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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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Hadoop HDFS Comics to Understand Quickly

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HDFS file system in Hadoop helps to store data supplied as input. Its fault-tolerant nature avoids data loss. About HDFS, the real story of fault-tolerant  given in Comic book for you to understand in less time.

What is HDFS in Hadoop

HDFS is optimized to support high-streaming read performance, and this comes at the expense of random seek performance.
hdfs file system

This means that if an application is reading from HDFS, it should avoid (or at least minimize) the number of seeks. Sequential reads are the preferred way to access HDFS files.

HDFS supports only a limited set of operations on files — writes, deletes, appends, and reads, but not updates. It assumes that the data will be written to the HDFS once, and then read multiple times.

HDFS does not provide a mechanism for local caching of data. The overhead of caching is large enough that data should simply be re-read from the source, which is not a problem for applications that are mostly doing sequential reads of large-sized data files.

Hadoop Vs HDFS Comic Book


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