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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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Poor Data Quality New Job Roles in Data Quality

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Data quality is on rising and important to organizations today. Since in Experian research it has found that poor data quality causing losses to the companies.

Experian research suggests companies in the UK, the US, Australia, and western Europe have poorer quality data this year than last. The credit information company’s 2015 Global Data Quality Research among 1,239 organizations found a dramatic lack of data quality “ownership”, and 29% of respondents were still cleaning their data by hand.
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The number of organizations that suspect inaccurate data has jumped from 86% in 2014 to 92%. Also, respondents reckoned 26% of their data to be wrong, up from 22% in 2014 and 17% in 2013. Some 23% of respondents said this meant lost sales, up from 19% in 2013.

Boris Huard, managing director of Experian Data Quality, said: “Getting your data strategy right is vital if you want to be successful in this consumer-driven, digitalized age. 

It is encouraging that companies are increasingly switching on to the value of their data assets, with 95% of respondents stating that they feel driven to use their data to understand customer needs, find new customers or increase the value of each customer.”

Poor Data Quality costs millions of pounds to the companies. About one-third of organizations use automated systems, such as monitoring and audit technology (34%), data profiling (32%) or matching and linkage technology (31%) to clean their data. A total of 29% still use manual checking to clean their data.

Huard added: “As our Dawn of the CDO research demonstrated, a new breed of chief data officers, chief digital officers, and director of insights are emerging – new roles that have come about in response to the pressure and opportunity presented by big data.”

However, only 35% of respondents said they manage data quality by way of a single director and nearly 63% are missing a coherent, centralized approach to data quality. More than half said individual departments still go their own way with respect to data quality enforcement, and 12% described their data quality efforts as “ad hoc”.

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