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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

Why MySQL You Need to Master for Data Analytics Jobs

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MySQL Before you can start analysing data, you are going to actually have to have some data on hand. That means a database – preferably a relational one. If you had your sights set on a non-relational, NoSQL database solution, you might want to step back and catch your breath. NoSQL databases are unique because of their independence from the Structured Query Language (SQL) found in relational databases. Relational databases all use SQL as the domain-specific language for ad hoc queries, whereas non-relational databases have no such standard query language, so they can use whatever they want –including SQL. Non-relational databases also have their own APIs designed for maximum scalability and flexibility. When You Need to Learn NoSQL Databases? NoSQL databases are typically designed to excel in two specific areas: speed and scalability. But for the purposes of learning about data concepts and analysis, such super-powerful tools are pretty much overkill. In other words, you