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If I follow the tutorial correctly, the following method will not be useful if the sample info has continuous values (such as height, weight). It will create a lot of columns for each unique number.
def getDatTraits(self, metaData):
data = self.datExpr.obs.copy()[metaData]
datTraits = pd.DataFrame(index=data.index)
for i in range(data.shape[1]):
data.iloc[:, i] = data.iloc[:, i].astype(str)
if len(np.unique(data.iloc[:, i])) == 2:
datTraits[data.columns[i]] = data.iloc[:, i]
org = np.unique(data.iloc[:, i]).tolist()
rep = list(range(len(org)))
datTraits.replace(to_replace=org, value=rep,
inplace=True)
elif len(np.unique(data.iloc[:, i])) > 2:
for name in np.unique(data.iloc[:, i]):
datTraits[name] = data.iloc[:, i]
org = np.unique(data.iloc[:, i])
rep = np.repeat(0, len(org))
rep[np.where(org == name)] = 1
org = org.tolist()
rep = rep.tolist()
datTraits.replace(to_replace=org, value=rep, inplace=True)
return datTraits
Are there any other function to correlate the module to continuous trais?
Thank you.
Wilson
The text was updated successfully, but these errors were encountered:
Hi,
If I follow the tutorial correctly, the following method will not be useful if the sample info has continuous values (such as height, weight). It will create a lot of columns for each unique number.
Are there any other function to correlate the module to continuous trais?
Thank you.
Wilson
The text was updated successfully, but these errors were encountered: