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Jupyter上使用matplotlib画图的一些有用的设置

我们在mac上使用jupyter的时候，用matplotlib画图，产出的图形的质量不是很高，可以在如下的文件中, 加上设置:

~/.ipython/profile_default/ipython_kernel_config.py

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c.IPKernelApp.matplotlib ='inline'
c.InlineBackend.figure_format ='retina'

默认的matplotlib画图的样式不是很美观， 我们可以做出如下的设置:

plt.style.use('seaborn')

我们可以使用如下的命令查看都有哪些可供选择的style

plt.style.available

Matplotlib和Seaborn使用方法集锦

1. 引入常用的包

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

2. 选定若干列，画pairplot

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sns.pairplot(college.iloc[:,1:11])

sns.pairplot(auto) # 选择全部列的数据

3. 选定两列，画出来boxplot

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sns.boxplot(x = college['Private'],y = college['Outstate'])

4. 通过bar画直方图

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fig = plt.figure()

plt.subplot(2,2,1) college['Enroll'].value_counts().plot.bar(title = 'Enroll')

plt.subplot(2,2,2) college['PhD'].value_counts().plot.bar(title = 'PhD')

plt.subplot(2,2,3) college['Terminal'].value_counts().plot.bar(title = 'Terminal')

#to add space in between the subplots 
fig.subplots_adjust(hspace=1)

5. 画出散点图和曲线图，并且设置线的颜色

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plt.figure(figsize = (14,8))
plt.scatter(auto['horsepower'],auto['mpg'])
plt.plot(auto['horsepower'],pred_1,color = 'orange')
plt.plot(auto['horsepower'],pred_2,color = 'green')
plt.plot(auto['horsepower'],pred_5,color = 'black')
plt.show()

6. 绘制平行于x轴的水平参考线

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error_1 = auto['mpg'] - pred_1
plt.figure(figsize = (12,6))
sns.scatterplot(x = auto['mpg'],y = error_1)
plt.axhline(y = 0,linestyle = 'dashed',color = 'black',linewidth = 0.5)

7. 用sns.regplot来比较两个变量的关系是否符合线性回归

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sns.regplot(data['LSTAT'],data['MEDV'])

# regplot中设置参数
sns.regplot(advertising.TV, advertising.Sales, order=1, ci=None, scatter_kws={'color':'r', 's':9})
plt.xlim(-10,310)
plt.ylim(ymin=0);

8. 设置label和title和

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plt.xlabel('Fitted Vales')
plt.ylabel('Residuals')
plt.title('Residual Plot')

9. 使用sns画相关系数矩阵的热力图

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sns.heatmap(auto.iloc[:,:-1].corr())

10. 设置线的label并且置成legend

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plt.figure(figsize = (14,6))
sns.scatterplot(X,Y)
plt.xlabel('X')
plt.ylabel('Y')

plt.plot(data['X'],lin_model.predict(data['X'].to_frame()),color = 'orange',label = 'Predicted Line')
plt.title('Relationship b/w X and Y')
plt.plot(tmp_x,tmp_y,color = 'green',label = 'True Line')
plt.legend()
plt.show()

11. 设置坐标范围, 设置相等的宽和高

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fig, ax = plt.subplots()

sns.scatterplot(simple_coeff,multi_coeff)

lims = [
    np.min([ax.get_xlim(), ax.get_ylim()]),  # min of both axes
    np.max([ax.get_xlim(), ax.get_ylim()]),  # max of both axes
]

ax.plot(lims, lims, 'k-', alpha=0.75, zorder=0,color = 'orange')
ax.set_aspect('equal')
ax.set_xlim(lims)
ax.set_ylim(lims)

12. 设置hue

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plt.figure(figsize = (12,8))

tmp = pd.DataFrame({'Lag1':X_train['Lag1'],'Lag2':X_train['Lag2'],'Direction':y_train})

sns.scatterplot(y = 'Lag2',x = 'Lag1',hue = 'Direction',data = tmp)

13. 画等高线和三维图

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fig = plt.figure(figsize=(15,6))
fig.suptitle('RSS - Regression coefficients', fontsize=20)

ax1 = fig.add_subplot(121)
ax2 = fig.add_subplot(122, projection='3d')

# Left plot
CS = ax1.contour(xx, yy, Z, cmap=plt.cm.Set1, levels=[2.15, 2.2, 2.3, 2.5, 3])
ax1.scatter(regr.intercept_, regr.coef_[0], c='r', label=min_RSS)
ax1.clabel(CS, inline=True, fontsize=10, fmt='%1.1f')

# Right plot
ax2.plot_surface(xx, yy, Z, rstride=3, cstride=3, alpha=0.3)
ax2.contour(xx, yy, Z, zdir='z', offset=Z.min(), cmap=plt.cm.Set1,
            alpha=0.4, levels=[2.15, 2.2, 2.3, 2.5, 3])
ax2.scatter3D(regr.intercept_, regr.coef_[0], min_rss, c='r', label=min_RSS)
ax2.set_zlabel('RSS')
ax2.set_zlim(Z.min(),Z.max())
ax2.set_ylim(0.02,0.07)

# settings common to both plots
for ax in fig.axes:
    ax.set_xlabel(r'$\beta_0$', fontsize=17)
    ax.set_ylabel(r'$\beta_1$', fontsize=17)
    ax.set_yticks([0.03,0.04,0.05,0.06])
    ax.legend()

14. 3D散点图

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# Create plot
fig = plt.figure(figsize=(10,6))
fig.suptitle('Regression: Sales ~ Radio + TV Advertising', fontsize=20)

ax = axes3d.Axes3D(fig)

ax.plot_surface(B1, B2, Z, rstride=10, cstride=5, alpha=0.4)
ax.scatter3D(advertising.Radio, advertising.TV, advertising.Sales, c='r')

ax.set_xlabel('Radio')
ax.set_xlim(0,50)
ax.set_ylabel('TV')
ax.set_ylim(ymin=0)
ax.set_zlabel('Sales')

15. suptitle、用clabel为等高线添加高程标签

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fig = plt.figure(figsize=(12,5))
fig.suptitle('RSS - Regression coefficients', fontsize=20)

ax1 = fig.add_subplot(121)
ax2 = fig.add_subplot(122)

min_RSS = r'$\beta_0$, $\beta_1$ for minimized RSS'

# Left plot
CS = ax1.contour(X1, Y1, Z1, cmap=plt.cm.Set1, levels=[21.25, 21.5, 21.8])
ax1.scatter(regr1.coef_[1], regr1.coef_[0], c='r', label=min_RSS)
ax1.clabel(CS, inline=True, fontsize=10, fmt='%1.1f')
ax1.set_ylabel(r'$\beta_{Age}$', fontsize=17)

# Right plot
CS = ax2.contour(X2, Y2, Z2, cmap=plt.cm.Set1, levels=[21.5, 21.8])
ax2.scatter(regr2.coef_[1], regr2.coef_[0], c='r', label=min_RSS)
ax2.clabel(CS, inline=True, fontsize=10, fmt='%1.1f')
ax2.set_ylabel(r'$\beta_{Rating}$', fontsize=17)
ax2.set_xticks([-0.1, 0, 0.1, 0.2])

for ax in fig.axes:
    ax.set_xlabel(r'$\beta_{Limit}$', fontsize=17)
    ax.legend()

16. 同时画两种不同的散点图

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fig = plt.figure(figsize=(12,5))
gs = mpl.gridspec.GridSpec(1, 4)
ax1 = plt.subplot(gs[0,:-2])
ax2 = plt.subplot(gs[0,-2])
ax3 = plt.subplot(gs[0,-1])

# Take a fraction of the samples where target value (default) is 'no'
df_no = df[df.default2 == 0].sample(frac=0.15)
# Take all samples  where target value is 'yes'
df_yes = df[df.default2 == 1]
df_ = df_no.append(df_yes)

ax1.scatter(df_[df_.default == 'Yes'].balance, df_[df_.default == 'Yes'].income, s=40, c='orange', marker='+',
            linewidths=1)
ax1.scatter(df_[df_.default == 'No'].balance, df_[df_.default == 'No'].income, s=40, marker='o', linewidths=1,
             edgecolors='lightblue', facecolors='white', alpha=.6)

ax1.set_ylim(ymin=0)
ax1.set_ylabel('Income')
ax1.set_xlim(xmin=-100)
ax1.set_xlabel('Balance')

c_palette = {'No':'lightblue', 'Yes':'orange'}
sns.boxplot('default', 'balance', data=df, orient='v', ax=ax2, palette=c_palette)
sns.boxplot('default', 'income', data=df, orient='v', ax=ax3, palette=c_palette)
gs.tight_layout(plt.gcf())

17. 使用注解(annotate)

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df.plot('Years', 'Hits', kind='scatter', color='orange', figsize=(7,6))
plt.xlim(0,25)
plt.ylim(ymin=-5)
plt.xticks([1, 4.5, 24])
plt.yticks([1, 117.5, 238])
plt.vlines(4.5, ymin=-5, ymax=250)
plt.hlines(117.5, xmin=4.5, xmax=25)
plt.annotate('R1', xy=(2,117.5), fontsize='xx-large')
plt.annotate('R2', xy=(11,60), fontsize='xx-large')
plt.annotate('R3', xy=(11,170), fontsize='xx-large')

参考资料

  • https://github.com/JWarmenhoven/ISLR-python.git
  • https://github.com/hardikkamboj/An-Introduction-to-Statistical-Learning.git

Pandas使用tips集锦

1. 替换字符

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auto['horsepower'] = auto['horsepower'].replace('?',np.nan)

2. 删除空值

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auto = auto.dropna()

3. 改变字段类型

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auto['horsepower'] = auto['horsepower'].astype('int')

4. 按照某一个/多个字段排序

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auto = auto.sort_values(by = ['horsepower'],ascending = True, axis = 0)

boston.sort_values(by= ['CRIM','TAX','PTRATIO'],ascending=False).head().index

5. pandas设置索引

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college = college.set_index(['Unnamed: 0'], append=True, verify_integrity=True) college.rename_axis([None, 'Name'], inplace=True)

6. 对某一列进行判断并且置值

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college['Elite'] = np.where(college['Top10perc'] > 50,'Yes','No')

# 使用map的方式
credit['Student2'] = credit.Student.map({'No':0, 'Yes':1})

7. 对某一列的值进行计数

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college['Elite'].value_counts()

8. 对某一列的值进行筛选

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elite_colleges = college[college['Elite'] == 'Yes']

9. 对某一列的数值进行分桶操作

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college['Enroll'] = pd.cut(college['Enroll'], bins=3, labels = ['Low','Medium','High'])

10. 查看df中的各个字段的unique的数目和字段类型信息等

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auto.nunique()
auto.info()
auto['horsepower'].unique() # 看某一列的unique数目

11. 筛选单列和多列

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info['range'] = info['max'] - info['min']
info = info[['mean','range','std']]

12. 根据index筛选和删除多列

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info = auto.drop(auto.index[10:85]).describe().T

13. load数据并且指定列名

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boston = pd.DataFrame(load_boston().data,columns = load_boston().feature_names )

data = pd.DataFrame(boston.data,columns = boston['feature_names'])

14. 通过iloc选取行和列

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corr_matrix = boston.corr()
corr_matrix.iloc[1:,0].sort_values()

# 选取所有行，和从第二列开始的所有列
data = data.iloc[:,1:]

15. 按照行/列进行df的拼接

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# 按行拼接
features = pd.concat([constant,features],axis = 1)

16. load数据并且选定列

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credit = pd.read_csv('Data/Credit.csv', usecols=list(range(1,12)))
advertising = pd.read_csv('Data/Advertising.csv', usecols=[1,2,3,4])

17. 读取数据的时候，就设置空值的表示方法

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auto = pd.read_csv('Data/Auto.csv', na_values='?').dropna()

18. 构造pandas的df

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X = np.random.normal(size = 100)
y = np.random.permutation(X)
data = pd.DataFrame({'X':X,'y':y})

19. 读取文本文件的时候，设置某列为索引列

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data = pd.read_csv('../data/Smarket.csv',index_col=0)

20. 原地替换

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df_x.replace(to_replace={0:'No', 1:'Yes', 'True':'Yes', 'False':'No'}, inplace=True)

参考链接