相关#
import polars as pl
from plotnine import *
Regression Plot#
mpg = pl.read_csv("data/mpg.csv")
mpg.head()
shape: (5, 11)
| manufacturer | model | displ | year | cyl | trans | drv | cty | hwy | fl | class |
|---|---|---|---|---|---|---|---|---|---|---|
| str | str | f64 | i64 | i64 | str | str | i64 | i64 | str | str |
| "audi" | "a4" | 1.8 | 1999 | 4 | "auto(l5)" | "f" | 18 | 29 | "p" | "compact" |
| "audi" | "a4" | 1.8 | 1999 | 4 | "manual(m5)" | "f" | 21 | 29 | "p" | "compact" |
| "audi" | "a4" | 2.0 | 2008 | 4 | "manual(m6)" | "f" | 20 | 31 | "p" | "compact" |
| "audi" | "a4" | 2.0 | 2008 | 4 | "auto(av)" | "f" | 21 | 30 | "p" | "compact" |
| "audi" | "a4" | 2.8 | 1999 | 6 | "auto(l5)" | "f" | 16 | 26 | "p" | "compact" |
mpg_select = mpg.filter((pl.col("cyl") == 4) | (pl.col("cyl") == 8))
(
ggplot(mpg_select, aes(x="displ", y="hwy", color="cyl", fill="cyl"))
+ geom_point(size=2)
+ geom_smooth(method="lm", size=1)
+ facet_wrap("cyl")
+ lims(x=(0, 8), y=(0, 50))
+ labs(title="Scatterplot with line of best fit grouped by number of cylinders")
+ theme(plot_title=element_text(hjust=0.5), legend_position="none")
+ scale_color_gradient(low="#E69F00", high="#56B4E9")
+ scale_fill_gradient(low="#E69F00", high="#56B4E9")
)
Jittering#
g = ggplot(mpg, aes(x="cty", y="hwy"))
(
g
+ geom_jitter(height=0.5, size=2, color="#06527f")
+ labs(title="Jittered Points")
+ theme(plot_title=element_text(hjust=0.5))
)
(
g
+ geom_count(color="red")
+ labs(title="Counts Plot")
+ theme(plot_title=element_text(hjust=0.5), legend_position="none")
)
FacetGrid#
mtcars = pl.read_csv("data/mtcars.csv")
mtcars.head()
shape: (5, 13)
| mpg | cyl | disp | hp | drat | wt | qsec | vs | am | gear | carb | fast | cars |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| f64 | i64 | f64 | i64 | f64 | f64 | f64 | i64 | i64 | i64 | i64 | i64 | str |
| 4.582576 | 6 | 160.0 | 110 | 3.9 | 2.62 | 16.46 | 0 | 1 | 4 | 4 | 1 | "Mazda RX4" |
| 4.582576 | 6 | 160.0 | 110 | 3.9 | 2.875 | 17.02 | 0 | 1 | 4 | 4 | 1 | "Mazda RX4 Wag" |
| 4.774935 | 4 | 108.0 | 93 | 3.85 | 2.32 | 18.61 | 1 | 1 | 4 | 1 | 1 | "Datsun 710" |
| 4.626013 | 6 | 258.0 | 110 | 3.08 | 3.215 | 19.44 | 1 | 0 | 3 | 1 | 1 | "Hornet 4 Drive" |
| 4.32435 | 8 | 360.0 | 175 | 3.15 | 3.44 | 17.02 | 0 | 0 | 3 | 2 | 1 | "Hornet Sportabout" |
(
ggplot(mtcars, aes("mpg", "wt"))
+ geom_point(color="#06527f")
+ facet_wrap(["am", "vs"])
)
Heatmap#
mtcars
shape: (32, 13)
| mpg | cyl | disp | hp | drat | wt | qsec | vs | am | gear | carb | fast | cars |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| f64 | i64 | f64 | i64 | f64 | f64 | f64 | i64 | i64 | i64 | i64 | i64 | str |
| 4.582576 | 6 | 160.0 | 110 | 3.9 | 2.62 | 16.46 | 0 | 1 | 4 | 4 | 1 | "Mazda RX4" |
| 4.582576 | 6 | 160.0 | 110 | 3.9 | 2.875 | 17.02 | 0 | 1 | 4 | 4 | 1 | "Mazda RX4 Wag" |
| 4.774935 | 4 | 108.0 | 93 | 3.85 | 2.32 | 18.61 | 1 | 1 | 4 | 1 | 1 | "Datsun 710" |
| 4.626013 | 6 | 258.0 | 110 | 3.08 | 3.215 | 19.44 | 1 | 0 | 3 | 1 | 1 | "Hornet 4 Drive" |
| 4.32435 | 8 | 360.0 | 175 | 3.15 | 3.44 | 17.02 | 0 | 0 | 3 | 2 | 1 | "Hornet Sportabout" |
| … | … | … | … | … | … | … | … | … | … | … | … | … |
| 5.51362 | 4 | 95.1 | 113 | 3.77 | 1.513 | 16.9 | 1 | 1 | 5 | 2 | 1 | "Lotus Europa" |
| 3.974921 | 8 | 351.0 | 264 | 4.22 | 3.17 | 14.5 | 0 | 1 | 5 | 4 | 0 | "Ford Pantera L" |
| 4.438468 | 6 | 145.0 | 175 | 3.62 | 2.77 | 15.5 | 0 | 1 | 5 | 6 | 1 | "Ferrari Dino" |
| 3.872983 | 8 | 301.0 | 335 | 3.54 | 3.57 | 14.6 | 0 | 1 | 5 | 8 | 0 | "Maserati Bora" |
| 4.626013 | 4 | 121.0 | 109 | 4.11 | 2.78 | 18.6 | 1 | 1 | 4 | 2 | 1 | "Volvo 142E" |
corr_matrix = mtcars.select(pl.selectors.by_dtype([pl.Float64, pl.Int64])).corr()
corr_matrix.head()
shape: (5, 12)
| mpg | cyl | disp | hp | drat | wt | qsec | vs | am | gear | carb | fast |
|---|---|---|---|---|---|---|---|---|---|---|---|
| f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 | f64 |
| 1.0 | -0.858539 | -0.867536 | -0.787309 | 0.680312 | -0.883453 | 0.420317 | 0.66926 | 0.593153 | 0.487226 | -0.553703 | 0.730748 |
| -0.858539 | 1.0 | 0.902033 | 0.832447 | -0.699938 | 0.782496 | -0.591242 | -0.810812 | -0.522607 | -0.492687 | 0.526988 | -0.695182 |
| -0.867536 | 0.902033 | 1.0 | 0.790949 | -0.710214 | 0.88798 | -0.433698 | -0.710416 | -0.591227 | -0.555569 | 0.394977 | -0.732073 |
| -0.787309 | 0.832447 | 0.790949 | 1.0 | -0.448759 | 0.658748 | -0.708223 | -0.723097 | -0.243204 | -0.125704 | 0.749812 | -0.751422 |
| 0.680312 | -0.699938 | -0.710214 | -0.448759 | 1.0 | -0.712441 | 0.091205 | 0.440278 | 0.712711 | 0.69961 | -0.09079 | 0.40043 |
corr_matrix2 = (
corr_matrix.with_row_index()
.unpivot(index="index")
.with_columns(pl.col("index").cast(pl.Float32) + 1)
.sort(by="value", descending=True)
)
corr_matrix2.head()
shape: (5, 3)
| index | variable | value |
|---|---|---|
| f32 | str | f64 |
| 1.0 | "mpg" | 1.0 |
| 2.0 | "cyl" | 1.0 |
| 3.0 | "disp" | 1.0 |
| 4.0 | "hp" | 1.0 |
| 6.0 | "wt" | 1.0 |
(
ggplot(corr_matrix2, aes("index", "variable", fill="value"))
+ geom_tile()
+ scale_fill_gradient(low="#0c71b0", high="#ff0000")
+ geom_text(aes(label="value"), size=8, format_string="{:.2f}", color="white")
+ labs(title="Correlogram of mtcars")
+ scale_x_discrete(limits=corr_matrix.columns)
+ scale_y_discrete(limits=corr_matrix.columns[::-1])
+ coord_fixed()
)
Marginal Plot#
g1 = ggplot(mpg) + geom_point(aes("displ", "hwy", color="manufacturer"))
g2 = ggplot(mpg) + geom_boxplot(aes("displ", "hwy"))
g3 = ggplot(mpg) + geom_boxplot(aes("hwy", "displ")) + coord_flip()
g1
