I am a little confused about how this code works:

fig, axes = plt.subplots(nrows=2, ncols=2) plt.show() 

How does the fig, axes work in this case? What does it do?

Also why wouldn't this work to do the same thing:

fig = plt.figure() axes = fig.subplots(nrows=2, ncols=2) 

11 Answers

There are several ways to do it. The subplots method creates the figure along with the subplots that are then stored in the ax array. For example:

import matplotlib.pyplot as plt x = range(10) y = range(10) fig, ax = plt.subplots(nrows=2, ncols=2) for row in ax: for col in row: col.plot(x, y) plt.show() 

However, something like this will also work, it's not so "clean" though since you are creating a figure with subplots and then add on top of them:

fig = plt.figure() plt.subplot(2, 2, 1) plt.plot(x, y) plt.subplot(2, 2, 2) plt.plot(x, y) plt.subplot(2, 2, 3) plt.plot(x, y) plt.subplot(2, 2, 4) plt.plot(x, y) plt.show() 

import matplotlib.pyplot as plt fig, ax = plt.subplots(2, 2) ax[0, 0].plot(range(10), 'r') #row=0, col=0 ax[1, 0].plot(range(10), 'b') #row=1, col=0 ax[0, 1].plot(range(10), 'g') #row=0, col=1 ax[1, 1].plot(range(10), 'k') #row=1, col=1 plt.show() 

• You can also unpack the axes in the subplots call

• And set whether you want to share the x and y axes between the subplots

Like this:

import matplotlib.pyplot as plt fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2, ncols=2, sharex=True, sharey=True) ax1.plot(range(10), 'r') ax2.plot(range(10), 'b') ax3.plot(range(10), 'g') ax4.plot(range(10), 'k') plt.show() 

You might be interested in the fact that as of matplotlib version 2.1 the second code from the question works fine as well.

From the change log:

Figure class now has subplots method The Figure class now has a subplots() method which behaves the same as pyplot.subplots() but on an existing figure.

Example:

import matplotlib.pyplot as plt fig = plt.figure() axes = fig.subplots(nrows=2, ncols=2) plt.show() 

Read the documentation: matplotlib.pyplot.subplots

pyplot.subplots() returns a tuple fig, ax which is unpacked in two variables using the notation

fig, axes = plt.subplots(nrows=2, ncols=2) 

The code:

fig = plt.figure() axes = fig.subplots(nrows=2, ncols=2) 

does not work because subplots() is a function in pyplot not a member of the object Figure.

Iterating through all subplots sequentially:

fig, axes = plt.subplots(nrows, ncols) for ax in axes.flatten(): ax.plot(x,y) 

Accessing a specific index:

for row in range(nrows): for col in range(ncols): axes[row,col].plot(x[row], y[col]) 

Subplots with pandas

• This answer is for subplots with pandas, which, uses matplotlib as the default plotting backend.
• Here is four options to create subplots starting with a pandas.DataFrame
  • Implementation 1. and 2. are for the data in a wide format, creating subplots for each column.
  • Implementation 3. and 4. are for data in a long format, creating subplots for each unique value in a column.
• Tested in python 3.8.11, pandas 1.3.2, matplotlib 3.4.3, seaborn 0.11.2

Imports and Data

import seaborn as sns # data only import pandas as pd import matplotlib.pyplot as plt import seaborn as sns # wide dataframe df = sns.load_dataset('planets').iloc[:, 2:5] orbital_period mass distance 0 269.300 7.10 77.40 1 874.774 2.21 56.95 2 763.000 2.60 19.84 3 326.030 19.40 110.62 4 516.220 10.50 119.47 # long dataframe dfm = sns.load_dataset('planets').iloc[:, 2:5].melt() variable value 0 orbital_period 269.300 1 orbital_period 874.774 2 orbital_period 763.000 3 orbital_period 326.030 4 orbital_period 516.220 

1. subplots=True and layout, for each column

• Use the parameters subplots=True and layout=(rows, cols) in pandas.DataFrame.plot
• This example uses kind='density', but there are different options for kind, and this applies to them all. Without specifying kind, a line plot is the default.
• ax is array of AxesSubplot returned by pandas.DataFrame.plot
• See How to get a Figure object, if needed.
  • How to save pandas subplots

axes = df.plot(kind='density', subplots=True, layout=(2, 2), sharex=False, figsize=(10, 6)) # extract the figure object; only used for tight_layout in this example fig = axes[0][0].get_figure() # set the individual titles for ax, title in zip(axes.ravel(), df.columns): ax.set_title(title) fig.tight_layout() plt.show() 

2. plt.subplots, for each column

• Create an array of Axes with matplotlib.pyplot.subplots and then pass axes[i, j] or axes[n] to the ax parameter.
  • This option uses pandas.DataFrame.plot, but can use other axes level plot calls as a substitute (e.g. sns.kdeplot, plt.plot, etc.)
  • It's easiest to collapse the subplot array of Axes into one dimension with .ravel or .flatten. See .ravel vs .flatten.
  • Any variables applying to each axes, that need to be iterate through, are combined with .zip (e.g. cols, axes, colors, palette, etc.). Each object must be the same length.

fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(10, 6)) # define the figure and subplots axes = axes.ravel() # array to 1D cols = df.columns # create a list of dataframe columns to use colors = ['tab:blue', 'tab:orange', 'tab:green'] # list of colors for each subplot, otherwise all subplots will be one color for col, color, ax in zip(cols, colors, axes): df[col].plot(kind='density', ax=ax, color=color, label=col, title=col) ax.legend() fig.delaxes(axes[3]) # delete the empty subplot fig.tight_layout() plt.show() 

Result for 1. and 2.

3. plt.subplots, for each group in .groupby

• This is similar to 2., except it zips color and axes to a .groupby object.

fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(10, 6)) # define the figure and subplots axes = axes.ravel() # array to 1D dfg = dfm.groupby('variable') # get data for each unique value in the first column colors = ['tab:blue', 'tab:orange', 'tab:green'] # list of colors for each subplot, otherwise all subplots will be one color for (group, data), color, ax in zip(dfg, colors, axes): data.plot(kind='density', ax=ax, color=color, title=group, legend=False) fig.delaxes(axes[3]) # delete the empty subplot fig.tight_layout() plt.show() 

4. seaborn figure-level plot

• Use a seaborn figure-level plot, and use the col or row parameter. seaborn is a high-level API for matplotlib. See seaborn: API reference

p = sns.displot(data=dfm, kind='kde', col='variable', col_wrap=2, x='value', hue='variable', facet_kws={'sharey': False, 'sharex': False}, height=3.5, aspect=1.75) sns.move_legend(p, "upper left", bbox_to_anchor=(.55, .45)) 

The other answers are great, this answer is a combination which might be useful.

import numpy as np import matplotlib.pyplot as plt # Optional: define x for all the sub-plots x = np.linspace(0,2*np.pi,100) # (1) Prepare the figure infrastructure fig, ax_array = plt.subplots(nrows=2, ncols=2) # flatten the array of axes, which makes them easier to iterate through and assign ax_array = ax_array.flatten() # (2) Plot loop for i, ax in enumerate(ax_array): ax.plot(x , np.sin(x + )) #ax.set_title(f'plot {i}') # Optional: main title plt.suptitle('Plots') 

Summary

1. Prepare the figure infrastructure
   • Get ax_array, an array of the subplots
   • Flatten the array in order to use it in one 'for loop'
2. Plot loop
   • Loop over the flattened ax_array to update the subplots
   • optional: use enumeration to track subplot number
3. Once flattened, each ax_array can be individually indexed from 0 through nrows x ncols -1 (e.g. ax_array[0], ax_array[1], ax_array[2], ax_array[3]).

here is a simple solution

fig, ax = plt.subplots(nrows=2, ncols=3, sharex=True, sharey=False) for sp in fig.axes: sp.plot(range(10)) 

Convert the axes array to 1D

• Generating subplots with plt.subplots(nrows, ncols), where both nrows and ncols is greater than 1, returns a nested array of <AxesSubplot:> objects.
  • It is not necessary to flatten axes in cases where either nrows=1 or ncols=1, because axes will already be 1 dimensional, which is a result of the default parameter squeeze=True
• The easiest way to access the objects, is to convert the array to 1 dimension with .ravel(), .flatten(), or .flat.
  • .ravel vs. .flatten
    • flatten always returns a copy.
    • ravel returns a view of the original array whenever possible.
• Once the array of axes is converted to 1-d, there are a number of ways to plot.

import matplotlib.pyplot as plt import numpy as np # sample data only # example of data rads = np.arange(0, 2*np.pi, 0.01) y_data = np.array([np.sin(t*rads) for t in range(1, 5)]) x_data = [rads, rads, rads, rads] # Generate figure and its subplots fig, axes = plt.subplots(nrows=2, ncols=2) # axes before array([[<AxesSubplot:>, <AxesSubplot:>], [<AxesSubplot:>, <AxesSubplot:>]], dtype=object) # convert the array to 1 dimension axes = axes.ravel() # axes after array([<AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>], dtype=object) 

1. Iterate through the flattened array
   • If there are more subplots than data, this will result in IndexError: list index out of range
     • Try option 3. instead, or select a subset of the axes (e.g. axes[:-2])

for i, ax in enumerate(axes): ax.plot(x_data[i], y_data[i]) 

2. Access each axes by index

axes[0].plot(x_data[0], y_data[0]) axes[1].plot(x_data[1], y_data[1]) axes[2].plot(x_data[2], y_data[2]) axes[3].plot(x_data[3], y_data[3]) 

3. Index the data and axes

for i in range(len(x_data)): axes[i].plot(x_data[i], y_data[i]) 

4. zip the axes and data together and then iterate through the list of tuples

for ax, x, y in zip(axes, x_data, y_data): ax.plot(x, y) 

Ouput

Go with the following if you really want to use a loop:

def plot(data): fig = plt.figure(figsize=(100, 100)) for idx, k in enumerate(data.keys(), 1): x, y = data[k].keys(), data[k].values plt.subplot(63, 10, idx) plt.bar(x, y) plt.show()