<transcript><audio_44>because they don't appear in both data set right you only obtain one yeah and then um similarly if you have like a lot of uh data that is collected across time and that's when you know you want to</transcript><transcript><audio_45>append data sets like uh to make your analysis um more robust and more meaningful like you have uh basically quarter one quality quality you know and then yeah each quarter they collect the same kind of data</transcript><transcript><audio_46>and so um every time you have new data set you want to add them together yeah so this is also something that is very common uh when working on any data science project when like you know you have so much data that you can work with</transcript><transcript><audio_47>yeah and um similarly like if you have a lot of um data that you know you are like pending then there could be cases of duplication and um these are the times where you know we want to</transcript><transcript><audio_48>duplicate our data so um there's different ways where we can choose to drop the earlier record of the late record you know kind of thing yeah and pandas can handle this very easily yeah and next yeah uh similar to pending</transcript><transcript><audio_49>say for example you have data from different sources and then you want to combine them together right so for example if you have like demographic data from one database and then you have transaction data and other database yeah then you want to match them</transcript> because they don't appear in both data set right you only obtain one yeah and then um similarly if you have like a lot of uh data that is collected across time and that's when you know you want to append data sets like uh to make your analysis um more robust and more meaningful like you have uh basically quarter one quality quality you know and then yeah each quarter they collect the same kind of data and so um every time you have new data set you want to add them together yeah so this is also something that is very common uh when working on any data science project when like you know you have so much data that you can work with yeah and um similarly like if you have a lot of um data that you know you are like pending then there could be cases of duplication and um these are the times where you know we want to duplicate our data so um there's different ways where we can choose to drop the earlier record of the late record you know kind of thing yeah and pandas can handle this very easily yeah and next yeah uh similar to pending say for example you have data from different sources and then you want to combine them together right so for example if you have like demographic data from one database and then you have transaction data and other database yeah then you want to match them
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