How to plot a time series graph using seaborn or plotly?

Question

I have a time series data as follows:

Datum   Menge
1/1/2018 0:00   19.5
1/1/2018 0:15   19.0
1/1/2018 0:30   19.5
1/1/2018 0:45   19.5
1/1/2018 1:00   21.0
1/1/2018 1:15   19.5
1/1/2018 1:30   20.0
1/1/2018 1:45   23.0

and the dataframe data has a shape of (14880, 2). In the Menge column, there are only 11807 values available and the rest are nan

I am trying to plot it as follows:

data.plot()
plt.show()

and this gives me

But I want to plot the same using seaborn or plotly

for seaborn I have tried:

x = data.Datum
y = data.Menge.values
sns.lineplot(x = x, y = y, data = data)

and it gives me the output as:

Out[3]: <matplotlib.axes._subplots.AxesSubplot at 0x21286bb8668>

and a new graph window is opened but it says Figure 1 (Not Responding)

So, I have 2 questions:

1. In the above graph, we can see the x-axis has index, but I would like it to be the Datum values there. How can it be changed?
2. I would like to achieve this in seaborn or plotly, so is there a way to achieve all this in either?

Answer 1

The cleanest setups, even for multiple time series, are:

• plotly: px.line()

• seaborn: lineplot()

---

plotly:

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

---

Seaborn:

sns.lineplot(data = df)

---

Complete code for both seaborn and plotly:

The following code sample will let you produce both plots.

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)

Another plotly option is:

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)

Answer 2

Considering a toy dataframe:

• seaborn solution

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()

• plotly solution

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)

Answer 3

This is now much easier than it was before in Plotly.

# 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 code is the same as the one in the top answer)

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