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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

Big Data: Top Hadoop Interview Questions (4 of 5)

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1) What is MAP Reduce program? - You need to give actual steps in this program - You have to write scripts and codes 2) What is MAPReduce? -Mapreduce is a data processing model -It is combination of 2 parts. One is Mappers and the other one is Reducers 3)What will happen in Mapping phase? It takes the input data, and feeds each data element into the mapper 4)What is the function of Reducer? The reducer process all outputs from mapper and arrives at a final result 5)What kind of input required for Mapreduce? It should be structured in the form of (Key,Value) pairs 6)What is HDFS? HDFS is a file system designed for large-scale data processing under frameworks such as MapReduce. 7) Is HDFS like UNIX? No, but commands in HDFS works similarly to UNIX 8) What is Simple file command? hadoop fs -ls 9) How to copy data into HDFS file system? Copy a file into HDFS from local system 10) What is default working directory in HDFS? /user/$USER $USER ==> Your log