23  Reshape

import polars as pl

23.1 Long to wide

Example 1:

dat_long = pl.DataFrame({"ID":list(range(1,4))*3,
                         "Fruit":["mango", "mango", "mango", 
                                  "banana", "banana", "banana", 
                                  "tangerine", "tangerine", "tangerine"],
                         "Score":[1.1, 2.1, 3.1, 1.2, 2.2, 3.2, 1.3, 2.3, 3.3]})
dat_long
shape: (9, 3)
ID Fruit Score
i64 str f64
1 "mango" 1.1
2 "mango" 2.1
3 "mango" 3.1
1 "banana" 1.2
2 "banana" 2.2
3 "banana" 3.2
1 "tangerine" 1.3
2 "tangerine" 2.3
3 "tangerine" 3.3 
dat_long.pivot(index = "ID", columns = "Fruit", values = "Score")
shape: (3, 4)
ID mango banana tangerine
i64 f64 f64 f64
1 1.1 1.2 1.3
2 2.1 2.2 2.3
3 3.1 3.2 3.3

Example 2:

dat_long = pl.DataFrame({
    "Account_ID": [8001, 8002, 8003, 8004, 8001, 8002, 8003, 8004, 
            8001, 8002, 8003, 8004, 8001, 8002, 8003, 8004], 
    "Age": [67.8017038366664, 42.9198507293701, 46.2301756642422, 
            39.665983196671, 67.8017038366664, 42.9198507293701, 
            46.2301756642422, 39.665983196671, 67.8017038366664, 
            42.9198507293701, 46.2301756642422, 39.665983196671, 
            67.8017038366664, 42.9198507293701, 46.2301756642422, 
            39.665983196671], 
    "Admission": ["ED", "Planned", "Planned", "ED", "ED", "Planned", 
            "Planned", "ED", "ED", "Planned", "Planned", "ED", "ED", 
            "Planned", "Planned", "ED"], 
    "Lab_key": ["RBC", "RBC", "RBC", "RBC", "WBC", "WBC", "WBC", "WBC", 
            "Hematocrit", "Hematocrit", "Hematocrit", "Hematocrit", 
            "Hemoglobin", "Hemoglobin", "Hemoglobin", "Hemoglobin"], 
    "Lab_value": [4.63449321082268, 3.34968550627897, 4.27037213597765, 
            4.93897736897793, 8374.22887757195, 7612.37380499927, 
            8759.27855519425, 6972.28096216548, 36.272693147236, 
            40.5716317809522, 39.9888624177955, 39.8786884058422, 
            12.6188444991545, 12.1739747363806, 15.1293426442183, 
            14.8885696185238]
})
dat_long
shape: (16, 5)
Account_ID Age Admission Lab_key Lab_value
i64 f64 str str f64
8001 67.801704 "ED" "RBC" 4.634493
8002 42.919851 "Planned" "RBC" 3.349686
8003 46.230176 "Planned" "RBC" 4.270372
8004 39.665983 "ED" "RBC" 4.938977
8001 67.801704 "ED" "WBC" 8374.228878
8002 42.919851 "Planned" "WBC" 7612.373805
8003 46.230176 "Planned" "WBC" 8759.278555
8004 39.665983 "ED" "WBC" 6972.280962
8001 67.801704 "ED" "Hematocrit" 36.272693
8002 42.919851 "Planned" "Hematocrit" 40.571632
8003 46.230176 "Planned" "Hematocrit" 39.988862
8004 39.665983 "ED" "Hematocrit" 39.878688
8001 67.801704 "ED" "Hemoglobin" 12.618844
8002 42.919851 "Planned" "Hemoglobin" 12.173975
8003 46.230176 "Planned" "Hemoglobin" 15.129343
8004 39.665983 "ED" "Hemoglobin" 14.88857

Usage: df.pivot(value, index, columns)

Unfortunately, including Age in the index currently leads to an error (not executed)

dat_long.pivot(values = "Lab_value",
               index = ["Account_ID", "Age", "Admission"], 
               columns = "Lab_key")

Works if Age is cast to UTF-8 (and back to Float64)

dat_long = dat_long.with_column(
    pl.col("Age").cast(pl.Utf8)
)
dat_wide = dat_long.pivot(values = "Lab_value",
               index = ["Account_ID", "Age", "Admission"], 
               columns = "Lab_key")
dat_wide = dat_wide.with_column(
    pl.col("Age").cast(pl.Float64)
)
dat_wide
shape: (4, 7)
Account_ID Age Admission RBC WBC Hematocrit Hemoglobin
i64 f64 str f64 f64 f64 f64
8001 67.801704 "ED" 4.634493 8374.228878 36.272693 12.618844
8002 42.919851 "Planned" 3.349686 7612.373805 40.571632 12.173975
8003 46.230176 "Planned" 4.270372 8759.278555 39.988862 15.129343
8004 39.665983 "ED" 4.938977 6972.280962 39.878688 14.88857

Excluding Age, the pivot works:

dat_long.pivot(values = "Lab_value",
               index = ["Account_ID", "Admission"], 
               columns = "Lab_key")
shape: (4, 6)
Account_ID Admission RBC WBC Hematocrit Hemoglobin
i64 str f64 f64 f64 f64
8001 "ED" 4.634493 8374.228878 36.272693 12.618844
8002 "Planned" 3.349686 7612.373805 40.571632 12.173975
8003 "Planned" 4.270372 8759.278555 39.988862 15.129343
8004 "ED" 4.938977 6972.280962 39.878688 14.88857

23.2 Wide to Long

Example 1:

dat_wide = pl.DataFrame({"ID":[1, 2, 3],
                          "mango":[1.1, 2.1, 3.1],
                          "banana":[1.2, 2.2, 3.2],
                          "tangerine":[1.3, 2.3, 3.3]})
dat_wide
shape: (3, 4)
ID mango banana tangerine
i64 f64 f64 f64
1 1.1 1.2 1.3
2 2.1 2.2 2.3
3 3.1 3.2 3.3

Usage: df.metl(id_vars, value_vars, variable_name, value_name)

dat_wide.melt("ID", value_vars = ['mango', "banana", "tangerine"])
shape: (9, 3)
ID variable value
i64 str f64
1 "mango" 1.1
2 "mango" 2.1
3 "mango" 3.1
1 "banana" 1.2
2 "banana" 2.2
3 "banana" 3.2
1 "tangerine" 1.3
2 "tangerine" 2.3
3 "tangerine" 3.3

In the above example, value_vars can be ommitted: all columns other than those defined as ids will be used.

Example 2:

dat_wide = pl.DataFrame({
    "Account_ID": [8001, 8002, 8003, 8004], 
    "Age": [67.8017038366664, 42.9198507293701, 46.2301756642422, 
        39.665983196671], 
    "RBC": [4.63449321082268, 3.34968550627897, 4.27037213597765, 
        4.93897736897793], 
    "WBC": [8374.22887757195, 7612.37380499927, 8759.27855519425, 
        6972.28096216548], 
    "Hematocrit": [36.272693147236, 40.5716317809522, 39.9888624177955, 
        39.8786884058422], 
    "Hemoglobin": [12.618444991545, 12.1739747363806, 15.1293426442183, 
        14.8885696185238], 
    "Admission": ["ED", "Planned", "Planned", "Planned"]
})
dat_wide
shape: (4, 7)
Account_ID Age RBC WBC Hematocrit Hemoglobin Admission
i64 f64 f64 f64 f64 f64 str
8001 67.801704 4.634493 8374.228878 36.272693 12.618445 "ED"
8002 42.919851 3.349686 7612.373805 40.571632 12.173975 "Planned"
8003 46.230176 4.270372 8759.278555 39.988862 15.129343 "Planned"
8004 39.665983 4.938977 6972.280962 39.878688 14.88857 "Planned"

Usage: df.metl(id_vars, value_vars, variable_name, value_name)

dat_wide.melt(id_vars = ["Account_ID", "Age", "Admission"],
              value_vars = ["RBC", "WBC", "Hematocrit", "Hemoglobin"],
              variable_name = "Lab",
              value_name = "Value")
shape: (16, 5)
Account_ID Age Admission Lab Value
i64 f64 str str f64
8001 67.801704 "ED" "RBC" 4.634493
8002 42.919851 "Planned" "RBC" 3.349686
8003 46.230176 "Planned" "RBC" 4.270372
8004 39.665983 "Planned" "RBC" 4.938977
8001 67.801704 "ED" "WBC" 8374.228878
8002 42.919851 "Planned" "WBC" 7612.373805
8003 46.230176 "Planned" "WBC" 8759.278555
8004 39.665983 "Planned" "WBC" 6972.280962
8001 67.801704 "ED" "Hematocrit" 36.272693
8002 42.919851 "Planned" "Hematocrit" 40.571632
8003 46.230176 "Planned" "Hematocrit" 39.988862
8004 39.665983 "Planned" "Hematocrit" 39.878688
8001 67.801704 "ED" "Hemoglobin" 12.618445
8002 42.919851 "Planned" "Hemoglobin" 12.173975
8003 46.230176 "Planned" "Hemoglobin" 15.129343
8004 39.665983 "Planned" "Hemoglobin" 14.88857

Note: value_vars above can be omitted and will default to remaining columns