Seaborn - lineplot 차트/그래프 그리기 Cheet Sheet

Timeseries plot with error bands

seaborn components used: set_theme(), load_dataset(), lineplot()

import seaborn as sns
sns.set_theme(style="darkgrid")

# Load an example dataset with long-form data
fmri = sns.load_dataset("fmri")

# Plot the responses for different events and regions
sns.lineplot(x="timepoint", y="signal",
             hue="region", style="event",
             data=fmri)

---

Lineplot from a wide-form dataset

seaborn components used: set_theme(), lineplot()

import numpy as np
import pandas as pd
import seaborn as sns
sns.set_theme(style="whitegrid")

rs = np.random.RandomState(365)
values = rs.randn(365, 4).cumsum(axis=0)
dates = pd.date_range("1 1 2016", periods=365, freq="D")
data = pd.DataFrame(values, dates, columns=["A", "B", "C", "D"])
data = data.rolling(7).mean()

sns.lineplot(data=data, palette="tab10", linewidth=2.5)

---

Small multiple time series

seaborn components used: set_theme(), load_dataset(), relplot(), lineplot()

import seaborn as sns

sns.set_theme(style="dark")
flights = sns.load_dataset("flights")

# Plot each year's time series in its own facet
g = sns.relplot(
    data=flights,
    x="month", y="passengers", col="year", hue="year",
    kind="line", palette="crest", linewidth=4, zorder=5,
    col_wrap=3, height=2, aspect=1.5, legend=False,
)

# Iterate over each subplot to customize further
for year, ax in g.axes_dict.items():

    # Add the title as an annotation within the plot
    ax.text(.8, .85, year, transform=ax.transAxes, fontweight="bold")

    # Plot every year's time series in the background
    sns.lineplot(
        data=flights, x="month", y="passengers", units="year",
        estimator=None, color=".7", linewidth=1, ax=ax,
    )

# Reduce the frequency of the x axis ticks
ax.set_xticks(ax.get_xticks()[::2])

# Tweak the supporting aspects of the plot
g.set_titles("")
g.set_axis_labels("", "Passengers")
g.tight_layout()

---

Line plots on multiple facets

seaborn components used: set_theme(), load_dataset(), color_palette(), relplot()

import seaborn as sns
sns.set_theme(style="ticks")

dots = sns.load_dataset("dots")

# Define the palette as a list to specify exact values
palette = sns.color_palette("rocket_r")

# Plot the lines on two facets
sns.relplot(
    data=dots,
    x="time", y="firing_rate",
    hue="coherence", size="choice", col="align",
    kind="line", size_order=["T1", "T2"], palette=palette,
    height=5, aspect=.75, facet_kws=dict(sharex=False),
)

---

Plotting a three-way ANOVA

seaborn components used: set_theme(), load_dataset(), catplot()

import seaborn as sns
sns.set_theme(style="whitegrid")

# Load the example exercise dataset
df = sns.load_dataset("exercise")

# Draw a pointplot to show pulse as a function of three categorical factors
g = sns.catplot(x="time", y="pulse", hue="kind", col="diet",
                capsize=.2, palette="YlGnBu_d", height=6, aspect=.75,
                kind="point", data=df)
g.despine(left=True)

 

출처 : https://seaborn.pydata.org/