As Ronak Shah said in a comment to your question, R 4.0.0 changed the default behavior in how read.table() (and so its wrappers including read.csv()) treats character vectors. There has been a long debate over that issue, but basically stringsAsFactors == T setting was a default since the inception of R because it helped to … Read more
series RowX yes RowY no RowW yes RowJ no RowA yes RowR no RowX yes RowY yes RowW yes RowJ yes RowA yes RowR no Name: Column 3, dtype: object pd.factorize 1 – series.factorize()[0] array([1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0]) np.where np.where(series == ‘yes’, 1, 0) array([1, 0, 1, … Read more
I just wanted to complete Holden’s answer. Since Spark 2.3.0,OneHotEncoder has been deprecated and it will be removed in 3.0.0. Please use OneHotEncoderEstimator instead. In Scala: import org.apache.spark.ml.Pipeline import org.apache.spark.ml.feature.{OneHotEncoderEstimator, StringIndexer} val df = Seq((0, “a”, 1), (1, “b”, 2), (2, “c”, 3), (3, “a”, 4), (4, “a”, 4), (5, “c”, 3)).toDF(“id”, “category1”, “category2”) val … Read more
See the relevel() function. Here is an example: set.seed(123) x <- rnorm(100) DF <- data.frame(x = x, y = 4 + (1.5*x) + rnorm(100, sd = 2), b = gl(5, 20)) head(DF) str(DF) m1 <- lm(y ~ x + b, data = DF) summary(m1) Now alter the factor b in DF by use of the … Read more
First, change the type of the column: df.cc = pd.Categorical(df.cc) Now the data look similar but are stored categorically. To capture the category codes: df[‘code’] = df.cc.cat.codes Now you have: cc temp code 0 US 37.0 2 1 CA 12.0 1 2 US 35.0 2 3 AU 20.0 0 If you don’t want to modify … Read more