Move from Excel to Python with Pandas Transcripts
Chapter: Data wrangling with Pandas
Lecture: Concept: Filtering and cleaning text and dates
0:00 in the previous examples, I've covered a lot of different content,
0:04 so I want to summarize it for you.
0:06 When cleaning and transforming dates or strings,
0:10 you'll use functions like year, month or quarter that actually return a value or string
0:16 functions such as Lower Upper, which will transform a value to do additional calculations or
0:23 add new columns to your data.
0:26 If you want to filter the data,
0:28 you need to use functions that return a true or false Siris of values.
0:33 So for dates is month start or his quarter starter example,
0:37 and then string has various search functions that you can use to determine if the value
0:43 meet your criteria. But the end of the day you'll need to refer to the
0:47 pandas documentation for all the available methods.
0:50 And when you're working with numbers,
0:51 they're similar approaches. So comparison options such as greater than less center equal to you
0:57 can use either comparing absolute numbers or other columns their equivalent functions,
1:03 such as greater than less than or equal that you can use as well.
1:06 But I encourage you to use the numeric functions as you get started and get comfortable
1:12 with python, and then when you need to use the additional functions.
1:17 You can do that in the future.
1:18 And then, from a math perspective,
1:20 it's a similar approach. You can adds track,
1:22 multiply and divide either whole numbers,
1:25 floats or other values and columns,
1:28 as well as using theme math operation functions.
1:31 But once again, I recommend that you use the standard math nomenclature until you get