Correcting Right Skew Data
In the slides we showed how you might use log transforms to fix positively skewed data (data whose distribution is mostly to the left). To correct negative skew (data mostly to the right) you need to take an extra step called "reflecting" before you can apply the inverse of \(\log\), written as (1/\(\log\)) to make the data look more like normal a normal distribution. Reflecting data uses the following formula to reflect each value: \((x_{\text{max}} +1) – x\).
Questo esercizio fa parte del corso
Feature Engineering with PySpark
Istruzioni dell'esercizio
- Use the aggregate function
skewness()to verify that'YEARBUILT'has negative skew. - Use the
withColumn()to create a new column'Reflect_YearBuilt'and reflect the values of'YEARBUILT'. - Using
'Reflect_YearBuilt'column, create another column'adj_yearbuilt'by taking 1/log() of the values.
esercizio interattivo pratico
Prova questo esercizio completando questo codice di esempio.
from pyspark.sql.functions import log
# Compute the skewness
print(df.____({____: ____}).____())
# Calculate the max year
max_year = df.____({____: ____}).____()[0][0]
# Create a new column of reflected data
df = df.____(____, (max_year + 1) - df[____])
# Create a new column based reflected data
df = df.____(____, 1 / ____(df[____]))