最干净的设置，即使是多个时间序列，是：

• plotly：px.line()
• 海运：lineplot()
  plotly：

px.line(df, x = df.index, y = df.columns)

海运：

sns.lineplot(data = df)

seaborn和plotly的完整代码：

下面的代码示例将允许您生成两个图。

import plotly.graph_objs as go
from datetime import datetime
import plotly.express as px
import matplotlib as mpl
import seaborn as sns
import pandas as pd
import numpy as np

# sample data in a pandas dataframe

np.random.seed(23)
observations = 75
df=pd.DataFrame(dict(A=np.random.uniform(low=-1, high=1.1, size=observations).tolist(),
                    B=np.random.uniform(low=-1, high=1.1, size=observations).tolist(),
                    C=np.random.uniform(low=-1, high=1.1, size=observations).tolist(),
                    ))
df.iloc[0,] = 0
df = df.cumsum()

firstdate = datetime(2020,1,1)
df['date'] = pd.date_range(firstdate, periods=df.shape[0]).tolist()
df.set_index('date', inplace=True)

px.line(df, x = df.index, y = df.columns)


# fig = go.Figure([{
#     'x': df.index,
#     'y': df[col],
#     'name': col
# }  for col in df.columns])
# fig.show()

#  sns.set_style("darkgrid")
#sns.lineplot(data = df)

plotly express

px.line(df, x = df.index, y = df.columns)

另一个plotly选项是：

plotly graph_objects

fig = go.Figure([{
    'x': df.index,
    'y': df[col],
    'name': col
}  for col in df.columns])
fig.show()

seaborn

sns.set_style("darkgrid")
sns.lineplot(data = df)

考虑一个玩具数据框架：

*海运解决方案

import pandas as pd
import matplotlib.pyplot as plt

import seaborn as sns

df = pd.DataFrame({"Datum": ['1/1/2018 0:00',
                             '1/1/2018 0:15',
                             '1/1/2018 0:30',
                             '1/1/2018 0:45',
                             '1/1/2018 1:00',
                             '1/1/2018 1:15',
                             '1/1/2018 1:30',
                             '1/1/2018 1:45 '],
                   "Menge": [19.5, 19.,19.5,19.5,21,19.5,20,23]})
sns.lineplot(x="Datum", y="Menge", data=df)
plt.xticks(rotation=15)
plt.title('seaborn-matplotlib example')
plt.show()

*解决方案

import pandas as pd
import numpy as np

import plotly.graph_objs as go
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
init_notebook_mode(connected=True)

trace1 = go.Scatter(x=df.Datum,
                    y=df.Menge,
                    name = "plotly example",
                    line = dict(color = 'blue'),
                    opacity = 0.4)

layout = dict(title='plotly example',)

fig = dict(data=[trace1], layout=layout)
iplot(fig)

这比以前在Plotly中容易得多。

# IMPORTS
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import plotly.express as px

# EXTRACT THE DATA
df = pd.DataFrame(
    {
        "Datum": [
            "1/1/2018 0:00",
            "1/1/2018 0:15",
            "1/1/2018 0:30",
            "1/1/2018 0:45",
            "1/1/2018 1:00",
            "1/1/2018 1:15",
            "1/1/2018 1:30",
            "1/1/2018 1:45 ",
        ],
        "Menge": [19.5, 19.0, 19.5, 19.5, 21, 19.5, 20, 23],
    }
)

Plotly

px.line(x="Datum", y="Menge", data_frame=df, title="plotly example")

Seaborn/Matplotlib

(The代码与顶部答案中的代码相同）

sns.lineplot(x="Datum", y="Menge", data=df)
plt.xticks(rotation=15)
plt.title('seaborn-matplotlib example')

这里最后给出的Plotly解决方案非常棒。使用figure对象，还可以很容易地为我的数据建立一个标签字典--因为政府给出的数据名称几乎没有描述性。所以这几行代码：

columns = ['CPALTT01USM657N', 'UNRATE', 'TB3MS']
names = {'CPALTT01USM657N': 'Inflation Rate',
         'UNRATE': 'Unemployment Rate', 
         'TB3MS': '3 Mo. T-Bill Rate'}

fig = go.Figure([{
    'x': monthly_data['DATE'],
    'y': monthly_data[col],
    'name': names[col]
}  for col in columns])
fig.show(renderer='iframe')

生成了以下Plotly图：