Showing posts with the label Bank ATM Analytics - SAP Hana

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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

Bank ATM Analytics - SAP Hana

Problem: ATM(Automated Teller Machine) has become an integral part of our society. Recent estimates developed by ATMIA place the number of ATMs in use currently at over 2.2 million, or 1 ATM per around over 3000 people in the world. If we think of very specific banks than a recent information in wikipedia states that SBI Group (State Bank Of India) has around 45000+ ATM. So we see the numbers are huge. But there is a very big problem and i would like to use a typical example from India to illustrate this. Here, many times the ATM runs out of currency notes of specific denomination as lets say Rs 100. The end consumer is left with the option of either accepting currency of higher denomination or seek a different ATM. Here we also have a charge applied by the bank if a consumer withdraws money from a ATM of a bank different from the one in which he holds his account. Now this is a major inconvenience for the end consumer and also a loss for the bank as their reputation is effecte