We Keep Coding

Placing multiple histograms in a stack with matplotlib - python

I am trying to place multiple histograms in a vertical stack. I am able to get the plots in a stack but then all the histograms are in the same graph.
fig, ax = plt.subplots(2, 1, sharex=True, figsize=(20, 18))
n = 2
axes = ax.flatten()
for i, j in zip(range(n), axes):
plt.hist(np.array(dates[i]), bins=20, alpha=0.3)
plt.show()

You have a 2x1 grid of axis objects. By directly looping over axes.flatten(), you are accessing one subplot at a time. You then need to use the corresponding axis instance for plotting the histogram.
fig, axes = plt.subplots(2, 1, sharex=True, figsize=(20, 18)) # axes here
n = 2
for i, ax in zip(range(n), axes.flatten()): # <--- flatten directly in the zip
ax.hist(np.array(dates[i]), bins=20, alpha=0.3)
Your original version was also correct except the fact that instead of plt, you just should have used the correct variable i.e. j instead of plt
for i, j in zip(range(n), axes):
j.hist(np.array(dates[i]), bins=20, alpha=0.3)

Related

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)

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)
fig, axes = plt.subplots(nrows=2, ncols=2, sharex=True, sharey=True)
ax1, ax2, ax3, ax4 = axes.flatten()
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 are 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))

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’s 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.
This answer is relevant to seaborn axes-level plots, which have the ax= parameter (e.g. sns.barplot(…, ax=ax[0]).
seaborn is a high-level API for matplotlib. See Figure-level vs. axes-level functions and seaborn is not plotting within defined subplots
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)
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])
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])
Index the data and axes
for i in range(len(x_data)):
axes[i].plot(x_data[i], y_data[i])
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
An option is to assign each axes to a variable, fig, (ax1, ax2, ax3) = plt.subplots(1, 3). However, as written, this only works in cases with either nrows=1 or ncols=1. This is based on the shape of the array returned by plt.subplots, and quickly becomes cumbersome.
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2) for a 2 x 2 array.
This option is most useful for two subplots (e.g.: fig, (ax1, ax2) = plt.subplots(1, 2) or fig, (ax1, ax2) = plt.subplots(2, 1)). For more subplots, it's more efficient to flatten and iterate through the array of axes.

You could use the following:
import numpy as np
import matplotlib.pyplot as plt
fig, _ = plt.subplots(nrows=2, ncols=2)
for i, ax in enumerate(fig.axes):
ax.plot(np.sin(np.linspace(0,2*np.pi,100) + np.pi/2*i))
Or alternatively, using the second variable that plt.subplot returns:
fig, ax_mat = plt.subplots(nrows=2, ncols=2)
for i, ax in enumerate(ax_mat.flatten()):
...
ax_mat is a matrix of the axes. It's shape is nrows x ncols.

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))

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()

Another concise solution is:
// set up structure of plots
f, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(20,10))
// for plot 1
ax1.set_title('Title A')
ax1.plot(x, y)
// for plot 2
ax2.set_title('Title B')
ax2.plot(x, y)
// for plot 3
ax3.set_title('Title C')
ax3.plot(x,y)

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)

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)
fig, axes = plt.subplots(nrows=2, ncols=2, sharex=True, sharey=True)
ax1, ax2, ax3, ax4 = axes.flatten()
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 are 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))

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’s 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.
This answer is relevant to seaborn axes-level plots, which have the ax= parameter (e.g. sns.barplot(…, ax=ax[0]).
seaborn is a high-level API for matplotlib. See Figure-level vs. axes-level functions and seaborn is not plotting within defined subplots
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)
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])
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])
Index the data and axes
for i in range(len(x_data)):
axes[i].plot(x_data[i], y_data[i])
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
An option is to assign each axes to a variable, fig, (ax1, ax2, ax3) = plt.subplots(1, 3). However, as written, this only works in cases with either nrows=1 or ncols=1. This is based on the shape of the array returned by plt.subplots, and quickly becomes cumbersome.
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2) for a 2 x 2 array.
This option is most useful for two subplots (e.g.: fig, (ax1, ax2) = plt.subplots(1, 2) or fig, (ax1, ax2) = plt.subplots(2, 1)). For more subplots, it's more efficient to flatten and iterate through the array of axes.

You could use the following:
import numpy as np
import matplotlib.pyplot as plt
fig, _ = plt.subplots(nrows=2, ncols=2)
for i, ax in enumerate(fig.axes):
ax.plot(np.sin(np.linspace(0,2*np.pi,100) + np.pi/2*i))
Or alternatively, using the second variable that plt.subplot returns:
fig, ax_mat = plt.subplots(nrows=2, ncols=2)
for i, ax in enumerate(ax_mat.flatten()):
...
ax_mat is a matrix of the axes. It's shape is nrows x ncols.

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))

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()

Another concise solution is:
// set up structure of plots
f, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(20,10))
// for plot 1
ax1.set_title('Title A')
ax1.plot(x, y)
// for plot 2
ax2.set_title('Title B')
ax2.plot(x, y)
// for plot 3
ax3.set_title('Title C')
ax3.plot(x,y)

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)

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)
fig, axes = plt.subplots(nrows=2, ncols=2, sharex=True, sharey=True)
ax1, ax2, ax3, ax4 = axes.flatten()
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 are 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))

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’s 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.
This answer is relevant to seaborn axes-level plots, which have the ax= parameter (e.g. sns.barplot(…, ax=ax[0]).
seaborn is a high-level API for matplotlib. See Figure-level vs. axes-level functions and seaborn is not plotting within defined subplots
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)
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])
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])
Index the data and axes
for i in range(len(x_data)):
axes[i].plot(x_data[i], y_data[i])
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
An option is to assign each axes to a variable, fig, (ax1, ax2, ax3) = plt.subplots(1, 3). However, as written, this only works in cases with either nrows=1 or ncols=1. This is based on the shape of the array returned by plt.subplots, and quickly becomes cumbersome.
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2) for a 2 x 2 array.
This option is most useful for two subplots (e.g.: fig, (ax1, ax2) = plt.subplots(1, 2) or fig, (ax1, ax2) = plt.subplots(2, 1)). For more subplots, it's more efficient to flatten and iterate through the array of axes.

You could use the following:
import numpy as np
import matplotlib.pyplot as plt
fig, _ = plt.subplots(nrows=2, ncols=2)
for i, ax in enumerate(fig.axes):
ax.plot(np.sin(np.linspace(0,2*np.pi,100) + np.pi/2*i))
Or alternatively, using the second variable that plt.subplot returns:
fig, ax_mat = plt.subplots(nrows=2, ncols=2)
for i, ax in enumerate(ax_mat.flatten()):
...
ax_mat is a matrix of the axes. It's shape is nrows x ncols.

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))

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()

Another concise solution is:
// set up structure of plots
f, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(20,10))
// for plot 1
ax1.set_title('Title A')
ax1.plot(x, y)
// for plot 2
ax2.set_title('Title B')
ax2.plot(x, y)
// for plot 3
ax3.set_title('Title C')
ax3.plot(x,y)

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)

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)
fig, axes = plt.subplots(nrows=2, ncols=2, sharex=True, sharey=True)
ax1, ax2, ax3, ax4 = axes.flatten()
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 are 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))

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’s 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.
This answer is relevant to seaborn axes-level plots, which have the ax= parameter (e.g. sns.barplot(…, ax=ax[0]).
seaborn is a high-level API for matplotlib. See Figure-level vs. axes-level functions and seaborn is not plotting within defined subplots
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)
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])
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])
Index the data and axes
for i in range(len(x_data)):
axes[i].plot(x_data[i], y_data[i])
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
An option is to assign each axes to a variable, fig, (ax1, ax2, ax3) = plt.subplots(1, 3). However, as written, this only works in cases with either nrows=1 or ncols=1. This is based on the shape of the array returned by plt.subplots, and quickly becomes cumbersome.
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2) for a 2 x 2 array.
This option is most useful for two subplots (e.g.: fig, (ax1, ax2) = plt.subplots(1, 2) or fig, (ax1, ax2) = plt.subplots(2, 1)). For more subplots, it's more efficient to flatten and iterate through the array of axes.

You could use the following:
import numpy as np
import matplotlib.pyplot as plt
fig, _ = plt.subplots(nrows=2, ncols=2)
for i, ax in enumerate(fig.axes):
ax.plot(np.sin(np.linspace(0,2*np.pi,100) + np.pi/2*i))
Or alternatively, using the second variable that plt.subplot returns:
fig, ax_mat = plt.subplots(nrows=2, ncols=2)
for i, ax in enumerate(ax_mat.flatten()):
...
ax_mat is a matrix of the axes. It's shape is nrows x ncols.

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))

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()

Another concise solution is:
// set up structure of plots
f, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(20,10))
// for plot 1
ax1.set_title('Title A')
ax1.plot(x, y)
// for plot 2
ax2.set_title('Title B')
ax2.plot(x, y)
// for plot 3
ax3.set_title('Title C')
ax3.plot(x,y)

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)

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)
fig, axes = plt.subplots(nrows=2, ncols=2, sharex=True, sharey=True)
ax1, ax2, ax3, ax4 = axes.flatten()
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 are 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))

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’s 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.
This answer is relevant to seaborn axes-level plots, which have the ax= parameter (e.g. sns.barplot(…, ax=ax[0]).
seaborn is a high-level API for matplotlib. See Figure-level vs. axes-level functions and seaborn is not plotting within defined subplots
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)
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])
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])
Index the data and axes
for i in range(len(x_data)):
axes[i].plot(x_data[i], y_data[i])
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
An option is to assign each axes to a variable, fig, (ax1, ax2, ax3) = plt.subplots(1, 3). However, as written, this only works in cases with either nrows=1 or ncols=1. This is based on the shape of the array returned by plt.subplots, and quickly becomes cumbersome.
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2) for a 2 x 2 array.
This option is most useful for two subplots (e.g.: fig, (ax1, ax2) = plt.subplots(1, 2) or fig, (ax1, ax2) = plt.subplots(2, 1)). For more subplots, it's more efficient to flatten and iterate through the array of axes.

You could use the following:
import numpy as np
import matplotlib.pyplot as plt
fig, _ = plt.subplots(nrows=2, ncols=2)
for i, ax in enumerate(fig.axes):
ax.plot(np.sin(np.linspace(0,2*np.pi,100) + np.pi/2*i))
Or alternatively, using the second variable that plt.subplot returns:
fig, ax_mat = plt.subplots(nrows=2, ncols=2)
for i, ax in enumerate(ax_mat.flatten()):
...
ax_mat is a matrix of the axes. It's shape is nrows x ncols.

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))

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()

Another concise solution is:
// set up structure of plots
f, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(20,10))
// for plot 1
ax1.set_title('Title A')
ax1.plot(x, y)
// for plot 2
ax2.set_title('Title B')
ax2.plot(x, y)
// for plot 3
ax3.set_title('Title C')
ax3.plot(x,y)