import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
np.random.seed(30)
x=np.random.normal(0,1,1000)
y=np.random.normal(0,1,1000)
六角图
sns.jointplot(x=x,y=y,kind='hex')
<seaborn.axisgrid.JointGrid at 0x1a1fb97978>
六角图可以显示出点集中的区域
plt.rcParams['figure.figsize']=(6,6)
import warnings
warnings.simplefilter('error', 'UserWarning')
密度分布图
sns.set()
ax=plt.subplot(111)
sns.kdeplot(x,y,ax=ax,color='m')
# sns.jointplot(x,y,kind='kde')
sns.rugplot(x, ax=ax,color='g')
sns.rugplot(y, vertical=True,ax=ax)
# plt.grid(True)
<matplotlib.axes._subplots.AxesSubplot at 0x1a219b3cf8>
iris=sns.load_dataset('iris')
PairPlot绘制出多个变量两两组合的绘图
help(sns.pairplot)
Help on function pairplot in module seaborn.axisgrid:
pairplot(data, hue=None, hue_order=None, palette=None, vars=None, x_vars=None, y_vars=None, kind='scatter', diag_kind='auto', markers=None, height=2.5, aspect=1, dropna=True, plot_kws=None, diag_kws=None, grid_kws=None, size=None)
Plot pairwise relationships in a dataset.
By default, this function will create a grid of Axes such that each
variable in ``data`` will by shared in the y-axis across a single row and
in the x-axis across a single column. The diagonal Axes are treated
differently, drawing a plot to show the univariate distribution of the data
for the variable in that column.
It is also possible to show a subset of variables or plot different
variables on the rows and columns.
This is a high-level interface for :class:`PairGrid` that is intended to
make it easy to draw a few common styles. You should use :class:`PairGrid`
directly if you need more flexibility.
Parameters
----------
data : DataFrame
Tidy (long-form) dataframe where each column is a variable and
each row is an observation.
hue : string (variable name), optional
Variable in ``data`` to map plot aspects to different colors.
hue_order : list of strings
Order for the levels of the hue variable in the palette
palette : dict or seaborn color palette
Set of colors for mapping the ``hue`` variable. If a dict, keys
should be values in the ``hue`` variable.
vars : list of variable names, optional
Variables within ``data`` to use, otherwise use every column with
a numeric datatype.
{x, y}_vars : lists of variable names, optional
Variables within ``data`` to use separately for the rows and
columns of the figure; i.e. to make a non-square plot.
kind : {'scatter', 'reg'}, optional
Kind of plot for the non-identity relationships.
diag_kind : {'auto', 'hist', 'kde'}, optional
Kind of plot for the diagonal subplots. The default depends on whether
``"hue"`` is used or not.
markers : single matplotlib marker code or list, optional
Either the marker to use for all datapoints or a list of markers with
a length the same as the number of levels in the hue variable so that
differently colored points will also have different scatterplot
markers.
height : scalar, optional
Height (in inches) of each facet.
aspect : scalar, optional
Aspect * height gives the width (in inches) of each facet.
dropna : boolean, optional
Drop missing values from the data before plotting.
{plot, diag, grid}_kws : dicts, optional
Dictionaries of keyword arguments.
Returns
-------
grid : PairGrid
Returns the underlying ``PairGrid`` instance for further tweaking.
See Also
--------
PairGrid : Subplot grid for more flexible plotting of pairwise
relationships.
Examples
--------
Draw scatterplots for joint relationships and histograms for univariate
distributions:
.. plot::
:context: close-figs
>>> import seaborn as sns; sns.set(style="ticks", color_codes=True)
>>> iris = sns.load_dataset("iris")
>>> g = sns.pairplot(iris)
Show different levels of a categorical variable by the color of plot
elements:
.. plot::
:context: close-figs
>>> g = sns.pairplot(iris, hue="species")
Use a different color palette:
.. plot::
:context: close-figs
>>> g = sns.pairplot(iris, hue="species", palette="husl")
Use different markers for each level of the hue variable:
.. plot::
:context: close-figs
>>> g = sns.pairplot(iris, hue="species", markers=["o", "s", "D"])
Plot a subset of variables:
.. plot::
:context: close-figs
>>> g = sns.pairplot(iris, vars=["sepal_width", "sepal_length"])
Draw larger plots:
.. plot::
:context: close-figs
>>> g = sns.pairplot(iris, height=3,
... vars=["sepal_width", "sepal_length"])
Plot different variables in the rows and columns:
.. plot::
:context: close-figs
>>> g = sns.pairplot(iris,
... x_vars=["sepal_width", "sepal_length"],
... y_vars=["petal_width", "petal_length"])
Use kernel density estimates for univariate plots:
.. plot::
:context: close-figs
>>> g = sns.pairplot(iris, diag_kind="kde")
Fit linear regression models to the scatter plots:
.. plot::
:context: close-figs
>>> g = sns.pairplot(iris, kind="reg")
Pass keyword arguments down to the underlying functions (it may be easier
to use :class:`PairGrid` directly):
.. plot::
:context: close-figs
>>> g = sns.pairplot(iris, diag_kind="kde", markers="+",
... plot_kws=dict(s=50, edgecolor="b", linewidth=1),
... diag_kws=dict(shade=True))
sns.pairplot(iris,kind='reg',diag_kind='kde',markers='o')
<seaborn.axisgrid.PairGrid at 0x1a2145a4a8>
PairGrid的绘图原理是先产生个数据组合，然后再分别选择对角线和非对角线上的映射形式。
g=sns.PairGrid(iris)
g.map_diag(sns.kdeplot)
g.map_offdiag(sns.kdeplot,n_levels=20)
<seaborn.axisgrid.PairGrid at 0x1a22dd5d30>
探索变量间的关系
tips=sns.load_dataset('tips')
tips.head()
散点+线性回归拟合线+95%置信区间
sns.lmplot(data=tips,x='size', y='tip')
<seaborn.axisgrid.FacetGrid at 0x1a28925b00>
看不清楚点的时候，因为x方向上很多点都重合了
方法一：加个抖动
sns.lmplot(data=tips,x='size', y='tip',x_jitter=0.08)
<seaborn.axisgrid.FacetGrid at 0x1a28b979b0>
anscombe=sns.load_dataset('anscombe')
anscombe.head()
我们可以通过设定order的参数来限定用来拟合的次数
sns.lmplot(data=anscombe.query("dataset == 'II'"), x='x', y='y',order=2)
<seaborn.axisgrid.FacetGrid at 0x1a28c7ed30>
如果有异常值，需要传入一个robust=True的参数来限定不将异常值点也纳入到拟合内
sns.lmplot(data=anscombe.query("dataset == 'III'"), x='x', y='y')
<seaborn.axisgrid.FacetGrid at 0x1a28c68240>
sns.lmplot(data=anscombe.query("dataset == 'III'"), x='x', y='y', robust=True,ci=None)
<seaborn.axisgrid.FacetGrid at 0x1a28d267f0>
sns.lmplot(data=tips,x='total_bill',y='if_smoker',logistic=True)
<seaborn.axisgrid.FacetGrid at 0x1a26c0b4a8>
我们可以设定一个logistic=True的参数来拟合二分类问题。
x=np.random.normal(0,1,1000)
y=np.random.normal(1,3,1000)
ax=plt.subplot(111)
ax2=ax.twinx()
sns.kdeplot(x,ax=ax)
sns.kdeplot(y,ax=ax2)
<matplotlib.axes._subplots.AxesSubplot at 0x1a25ddb6d8>
tips.head()
回归散点图
sns.lmplot(data=tips,x='total_bill', y='tip',hue='smoker')
<seaborn.axisgrid.FacetGrid at 0x1a25e60f28>
我们可以传入hue参数对数据进行分类别的展示
sns.lmplot(data=tips,x='total_bill', y='tip',hue='day')
<seaborn.axisgrid.FacetGrid at 0x1a2a3ec940>
结论：从上面大致可以看出星期天人们不太愿意给小费
还可以传入col和row参数进行分子图展示
height设置图的高度，aspect设置图的压缩比
sns.lmplot(data=tips,x='total_bill', y='tip',hue='smoker', col='time', row='sex',height=10,aspect=0.7)
<seaborn.axisgrid.FacetGrid at 0x1a2f38e978>
swarmplot
swarmplot用于分类散点图，避免点的重叠
sns.swarmplot(data=tip,x='day',y='tip')
<matplotlib.axes._subplots.AxesSubplot at 0x1a27dba208>
小提琴图
箱图+kde图
sns.violinplot(data=tips,x='day',y='tip',hue='sex')
<matplotlib.axes._subplots.AxesSubplot at 0x1a275cee10>
非对称小提琴图
非对称小提琴图适用于两种类别的hue同时画在左右两边一起对比
sns.violinplot(data=tips,x='day',y='tip',split=True,hue='sex',inner='stick')
<matplotlib.axes._subplots.AxesSubplot at 0x1a2636fdd8>
灰度柱状图
类似于pandas的value_counts()函数对某个列进行分类统计后绘制的图。
sns.countplot(tips.smoker)
plt.legend(('Yes', 'No'))
<matplotlib.legend.Legend at 0x1a26b898d0>
tips['smoker'].head()
0 No
1 No
2 No
3 No
4 No
Name: smoker, dtype: category
Categories (2, object): [Yes, No]
type(tips)
pandas.core.frame.DataFrame
tips['if_smoker']=tips['smoker'].map({'Yes':1, 'No':0})
tips.if_smoker.head()
0 0
1 0
2 0
3 0
4 0
Name: if_smoker, dtype: int64
我们可以自由选择进入PairGrid的变量
tips.head()
g=sns.PairGrid(tips,x_vars=['smoker','sex', 'day'],y_vars=['total_bill','tip'],height=5, aspect=0.7)
g.map(sns.violinplot, palette='bright')
<seaborn.axisgrid.PairGrid at 0x1a278fa4e0>
前面用到的是g.map_diag和g.map_offdiag分别是设置对角线和非对角线的绘制图类型，适用于方阵的情况，如果是统一设置则用g.map即可
