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How to Change Color of Line graphs in Statsmodel Decomposition Plots

How can I do a line break (line continuation) in Python?How do you change the size of figures drawn with matplotlib?Plot two graphs in same plot in RHow to read a file line-by-line into a list?How to put the legend out of the plotHaving line color vary with data index for line graph in matplotlib?Plotting line(color, attribue defined) graph using pandasHow to change colors of points in Python's ggplotAnimated plot with different color for every data pointPandas Series not plotting to timeseries chart

.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty margin-bottom:0;

1

The default color of the seasonal decomposition graphs is a light blue.
1. How can you change the colors so that each of the lines is a different color?
2. If each plot can not have a separate color, how would I change all of the colors to say red?

I've tried adding arguments to decomposition.plot(color = 'red')
and searching the documentation for clues.

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# I want 7 days of 24 hours with 60 minutes each
periods = 7 * 24 * 60
tidx = pd.date_range('2016-07-01', periods=periods, freq='D')
np.random.seed([3,1415])

# This will pick a number of normally distributed random numbers
# where the number is specified by periods
data = np.random.randn(periods)

ts = pd.Series(data=data, index=tidx, name='TimeSeries')

decomposition = sm.tsa.seasonal_decompose(ts, model ='additive')
fig = decomposition.plot()
plt.show()

A decomposition plot in which each graph is a different color.

python plot statsmodels

share|improve this question

asked Mar 26 at 4:48

rer49rer49

10888 bronze badges

add a comment | 

1

The default color of the seasonal decomposition graphs is a light blue.
1. How can you change the colors so that each of the lines is a different color?
2. If each plot can not have a separate color, how would I change all of the colors to say red?

I've tried adding arguments to decomposition.plot(color = 'red')
and searching the documentation for clues.

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# I want 7 days of 24 hours with 60 minutes each
periods = 7 * 24 * 60
tidx = pd.date_range('2016-07-01', periods=periods, freq='D')
np.random.seed([3,1415])

# This will pick a number of normally distributed random numbers
# where the number is specified by periods
data = np.random.randn(periods)

ts = pd.Series(data=data, index=tidx, name='TimeSeries')

decomposition = sm.tsa.seasonal_decompose(ts, model ='additive')
fig = decomposition.plot()
plt.show()

A decomposition plot in which each graph is a different color.

python plot statsmodels

share|improve this question

asked Mar 26 at 4:48

rer49rer49

10888 bronze badges

add a comment | 

The default color of the seasonal decomposition graphs is a light blue.
1. How can you change the colors so that each of the lines is a different color?
2. If each plot can not have a separate color, how would I change all of the colors to say red?

I've tried adding arguments to decomposition.plot(color = 'red')
and searching the documentation for clues.

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# I want 7 days of 24 hours with 60 minutes each
periods = 7 * 24 * 60
tidx = pd.date_range('2016-07-01', periods=periods, freq='D')
np.random.seed([3,1415])

# This will pick a number of normally distributed random numbers
# where the number is specified by periods
data = np.random.randn(periods)

ts = pd.Series(data=data, index=tidx, name='TimeSeries')

decomposition = sm.tsa.seasonal_decompose(ts, model ='additive')
fig = decomposition.plot()
plt.show()

A decomposition plot in which each graph is a different color.

python plot statsmodels

share|improve this question

asked Mar 26 at 4:48

rer49rer49

10888 bronze badges

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# I want 7 days of 24 hours with 60 minutes each
periods = 7 * 24 * 60
tidx = pd.date_range('2016-07-01', periods=periods, freq='D')
np.random.seed([3,1415])

# This will pick a number of normally distributed random numbers
# where the number is specified by periods
data = np.random.randn(periods)

ts = pd.Series(data=data, index=tidx, name='TimeSeries')

decomposition = sm.tsa.seasonal_decompose(ts, model ='additive')
fig = decomposition.plot()
plt.show()

share|improve this question

asked Mar 26 at 4:48

rer49rer49

10888 bronze badges

share|improve this question

asked Mar 26 at 4:48

rer49rer49

10888 bronze badges

asked Mar 26 at 4:48

rer49rer49

10888 bronze badges

asked Mar 26 at 4:48

rer49rer49

10888 bronze badges

1 Answer
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1

The decomposition object in the code you posted uses pandas in the plotting method. I don't see a way of passing colors directly to the plot method, and it doesn't take **kwargs.

A work around would be to call the pandas plotting code directly on the object:

fig, axes = plt.subplots(4, 1, sharex=True)

decomposition.observed.plot(ax=axes[0], legend=False, color='r')
axes[0].set_ylabel('Observed')
decomposition.trend.plot(ax=axes[1], legend=False, color='g')
axes[1].set_ylabel('Trend')
decomposition.seasonal.plot(ax=axes[2], legend=False)
axes[2].set_ylabel('Seasonal')
decomposition.resid.plot(ax=axes[3], legend=False, color='k')
axes[3].set_ylabel('Residual')

share|improve this answer

answered Mar 26 at 5:11

johnchasejohnchase

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1

The decomposition object in the code you posted uses pandas in the plotting method. I don't see a way of passing colors directly to the plot method, and it doesn't take **kwargs.

A work around would be to call the pandas plotting code directly on the object:

fig, axes = plt.subplots(4, 1, sharex=True)

decomposition.observed.plot(ax=axes[0], legend=False, color='r')
axes[0].set_ylabel('Observed')
decomposition.trend.plot(ax=axes[1], legend=False, color='g')
axes[1].set_ylabel('Trend')
decomposition.seasonal.plot(ax=axes[2], legend=False)
axes[2].set_ylabel('Seasonal')
decomposition.resid.plot(ax=axes[3], legend=False, color='k')
axes[3].set_ylabel('Residual')

share|improve this answer

answered Mar 26 at 5:11

johnchasejohnchase

5,38511 gold badge2121 silver badges3737 bronze badges

add a comment | 

1

The decomposition object in the code you posted uses pandas in the plotting method. I don't see a way of passing colors directly to the plot method, and it doesn't take **kwargs.

A work around would be to call the pandas plotting code directly on the object:

fig, axes = plt.subplots(4, 1, sharex=True)

decomposition.observed.plot(ax=axes[0], legend=False, color='r')
axes[0].set_ylabel('Observed')
decomposition.trend.plot(ax=axes[1], legend=False, color='g')
axes[1].set_ylabel('Trend')
decomposition.seasonal.plot(ax=axes[2], legend=False)
axes[2].set_ylabel('Seasonal')
decomposition.resid.plot(ax=axes[3], legend=False, color='k')
axes[3].set_ylabel('Residual')

share|improve this answer

answered Mar 26 at 5:11

johnchasejohnchase

5,38511 gold badge2121 silver badges3737 bronze badges

add a comment | 

The decomposition object in the code you posted uses pandas in the plotting method. I don't see a way of passing colors directly to the plot method, and it doesn't take **kwargs.

A work around would be to call the pandas plotting code directly on the object:

fig, axes = plt.subplots(4, 1, sharex=True)

decomposition.observed.plot(ax=axes[0], legend=False, color='r')
axes[0].set_ylabel('Observed')
decomposition.trend.plot(ax=axes[1], legend=False, color='g')
axes[1].set_ylabel('Trend')
decomposition.seasonal.plot(ax=axes[2], legend=False)
axes[2].set_ylabel('Seasonal')
decomposition.resid.plot(ax=axes[3], legend=False, color='k')
axes[3].set_ylabel('Residual')

share|improve this answer

answered Mar 26 at 5:11

johnchasejohnchase

5,38511 gold badge2121 silver badges3737 bronze badges

fig, axes = plt.subplots(4, 1, sharex=True)

decomposition.observed.plot(ax=axes[0], legend=False, color='r')
axes[0].set_ylabel('Observed')
decomposition.trend.plot(ax=axes[1], legend=False, color='g')
axes[1].set_ylabel('Trend')
decomposition.seasonal.plot(ax=axes[2], legend=False)
axes[2].set_ylabel('Seasonal')
decomposition.resid.plot(ax=axes[3], legend=False, color='k')
axes[3].set_ylabel('Residual')

share|improve this answer

answered Mar 26 at 5:11

johnchasejohnchase

5,38511 gold badge2121 silver badges3737 bronze badges

answered Mar 26 at 5:11

johnchasejohnchase

5,38511 gold badge2121 silver badges3737 bronze badges

answered Mar 26 at 5:11

johnchasejohnchase

5,38511 gold badge2121 silver badges3737 bronze badges