@@ -216,19 +216,19 @@ def outlier_identifier(dataframe, columns=None, method="trim"):
216216 >> 'SepalLengthCm' : [5.1, 4.9, 4.7, 5.5, 5.1, 50, 5.4, 5.0, 5.2, 5.3, 5.1],
217217 >> 'SepalWidthCm' : [1.4, 1.4, 20, 2.0, 0.7, 1.6, 1.2, 1.4, 1.8, 1.5, 2.1],
218218 >> 'PetalWidthCm' : [0.2, 0.2, 0.2, 0.3, 0.4, 0.5, 0.5, 0.6, 0.4, 0.2, 5]
219- >>})
219+ >> })
220220
221221
222222 >> outlier_identifier(data)
223- >> SepalLengthCm SepalWidthCm PetalWidthCm
224- >> 0 5.1 1.4 0.2
225- >> 1 4.9 1.4 0.2
226- >> 2 5.5 2.0 0.3
227- >> 3 5.1 0.7 0.4
228- >> 4 5.4 1.2 0.5
229- >> 5 5.0 1.4 0.6
230- >> 6 5.2 1.8 0.4
231- >> 7 5.3 1.5 0.2
223+ SepalLengthCm SepalWidthCm PetalWidthCm
224+ 0 5.1 1.4 0.2
225+ 1 4.9 1.4 0.2
226+ 2 5.5 2.0 0.3
227+ 3 5.1 0.7 0.4
228+ 4 5.4 1.2 0.5
229+ 5 5.0 1.4 0.6
230+ 6 5.2 1.8 0.4
231+ 7 5.3 1.5 0.2
232232
233233 """
234234 if not isinstance (dataframe , pd .DataFrame ):
@@ -325,13 +325,12 @@ def scale(dataframe, columns, scaler="standard"):
325325 >> numerical_columns = ['SepalLengthCm','SepalWidthCm','PetalWidthCm']
326326
327327 >> scale(data, numerical_columns, scaler="minmax")
328-
329- >> SepalLengthCm SepalWidthCm PetalWidthCm
330- >> 0 0.25 1.00 1.0
331- >> 1 0.00 0.25 0.0
332- >> 2 0.00 0.25 0.0
333- >> 3 0.75 0.00 1.0
334- >> 4 1.00 0.25 0.5
328+ SepalLengthCm SepalWidthCm PetalWidthCm
329+ 0 0.25 1.00 1.0
330+ 1 0.00 0.25 0.0
331+ 2 0.00 0.25 0.0
332+ 3 0.75 0.00 1.0
333+ 4 1.00 0.25 0.5
335334 """
336335
337336 # Check if input data is of pd.DataFrame type
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