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))