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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0:00 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,
0:11 you'll use functions like year, month or quarter that actually return a value or string
0:17 functions such as Lower Upper, which will transform a value to do additional calculations or add new columns to your data.
0:27 If you want to filter the data, you need to use functions that return a true or false Siris of values.
0:34 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
0:44 meet your criteria. But the end of the day you'll need to refer to the pandas documentation for all the available methods.
0:51 And when you're working with numbers, they're similar approaches. So comparison options such as greater than less center equal to you
0:58 can use either comparing absolute numbers or other columns their equivalent functions,
1:04 such as greater than less than or equal that you can use as well.
1:07 But I encourage you to use the numeric functions as you get started and get comfortable
1:13 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,
1:21 it's a similar approach. You can adds track, multiply and divide either whole numbers, floats or other values and columns,
1:29 as well as using theme math operation functions. But once again, I recommend that you use the standard math nomenclature until you get

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