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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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Cloudera Impala top features useful for developers

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Cloudera Impala that runs on Apache Hadoop. The program was proclaimed in October 2012 with a common beta trial dispersion. Popular usage is in data analytics.The key features useful for interviews.
Impala The Apache-licensed Impala program begets scalable collateral database techniques to Hadoop, authorizing consumers to subject low-latency SQL requests to information kept in HDFS and Apache HBase short of needing information motion either alteration.


Impala is amalgamated with Hadoop to employ the similar file and information setups, metadata, safeguarding and asset administration architectures applied by MapReduce, Apache Hive, Apache Pig and different Hadoop code.

Impala Applications

Impala is advanced for experts and information experts in science to accomplish systematic computational analysis of data or statistics on information kept in Hadoop through SQL either trade intellect implements. 

 
The effect is that extensive information handling (via MapReduce) and two-way requests may be completed on the similar configuration utilizing the similar information and metadata – eliminating the demand to wander information places in to specific setups and or exclusive setups plainly to accomplish examination. 


Features included
  • Supports HDFS#Hadoop_distributed_file_system|HDFS and Apache HBase storage
  • Reads Hadoop date setups, containing written material, LZO, SequenceFile, Avro and RCFile Supports Hadoop safeguarding (Kerberos authentication)
  • Fine-grained, Role-based allowance with Sentry Uses metadata, ODBC driver, and SQL structure as of Apache Hive

In first 2013, a column-oriented DBMS|column-oriented information setup named Parquet was proclaimed for designs containing Impala. In December 2013, Amazon Web Services proclaimed aid aimed at Impala.


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