数据可视化–Seaborn绘图总结2

Seaborn其实是在matplotlib的基础上进行了更高级的API封装，从而使得作图更加容易。同时它能高度兼容numpy与pandas数据结构以及scipy与statsmodels等统计模式

reference

1. seaborn官网

2. matplotlib实用绘图技巧总结

3. Seaborn教程

4. 10分钟python图表绘制

@[toc]

类型

• Relational plots 关系类图表

  1. relplot() 关系类图表的接口，其实是下面两种图的集成，通过指定kind参数可以画出下面的两种图
  2. scatterplot() 散点图
  3. lineplot() 折线图

• Categorical plots 分类图表

  1. catplot() 分类图表的接口，其实是下面八种图表的集成，通过指定kind参数可以画出下面的八种图
  2. stripplot() 分类散点图
  3. swarmplot() 能够显示分布密度的分类散点图
  4. boxplot() 箱图
  5. violinplot() 小提琴图
  6. boxenplot() 增强箱图
  7. pointplot() 点图
  8. barplot() 条形图
  9. countplot() 计数图

• Distribution plot 分布图

  1. jointplot() 双变量关系图
  2. pairplot() 变量关系组图
  3. distplot() 直方图，质量估计图
  4. kdeplot() 核函数密度估计图
  5. rugplot() 将数组中的数据点绘制为轴上的数据

• Regression plots 回归图

  1. lmplot() 回归模型图
  2. regplot() 线性回归图
  3. residplot() 线性回归残差图

• Matrix plots 矩阵图

  1. heatmap() 热力图
  2. clustermap() 聚集图

pip install -i https://pypi.tuna.tsinghua.edu.cn/simple jieba

import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import warnings
warnings.filterwarnings("ignore")

pointplot

点图

"""
Conditional means with observations
===================================

"""
sns.set(style="whitegrid")
iris = pd.read_csv("./seaborn-data-master/iris.csv")

# "Melt" the dataset to "long-form" or "tidy" representation
iris = pd.melt(iris, "species", var_name="measurement")

# Initialize the figure
f, ax = plt.subplots()
sns.despine(bottom=True, left=True)

# Show each observation with a scatterplot
sns.stripplot(x="value", y="measurement", hue="species",
              data=iris, dodge=True, jitter=True,
              alpha=.25, zorder=1)

# Show the conditional means
sns.pointplot(x="value", y="measurement", hue="species",
              data=iris, dodge=.532, join=False, palette="dark",
              markers="d", scale=.75, ci=None)

# Improve the legend 
handles, labels = ax.get_legend_handles_labels()
ax.legend(handles[3:], labels[3:], title="species",
          handletextpad=0, columnspacing=1,
          loc="lower right", ncol=3, frameon=True)

scatterplot

散点图

"""
Scatterplot with categorical and numerical semantics
====================================================
"""
sns.set(style="whitegrid")

# Load the example iris dataset
diamonds = pd.read_csv("./seaborn-data-master/diamonds.csv")

# Draw a scatter plot while assigning point colors and sizes to different
# variables in the dataset
f, ax = plt.subplots(figsize=(6.5, 6.5))
sns.despine(f, left=True, bottom=True)
clarity_ranking = ["I1", "SI2", "SI1", "VS2", "VS1", "VVS2", "VVS1", "IF"]
sns.scatterplot(x="carat", y="price",
                hue="clarity", size="depth",
                palette="ch:r=-.2,d=.3_r",
                hue_order=clarity_ranking,
                sizes=(1, 8), linewidth=0,
                data=diamonds, ax=ax)

<AxesSubplot:xlabel='carat', ylabel='price'>

boxenplot

增强箱图

"""
Plotting large distributions
============================

"""
sns.set(style="whitegrid")

diamonds = pd.read_csv("./seaborn-data-master/diamonds.csv")
clarity_ranking = ["I1", "SI2", "SI1", "VS2", "VS1", "VVS2", "VVS1", "IF"]

sns.boxenplot(x="clarity", y="carat",
              color="b", order=clarity_ranking,
              scale="linear", data=diamonds)

<AxesSubplot:xlabel='clarity', ylabel='carat'>

Scatterplot

散点图

"""
Scatterplot with continuous hues and sizes
==========================================

"""

sns.set()

# Load the example iris dataset
planets = pd.read_csv("./seaborn-data-master/planets.csv")

cmap = sns.cubehelix_palette(rot=-.2, as_cmap=True)
ax = sns.scatterplot(x="distance", y="orbital_period",
                     hue="year", size="mass",
                     palette=cmap, sizes=(10, 200),
                     data=planets)

"""
Scatterplot with marginal ticks
===============================
"""
sns.set(style="white", color_codes=True)

# Generate a random bivariate dataset
rs = np.random.RandomState(9)
mean = [0, 0]
cov = [(1, 0), (0, 2)]
x, y = rs.multivariate_normal(mean, cov, 100).T

# Use JointGrid directly to draw a custom plot
grid = sns.JointGrid(x, y, space=0, height=6, ratio=50)
grid.plot_joint(plt.scatter, color="g")
grid.plot_marginals(sns.rugplot, height=1, color="g")

PairGrid

用于绘制数据集中成对关系的子图网格。

"""
Paired density and scatterplot matrix
=====================================

"""

sns.set(style="white")

df = pd.read_csv("./seaborn-data-master/iris.csv")

g = sns.PairGrid(df, diag_sharey=False)
g.map_lower(sns.kdeplot)
g.map_upper(sns.scatterplot)
g.map_diag(sns.kdeplot, lw=3)

"""
Paired categorical plots
========================

"""
sns.set(style="whitegrid")

# Load the example Titanic dataset
titanic = pd.read_csv("./seaborn-data-master/titanic.csv")

# Set up a grid to plot survival probability against several variables
g = sns.PairGrid(titanic, y_vars="survived",
                 x_vars=["class", "sex", "who", "alone"],
                 height=5, aspect=.5)

# Draw a seaborn pointplot onto each Axes
g.map(sns.pointplot, scale=1.3, errwidth=4, color="xkcd:plum")
g.set(ylim=(0, 1))
sns.despine(fig=g.fig, left=True)

residplot

线性回归残差图

"""
Plotting model residuals
========================

"""

sns.set(style="whitegrid")

# Make an example dataset with y ~ x
rs = np.random.RandomState(7)
x = rs.normal(2, 1, 75)
y = 2 + 1.5 * x + rs.normal(0, 2, 75)

# Plot the residuals after fitting a linear model
sns.residplot(x, y, lowess=True, color="g")

"""
Scatterplot with varying point sizes and hues
==============================================

"""
sns.set(style="white")

# Load the example mpg dataset
mpg = pd.read_csv("./seaborn-data-master/mpg.csv")

# Plot miles per gallon against horsepower with other semantics
sns.relplot(x="horsepower", y="mpg", hue="origin", size="weight",
            sizes=(40, 400), alpha=.5, palette="muted",
            height=6, data=mpg)

swarmplot

能够显示分布密度的分类散点图

"""
Scatterplot with categorical variables
======================================

"""

sns.set(style="whitegrid", palette="muted")

# Load the example iris dataset
iris = pd.read_csv("./seaborn-data-master/iris.csv")

# "Melt" the dataset to "long-form" or "tidy" representation
iris = pd.melt(iris, "species", var_name="measurement")

# Draw a categorical scatterplot to show each observation
sns.swarmplot(x="measurement", y="value", hue="species",
              palette=["r", "c", "y"], data=iris)

pairplot

变量关系组图

"""
Scatterplot Matrix
==================

"""

sns.set(style="ticks")

df = pd.read_csv("./seaborn-data-master/iris.csv")
sns.pairplot(df, hue="species")

<seaborn.axisgrid.PairGrid at 0x2c7c11763a0>

clustermap

聚集图

"""
Discovering structure in heatmap data
=====================================

"""

sns.set()

# Load the brain networks example dataset
df = pd.read_csv("./seaborn-data-master/brain_networks.csv", 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
sns.clustermap(df.corr(), center=0, cmap="vlag",
               row_colors=network_colors, col_colors=network_colors,
               linewidths=.75, figsize=(13, 13))

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