Reputation: 3528
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:
Datum
values there. How can it be changed?Upvotes: 16
Views: 78839
Reputation: 61074
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)
px.line(df, x = df.index, y = df.columns)
Another plotly option is:
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)
Upvotes: 19
Reputation: 3298
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],
}
)
px.line(x="Datum", y="Menge", data_frame=df, title="plotly example")
(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')
Upvotes: 2
Reputation: 19
The Plotly solution last given here is wonderful. Using the figure object it was also easy to set up a dictionary of labels for my data - since the data names given by the government are barely descriptive. So the mere handful of lines of code:
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')
Produced the following Plotly graph:
Upvotes: 0
Reputation: 8913
Considering a toy dataframe:
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()
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)
Upvotes: 13