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14 Top Data Pipeline Key Terms Explained

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 Here are some key terms commonly used in data pipelines 1. Data Sources Definition: Points where data originates (e.g., databases, APIs, files, IoT devices). Examples: Relational databases (PostgreSQL, MySQL), APIs, cloud storage (S3), streaming data (Kafka), and on-premise systems. 2. Data Ingestion Definition: The process of importing or collecting raw data from various sources into a system for processing or storage. Methods: Batch ingestion, real-time/streaming ingestion. 3. Data Transformation Definition: Modifying, cleaning, or enriching data to make it usable for analysis or storage. Examples: Data cleaning (removing duplicates, fixing missing values). Data enrichment (joining with other data sources). ETL (Extract, Transform, Load). ELT (Extract, Load, Transform). 4. Data Storage Definition: Locations where data is stored after ingestion and transformation. Types: Data Lakes: Store raw, unstructured, or semi-structured data (e.g., S3, Azure Data Lake). Data Warehous...
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How to use Pandas Series Method top ideas

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How to use Pandas Series Method top ideas

Here is an example of how to use a Series constructor in Pandas. A one-dimensional array capable of holding any data type (integers, strings, floating-point numbers, Python objects, etc.) is called a Series object in pandas.

Sample DataFrame




Single dimension data


Below is the single dimension data of Index and Value.


 Index Value
 1 10           
 2 40
 3 01
 4 99

Having single value for an index is called Single dimensional data. On the other hand, when one index has multiple values, it is called multi-dimensional array.  

Below is the example for Multi-dimensional array. 

a = (1, (10,20))
mySeries = pd.Series(data, index=index)
Here, pd is a Pandas object. The data and index are two arguments. The data refers to a Python dictionary of "ndarray"  and index is index of data.

Generating DataFrame from single dimension data

The below example shows, how to construct single dimension data (Values and Index).

>>>mySeries = pd.Series([10,20,30], index=[1,2, 'a'])

Special Notes: In the above index list the 'a' represents alpha type.

Once mySeries object created, you can verify Values and Index. Do follow the steps in the screen.

series data 

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