Python plotnine

In [1]:

import pandas as pd
import plotnine as p9
from pyprojroot import here

p9.theme_set(p9.theme_minimal)
print(f"plotnine=={p9.__version__}")

import pandas as pd import plotnine as p9 from pyprojroot import here p9.theme_set(p9.theme_minimal) print(f"plotnine=={p9.__version__}")

plotnine==0.7.1

In [2]:

df = pd.read_csv(here() / ".data" / "titanic.csv")
df.head(3)

df = pd.read_csv(here() / ".data" / "titanic.csv") df.head(3)

Out[2]:

	pclass	survived	name	sex	age	sibsp	parch	ticket	fare	cabin	embarked	boat	body	home.dest
0	1	1	Allen, Miss. Elisabeth Walton	female	29.0000	0	0	24160	211.3375	B5	S	2	NaN	St Louis, MO
1	1	1	Allison, Master. Hudson Trevor	male	0.9167	1	2	113781	151.5500	C22 C26	S	11	NaN	Montreal, PQ / Chesterville, ON
2	1	0	Allison, Miss. Helen Loraine	female	2.0000	1	2	113781	151.5500	C22 C26	S	NaN	NaN	Montreal, PQ / Chesterville, ON

Univariate, Continuous Distribution¶

Histogram¶

In [3]:

(
    p9.ggplot(df[~df["age"].isna()], p9.aes(x="age"))
    + p9.geom_histogram(binwidth=5)
    + p9.ggtitle("Histogram")
)

( p9.ggplot(df[~df["age"].isna()], p9.aes(x="age")) + p9.geom_histogram(binwidth=5) + p9.ggtitle("Histogram") )

Out[3]:

<ggplot: (8775978535888)>

ECDF¶

In [4]:

(
    p9.ggplot(df[~df["age"].isna()], p9.aes(x="age"))
    + p9.stat_ecdf()
    + p9.ggtitle("ECDF")
)

( p9.ggplot(df[~df["age"].isna()], p9.aes(x="age")) + p9.stat_ecdf() + p9.ggtitle("ECDF") )

Out[4]:

<ggplot: (8775979585440)>

Continuous Distribution, grouped by Categorical¶

Box Plots¶

In [5]:

(
    p9.ggplot(df[~df["age"].isna()], p9.aes(x="sex", y="age"))
    + p9.geom_boxplot()
    + p9.coord_flip()
    + p9.ggtitle("Box plot")
)

( p9.ggplot(df[~df["age"].isna()], p9.aes(x="sex", y="age")) + p9.geom_boxplot() + p9.coord_flip() + p9.ggtitle("Box plot") )

Out[5]:

<ggplot: (8775979592789)>

Violin Plots¶

In [6]:

(
    p9.ggplot(df[~df["age"].isna()], p9.aes(x="sex", y="age"))
    + p9.geom_violin()
    + p9.coord_flip()
    + p9.ggtitle("Violin Plot")
)

( p9.ggplot(df[~df["age"].isna()], p9.aes(x="sex", y="age")) + p9.geom_violin() + p9.coord_flip() + p9.ggtitle("Violin Plot") )

Out[6]:

<ggplot: (8775979618829)>

Continuous Metric, grouped by Categorical¶

In [7]:

mean_age_by_pclass = (
    df.groupby("pclass").agg(mean_age=("age", "mean")).reset_index()
)
mean_age_by_pclass

mean_age_by_pclass = ( df.groupby("pclass").agg(mean_age=("age", "mean")).reset_index() ) mean_age_by_pclass

Out[7]:

	pclass	mean_age
0	1	39.159918
1	2	29.506705
2	3	24.816367

Bar Chart¶

In [8]:

(
    p9.ggplot(mean_age_by_pclass, p9.aes(x="pclass", y="mean_age"))
    + p9.geom_bar(stat="identity")
    + p9.coord_flip()
)

( p9.ggplot(mean_age_by_pclass, p9.aes(x="pclass", y="mean_age")) + p9.geom_bar(stat="identity") + p9.coord_flip() )

Out[8]:

<ggplot: (8775979668294)>

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