利用 Matplotlib 绘图

各类绘图

## 导入包
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns

## 参数设置
#-------------------------01-------------------------
large = 22; med = 16; small = 12
params = {'axes.titlesize': large,
          'legend.fontsize': med,
          'figure.figsize': (16, 10),
          'axes.labelsize': med,
          'axes.titlesize': med,
          'xtick.labelsize': med,
          'ytick.labelsize': med,
          'figure.titlesize': large}
plt.rcParams.update(params)
plt.style.use('seaborn-whitegrid')
sns.set_style("white")
# 显示中文
plt.rcParams['font.sans-serif']=['SimHei']
plt.rcParams['axes.unicode_minus']=False

## box
#-------------------------01-------------------------
fig=plt.figure(figsize=(10,6), dpi= 80)
ax1=fig.add_subplot(121)
sns.boxplot(x=personal_df.minutes,ax=ax1)
ax1.set_xlabel('个人客户')
ax1.set_xlim(0,500)
ax1.set_title('空调开启时长分布', fontsize=16, loc='center')
ax1.legend()

#-------------------------02-------------------------
fig=plt.figure(figsize=(12,6), dpi= 80)
ax2=fig.add_subplot(121)
sns.boxplot(x='company_type', y='minutes',data = company_df,ax=ax2)
ax2.set_xlabel('公司客户')
ax2.set_title('空调开启时长分布', fontsize=16, loc='center')
ax2.legend()

## bar
#-------------------------01-------------------------
plt.figure(figsize=(10,6),dpi = 72)
ax = sns.barplot(x=p_vehacfanspeed_times_mean_df.index.values, y=p_vehacfanspeed_times_mean_df.times_minutes.values, palette="rocket")
ax.axhline(0, color="k", clip_on=False)
ax.set_ylabel('平均时长/分钟')
ax.set_xlabel('空调风速')
# ax.set_axis_off()
ax.set_title('个人用户', fontsize=24, loc="center", pad=10)
# Annotate Text
for i, cty in enumerate(p_vehacfanspeed_times_mean_df.times_minutes.values):
    ax.text(i, cty+0.5, round(cty, 1),fontsize=12, horizontalalignment='center')

#-------------------------02-------------------------
otal_width, n = 0.8, 2     # 有多少个类型，只需更改n即可
width = total_width / n       # the width of the bars
# x = x - (total_width - width) / 2

fig, ax = plt.subplots(figsize=(10,8),dpi= 80)
rects1 = ax.bar(company_out_freq_count_s.index.values+width, company_out_freq_count_s.values, width,label = '公司客户', color='r')
rects2 = ax.bar(personal_out_freq_count_s.index.values, personal_out_freq_count_s.values, width,label = '个人客户', color='b')

def autolabel(rects):
    # attach some text labels
    for rect in rects:
        height = rect.get_height()
        ax.text(rect.get_x() + rect.get_width()/2., 1.02*height,
                '%d' % int(height),
                ha='center', va='bottom')

autolabel(rects1)
autolabel(rects2)

ax.set_ylabel('频数')
ax.set_title('个人/公司车辆一天出行次数分布')
plt.legend()

## distplot
#-------------------------01-------------------------
fig=plt.figure(figsize=(16,6), dpi= 80)

ax1=fig.add_subplot(121) #1*2的图形 在第一个位置
sns.distplot(company_out_df['delta_time_h'], color="dodgerblue", label="company_out", hist_kws={'alpha':.7}, kde_kws={'linewidth':3},ax=ax1)
sns.distplot(personal_out_df['delta_time_h'], color="orange", label="personal_out", hist_kws={'alpha':.7}, kde_kws={'linewidth':3},ax=ax1)
ax1.set_ylim(0,1)
# Decoration
ax1.set_xlabel("单次出行时长/h")
ax1.set_title('单次出行时长密度分布', fontsize=22)
ax1.legend()

## pie
#-------------------------01-------------------------
fig = plt.figure(figsize=(12, 8), dpi= 80)
def func(pct, allvals):
    absolute = int(pct/100.*np.sum(allvals))
    return "{:.1f}% ({:d} )".format(pct, absolute)

ax1=fig.add_subplot(221)
personal_charge_data = [personal_fcharge_car_nums, personal_scharge_car_nums]
categories = pd.Series(data = ['快充', '慢充'])
explode = [0,0.1]
wedges, texts, autotexts = ax1.pie(personal_charge_data,
                                  autopct=lambda pct: func(pct, personal_charge_data),
                                  textprops=dict(color="w"),
                                  colors=plt.cm.Dark2.colors,
                                 startangle=140,
                                 explode=explode)

# ax1.legend(wedges, categories, title="充电类型", loc="center left", bbox_to_anchor=(1, 0, 0.5, 1))
ax1.set_title('个人充电类型占比')

相关资源

帮助文档：

Matplotlib tutorial for beginner: https://github.com/rougier/matplotlib-tutorial

图之典:http://tuzhidian.com/

例子：

effective data visualization

Matplotlib 中文文档

notebook_matplotlib_visualizations.ipynb

top-50-matplotlib-visualizations-the-master-plots-python

art_of_data_visualization: text pre-processing