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How to Check Column Nulls and Replace: Pandas

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Here is a post that shows how to count Nulls and replace them with the value you want in the Pandas Dataframe. We have explained the process in two steps - Counting and Replacing the Null values. Count null values (column-wise) in Pandas ## count null values column-wise null_counts = df.isnull(). sum() print(null_counts) ``` Output: ``` Column1    1 Column2    1 Column3    5 dtype: int64 ``` In the above code, we first create a sample Pandas DataFrame `df` with some null values. Then, we use the `isnull()` function to create a DataFrame of the same shape as `df`, where each element is a boolean value indicating whether that element is null or not. Finally, we use the `sum()` function to count the number of null values in each column of the resulting DataFrame. The output shows the count of null values column-wise. to count null values column-wise: ``` df.isnull().sum() ``` ##Code snippet to count null values row-wise: ``` df.isnull().sum(axis=1) ``` In the above code, `df` is the Panda
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Kafka Flowchart Useful for Dummies

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Architecture for dummies


How Kafka Works


Here're the prime points on Kafka stream-processing. In Mainframe, the data you receive/process in two methods (Batch and online). In Kafka, it receives data and sends it to consumers. Here're the details with Architecture, Logs, and applications that use Kafka.

The streaming data is different (YouTube Live). When the data comes into Queue the data will then be processed. In the batch process, you need to wait till you get the Batch completes. In the case of stream processing, it is on the fly.

1. Architecture



Kafka Architecture

2. Process

  • Kafka is a publish/subscribe system, but it would be more precise to say that Kafka acts as a message broker. A broker is an intermediary that brings together two parties that don’t necessarily know each other for a mutually beneficial exchange or deal.
  • Kafka stores messages in topics and retrieves messages from topics. There’s no direct connection between the producers and the consumers of the messages. Additionally, Kafka doesn’t keep any state regarding the producers or consumers. It acts solely as a message clearinghouse.
  • The underlying technology of a Kafka topic is a log, which is a file that Kafka appends incoming records to. To help manage a load of messages coming into a topic, Kafka uses partitions.
  • The use of partitions is to bring data located on different machines together on the same server. We’ll discuss partitions in detail shortly.

Kafka Logs

  • It writes the incoming messages to logs.
  • It appends to the logs. it will not delete the existing records. 

Zookeeper 

  • ZooKeeper is a centralized service for maintaining configuration information, naming, providing distributed synchronization, and providing group services. 
  • All of these kinds of services are used in some form or another by distributed applications.

3. Stream Data Structure

Stream Data Key Points:

  • Here are the key points to remember: If you need to report on or take action immediately as data arrives, stream processing is a good approach.
  • If you want to Carry detailed analysis- If you need to perform in-depth analysis or are compiling a large repository of data for later analysis, a stream-processing approach may not be a good fit.
How the stream data comes into the Kafka server you can see here.

4. The Kafka Based Applications

Credit card fraud—A credit card owner may not notice a card has been stolen, but by reviewing purchases as they happen against established patterns (location, general spending habits), you may be able to detect a stolen credit card and alert the owner.


Intrusion detection—Analyzing application log files after a breach has occurred may be helpful to prevent future attacks or to improve security, but the ability to monitor aberrant behavior in real-time is critical.


In a large race, such as the New York City Marathon—Almost all runners will have a chip on their shoe, and when runners pass sensors along the course, you can use that information to track the runners’ positions. By using the sensor data, you can determine the leaders, spot potential cheating, and detect whether a runner is potentially having problems.


The financial industry—The ability to track market prices and direction in real-time is essential for brokers and consumers to make effective decisions about when to sell or buy.

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