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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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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

Google MapReduce Vs IBM PML

  1. Like MapReduce and Hadoop, PML supports associative-commutative computations as its primary parallelization mechanism. 
  2. Unlike MapReduce and Hadoop, PML fundamentally assumes that learning algorithms can be iterative in nature, requiring multiple passes over data.
  3. 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
  4. 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 dataset.
  5. Access to both sparse and dense datasetsParallel merge operations using tree structures for efficient collection of worker results on very large clusters.
  6. In order to make these extensions to the computational model and still address ease of use, PML provides an object-oriented API in which algorithms are objects that implement a predefined set of interface methods.

PML Unique Features

  • The PML infrastructure then uses these interface methods to distribute algorithm objects and their computations across multiple compute nodes-An object-oriented approach is employed to simplify the task of writing code to maintain, update, and distribute complex data structures in parallel environments.
  • Several parallel machine learning and data mining algorithms have already been implemented in PML, including Support Vector Machine (SVM) classifiers, linear regression, transform regression, nearest neighbors classifiers, decision tree classifiers, k-means, fuzzy k-means, kernel k-means, principal component analysis (PCA), kernel PCA, and frequent pattern mining.

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