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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

New Wave in Data Analytics in 2014

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 SrnimfJobs N ow that we’re in the swing of a new year, we’ve taken stock of the data analytics trends that are brewing and developed a list of the Top 5 trends we believe are going to dominate the industry this year. Even if some of them don’t realize their full potential in 2014, it promises to be an important year in which consumer trends and technology innovation will further shape a future in which companies make data-driven decisions. 1. Data Visualization Goes Mainstream In the mid-90s, e-mail introduced the Internet to consumers, made it more accessible, and catalyzed user adoption. Similarly, data visualization will make data analytics more accessible in 2014. Visual analytics allows business users to ask interactive questions of their prepared data sets and get immediate visual responses, which makes the whole process engaging. This trend will democratize access to data and foster a strong data analysis culture where business users will look for data and perform