It can be really useful to split your graphic window in several parts, in order to display several charts in the same time. With matplotlib, this is done through the subplot function. It is possible to display your graphics in several rows or several columns, or both. You can also control the size of each part. The examples below should allow you to understand how to proceed:
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We use the subplot function to split the graphic window in several parts. It works like that:
plt.subplot(num1, num2, num3)
num1: how many row do you want?
num2: how many column do you want?
num3: Which part are you going to modify?Here we want 1 row and 2 columns: plt.subplot(121) and then plt.subplot(122) !
# libraries and data from matplotlib import pyplot as plt import pandas as pd import numpy as np df=pd.DataFrame({'x': range(1,101), 'y': np.random.randn(100)*15+range(1,101), 'z': (np.random.randn(100)*15+range(1,101))*2 }) # Cut your window in 1 row and 2 columns, and start a plot in the first part plt.subplot(121) plt.plot( 'x', 'y', data=df, marker='o', alpha=0.4) plt.title("A subplot with 2 lines") # And now add something in the second part: plt.subplot(122) plt.plot( 'x','z', data=df, linestyle='none', marker='o', color="orange", alpha=0.3) plt.savefig('PNG/#194_matplotlib_subplot1.png', dpi=96) # Show the graph plt.show() -
The concept is exactly the same, except that we cut the graphic window in 2 lines and 1 column: subplots(21x).
# libraries and data from matplotlib import pyplot as plt import pandas as pd import numpy as np df=pd.DataFrame({'x': range(1,101), 'y': np.random.randn(100)*15+range(1,101), 'z': (np.random.randn(100)*15+range(1,101))*2 }) # fist line: plt.subplot(211) plt.plot( 'x', 'y', data=df, marker='o', alpha=0.4) plt.title("A subplot with 2 lines") # second line plt.subplot(212) plt.plot( 'x','z', data=df, linestyle='none', marker='o', color="orange", alpha=0.3) plt.show() -
Once you have 2 aligned graphics, you need to decide wether or not you want to share axis. If you do, the limits of the axis will be the same for all plots. If you don’t, limits will be adjusted for each plot.
Think about it! The message is not the same in both case. Do you want to compare the average value in your plots? Or do you want to study the trend in both plot?
Here the 2 plots share the axis. It allows to detect that the blue values are lower than the oranges. But the trend of the blue value is hard to observe!
# libraries from matplotlib import pyplot as plt import pandas as pd import numpy as np df=pd.DataFrame({'x': range(1,101), 'y': np.random.randn(100)*15+range(1,101), 'z': (np.random.randn(100)*15+range(1,101))*10 }) # initialise the figure. here we share X and Y axis fig, axes = plt.subplots(nrows=2, ncols=1, sharex=True, sharey=True) axes[0].plot( 'x', 'y', data=df, marker='o', alpha=0.4) axes[1].plot( 'x','z', data=df, linestyle='none', marker='o', color="orange", alpha=0.3) axes[0].title.set_text('These 2 plots have the same limit for the Y axis') plt.show() -
Following the same idea it is straightforward to make a 2 x 2 grid:
# libraries from matplotlib import pyplot as plt import pandas as pd import numpy as np df=pd.DataFrame({'x': range(1,101), 'y': np.random.randn(100)*15+range(1,101), 'z': (np.random.randn(100)*15+range(1,101))*2 }) # Cut the window with 2 rows and 2 columns: plt.subplot(221) plt.plot( 'x', 'y', data=df, marker='o', alpha=0.4) plt.subplot(222) plt.plot( 'x','z', data=df, linestyle='none', marker='o', color="orange", alpha=0.3) plt.subplot(223) plt.plot( 'x','z', data=df, linestyle='none', marker='D', color="green", alpha=0.3) plt.subplot(224) plt.plot( 'x','z', data=df, marker='o', color="grey", alpha=0.3) plt.show() -
Here are a few tips to add a general title to your figure. It is done through the suptitle command. Do not forget to use y=1.0.. to add more space on the top of the figure! See more about titles here.
# libraries from matplotlib import pyplot as plt import pandas as pd import numpy as np df=pd.DataFrame({'x': range(1,101), 'y': np.random.randn(100)*15+range(1,101), 'z': (np.random.randn(100)*15+range(1,101))*2 }) # initialize a figure fig=plt.figure() # Do a 2x2 chart plt.subplot(221) plt.plot( 'x', 'y', data=df, marker='o', alpha=0.4) plt.title('title of fig A', fontsize=12, color='grey', loc='left', style='italic') plt.subplot(222) plt.plot( 'x','z', data=df, linestyle='none', marker='o', color="orange", alpha=0.3) plt.title('title of fig A', fontsize=12, color='grey', loc='left', style='italic') plt.subplot(223) plt.plot( 'x','z', data=df, linestyle='none', marker='D', color="green", alpha=0.3) plt.title('title of fig A', fontsize=12, color='grey', loc='left', style='italic') plt.subplot(224) plt.plot( 'x','z', data=df, marker='o', color="grey", alpha=0.3) plt.title('title of fig A', fontsize=12, color='grey', loc='left', style='italic') # Add a title: plt.suptitle('A title common to my 4 plots', y=1.02) plt.show() -
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- #194 Custom proportion on subplot
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- #194 Subplot with custom proportion
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- #194 Subplot and custom proportions
It is possible to custom the proportion of each graph of your split window. Here are 3 examples that illustrate this concept. You can read more about that here. It is done using the subplot2grid function. It works like that:
plt.subplot2grid((2, 2), (0, 0), colspan=2)
(2, 2): I cut my window in 2 lines and 2 columns
(2, 2): I am going to add a plot in the line 0+1=1 of the column 0+1=1. This part starts counting on 0. So the first line has the number 0.
colspan=2: This chart will be spread on 2 columnsExample 1
from matplotlib import pyplot as plt import pandas as pd import numpy as np df=pd.DataFrame({'x': range(1,101), 'y': np.random.randn(100)*15+range(1,101), 'z': (np.random.randn(100)*15+range(1,101))*2 }) ax1 = plt.subplot2grid((2, 2), (0, 0), colspan=2) ax1.plot( 'x', 'y', data=df, marker='o', alpha=0.4) ax2 = plt.subplot2grid((2, 2), (1, 0), colspan=1) ax2.plot( 'x','z', data=df, marker='o', color="grey", alpha=0.3) ax3 = plt.subplot2grid((2, 2), (1, 1), colspan=1) ax3.plot( 'x','z', data=df, marker='o', color="orange", alpha=0.3)Example 2
from matplotlib import pyplot as plt import pandas as pd import numpy as np df=pd.DataFrame({'x': range(1,101), 'y': np.random.randn(100)*15+range(1,101), 'z': (np.random.randn(100)*15+range(1,101))*2 }) # 4 columns and 2 rows # The first plot is on line 1, and is spread all along the 4 columns ax1 = plt.subplot2grid((2, 4), (0, 0), colspan=4) ax1.plot( 'x', 'y', data=df, marker='o', alpha=0.4) # The second one is on column2, spread on 3 columns ax2 = plt.subplot2grid((2, 4), (1, 0), colspan=3) ax2.plot( 'x','z', data=df, marker='o', color="grey", alpha=0.3) # The last one is spread on 1 column only, on the 4th column of the second line. ax3 = plt.subplot2grid((2, 4), (1, 3), colspan=1) ax3.plot( 'x','z', data=df, marker='o', color="orange", alpha=0.3)Example 3
from matplotlib import pyplot as plt import pandas as pd import numpy as np df=pd.DataFrame({'x': range(1,101), 'y': np.random.randn(100)*15+range(1,101), 'z': (np.random.randn(100)*15+range(1,101))*2 }) ax1 = plt.subplot2grid((2, 2), (0, 0), colspan=1) ax1.plot( 'x', 'y', data=df, marker='o', alpha=0.4) ax2 = plt.subplot2grid((2, 2), (1, 0), colspan=1) ax2.plot( 'x','z', data=df, marker='o', color="grey", alpha=0.3) ax3 = plt.subplot2grid((2, 2), (0, 1), rowspan=2) ax3.plot( 'x','z', data=df, marker='o', color="orange", alpha=0.3) -
you can also inlcude seaborn graph on that one. thanks yan for your offline help