BrkTil,TA,GasA,Ex,Y,SBrkr,2,0,4,1,TA,7,0,NA,NA,NA,0,NA,0,NA,NA,6,2006,102000 +1011,21286,1Fam,1.5Fin,5,1948,Gable,TA,CBlock,TA,GasA,TA,Y,SBrkr,2,0,4,1,TA,7,1,Gd,Attchd,Unf,1,TA,0,NA,NA,8,2008,135000 +1012,9825,Duplex,1Story,5,1965,Hip,TA,CBlock,NA,GasA,TA,N,SBrkr,2,0,4,2,TA,8,0,NA,NA,NA,0,NA,0,NA,NA,5,2010,100000 +1013,10592,1Fam,2Story,7,1923,Hip,Gd,PConc,Fa,GasA,TA,Y,SBrkr,1,1,3,1,Gd,7,2,TA,Detchd,Unf,1,TA,0,NA,NA,8,2007,165000 +1014,7200,1Fam,1Story,4,1910,Hip,TA,BrkTil,TA,GasW,TA,N,SBrkr,1,0,2,1,TA,4,0,NA,Detchd,Unf,1,TA,0,NA,NA,6,2009,85000 +1015,11664,1Fam,1Story,5,1948,Gable,TA,CBlock,Fa,GasA,TA,Y,SBrkr,1,0,2,1,TA,5,1,Gd,Detchd,Unf,1,TA,0,NA,NA,11,2007,119200 +1016,8400,1Fam,2Story,6,2001,Gable,TA,PConc,TA,GasA,Ex,Y,SBrkr,2,1,3,1,Gd,6,0,NA,Attchd,Fin,2,TA,0,NA,NA,11,2009,227000 +1017,11883,1Fam,1Story,5,1996,Hip,TA,PConc,TA,GasA,Ex,Y,SBrkr,2,0,3,1,Gd,6,1,TA,Attchd,Fin,2,TA,0,NA,NA,6,2009,203000 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[ + "### Import Python Modules" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In some cases if your `import ibm_db` and `import ibm_db_dbi` does not work. Please refer to github issue [here](https://github.com/ibmdb/python-ibmdb/issues/276).\n", + "\n", + "#### IMPORTANT NOTE: \n", + "If you have a Mac, you will need to refer to the github link above and run the `install_name_tool`. Please make sure that you edit that line properly and make sure the paths are correct, otherwise it will affect other python dependencies!!!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!easy_install ibm_db" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import ibm_db\n", + "import ibm_db_dbi\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Connect to DB2 Instance\n", + "Here we will be connecting to our Db2 instance with the following service credentials. Please also enter the schema name you would like your table to be under." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Connection To DB2 Instance Has Been Created!\n" + ] + } + ], + "source": [ + "# DON'T TOUCH THE `DRIVER` ATTRIBUTE! \n", + "dsn = \"DRIVER={{IBM DB2 ODBC DRIVER}};\" + \\\n", + " \"DATABASE={DATABASE_NAME};\" + \\\n", + " \"HOSTNAME={HOST_NAME};\" + \\\n", + " \"PORT=50000;\" + \\\n", + " \"PROTOCOL=TCPIP;\" + \\\n", + " \"UID={UID};\" + \\\n", + " \"PWD={PWD};\"\n", + "SCHEMA_NAME = 'KXQ49540'\n", + "hdbc = ibm_db.connect(dsn, \"\", \"\")\n", + "hdbi = ibm_db_dbi.Connection(hdbc)\n", + "print('Connection To DB2 Instance Has Been Created!')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Creat Table With JSON Columns\n", + "Here we will be creating a table for our House Value dataset. Notice how `BLDGTYPE` and `HOUSESTYLE` have type `BLOB`. This indicates that those columns will be BSON columns. " + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "sql = 'CREATE TABLE '+SCHEMA_NAME+'.HOME_SALES ( ' + \\\n", + " 'ID SMALLINT, ' + \\\n", + " 'LOTAREA INTEGER, ' + \\\n", + " 'BLDGTYPE BLOB,' + \\\n", + " 'HOUSESTYLE BLOB, ' + \\\n", + " 'OVERALLCOND INTEGER, ' + \\\n", + " 'YEARBUILT INTEGER, ' + \\\n", + " 'ROOFSTYLE VARCHAR(50), ' + \\\n", + " 'EXTERCOND VARCHAR(50), ' + \\\n", + " 'FOUNDATION VARCHAR(50), ' + \\\n", + " 'BSMTCOND VARCHAR(50), ' + \\\n", + " 'HEATING VARCHAR(50), ' + \\\n", + " 'HEATINGQC VARCHAR(50),' + \\\n", + " 'CENTRALAIR VARCHAR(50), ' + \\\n", + " 'ELECTRICAL VARCHAR(50), ' + \\\n", + " 'FULLBATH INTEGER, ' + \\\n", + " 'HALFBATH INTEGER, ' + \\\n", + " 'BEDROOMABVGR INTEGER, ' + \\\n", + " 'KITCHENABVGR INTEGER, ' + \\\n", + " 'KITCHENQUAL VARCHAR(50), ' + \\\n", + " 'TOTRMSABVGRD INTEGER, ' + \\\n", + " 'FIREPLACES INTEGER, ' + \\\n", + " 'FIREPLACEQU VARCHAR(50), ' + \\\n", + " 'GARAGETYPE VARCHAR(50), ' + \\\n", + " 'GARAGEFINISH VARCHAR(50), ' + \\\n", + " 'GARAGECARS INTEGER, ' + \\\n", + " 'GARAGECOND VARCHAR(50), ' + \\\n", + " 'POOLAREA INTEGER, ' + \\\n", + " 'POOLQC VARCHAR(50), ' + \\\n", + " 'FENCE VARCHAR(50), ' + \\\n", + " 'MOSOLD INTEGER, ' + \\\n", + " 'YRSOLD INTEGER, ' + \\\n", + " 'SALEPRICE INTEGER )'\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Time to execute our SQL statement and create a table. You may go to your DB2 instance and verify that the table has been created." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR: Table Already Exist Or Some Other Error\n" + ] + } + ], + "source": [ + "try:\n", + " stmt = ibm_db.exec_immediate(hdbc, sql)\n", + " print('Table HOME_SALES Has Been Created Under ' + str(SCHEMA_NAME) + ' Schema!')\n", + "except:\n", + " print('ERROR: Table Already Exist Or Some Other Error')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Prepare and Load File into Database\n", + "As mentioned before, we will be using the home_sales dataset. We will be reading each row at a time since we need to convert two columns into a JSON before inserting into the db." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

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IDLOTAREABLDGTYPEHOUSESTYLEOVERALLCONDYEARBUILTROOFSTYLEEXTERCONDFOUNDATIONBSMTCOND...GARAGETYPEGARAGEFINISHGARAGECARSGARAGECONDPOOLAREAPOOLQCFENCEMOSOLDYRSOLDSALEPRICE
0184501Fam2Story52003GableTAPConcTA...AttchdRFn2TA0NaNNaN22008208500
1296001Fam1Story81976GableTACBlockTA...AttchdRFn2TA0NaNNaN52007181500
23112501Fam2Story52001GableTAPConcTA...AttchdRFn2TA0NaNNaN92008223500
3495501Fam2Story51915GableTABrkTilGd...DetchdUnf3TA0NaNNaN22006140000
45142601Fam2Story52000GableTAPConcTA...AttchdRFn3TA0NaNNaN122008250000
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5 rows × 32 columns

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" + ], + "text/plain": [ + " ID LOTAREA BLDGTYPE HOUSESTYLE OVERALLCOND YEARBUILT ROOFSTYLE \\\n", + "0 1 8450 1Fam 2Story 5 2003 Gable \n", + "1 2 9600 1Fam 1Story 8 1976 Gable \n", + "2 3 11250 1Fam 2Story 5 2001 Gable \n", + "3 4 9550 1Fam 2Story 5 1915 Gable \n", + "4 5 14260 1Fam 2Story 5 2000 Gable \n", + "\n", + " EXTERCOND FOUNDATION BSMTCOND ... GARAGETYPE GARAGEFINISH GARAGECARS \\\n", + "0 TA PConc TA ... Attchd RFn 2 \n", + "1 TA CBlock TA ... Attchd RFn 2 \n", + "2 TA PConc TA ... Attchd RFn 2 \n", + "3 TA BrkTil Gd ... Detchd Unf 3 \n", + "4 TA PConc TA ... Attchd RFn 3 \n", + "\n", + " GARAGECOND POOLAREA POOLQC FENCE MOSOLD YRSOLD SALEPRICE \n", + "0 TA 0 NaN NaN 2 2008 208500 \n", + "1 TA 0 NaN NaN 5 2007 181500 \n", + "2 TA 0 NaN NaN 9 2008 223500 \n", + "3 TA 0 NaN NaN 2 2006 140000 \n", + "4 TA 0 NaN NaN 12 2008 250000 \n", + "\n", + "[5 rows x 32 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = pd.read_csv(\"../data/home-sales-training-data.csv\") \n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "sql_p1 = 'INSERT INTO '+SCHEMA_NAME+'.HOME_SALES (' + \\\n", + " 'ID, ' + \\\n", + " 'LOTAREA, ' + \\\n", + " 'BLDGTYPE,' + \\\n", + " 'HOUSESTYLE, ' + \\\n", + " 'OVERALLCOND, ' + \\\n", + " 'YEARBUILT, ' + \\\n", + " 'ROOFSTYLE, ' + \\\n", + " 'EXTERCOND, ' + \\\n", + " 'FOUNDATION, ' + \\\n", + " 'BSMTCOND, ' + \\\n", + " 'HEATING, ' + \\\n", + " 'HEATINGQC,' + \\\n", + " 'CENTRALAIR, ' + \\\n", + " 'ELECTRICAL, ' + \\\n", + " 'FULLBATH, ' + \\\n", + " 'HALFBATH, ' + \\\n", + " 'BEDROOMABVGR, ' + \\\n", + " 'KITCHENABVGR, ' + \\\n", + " 'KITCHENQUAL, ' + \\\n", + " 'TOTRMSABVGRD, ' + \\\n", + " 'FIREPLACES, ' + \\\n", + " 'FIREPLACEQU, ' + \\\n", + " 'GARAGETYPE, ' + \\\n", + " 'GARAGEFINISH, ' + \\\n", + " 'GARAGECARS , ' + \\\n", + " 'GARAGECOND, ' + \\\n", + " 'POOLAREA , ' + \\\n", + " 'POOLQC, ' + \\\n", + " 'FENCE, ' + \\\n", + " 'MOSOLD, ' + \\\n", + " 'YRSOLD, ' + \\\n", + " 'SALEPRICE )'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As you can see, we are going through each row of the pandas dataframe and extracting each value. Notice how we are wrapper `BLDGTYPE` and `HOUSESTYLE` in a JSON object. We then put the JSON object in a system call function - `JSON2BSON`. This function converts the JSON object into a BSON, which is a binary representation of the JSON object. So when you view your data in the table, these two columns will be representated in binaray form. " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "for index, row in data.iterrows():\n", + " sql_p2 = ' VALUES ('+str(row['ID'])+' , ' + \\\n", + " ''+str(row['LOTAREA'])+' , ' + \\\n", + " 'SYSTOOLS.JSON2BSON(\\' { \"BLDGTYPE\": \"'+str(row['BLDGTYPE'])+'\"} \\') , ' + \\\n", + " 'SYSTOOLS.JSON2BSON(\\' { \"HOUSESTYLE\": \"'+str(row['HOUSESTYLE'])+'\"} \\') , ' + \\\n", + " ''+str(row['OVERALLCOND'])+' , ' + \\\n", + " ''+str(row['YEARBUILT'])+' , ' + \\\n", + " '\\''+str(row['ROOFSTYLE'])+'\\' , ' + \\\n", + " '\\''+str(row['EXTERCOND'])+'\\' , ' + \\\n", + " '\\''+str(row['FOUNDATION'])+'\\' , ' + \\\n", + " '\\''+str(row['BSMTCOND'])+'\\' , ' + \\\n", + " '\\''+str(row['HEATING'])+'\\' , ' + \\\n", + " '\\''+str(row['HEATINGQC'])+'\\' , ' + \\\n", + " '\\''+str(row['CENTRALAIR'])+'\\' , ' + \\\n", + " '\\''+str(row['ELECTRICAL'])+'\\' , ' + \\\n", + " ''+str(row['FULLBATH'])+' , ' + \\\n", + " ''+str(row['HALFBATH'])+' , ' + \\\n", + " ''+str(row['BEDROOMABVGR'])+' , ' + \\\n", + " ''+str(row['KITCHENABVGR'])+' , ' + \\\n", + " '\\''+str(row['KITCHENQUAL'])+'\\' , ' + \\\n", + " ''+str(row['TOTRMSABVGRD'])+' , ' + \\\n", + " ''+str(row['FIREPLACES'])+' , ' + \\\n", + " '\\''+str(row['FIREPLACEQU'])+'\\' , ' + \\\n", + " '\\''+str(row['GARAGETYPE'])+'\\' , ' + \\\n", + " '\\''+str(row['GARAGEFINISH'])+'\\' , ' + \\\n", + " ''+str(row['GARAGECARS'])+' , ' + \\\n", + " '\\''+str(row['GARAGECOND'])+'\\' , ' + \\\n", + " ''+str(row['POOLAREA'])+' , ' + \\\n", + " '\\''+str(row['POOLQC'])+'\\' , ' + \\\n", + " '\\''+str(row['FENCE'])+'\\' , ' + \\\n", + " ''+str(row['MOSOLD'])+' , ' + \\\n", + " ''+str(row['YRSOLD'])+' , ' + \\\n", + " ''+str(row['SALEPRICE'])+' ' + \\\n", + " ')'\n", + " sql_final = sql_p1 + sql_p2\n", + " stmt = ibm_db.exec_immediate(hdbc, sql_final)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Viewing and Pulling Data From Database\n", + "Now that we have inserted all our data into our database, we want to be able to read and extract information. For that we will use the `SELECT` statement. Notice that we will be using the system call `BSON2JSON`. Since two of your columns are JSON objects and stored in the database as binary objects, we need to convert them back to JSON so that we can read and use that data effectively. " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "sql = 'SELECT LOTAREA, MOSOLD, YRSOLD, SALEPRICE, SYSTOOLS.BSON2JSON(BLDGTYPE) AS BLDGTYPE , SYSTOOLS.BSON2JSON(HOUSESTYLE) AS HOUSESTYLE FROM '+SCHEMA_NAME+'.HOME_SALES;'" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " LOTAREA MOSOLD YRSOLD SALEPRICE BLDGTYPE \\\n", + "0 8450 2 2008 208500 {\"BLDGTYPE\":\"1Fam\"} \n", + "1 9600 5 2007 181500 {\"BLDGTYPE\":\"1Fam\"} \n", + "2 11250 9 2008 223500 {\"BLDGTYPE\":\"1Fam\"} \n", + "3 9550 2 2006 140000 {\"BLDGTYPE\":\"1Fam\"} \n", + "4 14260 12 2008 250000 {\"BLDGTYPE\":\"1Fam\"} \n", + "\n", + " HOUSESTYLE \n", + "0 {\"HOUSESTYLE\":\"2Story\"} \n", + "1 {\"HOUSESTYLE\":\"1Story\"} \n", + "2 {\"HOUSESTYLE\":\"2Story\"} \n", + "3 {\"HOUSESTYLE\":\"2Story\"} \n", + "4 {\"HOUSESTYLE\":\"2Story\"} " + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_sql(sql,hdbi)\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "sql = 'SELECT LOTAREA, MOSOLD, YRSOLD, SALEPRICE, JSON_VAL(BLDGTYPE,\\'BLDGTYPE\\',\\'s:36\\') AS BLDGTYPE , JSON_VAL(HOUSESTYLE,\\'HOUSESTYLE\\',\\'s:36\\') AS HOUSESTYLE FROM '+SCHEMA_NAME+'.HOME_SALES;'" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " LOTAREA MOSOLD YRSOLD SALEPRICE BLDGTYPE HOUSESTYLE\n", + "0 8450 2 2008 208500 1Fam 2Story\n", + "1 9600 5 2007 181500 1Fam 1Story\n", + "2 11250 9 2008 223500 1Fam 2Story\n", + "3 9550 2 2006 140000 1Fam 2Story\n", + "4 14260 12 2008 250000 1Fam 2Story" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_sql(sql,hdbi)\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}

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