The biography of Muhammad
Posted: Wed Dec 18, 2024 4:21 am
Meg said that was impossible, since she didn't actually have ovaries anymore.
The doctor explained that she had Ovarian Remnant Syndrome. A small part of her ovarian tissue had been left behind when it was removed and, in a rare twist, it regenerated itself.
"My chances of developing it were like one in a million," he says.
Meg has had her remains removed, but she will no longer let herself believe she has seen the last of her health problems. She is still experiencing issues with endometriosis, and is afraid she will “end up growing sideshow starfish ovaries again,” referencing how the marine animal can regrow lost or damaged arms.
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Still, she says her pain levels have dropped, putting italy email list them at a four out of ten, adding that's "huge" for her.
dataset = pd.read_csv('made_up_thing.csv')
Hopefully that's self-explanatory. The next one is a little more complicated. The thing about spreadsheets is that you may or may not care about some of the columns. For the sake of simplicity, let's say we care about everything except the last column. We're going to use the pandas .iloc function, which gets the data in whatever column(s) it's given:
X = dataset.iloc.values
.actually, iloc accepts it, so we enter . The : is because we want all the rows in those columns, and : is the way you do it. We add the .values to, well, get the values in the segments we've selected. In other words, the first part selects the values, the second part gets the values.
Now let’s do the actual encoding. Sklearn makes this incredibly easy, but there’s a catch. You may have noticed that we imported both the label encoder and the one hot encoder. Sklearn’s one hot encoder doesn’t actually know how to convert categories to numbers, it only knows how to convert numbers to binary. We need to use the label encoder first.
The doctor explained that she had Ovarian Remnant Syndrome. A small part of her ovarian tissue had been left behind when it was removed and, in a rare twist, it regenerated itself.
"My chances of developing it were like one in a million," he says.
Meg has had her remains removed, but she will no longer let herself believe she has seen the last of her health problems. She is still experiencing issues with endometriosis, and is afraid she will “end up growing sideshow starfish ovaries again,” referencing how the marine animal can regrow lost or damaged arms.
ADVERTISEMENT
Still, she says her pain levels have dropped, putting italy email list them at a four out of ten, adding that's "huge" for her.
dataset = pd.read_csv('made_up_thing.csv')
Hopefully that's self-explanatory. The next one is a little more complicated. The thing about spreadsheets is that you may or may not care about some of the columns. For the sake of simplicity, let's say we care about everything except the last column. We're going to use the pandas .iloc function, which gets the data in whatever column(s) it's given:
X = dataset.iloc.values
.actually, iloc accepts it, so we enter . The : is because we want all the rows in those columns, and : is the way you do it. We add the .values to, well, get the values in the segments we've selected. In other words, the first part selects the values, the second part gets the values.
Now let’s do the actual encoding. Sklearn makes this incredibly easy, but there’s a catch. You may have noticed that we imported both the label encoder and the one hot encoder. Sklearn’s one hot encoder doesn’t actually know how to convert categories to numbers, it only knows how to convert numbers to binary. We need to use the label encoder first.