Move from Excel to Python with Pandas Transcripts
Chapter: Data wrangling with Pandas
Lecture: Concept: Filtering and cleaning text and dates
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in the previous examples, I've covered a lot of different content, so I want to summarize it for you. When cleaning and transforming dates or strings,
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you'll use functions like year, month or quarter that actually return a value or string
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functions such as Lower Upper, which will transform a value to do additional calculations or add new columns to your data.
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If you want to filter the data, you need to use functions that return a true or false Siris of values.
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So for dates is month start or his quarter starter example, and then string has various search functions that you can use to determine if the value
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meet your criteria. But the end of the day you'll need to refer to the pandas documentation for all the available methods.
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And when you're working with numbers, they're similar approaches. So comparison options such as greater than less center equal to you
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can use either comparing absolute numbers or other columns their equivalent functions,
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such as greater than less than or equal that you can use as well.
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But I encourage you to use the numeric functions as you get started and get comfortable
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with Python, and then when you need to use the additional functions. You can do that in the future. And then, from a math perspective,
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it's a similar approach. You can adds track, multiply and divide either whole numbers, floats or other values and columns,
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as well as using theme math operation functions. But once again, I recommend that you use the standard math nomenclature until you get