<transcript><audio_80>or rather like um do different kinds of uh recording of the platforms where facebook and fb they are actually the same thing and then youtube you can have like you know people not</transcript><transcript><audio_81>uh doing caps for the key and also just evading it as yt so they are referring to the same thing so this is something that we want to clean as well okay so um yeah over here we can make use of the describe function you know to look at um</transcript><transcript><audio_82>variables uh that are numeric in nature okay yeah and for uh variables that are categorical in nature we can make use of value count okay and then similarly like if you want to look at more detailed breakdown</transcript><transcript><audio_83>by different levels okay so this is by market and by vertical okay you can make use of the dot count function and then next um we can also you know um instead of uh outputting all the columns okay they are all the same because there's like</transcript><transcript><audio_84>three records for you know all these uh columns we can just choose a particular column to output yeah so these numbers over here is the same that's what you see at the top okay so in cases where you know we want to rename our column names okay so this is how we</transcript><transcript><audio_85>can do the renaming yeah and this is just directly replacing them in the data set okay so uh again like to remove symbols in values we can make use of the replace function okay so now</transcript><transcript><audio_86>that the data for spans is clean yeah we have removed those unnecessary symbols and then similarly like for converting string to numeric and string to date there is um this two numeric function in</transcript><transcript><audio_87>pandas okay and then over here when you see this section i mean this part of the code um that says errors equal scores means like whenever there is a</transcript> or rather like um do different kinds of uh recording of the platforms where facebook and fb they are actually the same thing and then youtube you can have like you know people not uh doing caps for the key and also just evading it as yt so they are referring to the same thing so this is something that we want to clean as well okay so um yeah over here we can make use of the describe function you know to look at um variables uh that are numeric in nature okay yeah and for uh variables that are categorical in nature we can make use of value count okay and then similarly like if you want to look at more detailed breakdown by different levels okay so this is by market and by vertical okay you can make use of the dot count function and then next um we can also you know um instead of uh outputting all the columns okay they are all the same because there's like three records for you know all these uh columns we can just choose a particular column to output yeah so these numbers over here is the same that's what you see at the top okay so in cases where you know we want to rename our column names okay so this is how we can do the renaming yeah and this is just directly replacing them in the data set okay so uh again like to remove symbols in values we can make use of the replace function okay so now that the data for spans is clean yeah we have removed those unnecessary symbols and then similarly like for converting string to numeric and string to date there is um this two numeric function in pandas okay and then over here when you see this section i mean this part of the code um that says errors equal scores means like whenever there is a
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