matplotlib数据可视化--折线图

折线图主要是看内容随时间变化的趋势情况

# 导入模块
import pandas as pd
import matplotlib.pyplot as plt

# 设置中文编码和负号的正常显示
plt.rcParams['font.sans-serif'] = 'SimHei'
plt.rcParams['axes.unicode_minus'] = False

# 读取需要绘图的数据
df = pd.read_excel(r'user_analysis.xlsx')

df

	时间	新关注人数	取消关注人数	净增关注人数
0	NaT	NaN	NaN	NaN
1	2017-09-01	87.0	7.0	80.0
2	2017-09-02	85.0	20.0	65.0
3	2017-09-03	90.0	10.0	80.0
4	2017-09-04	88.0	17.0	71.0
5	2017-09-05	78.0	19.0	59.0
6	2017-09-06	81.0	8.0	73.0
7	2017-09-07	224.0	11.0	213.0
8	2017-09-08	124.0	12.0	112.0
9	2017-09-09	59.0	6.0	53.0
10	2017-09-10	72.0	15.0	57.0
11	2017-09-11	94.0	10.0	84.0
12	2017-09-12	68.0	14.0	54.0
13	2017-09-13	110.0	7.0	103.0
14	2017-09-14	108.0	13.0	95.0
15	2017-09-15	142.0	10.0	132.0
16	2017-09-16	75.0	13.0	62.0
17	2017-09-17	85.0	9.0	76.0
18	2017-09-18	114.0	15.0	99.0
19	2017-09-19	95.0	10.0	85.0
20	2017-09-20	118.0	13.0	105.0
21	2017-09-21	87.0	7.0	80.0
22	2017-09-22	108.0	10.0	98.0
23	2017-09-23	103.0	12.0	91.0
24	2017-09-24	79.0	9.0	70.0
25	2017-09-25	93.0	6.0	87.0
26	2017-09-26	309.0	19.0	290.0
27	2017-09-27	394.0	19.0	375.0
28	2017-09-28	310.0	24.0	286.0
29	2017-09-29	756.0	13.0	743.0
30	2017-09-30	510.0	47.0	463.0

df.dropna(axis=0)

	时间	新关注人数	取消关注人数	净增关注人数
1	2017-09-01	87.0	7.0	80.0
2	2017-09-02	85.0	20.0	65.0
3	2017-09-03	90.0	10.0	80.0
4	2017-09-04	88.0	17.0	71.0
5	2017-09-05	78.0	19.0	59.0
6	2017-09-06	81.0	8.0	73.0
7	2017-09-07	224.0	11.0	213.0
8	2017-09-08	124.0	12.0	112.0
9	2017-09-09	59.0	6.0	53.0
10	2017-09-10	72.0	15.0	57.0
11	2017-09-11	94.0	10.0	84.0
12	2017-09-12	68.0	14.0	54.0
13	2017-09-13	110.0	7.0	103.0
14	2017-09-14	108.0	13.0	95.0
15	2017-09-15	142.0	10.0	132.0
16	2017-09-16	75.0	13.0	62.0
17	2017-09-17	85.0	9.0	76.0
18	2017-09-18	114.0	15.0	99.0
19	2017-09-19	95.0	10.0	85.0
20	2017-09-20	118.0	13.0	105.0
21	2017-09-21	87.0	7.0	80.0
22	2017-09-22	108.0	10.0	98.0
23	2017-09-23	103.0	12.0	91.0
24	2017-09-24	79.0	9.0	70.0
25	2017-09-25	93.0	6.0	87.0
26	2017-09-26	309.0	19.0	290.0
27	2017-09-27	394.0	19.0	375.0
28	2017-09-28	310.0	24.0	286.0
29	2017-09-29	756.0	13.0	743.0
30	2017-09-30	510.0	47.0	463.0

# 设置图框的大小
fig = plt.figure(figsize=(8,4))
# 绘图
plt.plot(df['时间'], # x轴数据
         df['新关注人数'], # y轴数据
        ) 

# 添加标题和坐标轴标签
plt.title('公众号每天新增用户数')
plt.xlabel('日期')
plt.ylabel('新增人数')

fig.autofmt_xdate(rotation = 45)

# 显示图形
plt.show()

C:\Users\Dell\AppData\Roaming\Python\Python36\site-packages\pandas\plotting\_converter.py:129: FutureWarning: Using an implicitly registered datetime converter for a matplotlib plotting method. The converter was registered by pandas on import. Future versions of pandas will require you to explicitly register matplotlib converters.

To register the converters:
	>>> from pandas.plotting import register_matplotlib_converters
	>>> register_matplotlib_converters()
  warnings.warn(msg, FutureWarning)

# 设置图框的大小
fig = plt.figure(figsize=(8,4))

# 设置绘图风格
plt.style.use('seaborn')

# 设置中文编码和负号的正常显示
plt.rcParams['font.sans-serif'] = 'SimHei'
plt.rcParams['axes.unicode_minus'] = False

# 绘图
plt.plot(df['时间'], # x轴数据
         df['新关注人数'], # y轴数据
        ) 

# 添加标题和坐标轴标签
plt.title('公众号每天新增用户数')
plt.xlabel('日期')
plt.ylabel('新增人数')

fig.autofmt_xdate(rotation = 45)

# 显示图形
plt.show()


#一张图上多个折线图

# 设置图框的大小
fig = plt.figure(figsize=(8,4))

# 设置绘图风格
plt.style.use('seaborn')

# 设置中文编码和负号的正常显示
plt.rcParams['font.sans-serif'] = 'SimHei'
plt.rcParams['axes.unicode_minus'] = False

# 绘图
plt.plot(
         df['新关注人数'], # y轴数据
         marker = 'o', # 点的形状
         markersize = 5, # 点的大小
         markerfacecolor='mediumpurple' # 点的填充色
        ) 

plt.plot(
         df['取消关注人数'], # y轴数据
         marker = 'o', # 点的形状
         markersize = 5, # 点的大小
         markerfacecolor='orangered' # 点的填充色
) 


# 添加标题和坐标轴标签
plt.title('公众号关注情况')
plt.xlabel('日期')
plt.ylabel('新增人数')

fig.autofmt_xdate(rotation = 45)

# 显示图例
plt.legend()
# 显示图形
plt.show()

#一张图上多个折线图

# 设置图框的大小
fig = plt.figure(figsize=(8,4))

# 设置绘图风格
plt.style.use('seaborn')

# 设置中文编码和负号的正常显示
plt.rcParams['font.sans-serif'] = 'SimHei'
plt.rcParams['axes.unicode_minus'] = False

# 绘图
plt.plot(
         df['新关注人数'], # y轴数据
        ) 

plt.plot(
         df['净增关注人数'], # y轴数据
        ) 


# 添加标题和坐标轴标签
plt.title('公众号每天新增用户数')
plt.xlabel('日期')
plt.ylabel('新增人数')

fig.autofmt_xdate(rotation = 45)

# 显示图例
plt.legend()
# 显示图形
plt.show()