Showing posts with the label business analytics

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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

Business Vs Demographic Vs Product Analytics

List of top analytics areas and their differences 1. Analytics in Business Advertising Analytics Brand Analytics Promotion Analytics Business-to-business marketing Analytics Social Media Analytics Tracking Studies 2. Demographic Analytics Consumer Analytics Concept Testing Data Mining Customer Satisfaction Study Analytics Demographic Analytics Employee Satisfaction Analysis Text Mining Ethnographic Analytics Media Testing Opinion Polling and Predictive Analytics Usage & Attitude Studies Segmentation Analytics Semiotic and Cultural Analysis 3. Product Analytics Packaging and Design Effectiveness Analytics New Product Development Pricing Studies Product Testing Scenario Planning  Also Read Top IT Skills You Need to Become Data Analyst