python之数据可视化

各种图形简介

线性图:plt.plot(x,y,*argv)

条形图：plt.bar(x,y)x和y的长度应相等

水平条形图：plt.barh(x,y)x轴成垂直,y轴水平而已

条形图高度表示某项目内的数据个数，由于分组数据具有连续性，直方图的各矩形通常是连续排列，而条形图则是分开排列

直方图：plt.hist(x)，数据集种各数据出现的频数/频率图

2d直方图：plt.hist2d(x,y)

直方图是用面积表示各组频数的多少，矩形的高度表示每一组的频数或频率，宽度则表示各组的组距，其高度与宽度均有意义

饼状图:plt.pie(a,labels=list('abcde'),autopct='%.2f%%'),

散点图：plt.scatter(x,y,*argv)

箱形图:plt.boxplot(x)

词云图:wordcloud.WordCloud(*argv)

根据词频和背景图产生的图

直方图/分布:sns.distplot()

制图实例

In [79]: import numpy as np

In [80]: import pandas as pd

In [81]: import matplotlib.pyplot as plt

In [82]: import wordcloud

In [83]: import seaborn
plt.rcParams['font.serif'] = ['KaiTi']
plt.rcParams['axes.unicode_minus'] = False 

names = ['mpg','cylinders','displacement','horsepower','weight','acceleration','model_year','origin','car_name']
df = pd.read_csv("http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data", sep='\s+', names=names)

In [154]: df['maker'] = df.car_name.apply(lambda x: x.split()[0]).str.title()
     ...: df['origin'] = df.origin.map({1: 'America', 2: 'Europe', 3: 'Asia'})
     ...: df=df.applymap(lambda x: np.nan if x == '?' else x).dropna()
     ...: df['horsepower'] = df.horsepower.astype(float)

1.云词

names = ['mpg','cylinders','displacement','horsepower','weight','acceleration','model_year','origin','car_name']
df = pd.read_csv("http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data", sep='\s+', names=names)
word_dict=dict(df['car_name'])
background=plt.imread('data/back.jpg')
wc = wordcloud.WordCloud(
     background_color='white',#背景颜色
     font_path='data/simhei.ttf',#字体
     mask=background,#背景图
     max_words=1000,#最大的字数
     max_font_size=100,#最大的字体
     colormap='hsv',#色谱
     random_state=100#随机种子
    )
    wc.generate_from_frequencies(word_dict)#根据词频生成wordcloud
    plt.imshow(wc)#将wordcloud投影到plt上
    plt.axis('off')#去除坐标
    plt.savefig('image/DesriptionWordCloud.png', dpi=400, bbox_inches='tight')

2.线性图

In [100]: plt.plot(df.displacement.index,df.displacement.values)
Out[100]: [<matplotlib.lines.Line2D at 0x7f1378501c50>]

In [101]: plt.show()

3.条形图

In [104]: plt.bar(df.displacement.index[:10],df.displacement.values[:10])
Out[104]: <BarContainer object of 10 artists>

In [105]: plt.show()

4.水平条形图

In [106]: plt.barh(df.displacement.index[:10],df.displacement.values[:10])
Out[106]: <BarContainer object of 10 artists>

In [107]: plt.show()

5.直方图

In [116]: a=pd.Series([1,2,3,1,2,3,3,4,2,1])

In [117]: plt.hist(a)
Out[117]: 
(array([3., 0., 0., 3., 0., 0., 3., 0., 0., 1.]),
 array([1. , 1.3, 1.6, 1.9, 2.2, 2.5, 2.8, 3.1, 3.4, 3.7, 4. ]),
 <a list of 10 Patch objects>)

In [118]: plt.show()

6.饼状图

In [124]: data=[0.2,0.1,0.33,0.27,0.1]

In [125]: plt.pie(data,autopct='%.2f%%',labels=list('abcde'))
Out[125]: 
([<matplotlib.patches.Wedge at 0x7f136fe5af28>,
  <matplotlib.patches.Wedge at 0x7f136fe636a0>,
  <matplotlib.patches.Wedge at 0x7f136fe63da0>,
  <matplotlib.patches.Wedge at 0x7f136fe6c4e0>,
  <matplotlib.patches.Wedge at 0x7f136fe6cbe0>],
 [Text(0.889919,0.646564,'a'),
  Text(-2.57474e-08,1.1,'b'),
  Text(-1.07351,0.239957,'c'),
  Text(0.103519,-1.09512,'d'),
  Text(1.04616,-0.339919,'e')],
 [Text(0.48541,0.352671,'20.00%'),
  Text(-1.4044e-08,0.6,'10.00%'),
  Text(-0.58555,0.130886,'33.00%'),
  Text(0.0564651,-0.597337,'27.00%'),
  Text(0.570634,-0.18541,'10.00%')])

In [126]: plt.show()

7.散点图

In [130]: plt.scatter(df.displacement.index,df.displacement.values,color='red')
Out[130]: <matplotlib.collections.PathCollection at 0x7f136faf9470>

In [131]: plt.show()

8.箱形图

In [147]: plt.boxplot(df.iloc[[1,2,3],[1,6]])
Out[147]: 
{'whiskers': [<matplotlib.lines.Line2D at 0x7f136f0d9e48>,
  <matplotlib.lines.Line2D at 0x7f136f0d9f60>,
  <matplotlib.lines.Line2D at 0x7f136f0e8d68>,
  <matplotlib.lines.Line2D at 0x7f136f0e8e80>,
  <matplotlib.lines.Line2D at 0x7f136f0f8c88>,
  <matplotlib.lines.Line2D at 0x7f136f0f8da0>],
 'caps': [<matplotlib.lines.Line2D at 0x7f136f0e0748>,
  <matplotlib.lines.Line2D at 0x7f136f0e0ba8>,
  <matplotlib.lines.Line2D at 0x7f136f0f1668>,
  <matplotlib.lines.Line2D at 0x7f136f0f1ac8>,
  <matplotlib.lines.Line2D at 0x7f136f100588>,
  <matplotlib.lines.Line2D at 0x7f136f1009e8>],
 'boxes': [<matplotlib.lines.Line2D at 0x7f136f0d9898>,
  <matplotlib.lines.Line2D at 0x7f136f0e8908>,
  <matplotlib.lines.Line2D at 0x7f136f0f8828>],
 'medians': [<matplotlib.lines.Line2D at 0x7f136f0e0cc0>,
  <matplotlib.lines.Line2D at 0x7f136f0f1f28>,
  <matplotlib.lines.Line2D at 0x7f136f100e48>],
 'fliers': [<matplotlib.lines.Line2D at 0x7f136f0e84a8>,
  <matplotlib.lines.Line2D at 0x7f136f0f83c8>,
  <matplotlib.lines.Line2D at 0x7f136f100f60>],
 'means': []}

In [148]: plt.show()

8.直方分布图

#方法一
In [150]: sns.distplot(df.displacement.values)
/home/zelin/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval
Out[150]: <matplotlib.axes._subplots.AxesSubplot at 0x7f136f0c7668>

#方法二
In [166]: g = sns.FacetGrid(df, col="origin")
     ...: g.map(sns.distplot, "mpg")
     ...: 
     ...: 
/home/zelin/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval
Out[166]: <seaborn.axisgrid.FacetGrid at 0x7f136e0e7f98>



In [151]: plt.show()

9.关系图

#根据两个维度绘制关系图，即DataFrame的两列
In [155]: sns.factorplot(data=df,x='model_year',y='mpg')
#根据三各维度绘制关系图
sns.factorplot(data=df,x='model_year',y='mpg',col='origin')
#从折线图切成柱状图
sns.factorplot(data=df, x="model_year", y="mpg", col="origin",kind='bar')

10.绘图同时还做回归

In [168]: g = sns.FacetGrid(df, col="origin")
     ...: g.map(sns.regplot, "horsepower", "mpg")
     ...: plt.xlim(0, 250)#x轴刻度最大值
     ...: plt.ylim(0, 60)#y轴刻度最大值
     ...: 
     ...: 
/home/zelin/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval
Out[168]: (0, 60)

11.等高线图

In [170]: df['tons'] = (df.weight/2000).astype(int)
     ...: g = sns.FacetGrid(df, col="origin", row="tons")
     ...: g.map(sns.kdeplot, "horsepower", "mpg")
     ...: plt.xlim(0, 250)
     ...: plt.ylim(0, 60)

12.按照两个维度展开画图

g = sns.FacetGrid(df, col="origin", row="tons")
g.map(plt.hist, "mpg", bins=np.linspace(0, 50, 11))

13.多个维度两两组合绘图

g = sns.pairplot(df[["mpg", "horsepower", "weight", "origin"]], hue="origin", diag_kind="hist")
for ax in g.axes.flat:
    plt.setp(ax.get_xticklabels(), rotation=45)

14.组合绘图时做回归

g = sns.PairGrid(df[["mpg", "horsepower", "weight", "origin"]], hue="origin")
g.map_upper(sns.regplot)
g.map_lower(sns.residplot)
g.map_diag(plt.hist)
for ax in g.axes.flat:
    plt.setp(ax.get_xticklabels(), rotation=45)
g.add_legend()
g.set(alpha=0.5)

15.联合绘图(等高图)

sns.jointplot("mpg", "horsepower", data=df, kind='kde')

16.联合绘图加回归(散点图)

sns.jointplot("horsepower", "mpg", data=df, kind="reg")