matplotlib模块之绘图设计

matplotlib模块

In [11]:

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

饼图绘制¶

In [4]:

# 解决中文乱码问题
plt.rcParams['font.sans-serif'] = ['SimHei']
# 构造数据
edu = [0.2515, 0.3234, 0.3336, 0.0367, 0.0057]
labels = ['中专', '大专', '本科', '硕士', '其他']
explode = [0,0.1,0,0,0]
plt.pie(x=edu,#绘图数据
       labels = labels,# 添加标签
        autopct = '%.2f%%',#设置百分比格式，这里保留两位小数
        explode = explode,#呈现爆炸式突出 离圆心点位置
       )
# %matplotlib
plt.show()
'''
默认情况下，使用matplotlib绘制图片是嵌入式展示的，可以设置弹窗式展示。 
使用步骤：代码输入 %matplotlib，运行没反应再注释掉运行就有反应了。
'''

Out[4]:

'\n默认情况下，使用matplotlib绘制图片是嵌入式展示的，可以设置弹窗式展示。 \n使用步骤：代码输入 %matplotlib，运行没反应再注释掉运行就有反应了。\n'

条形图¶

In [5]:

# 文件中读取数据
GDP = pd.read_excel(r'GDP1.xlsx')
GDP.head()

Out[5]:

	Province	GDP
0	北京	2.80
1	上海	3.01
2	广东	8.99
3	江苏	8.59
4	重庆	1.95

In [11]:

# 绘制条形图
plt.bar(x=range(GDP.shape[0]),# 指定条形图轴的刻度值
       height = GDP['GDP'], #指定条形图的y轴
       tick_label = GDP.Province, # 指定条形图x轴的刻度标签
        color = 'red',# 指定条形图的填充色
       edgecolor = 'black', # 指定条形图的边框色
       )
# 给y轴添加一个标签注释
plt.ylabel('GDP(万亿)')
# 添加条形图的标题
plt.title('2017年度6个省份GDP分布')
##########################################
# 为每个条形图添加数值标签
for x, y in enumerate(GDP.GDP):
    plt.text(x, y+0.1, '%s'%round(y,1), ha='center')
##########################################
plt.show()

In [12]:

# 水平条形图
# 对读入数据做升序排序
GDP.sort_values(by = 'GDP', inplace = True)
"""
ascending 决定升序还是降序
"""

In [24]:

# 绘制水平条形图
plt.barh(y=range(GDP.shape[0]),# 指定条形图轴的刻度值
       width = GDP['GDP'], #指定条形图的y轴
       tick_label = GDP.Province, # 指定条形图x轴的刻度标签
       color = 'green',# 指定条形图的填充色
       edgecolor = 'black', # 指定条形图的边框色
       )
# 给x轴添加一个标签注释
plt.xlabel('GDP(万亿)')
# 添加条形图的标题
plt.title('2017年度6个省份GDP分布')
# 为每个条形图添加数值标签
for y, x in enumerate(GDP.GDP):
    plt.text(x+0.3, y, '%s'%round(x,2), ha='center')
plt.show()

In [45]:

# 交叉条形图
HuaRen = pd.read_excel(r'Count1.xlsx')
HuaRen.head()

Out[45]:

	Year	Counts	City
0	2016	15600	北京
1	2016	12700	上海
2	2016	11300	香港
3	2016	4270	深圳
4	2016	3620	广州

In [46]:

# Pandas模块之水平交错条形图
# pivot_table 数据透视表
HuaRen_reshape = HuaRen.pivot_table(
            index = 'City', 
            columns = 'Year',
            values = 'Counts').reset_index()
HuaRen_reshape

Out[46]:

Year	City	2016	2017	2107
0	上海	12700.0	14700.0	NaN
1	北京	15600.0	19600.0	NaN
2	广州	3620.0	NaN	8620.0
3	深圳	4270.0	NaN	6270.0
4	香港	11300.0	NaN	13300.0

In [50]:

#对数据集降序排序
HuaRen_reshape.sort_values(by = 2016, ascending = False, inplace = True)
HuaRen_reshape.plot(
    x='City',
    y = [2016,2107, 2017],
    kind = 'bar', 
    color = ['steelblue', 'indianred', 'yellow'], 
    # 用于旋转x轴刻度标签的角度，0表示水平显示刻度标签
    rot = 0,
    width = 0.8,
    title = '近两年内5个城市亿万资产家庭数比较',
)
# 添加y轴标签
plt.ylabel('亿万资产家庭数')
# 将x轴标签去除
plt.xlabel('')
plt.show()

直方图¶

折线图¶

In [12]:

# 数据读取
wechat = pd.read_excel(r'Wechat.xlsx')
wechat.head()                      

Out[12]:

	Date	Counts	Times
0	2017-10-01	399	453
1	2017-10-02	126	234
2	2017-10-03	76	645
3	2017-10-04	59	235
4	2017-10-05	60	654

In [13]:

plt.plot(
    wechat.Date, # x轴数据
    wechat.Counts, # y轴数据
    linestyle = '-', # 折线的类型
    linewidth = 2, # 折线的宽度
    color = 'steelblue', # 折线的颜色
    marker = 'o', # 折线图中添加圆点
    markersize = 6, # 点的大少
    markeredgecolor = 'black', # 点的边框色
    markerfacecolor = 'brown', # 点的天从折
        )
# 获取图的坐标位置
ax = plt.gca()
# 设置日期的显示格式
import matplotlib as mpl
date_format = mpl.dates.DateFormatter("%m-%d")
ax.xaxis.set_major_formatter(date_format)
# # 设置x轴每个刻度的间隔天数
# xlocator = mpl.ticker.MultipleLocator(7)
# ax.xaxis.set_major_locator(xlocator)
# 添加y轴标签
plt.ylabel('人数')
# 显示图形
plt.show()

D:\Users\24579\anaconda3\Lib\site-packages\IPython\core\pylabtools.py:152: UserWarning: Glyph 20154 (\N{CJK UNIFIED IDEOGRAPH-4EBA}) missing from current font.
  fig.canvas.print_figure(bytes_io, **kw)
D:\Users\24579\anaconda3\Lib\site-packages\IPython\core\pylabtools.py:152: UserWarning: Glyph 25968 (\N{CJK UNIFIED IDEOGRAPH-6570}) missing from current font.
  fig.canvas.print_figure(bytes_io, **kw)

热力图¶

In [26]:

import numpy as np
import seaborn as sns
# 读取数据
Sales = pd.read_excel(r'Sales.xlsx')
Sales.head()

Out[26]:

	Date	Sales
0	2010-10-13	261.5400
1	2012-02-20	435.7500
2	2011-07-15	2808.0800
3	2011-07-15	1761.4000
4	2011-07-15	160.2335

In [27]:

# 根据交易日期，衍生出年份和月份字段
# 将date拆分出 year、month
Sales['year'] = Sales.Date.dt.year
Sales['month'] = Sales.Date.dt.month
Sales.head()

Out[27]:

	Date	Sales	year	month
0	2010-10-13	261.5400	2010	10
1	2012-02-20	435.7500	2012	2
2	2011-07-15	2808.0800	2011	7
3	2011-07-15	1761.4000	2011	7
4	2011-07-15	160.2335	2011	7

In [28]:

# 统计每年各月份的销售总额（绘制热力图之前，必须将数据转换为交叉表形式）
Summary = Sales.pivot_table(index = 'month', columns = 'year', values = 'Sales', aggfunc = np.sum)
Summary

Out[28]:

year	2009	2010	2011	2012	2019
month
2	435.7500	261.5400	865.7040	435.7500	865.7040
3	489.3945	2808.0800	1761.4000	160.2335	261.5400
7	432.5300	123.9302	4853.6437	56.8645	435.7500
10	366.8887	261.5400	435.5640	445.7764	45.7855

In [30]:

# 绘制热力图
sns.heatmap(data = Summary, # 指定绘图数据
            cmap = 'PuBuGn', # 指定填充色
            linewidths = .1, # 设置每个单元格边框的宽度
            annot = True, # 显示数值
            fmt = '.1e' # 以科学计算法显示数据
            )
#添加标题
plt.title('每年各月份销售总额热力图')
# 显示图形
plt.show()

D:\Users\24579\anaconda3\Lib\site-packages\IPython\core\pylabtools.py:152: UserWarning: Glyph 27599 (\N{CJK UNIFIED IDEOGRAPH-6BCF}) missing from current font.
  fig.canvas.print_figure(bytes_io, **kw)
D:\Users\24579\anaconda3\Lib\site-packages\IPython\core\pylabtools.py:152: UserWarning: Glyph 24180 (\N{CJK UNIFIED IDEOGRAPH-5E74}) missing from current font.
  fig.canvas.print_figure(bytes_io, **kw)
D:\Users\24579\anaconda3\Lib\site-packages\IPython\core\pylabtools.py:152: UserWarning: Glyph 21508 (\N{CJK UNIFIED IDEOGRAPH-5404}) missing from current font.
  fig.canvas.print_figure(bytes_io, **kw)
D:\Users\24579\anaconda3\Lib\site-packages\IPython\core\pylabtools.py:152: UserWarning: Glyph 26376 (\N{CJK UNIFIED IDEOGRAPH-6708}) missing from current font.
  fig.canvas.print_figure(bytes_io, **kw)
D:\Users\24579\anaconda3\Lib\site-packages\IPython\core\pylabtools.py:152: UserWarning: Glyph 20221 (\N{CJK UNIFIED IDEOGRAPH-4EFD}) missing from current font.
  fig.canvas.print_figure(bytes_io, **kw)
D:\Users\24579\anaconda3\Lib\site-packages\IPython\core\pylabtools.py:152: UserWarning: Glyph 38144 (\N{CJK UNIFIED IDEOGRAPH-9500}) missing from current font.
  fig.canvas.print_figure(bytes_io, **kw)
D:\Users\24579\anaconda3\Lib\site-packages\IPython\core\pylabtools.py:152: UserWarning: Glyph 21806 (\N{CJK UNIFIED IDEOGRAPH-552E}) missing from current font.
  fig.canvas.print_figure(bytes_io, **kw)
D:\Users\24579\anaconda3\Lib\site-packages\IPython\core\pylabtools.py:152: UserWarning: Glyph 24635 (\N{CJK UNIFIED IDEOGRAPH-603B}) missing from current font.
  fig.canvas.print_figure(bytes_io, **kw)
D:\Users\24579\anaconda3\Lib\site-packages\IPython\core\pylabtools.py:152: UserWarning: Glyph 39069 (\N{CJK UNIFIED IDEOGRAPH-989D}) missing from current font.
  fig.canvas.print_figure(bytes_io, **kw)
D:\Users\24579\anaconda3\Lib\site-packages\IPython\core\pylabtools.py:152: UserWarning: Glyph 28909 (\N{CJK UNIFIED IDEOGRAPH-70ED}) missing from current font.
  fig.canvas.print_figure(bytes_io, **kw)
D:\Users\24579\anaconda3\Lib\site-packages\IPython\core\pylabtools.py:152: UserWarning: Glyph 21147 (\N{CJK UNIFIED IDEOGRAPH-529B}) missing from current font.
  fig.canvas.print_figure(bytes_io, **kw)
D:\Users\24579\anaconda3\Lib\site-packages\IPython\core\pylabtools.py:152: UserWarning: Glyph 22270 (\N{CJK UNIFIED IDEOGRAPH-56FE}) missing from current font.
  fig.canvas.print_figure(bytes_io, **kw)

组合图¶

In [ ]:

posted @ 2023-11-21 14:29 bjyxxc 阅读(129) 评论(0) 收藏举报

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