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Showing posts with the label google mapreduce

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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' ]) ...

IBM PML Vs Google MapReduce why you need to read

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IBM Parallel Machine Learning Toolbox (PML) is similar to that of Google's MapReduce programming model (Dean and Ghemawat, 2004) and the open source Hadoop system,which is to provide Application Programming Interfaces (APIs) that enable programmers who have no prior experience in parallel and distributed systems to nevertheless implement parallel algorithms with relative ease. Google MapReduce Vs IBM PML Like MapReduce and Hadoop, PML supports associative-commutative computations as its primary parallelization mechanism .  Unlike MapReduce and Hadoop, PML fundamentally assumes that learning algorithms can be iterative in nature, requiring multiple passes over data. The ability to maintain the state of each worker node between iterations, making it possible, for example, to partition and distribute data structures across workers Efficient distribution of data, including the ability of each worker to read a subset of the data, to sample the data, or to scan the entire data...