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Top Questions People Ask About Pandas, NumPy, Matplotlib & Scikit-learn — Answered!

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 Whether you're a beginner or brushing up on your skills, these are the real-world questions Python learners ask most about key libraries in data science. Let’s dive in! 🐍 🐼 Pandas: Data Manipulation Made Easy 1. How do I handle missing data in a DataFrame? df.fillna( 0 ) # Replace NaNs with 0 df.dropna() # Remove rows with NaNs df.isna(). sum () # Count missing values per column 2. How can I merge or join two DataFrames? pd.merge(df1, df2, on= 'id' , how= 'inner' ) # inner, left, right, outer 3. What is the difference between loc[] and iloc[] ? loc[] uses labels (e.g., column names) iloc[] uses integer positions df.loc[ 0 , 'name' ] # label-based df.iloc[ 0 , 1 ] # index-based 4. How do I group data and perform aggregation? df.groupby( 'category' )[ 'sales' ]. sum () 5. How can I convert a column to datetime format? df[ 'date' ] = pd.to_datetime(df[ 'date' ]) ...
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5 HBase Vs. RDBMS Top Functional Differences

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Here're the differences between RDBMS and HBase. HBase in the Big data context has a lot of benefits over RDBMS. The listed differences below make it understandable why HBASE is popular in Hadoop (or Bigdata) platform.

5 HBase Vs. RDBMS Top Functional Differences

5 HBase Vs. RDBMS Top Functional Differences


Here're the differences unlock now.

Random Accessing


HBase handles a large amount of data that is store in a distributed manner in the column-oriented format while RDBMS is systematic storage of a database that cannot support a random manner for accessing the database.

Database Rules


RDBMS strictly follows Codd's 12 rules with fixed schemas and row-oriented manner of database and also follows ACID properties.


HBase follows BASE properties and implements complex queries.
Secondary indexes, complex inner and outer joins, count, sum, sort, group, and data of page and table can easily be accessible by RDBMS.

Storage


From small to medium storage application there is the use of RDBMS that provides the solution with MySQL and PostgreSQL whose size increase with concurrency and performance. 


Codd's rules always need to keep in mind while extending the size of the database in the use of data processing.

Data Integrity


RDBMS focuses on and emphasizes consistency, referential integrity, abstraction from the physical layer, and complex queries through SQL language.

Takeaway

  • There is no single-point failure in HBASE. You always have backup data.
  • The server regions have the flexibility to share or rebalance the load among the servers.
  • Automatic partition helps to distribute its workload among servers. It happens with its in-built feature of HBASE.
  • The cost involved in the maintenance of HBASE is comparatively low.


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