{"id":46717,"date":"2022-08-19T00:00:00","date_gmt":"2022-08-19T07:00:00","guid":{"rendered":"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/blog\/exploring-consumer-purchases-patterns-using-griddb-and-python\/"},"modified":"2025-11-13T12:56:09","modified_gmt":"2025-11-13T20:56:09","slug":"exploring-consumer-purchases-patterns-using-griddb-and-python","status":"publish","type":"post","link":"https:\/\/www.griddb.net\/en\/blog\/exploring-consumer-purchases-patterns-using-griddb-and-python\/","title":{"rendered":"Exploring Consumer Purchases Patterns using GridDB and Python"},"content":{"rendered":"

Understanding consumer behavior is important because it assists marketers in understanding what motivates consumers to make purchases. Each consumer follows his or her own set of buying patterns. By recognizing, analyzing, and measuring buying patterns, businesses can gain a better understanding of their target audience and potentially expand their reach.<\/p>\n

The repo for this blog can be found here: https:\/\/github.com\/griddbnet\/Blogs\/tree\/exploring_consumer_purchases<\/p>\n

The objective of this blog is to analyze the buying behavior of Google Merchandise Store customers using the power of GridDB. The results of the analysis might lead to more actionable operational changes and better use of marketing budgets for those companies who choose to use data analysis on top of Google analytics data.<\/p>\n

Full Jupyter File found here <\/a><\/p>\n

The outline of the tutorial is as follows:<\/p>\n

    \n
  1. Dataset overview<\/li>\n
  2. Importing required libraries<\/li>\n
  3. Loading the dataset<\/li>\n
  4. Data Cleaning and Preprocessing<\/li>\n
  5. Analysing with data visualization<\/li>\n
  6. Conclusion<\/li>\n<\/ol>\n

    Prerequisites and Environment setup<\/h2>\n

    This tutorial is carried out in Anaconda Navigator (Python version \u2013 3.8.3) on Windows Operating System. The following packages need to be installed before you continue with the tutorial \u2013<\/p>\n

      \n
    1. \n

      Pandas<\/p>\n<\/li>\n

    2. \n

      NumPy<\/p>\n<\/li>\n

    3. \n

      plotly<\/p>\n<\/li>\n

    4. \n

      Matplotlib<\/p>\n<\/li>\n

    5. \n

      Seaborn<\/p>\n<\/li>\n

    6. \n

      griddb_python<\/p>\n<\/li>\n

    7. \n

      scipy<\/p>\n<\/li>\n

    8. \n

      json<\/p>\n<\/li>\n

    9. \n

      squarify<\/p>\n<\/li>\n

    10. \n

      random<\/p>\n<\/li>\n<\/ol>\n

      You can install these packages in Conda\u2019s virtual environment using conda install package-name<\/code>. In case you are using Python directly via terminal\/command prompt, pip install package-name<\/code> will do the work.<\/p>\n

      GridDB installation<\/h2>\n

      While loading the dataset, this tutorial will cover two methods \u2013 Using GridDB as well as Using Pandas. To access GridDB using Python, the following packages also need to be installed beforehand:<\/p>\n

        \n
      1. GridDB C-client<\/a><\/li>\n
      2. SWIG (Simplified Wrapper and Interface Generator)<\/li>\n
      3. GridDB Python Client<\/a><\/li>\n<\/ol>\n

        1. Dataset Overview<\/h2>\n

        The dataset contains information about visits to GStore (Google swag online store), each row is a unique visit and each user has a unique ‘fullVisitorId’.<\/p>\n

        1) fullVisitorId<\/code>– A unique identifier for each user of the Google Merchandise Store.<\/p>\n

        2) channelGrouping<\/code> – The channel via which the user came to the Store.<\/p>\n

        3) date<\/code> – The date on which the user visited the Store.<\/p>\n

        4) device<\/code> – The specifications for the device used to access the Store.<\/p>\n

        5) geoNetwork<\/code> – This section contains information about the geography of the user.<\/p>\n

        6) socialEngagementType<\/code> – Engagement type, either “Socially Engaged” or “Not Socially Engaged”.<\/p>\n

        7) totals<\/code> – This section contains aggregate values across the session.<\/p>\n

        8) trafficSource<\/code> – This section contains information about the Traffic Source from which the session originated.<\/p>\n

        9) visitId<\/code> – An identifier for this session. This is part of the value usually stored as the _utmb cookie. This is only unique to the user. For a completely unique ID, you should use a combination of fullVisitorId and visitId.<\/p>\n

        10) visitNumber<\/code> – The session number for this user. If this is the first session, then this is set to 1.<\/p>\n

        11) visitStartTime<\/code> – The timestamp (expressed as POSIX time).<\/p>\n

        12) hits<\/code> – This row and nested fields are populated for any and all types of hits. Provides a record of all page visits.<\/p>\n

        13) customDimensions<\/code> – This section contains any user-level or session-level custom dimensions that are set for a session. This is a repeated field and has an entry for each dimension that is set.<\/p>\n

        14) totals<\/code> – This set of columns mostly includes high-level aggregate data.<\/p>\n

        https:\/\/www.kaggle.com\/competitions\/ga-customer-revenue-prediction\/data?select=train.csv<\/p>\n

        2. Importing Required Libraries<\/h2>\n
        \n
        import griddb_python as griddb\n\nimport gc\nimport numpy as np\nimport pandas as pd\nfrom pandas.io.json import json_normalize\nfrom scipy.stats import norm\nimport json\nimport datetime\nimport random\n\nimport squarify\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nfrom plotly.offline import init_notebook_mode, iplot\nimport plotly.graph_objs as go\nfrom plotly import tools\n\nimport warnings\nwarnings.filterwarnings('ignore')\n%matplotlib inline<\/code><\/pre>\n<\/div>\n
        3. Loading the Dataset<\/h2>\n
        Let\u2019s proceed and load the dataset into our notebook.<\/p>\n
        3.a Using GridDB<\/h3>\n
        Toshiba GridDB\u2122 is a highly scalable NoSQL database best suited for IoT and Big Data. The foundation of GridDB\u2019s principles is based upon offering a versatile data store that is optimized for IoT, provides high scalability, tuned for high performance, and ensures high reliability.<\/p>\n
        To store large amounts of data, a CSV file can be cumbersome. GridDB serves as a perfect alternative as it in open-source and a highly scalable database. GridDB is a scalable, in-memory, No SQL database which makes it easier for you to store large amounts of data. If you are new to GridDB, a tutorial on reading and writing to GridDB<\/a> can be useful.<\/p>\n
        Assuming that you have already set up your database, we will now write the SQL query in python to load our dataset.<\/p>\n
        \n
        factory = griddb.StoreFactory.get_instance()\n\nInitialize the GridDB container (enter your database credentials)\ntry:\n    gridstore = factory.get_store(host=host_name, port=your_port, \n            cluster_name=cluster_name, username=admin, \n            password=admin)\n\n    info = griddb.ContainerInfo(\"customer_data\",\n                    [[\"channelGrouping\", griddb.Type.STRING],[\"geoNetwork\", griddb.Type.STRING],[\"device\", griddb.Type.STRING],\n                     [\"date\", griddb.Type.TIMESTAMP],[\"sessionId\", griddb.Type.STRING],[\"socialEngagementType\", griddb.Type.STRING],\n                     [\"totals\", griddb.Type.STRING],[\"trafficSource\", griddb.Type.STRING],[\"visitStartTime\", griddb.Type.TIMESTAMP],\n                     [\"fullVisitorId\", griddb.Type.INTEGER],[\"visitNumber\", griddb.Type.INTEGER],[\"visitId\", griddb.Type.INTEGER],\n                    griddb.ContainerType.COLLECTION, True)\n    cont = gridstore.put_container(info) \n    data = pd.read_csv(\"False.csv\")\n    #Add data\n    for i in range(len(data)):\n        ret = cont.put(data.iloc[i, :])\n        print(\"Data added successfully\")<\/code><\/pre>\n<\/div>\n
        Data added successfully\n<\/code><\/pre>\n
        The read_sql_query function offered by the pandas library converts the data fetched into a panda data frame to make it easy for the user to work.<\/p>\n
        \n
        sql_statement = ('SELECT * FROM customer_data.csv')\ndf = pd.read_sql_query(sql_statement, cont)<\/code><\/pre>\n<\/div>\n
        Note that the cont<\/code> variable has the container information where our data is stored. Replace the credit_card_dataset<\/code> with the name of your container. More info can be found in this tutorial reading and writing to GridDB<\/a>.<\/p>\n
        When it comes to IoT and Big Data use cases, GridDB clearly stands out among other databases in the Relational and NoSQL space. Overall, GridDB offers multiple reliability features for mission-critical applications that require high availability and data retention.<\/p>\n
        3.b Using pandas read_csv<\/h3>\n
        We can also use Pandas’ read_csv<\/code> function to load our data. Both of the above methods will lead to the same output as the data is loaded in the form of a pandas dataframe using either of the methods.<\/p>\n
        \n
        df = pd.read_csv(\"customer_data.csv\")<\/code><\/pre>\n<\/div>\n
        \n
        df.head()<\/code><\/pre>\n<\/div>\n
        \n