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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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Top features of Apache Avro in Hadoop eco-System

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Avro defines a data format designed to support data-intensive applications, and provides support for this format in a variety of programming languages.

 The Hadoop ecosystem includes a new binary data serialization system — Avro. 

Avro provides:
·     Rich data structures.

·         A compact, fast, binary data format.
·         A container file, to store persistent data.
·         Remote procedure call (RPC).
·       Simple integration with dynamic languages. Code generation is not required to read or write data files nor to use or implement RPC protocols. Code generation as an optional optimization, only worth implementing for statically typed languages.

Its functionality is similar to the other marshaling systems such as Thrift, Protocol Buffers, and so on.

The main differentiators of Avro include the following:

[Hadoop Interview Questions]
[Hadoop Interview Questions]
Dynamic typing — The Avro implementation always keeps data and its corresponding schema together. As a result, marshaling/unmarshaling operations do not require either code generation or static data types. This also allows generic data processing.

Untagged data — Because it keeps data and schema together, Avro
marshaling/unmarshaling does not require type/size information or manually assigned IDs to be encoded in data. As a result, Avro serialization produces a smaller output.

Enhanced versioning support — In the case of schema changes, Avro contains both schemas, which enables you to resolve differences symbolically based on the field names.
Because of high performance, a small codebase, and compact resulting data, there is a wide adoption of Avro not only in the Hadoop community, but also by many other NoSQL implementations (including Cassandra).

At the heart of Avro is a data serialization system. Avro can either use reflection to dynamically generate schemas of the existing Java objects, or use an explicit Avro schema — a JavaScript Object Notation (JSON) document describing the data format. Avro schemas can contain both simple and complex types.

Simple data types supported by Avro include null, boolean, int, long, float, double, bytes, and string. Here, null is a special type, corresponding to no data, and can be used in place of any data type.

Complex types supported by Avro include the following:
Record — This is roughly equivalent to a C structure. A record has a name and optional namespace, document, and alias. It contains a list of named attributes that can be of any Avro type.
Enum — This is an enumeration of values. Enum has a name, an optional namespace, document, and alias, and contains a list of symbols (valid JSON strings).
Array — This is a collection of items of the same type.
Map — This is a map of keys of type string and values of the specified type.
Union — This represents an or option for the value. A common use for unions is to specify nullable values.

Related: Top 100 HADOOP Interview Questions-Part:1

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