Python data visualization: Seaborn

1. Introduction of Seaborn

Seaborn is a library based on matplotlib and its data structure is consistent with Pandas.

The Seaborn library is designed to mine and understand data around data visualization.

Seaborn provides the data set oriented mapping function, which mainly operates on column index and array, including internal semantic mapping and statistical integration of the whole data set.

It is no exaggeration to say that Seaborn can create any diagram you can imagine.

2. Sample data

All of the visual graphs in this article are based on Seaborn’s own restaurant customer consumption data set, TIPS. The first two pieces of the TIPS dataset are as follows:

(Total_bill, tip, sex, smoker, day, time, size, smoker)

3. Seaborn overview

• The diagram

A diagram is generally used to show a bivariate relationship.

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

sns.set(style='darkgrid')

tips = sns.load_dataset('tips')
sns.relplot(x='total_bill',y='tip',data=tips)
sns.relplot(x="total_bill", y="tip", hue="smoker", data=tips);

fmri = sns.load_dataset("fmri")
sns.relplot(x="timepoint", y="signal", kind="line", data=fmri);
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• Classification figure

Visualization of categorizable data; The classification diagram can be presented by scatter diagram, distribution diagram, estimation diagram and other forms.

import seaborn as sns
import matplotlib.pyplot as plt
sns.set(style='ticks',color_codes=True)

tips = sns.load_dataset('tips')
sns.catplot(x='day',y='total_bill',data=tips)
sns.catplot(x='day',y='total_bill',kind='swarm',data=tips)
sns.catplot(x='day',y='total_bill',kind='box',data=tips)

diamonds = sns.load_dataset('diamonds')
sns.catplot(x='color',y='price',kind='boxen',data=diamonds.sort_values('color'))
sns.catplot(x="total_bill", y="day", hue="time",kind="violin", data=tips)

titanic = sns.load_dataset("titanic")
sns.catplot(x="sex", y="survived", hue="class", kind="point", data=titanic)
sns.catplot(x="sex", y="survived", hue="class", kind="bar", data=titanic)
sns.catplot(x="deck", kind="count", palette="ch:.25", data=titanic)
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• Return to figure

Regression is performed on the data and the regression function is plotted.

import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt

sns.set(color_codes=True)
tips = sns.load_dataset("tips")
sns.lmplot(x="total_bill", y="tip", data=tips)
sns.residplot(x="x", y="y", data=anscombe.query("dataset == 'II'"),scatter_kws={"s": 80})

f, ax = plt.subplots(figsize=(5.6))
sns.regplot(x="total_bill", y="tip", data=tips, ax=ax)
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• Distribution of

A chart used to examine univariate or bivariate distributions.

import seaborn as sns
import matplotlib.pyplot as plt
from scipy import stats

sns.set(color_codes=True)
x = np.random.normal(size=100)
sns.distplot(x)
sns.kdeplot(x, shade=True)

mean, cov = [0.1], [[1.. 5), (. 5.1)]
data = np.random.multivariate_normal(mean, cov, 200)
df = pd.DataFrame(data, columns=["x"."y"])
sns.jointplot(x="x", y="y", data=df)

iris = sns.load_dataset("iris")
sns.pairplot(iris)
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• The matrix in figure

Visualize the data set as a matrix.

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd

sns.set_theme()

# Load the example flights dataset and convert to long-form
flights_long = sns.load_dataset("flights")
flights = flights_long.pivot("month"."year"."passengers")

# Draw a heatmap with the numeric values in each cell
f, ax = plt.subplots(figsize=(9.6))
sns.heatmap(flights, annot=True, fmt="d", linewidths=. 5, ax=ax)


sns.set_theme()

# Load the brain networks example dataset
df = sns.load_dataset("brain_networks", header=[0.1.2], index_col=0)

# Select a subset of the networks
used_networks = [1.5.6.7.8.12.13.17]
used_columns = (df.columns.get_level_values("network")
                          .astype(int)
                          .isin(used_networks))
df = df.loc[:, used_columns]

# Create a categorical palette to identify the networks
network_pal = sns.husl_palette(8, s=45.)
network_lut = dict(zip(map(str, used_networks), network_pal))

# Convert the palette to vectors that will be drawn on the side of the matrix
networks = df.columns.get_level_values("network")
network_colors = pd.Series(networks, index=df.columns).map(network_lut)

# Draw the full plot
g = sns.clustermap(df.corr(), center=0, cmap="vlag",
                   row_colors=network_colors, col_colors=network_colors,
                   dendrogram_ratio=(1..2.),
                   cbar_pos=(. 02.32...03.2.),
                   linewidths=75., figsize=(12.13))

g.ax_row_dendrogram.remove()
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• Structured multidrawing

Plots relationships between pairs of variables in the form of subgraphs.

Ending

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