In this post, we will learn how we can create pandas dataframe from MongoDB collection. MongoDB is a popular NoSQL database that stores data in a JSON-like format and offers a flexible and scalable solution for managing large volumes of data. When working with data stored in MongoDB, it is often necessary to analyze and […]
In the Python ecosystem, package distribution plays a crucial role in sharing and reusing code efficiently. While Python’s built-in package manager, pip, allows us to install packages effortlessly, sometimes it becomes necessary to distribute our own Python packages. In such cases, wheel files prove to be a valuable asset. A wheel file is a built
Creating a Wheel File in Python: Simplifying Package Distribution Read More »
In this post, we will learn how to create a requirements.txt file for a python project. The requirements.txt file contains the list of all the packages needed to execute the Python project. It is very helpful, especially during the deployment. Using the requirement.txt file, we can automate the deployment of the project to a different
Create requirements.txt file in Python automatically Read More »
In today’s digital age, organizations deal with large amounts of sensitive data that includes PII data such as names, addresses, phone numbers, and email addresses. Protecting this data is critical to prevent identity theft and other types of fraud, and PII detection is a key step in the process. In this post, we will discuss
PII Data Identification using Presidio Open Source ML Library Read More »
Pandas is one of the most popular Python libraries used by Data Scientists/Data Engineers for data wrangling and data analysis. Also, Pandas provide DataFrames (a table-like structure that stores data in rows and columns) to deal with structured datasets. These DataFrames are very similar to Spark’s DataFrames. However, Pandas dataframes are limited to a single
Databricks provides a wrapper file system API named DBFS (Databricks File System) to perform any file-level operation such as read, write, move, delete, rename, etc. However, sometimes we may need to read the underlying file system objects directly without using the DBFS wrapper APIs. To do so, we can use HDFS APIs available through py4j
Use HDFS API to read Azure Blob files in Databricks Read More »
In this post, we will learn how we can extract the physical HDFS file location path of the Hive table as a column along with other columns of the table. We will demonstrate this using HiveQL, PySpark, and Scala. We can create the Hive tables as internal or external tables. So, if we create an
Get HDFS file location of Hive table records as column Read More »
In the previous post, we had a brief discussion about the GridSearchCV and RandomizedSearchCV. Now, in this post, we will demonstrate that how we can use the GridSearchCV and RandomizedSearchCV methods available with the Sci-kit learn library for hyperparameter tuning in Python. We will use the sklearn built-in diabetes dataset in this demo. However, if
Hyperparameter tuning using GridSearchCV and RandomizedSearchCV in Python Read More »
This post briefs how we can display the full contents of data frame columns in Apache Spark. The default behavior of Spark truncates the column values if it is more than 20 characters. However, sometimes we need to display the full values rather than the truncated data. Having truncated data might not be useful in
Suppose, we have a CSV file that contains some non-English characters (Spanish, Japanese, and etc.) and we want to read this file into a Spark data frame. If we read this file without using the right character encoding, we will end up with some junk characters (like �) in the data frame. So, the files
Spark read file with special characters using PySpark Read More »
