Showing posts with the label data engineer career path

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How to Check Column Nulls and Replace: Pandas

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

5 Essential IT Skills for Data Engineers

Data engineers need the following skills. These skills help you get nice job in any analytics company. Photo Credit: Srini Five Top Skills Need Skill-1 Experience working with big data tools such as MapReduce, Pig, Spark, Kafka and NoSQL data stores such as MongoDB, Cassandra, HBase, etc. Skill-2 Expertise in multi-structured data modeling, reporting on NoSQL & structured database technologies such as HBase and Cassandra, SQL. Skill-3 Experience with languages such as Python, Perl, Ruby, Java, Scala, R etc. Skill-4 Strong data & visual presentation skills and ability to explain insights using tools like tableau, D3 charts or other tools. Skill-5 Basic knowledge and experience of statistical analysis tools such as R.