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: \((x_{\text{max}} +1) – x\).

Bu egzersiz

Feature Engineering with PySpark

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

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.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

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[____]))

Kodu Düzenle ve Çalıştır