Develop Reference

How to create multiple legends?

fig, ax = plt.subplots(figsize = (10,7))
sns.lineplot(data = dearborn_1111_groupby,
x = 'Date',
y = 'Rent',
hue = 'generic_type',
palette = 'husl',
ax = ax).set_title('1111 Dearborn Median In Place Rents (2018 - 2022)')
sns.lineplot(data = dearborn_1111_groupby,
x = 'Date',
y = 'Rent_apartlist',
color = 'black',
ax = ax)
ax.legend(bbox_to_anchor = (1.15, 0.95), title = 'Unit Type')
plt.show()
line plot
I'm trying to add a legend containing the black line. However the black line is a separate lineplot. How Do I include the black line into the existing legend or a separate legend?

You can to add a label to the line, via sns.lineplot(..., label=...).
Note that when using bbox_to_anchor for the legend, you also need to set loc=.... By default, loc='best', which change the anchor point depending on small changes in the plot or its parameters. plt.tight_layout() fits the legend and the labels nicely into the plot figure.
Here is some example code using Seaborn's flights dataset.
import matplotlib.pyplot as plt
import seaborn as sns
flights = sns.load_dataset('flights')
fig, ax = plt.subplots(figsize=(12, 5))
sns.lineplot(data=flights,
x='year',
y='passengers',
hue='month',
palette='husl',
ax=ax)
sns.lineplot(data=flights,
x='year',
y='passengers',
color='black',
label='Black Line',
ax=ax)
ax.legend(bbox_to_anchor=(1.02, 0.95), loc="upper left", title='Unit Type')
ax.margins(x=0)
plt.tight_layout()
plt.show()

How to loop over axis plots in python

I am trying to create an axis plot. I was trying to loop over it as I am plotting the same variable for two different categories. Currently, I have written code two times but I am looking for a smarter way with looping, if possible. Any other suggestion will also be helpful.
zone = ['AB','CD']
plt.style.use('default')
fig,(ax0,ax1) = plt.subplots(2,1, figsize = (18,18), sharex = False)
i = 0
while i < len(zone):
if zone[i] == zone[0]:
ax0.plot(df0['datetime'], df0['pnl1'], color='k', linewidth=1, label ='PnL1')
ax0.plot(df0['datetime'], df0['pnl2'], color='m', linewidth=1, label ='PnL2')
ax00 = ax0.twinx()
ax00.bar(df0['datetime'], df0['qty'], width = 1/96, color='g', align = 'edge', alpha = 0.5, label ='Qty')
elif zone[i] == zone[1]:
ax1.plot(df0['datetime'], df0['pnl1'], color='k', linewidth=1, label ='PnL1')
ax1.plot(df0['datetime'], df0['pnl2'], color='m', linewidth=1, label ='PnL2')
ax01 = ax1.twinx()
ax01.bar(df0['datetime'], df0['hedge'], width = 1/96, color='g', align = 'edge', alpha = 0.5, label ='Qty')
i = i + 1
I want to check if something like below can be done with axis plots or not.
zone = ['AB','CD']
plt.style.use('default')
fig,(ax0,ax1) = plt.subplots(2,1, figsize = (18,18), sharex = False)
i = 0
while i < len(zone):
ax{''}.format(i).plot(df0['datetime'], df0['pnl1'], color='k', linewidth=1, label ='PnL1')
ax{''}.format(i).plot(df0['datetime'], df0['pnl2'], color='m', linewidth=1, label ='PnL2')
ax0{''}.format(i) = ax{''}.format(i).twinx()
ax0{''}.format(i).bar(df0['datetime'], df0['qty'], width = 1/96, color='g', align = 'edge', alpha = 0.5, label ='Qty')
It did not work for me. Any leads to execute axis plot with loop will be helpful.

Here are some ways:
Simply loop over the list of axes
import matplotlib.pyplot as plt
import numpy as np
fig,axes = plt.subplots(2,1)
x = np.linspace(0,5,21)
for ax in axes:
ax.plot(x,np.sin(x))
plt.show()
Works also with index:
for i in range(len(axes)):
axes[i].plot(x,np.sin(x))
For a grid of plot you can use a similar approach:
import matplotlib.pyplot as plt
import numpy as np
fig,axes = plt.subplots(2,2)
x = np.linspace(0,5,21)
for i in range(len(axes)):
for j in range(len(axes[0])):
axes[i][j].plot(x,np.sin(x))
plt.show()
If you don't like double-loops, you can flatten the array with np.ravel()
fig,axes = plt.subplots(2,2)
x = np.linspace(0,5,21)
for ax in np.ravel(axes):
ax.plot(x,np.sin(x))
plt.show()

Seaborn plot with second y axis

i wanted to know how to make a plot with two y-axis so that my plot that looks like this :
to something more like this by adding another y-axis :
i'm only using this line of code from my plot in order to get the top 10 EngineVersions from my data frame :
sns.countplot(x='EngineVersion', data=train, order=train.EngineVersion.value_counts().iloc[:10].index);

I think you are looking for something like:
import matplotlib.pyplot as plt
x = [1,2,3,4,5]
y = [1000,2000,500,8000,3000]
y1 = [1050,3000,2000,4000,6000]
fig, ax1 = plt.subplots()
ax2 = ax1.twinx()
ax1.bar(x, y)
ax2.plot(x, y1, 'o-', color="red" )
ax1.set_xlabel('X data')
ax1.set_ylabel('Counts', color='g')
ax2.set_ylabel('Detection Rates', color='b')
plt.show()
Output:

#gdubs If you want to do this with Seaborn's library, this code set up worked for me. Instead of setting the ax assignment "outside" of the plot function in matplotlib, you do it "inside" of the plot function in Seaborn, where ax is the variable that stores the plot.
import seaborn as sns # Calls in seaborn
# These lines generate the data to be plotted
x = [1,2,3,4,5]
y = [1000,2000,500,8000,3000]
y1 = [1050,3000,2000,4000,6000]
fig, ax1 = plt.subplots() # initializes figure and plots
ax2 = ax1.twinx() # applies twinx to ax2, which is the second y axis.
sns.barplot(x = x, y = y, ax = ax1, color = 'blue') # plots the first set of data, and sets it to ax1.
sns.lineplot(x = x, y = y1, marker = 'o', color = 'red', ax = ax2) # plots the second set, and sets to ax2.
# these lines add the annotations for the plot.
ax1.set_xlabel('X data')
ax1.set_ylabel('Counts', color='g')
ax2.set_ylabel('Detection Rates', color='b')
plt.show(); # shows the plot.
Output:
Seaborn output example

You could try this code to obtain a very similar image to what you originally wanted.
import seaborn as sb
from matplotlib.lines import Line2D
from matplotlib.patches import Rectangle
x = ['1.1','1.2','1.2.1','2.0','2.1(beta)']
y = [1000,2000,500,8000,3000]
y1 = [3,4,1,8,5]
g = sb.barplot(x=x, y=y, color='blue')
g2 = sb.lineplot(x=range(len(x)), y=y1, color='orange', marker='o', ax=g.axes.twinx())
g.set_xticklabels(g.get_xticklabels(), rotation=-30)
g.set_xlabel('EngineVersion')
g.set_ylabel('Counts')
g2.set_ylabel('Detections rate')
g.legend(handles=[Rectangle((0,0), 0, 0, color='blue', label='Nontouch device counts'), Line2D([], [], marker='o', color='orange', label='Detections rate for nontouch devices')], loc=(1.1,0.8))

How do I plot this using seaborn?

import matplotlib.pyplot as plt
import seaborn as sns
rankings_by_age = star_wars.groupby("Age").agg(np.mean).iloc[:,8:]
age_first = rankings_by_age.iloc[0, :].values
age_second = rankings_by_age.iloc[1, :].values
age_third = rankings_by_age.iloc[2, :].values
age_fourth = rankings_by_age.iloc[3, :].values
fig, ax = plt.subplots(figsize=(12, 9))
ind = np.arange(6)
width = 0.2
rects_1 = ax.bar(ind, age_first, width, color=(114/255,158/255,206/255),
alpha=.8)
rects_2 = ax.bar(ind+width, age_second, width, color=
(255/255,158/255,74/255), alpha=.8)
rects_3 = ax.bar(ind+2*width, age_third, width, color=
(103/255,191/255,92/255), alpha=.8)
rects_4 = ax.bar(ind+3*width, age_fourth, width, color=
(237/255,102/255,93/255), alpha=.8)
ax.set_title("Star Wars Film Rankings by Age")
ax.set_ylabel("Ranking")
ax.set_xticks(ind)
ax.set_xticklabels(titles, rotation=45)
ax.tick_params(top='off', right='off', left='off', bottom='off')
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.legend((rects_1[0], rects_2[0], rects_3[0], rects_4[0]), ('18-29', '30-
44', '45-60', '> 60'), title="Age")
plt.show()
I want to replicate this plot using seaborn, but I am not sure how to go about plotting multiple bars for each category. I understand how to do it using one age group at a time, but getting more than one bar per age group seems tricky. Any help would be appreciated.

Quoting the seaborn bar plot documentation, you can use the hue argument to determine which column of the dataframe the bars should be grouped by.
import seaborn.apionly as sns
import matplotlib.pyplot as plt
df = sns.load_dataset("tips")
ax = sns.barplot(data=df, x="day", y="total_bill", hue="sex")
plt.show()

How to set a secondary y-axis in Python

I'm currently trying to change the secondary y-axis values in a matplot graph to ymin = -1 and ymax = 2. I can't find anything on how to change the values though. I am using the secondary_y = True argument in .plot(), so I am not sure if changing the secondary y-axis values is possible for this. I've included my current code for creating the plot.
df.plot()
df.plot(secondary_y = "Market")

From your example code, it seems you're using Pandas built in ploting capabilities. One option to add a second layer is by using matplotlib directly like in the example "two_scales.py".
It uses
import matplotlib.pyplot as plt
fig, ax1 = plt.subplots()
ax1.plot(df["..."])
# ...
ax2 = ax1.twinx()
ax2.plot(df["Market"])
ax2.set_ylim([0, 5])
where you can change the y-limits.

Setting ylim on plot does not appear to work in the case of secondary_y, but I was able to workaround with this:
import pandas as pd
df = pd.DataFrame({'one': range(10), 'two': range(10, 20)})
ax = df['one'].plot()
ax2 = df['two'].plot(secondary_y=True)
ax2.set_ylim(-20, 50)
fig = ax.get_figure()
fig.savefig('test.png')

This is a solution for showing as much y-axes as data columns the dataframe has
colors = ['tab:blue',
'tab:orange',
'tab:green',
'tab:red',
'tab:purple',
'tab:brown',
'tab:pink',
'tab:gray',
'tab:olive',
'tab:cyan']
#X axe and first Y axe
fig, ax1 = plt.subplots()
x_label = str( dataFrame.columns[0] )
index = dataFrame[x_label]
ax1.set_xlabel(x_label)
ax1.set_xticklabels(dataFrame[x_label], rotation=45, ha="right")
firstYLabel = str( dataFrame.columns[1] )
ax1.set_ylabel(firstYLabel, color = colors[0])
ax1.plot(index, dataFrame[firstYLabel], color = colors[0])
ax1.tick_params(axis='y', labelcolor = colors[0])
#Creates subplots with independet y-Axes
axS =[]
def newTwix(label, ax1, index, dataFrame):
print(label)
actualPos = len(axS)
axS.append(ax1.twinx())
axS[actualPos].set_ylabel(label, color = colors[actualPos%10 + 1])
axS[actualPos].plot(index, dataFrame[label], color=colors[actualPos%10 + 1])
axS[actualPos].tick_params(axis='y', labelcolor=colors[actualPos%10 + 1])
identation = 0.075 #would improve with a dynamic solution
p = 1 + identation
for i in range(2,len(dataFrame.columns)):
newTwix(str(dataFrame.columns[i]), ax1, index, dataFrame)
if (len(axS) == 1):
axS[len(axS)-1].spines.right.set_position(("axes", p))
else:
p = int((p + identation)*1000)/1000
axS[len(axS)-1].spines.right.set_position(("axes", p))
fig.tight_layout() # otherwise the right y-label is slightly clipped
plt.subplots_adjust(left=0.04, right=0.674, bottom=0.1)
mng = plt.get_current_fig_manager()
mng.full_screen_toggle()
plt.show()
multiple y-axes with independent scales