I'm trying to make a combined legend in a Jupiter Notebook. When I try various codes from examples, I get an empty legend. The examples work fine copied, but something goes wrong when I implement it into my own code. Any ideas?
Result:
Code:
fig, (ax1, ax2, ax3) = plt.subplots(3, 1, sharex=True, figsize=(15,10))
l1 = ax1.plot(time[18206:18226],tpm2[18206:18226], 'r', label='Chilbolton 2')
ax1.set_title('Difference in Hydrometeor Count Per Minute Over Time')
ax1.set_ylim([0,14000])
ax1.grid(b=True, which='major', color='k', linestyle='--', alpha=0.5)
l2 = ax2.plot(time[18206:18226],tpm1[18206:18226], 'b', label='Chilbolton 2')
ax2.set_ylim([0,14000])
ax2.grid(b=True, which='major', color='k', linestyle='--', alpha=0.5)
l3 = ax3.plot(time[18206:18226],diff[18206:18226], 'k', label='D.P.M.')
ax3.plot(time[18206:18226],np.zeros(20),'k--')
ax3.set_xlabel('Time (10th February to 29th April)')
ax3.set_ylim([-3000,3000])
ax3.grid(b=True, which='major', color='k', linestyle='--', alpha=0.5)
#plt.legend( handles=[l1, l2, l3], labels=['l1','l2','l3'],loc="upper left", bbox_to_anchor=[0, 1],
# ncol=2, shadow=True, title="Legend", fancybox=True)
fig.legend((l1, l2, l3), ('Line 1', 'Line 2', 'Line 3'), 'upper left')
# ('Chilbolton 2','Chilbolton 2','D.P.M.'), loc = (0.5, 0), ncol=1 )
plt.ylabel('Hydrometeor Count (#)')
# Fine-tune figure; make subplots close to each other and hide x ticks for
# all but bottom plot.
#f.subplots_adjust(hspace=0)
plt.setp([a.get_xticklabels() for a in f.axes[-1:]], rotation=90, visible=True)
plt.show()
ax.plot() returns a list of line artists, even if you are only plotting just one line. So when you write l1 = ax1.plot(...), a list of length-1 is assigned to l1. Ditto for l2 and l3. This causes a problem for fig.legend(), which needs just the line artist objects.
You can fix this problem in a number of ways. The most commonly-used method is syntax like:
l1, = ax1.plot(...
Inserting the comma assigns the only element of the returned list to l1. You could also do l1 = ax1.plot(...)[0]. Or, in your case, you could modify your legend call to fig.legend((l1[0],l2[0],l3[0]),...) .
So,
import maptlotlib.pyplot as plt
fig, (ax1, ax2, ax3) = plt.subplots(3, 1, sharex=True, figsize=(15,10))
l1, = ax1.plot([0,1],[0,14000])
ax1.set_title('Difference in Hydrometeor Count Per Minute Over Time')
ax1.set_ylim([0,14000])
ax1.grid(b=True, which='major', color='k', linestyle='--', alpha=0.5)
l2, = ax2.plot([0,1],[0,14000])
ax2.set_ylim([0,14000])
ax2.grid(b=True, which='major', color='k', linestyle='--', alpha=0.5)
l3, = ax3.plot([0,1],[-3000,3000])
ax3.plot(time[18206:18226],np.zeros(20),'k--')
ax3.set_xlabel('Time (10th February to 29th April)')
ax3.set_ylim([-3000,3000])
ax3.grid(b=True, which='major', color='k', linestyle='--', alpha=0.5)
fig.legend((l1, l2, l3), ('Line 1', 'Line 2', 'Line 3'), 'upper left')
As a workaround, you could create your own custom legend using Patch:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import random
tx = range(20)
t1 = np.random.randint(0, 14000, 20)
t2 = np.random.randint(0, 14000, 20)
t3 = np.random.randint(-3000, 3000, 20)
labels = ['Chilbolton 2', 'Chilbolton 2', 'D.P.M.']
fig, (ax1, ax2, ax3) = plt.subplots(3, 1, sharex=True, figsize=(15,10))
l1 = ax1.plot(tx, t1, 'r', label=labels[0])
ax1.set_title('Difference in Hydrometeor Count Per Minute Over Time')
ax1.set_ylim([0,14000])
ax1.grid(b=True, which='major', color='k', linestyle='--', alpha=0.5)
l2 = ax2.plot(tx,t2, 'b', label=labels[1])
ax2.set_ylim([0,14000])
ax2.grid(b=True, which='major', color='k', linestyle='--', alpha=0.5)
l3 = ax3.plot(tx,t3, 'k', label=labels[2])
ax3.plot(tx,np.zeros(20),'k--')
ax3.set_xlabel('Time (10th February to 29th April)')
ax3.set_ylim([-3000,3000])
ax3.grid(b=True, which='major', color='k', linestyle='--', alpha=0.5)
# Create custom legend
leg1 = mpatches.Patch(color='r')
leg2 = mpatches.Patch(color='b')
leg3 = mpatches.Patch(color='k')
fig.legend(handles=[leg1, leg2, leg3], labels=labels, loc="upper left")
plt.ylabel('Hydrometeor Count (#)')
# Fine-tune figure; make subplots close to each other and hide x ticks for
# all but bottom plot.
#f.subplots_adjust(hspace=0)
plt.setp([a.get_xticklabels() for a in fig.axes[-1:]], rotation=90, visible=True)
plt.show()
Giving you:
Related
I have the following code for a Matplotlib plot:
import pandas as pd
from matplotlib import pyplot as plt
columns = ['Price']
price_values = [[4.2],
[4.1],
[4],
[3.8],
[3.9],
[4.2],
[4.5],
[4.8],
[5.2],
[5.2],
[5.2],
[5.6],
[5.2],
[5.1],
[5.3],
[6],
[6.2],
[6.3],
[6.2],
[6],
[5.5] ,
[5.2],
[4.8],
[4.6]]
price_data = pd.DataFrame(price_values, index=range(0, 24), columns=columns)
fig = plt.figure(linewidth=1, figsize=(9, 5))
ax=plt.gca()
for column,color in zip(price_data.columns,['gold']):
ax.fill_between(
x=price_data.index,
y1=price_data[column],
y2=0,
label=column,
color=color,
alpha=.5,
step='post',
linewidth=5,
)
ax.set_facecolor("white")
ax.set_xlabel("Time of day", fontsize = 14, labelpad=8)
ax.set_ylabel("Price [Cent/kWh]", fontsize = 14,labelpad=8)
ax.set_xlim(0, 23)
ax.set_ylim(0, 8)
plt.xticks(price_data.index, labels=[f'{h:02d}:00' for h in price_data.index], rotation=90)
plt.tight_layout()
hours = list(range(25))
labels = [f'{h:02d}:00' for h in hours]
ax.tick_params(axis='both', which='major', labelsize=14)
ax.legend(loc='center left', bbox_to_anchor=(0.15, 1.07), fontsize = 14, ncol=3)
plt.savefig('Diagramm.png', edgecolor='black', dpi=400, bbox_inches='tight')
plt.show()
Now I would like to remove the area under the curve, sucht that I can only see the curve. I tried to use
plt.bar(fill=False)
but I get the error "TypeError: bar() missing 2 required positional arguments: 'x' and 'height'". Any suggestions how I can do that
Using fill_between and later remove the area under the curve seems like a pretty convoluted way to plot your data. But you could just set y2=price_data[column]:
price_data = pd.DataFrame(price_values, index=range(0, 24), columns=columns)
fig = plt.figure(linewidth=1, figsize=(9, 5))
ax=plt.gca()
for column,color in zip(price_data.columns,['gold']):
ax.fill_between(
x=price_data.index,
y1=price_data[column],
y2=price_data[column],
label=column,
color=color,
alpha=.5,
step='post',
linewidth=5,
)
ax.set_facecolor("white")
ax.set_xlabel("Time of day", fontsize = 14, labelpad=8)
ax.set_ylabel("Price [Cent/kWh]", fontsize = 14,labelpad=8)
ax.set_xlim(0, 23)
ax.set_ylim(0, 8)
plt.xticks(price_data.index, labels=[f'{h:02d}:00' for h in price_data.index], rotation=90)
plt.tight_layout()
hours = list(range(25))
labels = [f'{h:02d}:00' for h in hours]
ax.tick_params(axis='both', which='major', labelsize=14)
ax.legend(loc='center left', bbox_to_anchor=(0.15, 1.07), fontsize = 14, ncol=3)
plt.savefig('Diagramm.png', edgecolor='black', dpi=400, bbox_inches='tight')
plt.show()
Output:
Edit: #JohanC rightfully noted that the last value barely appears on the plot. One way to avoid this would be to replace your loop with the following:
price_data.plot(ax=ax, color="gold", drawstyle="steps-mid", linewidth=2)
Note that your solution is missing the last price value, the one between 23 and 24 h. You'll need to repeat the last value for this to work. To draw a step plot, the easiest way is ax.step.
The following example code changes the values for the first and the last value to make them stand out more.
from matplotlib import pyplot as plt
import pandas as pd
columns = ['Price']
price_values = [[1.2], [4.1], [4], [3.8], [3.9], [4.2], [4.5], [4.8], [5.2], [5.2], [5.2], [5.6], [5.2], [5.1], [5.3], [6], [6.2], [6.3], [6.2], [6], [5.5], [5.2], [4.8], [1.6]]
price_data = pd.DataFrame(price_values, index=range(0, 24), columns=columns)
fig, ax = plt.subplots(figsize=(9, 5))
for column, color in zip(price_data.columns, ['gold']):
ax.step(x=range(len(price_data) + 1), y=list(price_data[column]) + list(price_data[column][-1:]),
where='post', color=color, linewidth=5, label=column)
ax.set_xlabel("Time of day", fontsize=14, labelpad=8)
ax.set_ylabel("Price [Cent/kWh]", fontsize=14, labelpad=8)
ax.set_xlim(0, 24)
ax.set_ylim(0, 8)
xs = range(len(price_data) + 1)
ax.set_xticks(xs, labels=[f'{h:02d}:00' for h in xs], rotation=90)
ax.tick_params(axis='both', which='major', labelsize=14)
ax.legend(loc='lower left', bbox_to_anchor=(0.15, 1.01), fontsize=14, ncol=3)
plt.tight_layout()
plt.savefig('Diagramm.png', edgecolor='black', dpi=400, bbox_inches='tight')
plt.show()
Alternatively, you could use Seaborn's histplot, which has a step option (element='step', fill=False), but that works easiest if you'd let seaborn do the counting for the histogram. You could use sns.histplot's weights= parameter to fill in the values, e.g.
sns.histplot(x=price_data.index, weights=price_data[column].values, bins=len(price_data), binrange=(0, 24),
element='step', fill=False, color=color, linewidth=5, label=column, ax=ax)
I am outputting two scatter plots, but I want both graphs to be next to each other side by side.
When I use plt.subplots, my ax1 and ax2 aren't being recognized? How can I make the bottom two scatterplots next to each other? Whenever I use plt.subplots it just creates empty graphs.
# Scatter plots.
ax1 = df_Baker.plot(kind='scatter', x='HS_GPA', y='Course_Grade', color='black', alpha=0.5, figsize=(10, 7))
df_Muriel.plot(kind='scatter', x='HS_GPA', y='Course_Grade', color='black', alpha=0.5, figsize=(10, 7), ax=ax1)
df_Tanner.plot(kind='scatter', x='HS_GPA', y='Course_Grade', color='black', alpha=0.5, figsize=(10, 7), ax=ax1)
# regression lines
plt.plot(df_Baker.HS_GPA, Baker_fit[0] * df_Baker.HS_GPA + Baker_fit[1], color='darkblue', linewidth=2)
plt.plot(df_Tanner.HS_GPA, Tanner_fit[0] * df_Tanner.HS_GPA + Tanner_fit[1], color='deeppink', linewidth=2)
plt.plot(df_Muriel.HS_GPA, Muriel_fit[0] * df_Muriel.HS_GPA + Muriel_fit[1], color='deeppink', linewidth=2)
plt.legend(labels=['_h', '_hii', '_', '10 - 20','1 - 5'], title='Legend Test')
plt.title('BIO: Basic Concepts', size=24)
plt.xlabel('High school gpa', size=18)
plt.ylabel('cousre Grade', size=18);
#-----------------------------------------------------------------------------
# Scatter plots.
ax2 = df_Baker.plot(kind='scatter', x='HS_GPA', y='Course_Grade', color='black', alpha=0.5, figsize=(6, 3))
df_Muriel.plot(kind='scatter', x='HS_GPA', y='Course_Grade', color='black', alpha=0.5, figsize=(6, 3), ax=ax2)
df_Tanner.plot(kind='scatter', x='HS_GPA', y='Course_Grade', color='black', alpha=0.5, figsize=(6, 3), ax=ax2)
# regression lines
plt.plot(df_Baker.HS_GPA, Baker_fit[0] * df_Baker.HS_GPA + Baker_fit[1], color='black', linewidth=2)
plt.plot(df_Tanner.HS_GPA, Tanner_fit[0] * df_Tanner.HS_GPA + Tanner_fit[1], color='black', linewidth=2)
plt.plot(df_Muriel.HS_GPA, Muriel_fit[0] * df_Muriel.HS_GPA + Muriel_fit[1], color='black', linewidth=2)
plt.legend(labels=['_h', '_hii', '_', '10 - 20','1 - 5'], title='Legend Test')
plt.title('BIO: Basic Concepts', size=24)
plt.xlabel('High school gpa', size=18)
plt.ylabel('cousre Grade', size=18);
Output graphs so far
In this line:
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 3))
your create the two axes objects and attach the names ax1 and ax2 to them.
Later, in
ax1 = df_Baker.plot(kind='scatter', x='HS_GPA', y='Course_Grade', color='black', alpha=0.5, figsize=(10, 7))
(and similarely in the line for ax2), your create new axis objects, and assign the names ax1 and ax2 to them.
I seems that this is not what you want. Rather, I guess you want to use the previously generated axes objects in the calls to df.Bakker.plot(). You can achieve this by using the ax= keyword:
df_Baker.plot(kind='scatter', x='HS_GPA', y='Course_Grade', color='black', alpha=0.5, figsize=(10, 7), ax=ax1)
You will also have to change the plt.plot(...) calls to ax1.plot(...) or ax2.plot(...), and similar for the functions plt.xlabel, plt.ylabel, plt.legend.
I would suggest to read the Blog post https://matplotlib.org/matplotblog/posts/pyplot-vs-object-oriented-interface/ on the difference between the Pyplot vs. Object Oriented Interface to Matplotlib, and you can also have a look at the examples referenced in https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html
I have two grouped bar charts of value changes between cases for two systems among 3 groups as below. Here I was able to add data labels to the bars using the code below (figure produced attached)
What I want to do is on top (or bottom for the negative change in value cases), add an extra data label that captures the % of the value changes as shown in the second figure with the 33% in red (I edited it in by hands). How do I achieve that from this code? Thank you in advance.
import matplotlib.pyplot as plt
import numpy as np
value_case0_system1 = np.array([30, 20, 40])
value_case1_system1 = np.array([20, 25, 50])
value_case2_system1 = np.array([10, 35, 45])
value_case1_system2 = np.array([60, 50, 40])
value_case2_system2 = np.array([50, 40, 55])
change_case0_to_case1_system1 = np.subtract(value_case1_system1,value_case0_system1)
change_case1_to_case2_system1 = np.subtract(value_case2_system1,value_case1_system1)
change_case1_to_case2_system2 = np.subtract(value_case2_system2,value_case1_system2)
fig, (ax0, ax1) = plt.subplots(nrows=1, ncols=2, sharey=True, figsize=(18,10))
labels = ['Group 1', 'Group 2', 'Group 3']
x = np.arange(len(labels))
ax0.set_ylabel('Change in Values', va='center', rotation='vertical',
fontsize=17, fontweight='bold')
width = 0.28
ax0.set_title('System 1', fontsize=17, fontweight='bold')
ax0.axhline(y=0, color='black', ls=':', lw=2)
ax0.set_xticks(x)
ax0.set_xticklabels(labels,fontsize=15)
rects1 = ax0.bar(x-width/2, change_case0_to_case1_system1, width, label='Case 0 to Case 1',
color='#292929', edgecolor='black', linewidth=1)
rects2 = ax0.bar(x+width/2, change_case1_to_case2_system1, width, label='Case 1 to Case 2',
color='#7f6d5f', edgecolor='black', linewidth=1)
ax0.bar_label(rects1, padding=3, fontsize=11)
ax0.bar_label(rects2, padding=3, fontsize=11)
leg = ax0.legend(loc="upper left", bbox_to_anchor=[0, 1],
ncol=1, fancybox=True)
ax0.legend(fontsize=15)
ax1.set_title('System 2', fontsize=17, fontweight='bold')
ax1.axhline(y=0, color='black', ls=':', lw=2)
ax1.set_xticks(x)
ax1.set_xticklabels(labels,fontsize=15)
rects3 = ax1.bar(x, change_case1_to_case2_system2, width, label='Case 1 to Case 2',
color='#7f6d5f', edgecolor='black', linewidth=1)
ax1.legend(shadow=True, fancybox=True)
ax1.bar_label(rects3, padding=3, fontsize=11)
leg = ax1.legend(loc="upper left", bbox_to_anchor=[0, 1],
ncol=1, fancybox=True)
ax1.legend(fontsize=15)
plt.tight_layout()
plt.show()
The code for the extra plot formatting has been left out, because it's not relevant for the answer. It can be added back, as per your requirements.
Each .bar_label colors the label globally, so unlike this answer, a second .bar_label needs to be added for the percent change, with a different color and padding
For each case-to-case, calculate the percent change, and set the string format in a list comprehension.
Set the list of string formatted calculations to the labels parameter in .bar_label.
Given the code in the OP, 6 lines of code need to be added, 3 for creating the list of labels, and 3 for adding the labels to the plot.
Additional resources:
matplotlib: Bar Label Demo
Adding value labels on a matplotlib bar chart
Tested in python 3.8.11, matplotlib 3.4.3
change_case0_to_case1_system1 = np.subtract(value_case1_system1, value_case0_system1)
# add list of string formatted percent change calculation
per_change_case0_to_case1_system1 = [f'({v}%)' for v in (change_case0_to_case1_system1 / value_case0_system1).round(2)*100]
change_case1_to_case2_system1 = np.subtract(value_case2_system1, value_case1_system1)
# add list of string formatted percent change calculation
per_change_case1_to_case2_system1 = [f'({v}%)' for v in (change_case1_to_case2_system1 / value_case1_system1).round(2)*100]
change_case1_to_case2_system2 = np.subtract(value_case2_system2, value_case1_system2)
# add list of string formatted percent change calculation
per_case1_to_case2_system2 = [f'({v}%)' for v in (change_case1_to_case2_system2 / value_case1_system2).round(2)*100]
fig, (ax0, ax1) = plt.subplots(nrows=1, ncols=2, sharey=True, figsize=(18,10))
labels = ['Group 1', 'Group 2', 'Group 3']
x = np.arange(len(labels))
width = 0.28
ax0.set_xticks(x)
ax0.set_xticklabels(labels, fontsize=15)
rects1 = ax0.bar(x-width/2, change_case0_to_case1_system1, width, label='Case 0 to Case 1', color='#292929', edgecolor='black', linewidth=1)
rects2 = ax0.bar(x+width/2, change_case1_to_case2_system1, width, label='Case 1 to Case 2', color='#7f6d5f', edgecolor='black', linewidth=1)
ax0.bar_label(rects1, padding=3, fontsize=11)
# add a second annotation with the string formatted labels
ax0.bar_label(rects1, labels=per_change_case0_to_case1_system1, padding=15, fontsize=11, color='red')
ax0.bar_label(rects2, padding=3, fontsize=11)
# add a second annotation with the string formatted labels
ax0.bar_label(rects2, labels=per_change_case1_to_case2_system1, padding=15, fontsize=11, color='red')
rects3 = ax1.bar(x, change_case1_to_case2_system2, width, label='Case 1 to Case 2', color='#7f6d5f', edgecolor='black', linewidth=1)
ax1.set_xticks(x)
ax1.set_xticklabels(labels,fontsize=15)
ax1.bar_label(rects3, padding=3, fontsize=11)
# add a second annotation with the string formatted labels
ax1.bar_label(rects3, labels=per_case1_to_case2_system2, padding=15, fontsize=11, color='red')
plt.tight_layout()
plt.show()
When plotting with twinx how can multiple subplots be used?
%pylab inline
import pandas as pd
import seaborn as sns; sns.set()
df = pd.DataFrame({'dt':['2020-01-01', '2020-01-02', '2020-01-03', '2020-01-04'], 'category':['a', 'b', 'a', 'b'], 'foo':[10, 15, 8, 13], 'bar':[12, 8, 5, 18]})
df['dt'] = pd.to_datetime(df['dt'])
ax = sns.lineplot(x='dt', y='foo', data=df, hue='category')
ax.set_ylabel('asdf', fontsize=28)
ax.plot([], '-g', label = 'other axis in legend')
plt.legend(fontsize='x-large')
handles, labels = ax.get_legend_handles_labels()
ax.legend(handles=handles[1:], labels=labels[1:], fontsize='large', loc='lower left')
plt.xticks(rotation=90, horizontalalignment='center', fontsize=28)
plt.xlabel('')
plt.yticks(fontsize=16)
ax2 = ax.twinx()
ax2 = sns.lineplot(x='dt', y='bar', data=df, ax=ax2, color='green')
plt.yticks(fontsize=16)
ax2.plot([], '-g', label = 'other axis in legend')
ax2.set_ylabel('ratio', fontsize=28)
plt.axvline(x=np.datetime64('2020-01-02'),color='k', linestyle='--', lw=4)
plt.text(x=np.datetime64('2020-01-02'), y=10, s=' foo-the-bar ', fontsize=28, horizontalalignment='left')
plt.show()
d2 = pd.DataFrame({'dt':['2020-01-01', '2020-01-02', '2020-01-03', '2020-01-04'], 'category':['a', 'b', 'a', 'b'],'foo':[11, 16, 8, 14], 'bar':[11, 7, 4, 17]})
d2['dt'] = pd.to_datetime(d2['dt'])
ax = sns.lineplot(x='dt', y='foo', data=d2, hue='category')
ax.set_ylabel('something else', fontsize=28)
ax.plot([], '-g', label = 'other axis in legend')
plt.legend(fontsize='x-large')
handles, labels = ax.get_legend_handles_labels()
ax.legend(handles=handles[1:], labels=labels[1:], fontsize='large', loc='lower left')
plt.xticks(rotation=90, horizontalalignment='center', fontsize=28)
plt.xlabel('')
plt.yticks(fontsize=16)
plt.axvline(x=np.datetime64('2020-01-02'),color='k', linestyle='--', lw=4)
plt.text(x=np.datetime64('2020-01-02'), y=10, s=' foo-the-bar ', fontsize=28, horizontalalignment='left')
ax2 = ax.twinx()
ax2 = sns.lineplot(x='dt', y='bar', data=d2, ax=ax2, color='green')
plt.yticks(fontsize=16)
ax2.plot([], '-g', label = 'other axis in legend')
ax2.set_ylabel('ratio', fontsize=28)
plt.show()
It is more or less nice. But when adding in subplots to combine multiple measurements into a single figure sharing the x-axis (i.e. saving space and writing the dates over and over) the following fails to work and completely distorts the plot
ax0 = plt.subplot(211)
ax2 = ax0.twinx()
ax3 = plt.subplot(212)
ax4 = ax3.twinx()
ax = sns.lineplot(x='dt', y='foo', data=df, hue='category', ax=ax0)
ax.set_ylabel('asdf', fontsize=28)
ax.plot([], '-g', label = 'other axis in legend')
plt.legend(fontsize='x-large')
handles, labels = ax.get_legend_handles_labels()
ax.legend(handles=handles[1:], labels=labels[1:], fontsize='large', loc='lower left')
plt.xticks(rotation=90, horizontalalignment='center', fontsize=28)
plt.xlabel('')
plt.yticks(fontsize=16)
plt.axvline(x=np.datetime64('2020-01-02'),color='k', linestyle='--', lw=4)
plt.text(x=np.datetime64('2020-01-02'), y=10, s=' foo-the-bar ', fontsize=28, horizontalalignment='left')
#ax2 = ax.twinx()
ax2 = sns.lineplot(x='dt', y='bar', data=df, ax=ax2, color='green')
plt.yticks(fontsize=16)
ax2.plot([], '-g', label = 'other axis in legend')
ax2.set_ylabel('ratio', fontsize=28)
plt.show()
# TODO second plot is missing
Problems always arise when trying to mix the object-oriented syntax and the pyplot interface.
pyplot functions (plt.XXX) only affect the current axes (generally the latest created, in your case ax4). When you have several axes, it is generally much better to use the OO functions so that there is no ambiguity about which axes you are working on.
Additionally, you might want to through a tight_layout() at the end of your code to automatically adjust the position of your axes to give enough room for your labels
plt.figure()
ax0 = plt.subplot(211)
ax2 = ax0.twinx()
ax3 = plt.subplot(212)
ax4 = ax3.twinx()
ax = sns.lineplot(x='dt', y='foo', data=df, hue='category', ax=ax0)
ax.set_ylabel('asdf', fontsize=28)
ax.plot([], '-g', label = 'other axis in legend')
handles, labels = ax.get_legend_handles_labels()
ax.legend(handles=handles[1:], labels=labels[1:], fontsize='large', loc='lower left')
ax.set_xticklabels(ax.get_xticklabels(), rotation=90, horizontalalignment='center', fontsize=28)
ax.set_xlabel('')
ax.tick_params(axis='y', labelsize=16)
ax.axvline(x=np.datetime64('2020-01-02'),color='k', linestyle='--', lw=4)
ax.text(x=np.datetime64('2020-01-02'), y=10, s=' foo-the-bar ', fontsize=28, horizontalalignment='left')
#ax2 = ax.twinx()
ax2 = sns.lineplot(x='dt', y='bar', data=df, ax=ax2, color='green')
ax2.tick_params(axis='y', labelsize=16)
ax2.plot([], '-g', label = 'other axis in legend')
ax2.set_ylabel('ratio', fontsize=28)
plt.tight_layout()
plt.show()
I have the output of a group-by representing a sum of dates per week.
Date
2008-10-28 20.0
2008-11-04 25.0
2008-11-11 20.0
2008-11-18 40.0
2008-11-25 35.0
2008-12-02 35.0
2008-12-09 NaN
2008-12-16 NaN
2008-12-23 NaN
2008-12-30 NaN
Freq: W-TUE, Name: Count, dtype: float64
I'm trying to plot these using plot_date
fig, ax = plt.subplots(figsize=(2, 4))
# ax = plt.gca()
line = ax.plot_date(a.index, a.values, '.', label='a', alpha=0.5, linewidth=1)
ax.tick_params('y', colors='k')
ax.set_xlabel('Date')
ax.set_ylabel('Frequency')
ax.set_title('Daily Games')
ax.tick_params('y', colors='k')
ax.grid(b=True, which='major', color='w', linewidth=1.0)
ax.grid(b=True, which='minor', color='w', linewidth=0.5)
ax.yaxis.grid(True)
ax.get_xaxis().set_minor_locator(mpl.ticker.AutoMinorLocator())
ax.set_xticklabels(ax.xaxis.get_majorticklabels(),
rotation=70)
ax.set_xticklabels(ax.xaxis.get_minorticklabels(),
rotation=70)
plt.xticks(rotation=70)
plt.show()
This is producing a graph like so:
I've tried all manner of rearranging but I can't get both minor and major labels for the date to plot.
I'd like to have each month labelled at 70 degrees. How can I adjust what I have to do so?
You could use the AutoDateLocator() as follows:
import seaborn as sns
import matplotlib as mpl
import matplotlib.pyplot as plt
import pandas as pd
sns.set()
a = pd.DataFrame([
('2008-10-28', 20.0), ('2008-11-04', 25.0), ('2008-11-11', 20.0),
('2008-11-18', 40.0), ('2008-11-25', 35.0), ('2008-12-02', 35.0)], columns=['Date', 'Frequency'])
a['Date'] = pd.to_datetime(a['Date'], format='%Y-%m-%d')
fig, ax = plt.subplots(figsize=(5, 5))
# ax = plt.gca()
line = ax.plot_date(a.Date, a.Frequency, '.', label='a', alpha=0.5, linewidth=1)
ax.tick_params('y', colors='k')
ax.set_xlabel('Date')
ax.set_ylabel('Frequency')
ax.set_title('Daily Games')
ax.tick_params('y', colors='k')
ax.grid(b=True, which='major', color='w', linewidth=1.0)
ax.grid(b=True, which='minor', color='w', linewidth=0.5)
ax.yaxis.grid(True)
xtick_locator = mpl.dates.AutoDateLocator()
xtick_formatter = mpl.dates.AutoDateFormatter(xtick_locator)
ax.xaxis.set_major_locator(xtick_locator)
ax.xaxis.set_major_formatter(xtick_formatter)
fig.subplots_adjust(bottom=0.24)
plt.xticks(rotation=70)
plt.show()
This would then display as:
From #MartinEvans suggestion to use AutoDateLocator() I looked up more of the matplotlib documentation and found matplotlib.dates.MonthLocator along with the WeekdayLocator. This allowed tuning the major and minor xticks to change the format and appearance as required.
I then used this answer to set their rotation.
fig, ax = plt.subplots(figsize=(2, 4))
# ax = plt.gca()
line = ax.plot_date(a.Date, a.Frequency, '.', label='a', alpha=0.5, linewidth=1)
ax.tick_params('y', colors='k')
# ax.xticks(rotation=70)
ax.set_xlabel('Date')
# ax.xlabel('Date')
ax.set_ylabel('Frequency')
ax.set_title('Daily Games')
ax.tick_params('y', colors='k')
ax.grid(b=True, which='major', color='w', linewidth=1.0)
ax.grid(b=True, which='minor', color='w', linewidth=0.5)
ax.yaxis.grid(True)
xtick_locator = mpl.dates.MonthLocator(interval=1)
xtick_formatter = mpl.dates.AutoDateFormatter(xtick_locator)
ax.xaxis.set_major_locator(xtick_locator)
ax.xaxis.set_major_formatter(xtick_formatter)
xtick_locator = mpl.dates.WeekdayLocator(byweekday=3)
xtick_formatter = mpl.dates.AutoDateFormatter(xtick_locator)
ax.xaxis.set_minor_locator(xtick_locator)
ax.xaxis.set_minor_formatter(xtick_formatter)
plt.setp(ax.xaxis.get_minorticklabels(), rotation=90, size=10)
plt.setp(ax.xaxis.get_majorticklabels(), rotation=90, size=7)
fig.subplots_adjust(bottom=0.24)
plt.show()