I have two lists of numbers:
list_1 = [1,2,3,4,5,1,2,3,4,5,6,3,4,5,1,3,4,5,4,5,6,8,9,12,3,3,3,4,3,4,5,6,5,6,7,8,9,5,3,2,4,5,2,3,4,11,13,4,5,3,5,6,7,11,13,3,4,5,4,5]
list_2 = [4,5,6,7,8,9,4,5,6,7,8,9,5,6,7,8,9,6,7,8,9,12,15,16,11,12,7,8,9,7,8,9,5,6,7,8,9,7,8,9,8,9,11,10,12,16,7,8,9,10,10,8,9,8,9,10,10,10,15,16,19]
I want to plot two histograms with Python and Matplotlib so that i get result like this:
I need the line histogram, and I do not know how to plot it. I know how to make bar histogram, but I want to have line histogram so that I can see intersection of this two histograms.
plt.hist(list_1, bins = 10)
plt.hist(list_2, bins = 10)
plt.show()
The Seaborn Library can help you:
import matplotlib.pyplot as plt
import seaborn as sns
# Your Data
list_1 = [1,2,3,4,5,1,2,3,4,5,6,3,4,5,1,3,4,5,4,5,6,8,9,12,3,3,3,4,3,4,5,6,5,6,7,8,9,5,3,2,4,5,2,3,4,11,13,4,5,3,5,6,7,11,13,3,4,5,4,5]
list_2 = [4,5,6,7,8,9,4,5,6,7,8,9,5,6,7,8,9,6,7,8,9,12,15,16,11,12,7,8,9,7,8,9,5,6,7,8,9,7,8,9,8,9,11,10,12,16,7,8,9,10,10,8,9,8,9,10,10,10,15,16,19]
# Creating a displot
fig = plt.figure(figsize=(15,5))
ax = fig.add_subplot(111)
sns.distplot(list_1, kde=True, ax = ax, hist=False, bins = 10)
sns.distplot(list_2, kde=True, ax = ax, hist=False, bins = 10)
plt.show()
Related
I'm trying to change the labels in a violin plot on Seaborn. I wanna change the NU_NOTA_CN, NU_NOTA_CH, NU_NOTA_LC, NU_NOTA_MT and NU_NOTA_REDAÇÃO, and TP_ESCOLA, and the 2 and 3.
import pandas as pd
import numpy as np
import seaborn as sns
fig_dims = (10, 8)
fig, ax = plt.subplots(figsize=fig_dims)
sns.boxplot(x="DISCIPLINA", y="NOTA", hue="TP_ESCOLA", data=publica_privada_pivot)
plt.show()
plt.clf()
plt.close()
violin plot here
You can use the set_xticklabels
f, ax = plt.subplots()
sns.boxplot(x="DISCIPLINA", y="NOTA", hue="TP_ESCOLA", data=publica_privada_pivot, ax=ax)
ax.set_xticklabels([...]) # list of strings
In addition, you can use get_xticklabels, for example.
xticklabels = [t.get_text() for t in ax.get_xticklabels()]
xticklabels = [t.replace('NU_', '').replace('_', ' ').title()
ax.set_xticklabels(xticklabels)
tips = sns.load_dataset("tips")
foo = sns.boxplot(x="day", y="total_bill", data=tips)
plt.xticks([0, 1, 2, 3], ['x1', 'x2', 'x3', 'x4'])
plt.show()
The number of unique values in 'day' column (i.e. cardinality of the feature) should be the length of the lists passed to plt.xticks() function.
matplotlib.pyplot.xticks
I made a plot that looks like this
I want to turn off the ticklabels along the y axis. And to do that I am using
plt.tick_params(labelleft=False, left=False)
And now the plot looks like this. Even though the labels are turned off the scale 1e67 still remains.
Turning off the scale 1e67 would make the plot look better. How do I do that?
seaborn is used to draw the plot, but it's just a high-level API for matplotlib.
The functions called to remove the y-axis labels and ticks are matplotlib methods.
After creating the plot, use .set().
.set(yticklabels=[]) should remove tick labels.
This doesn't work if you use .set_title(), but you can use .set(title='')
.set(ylabel=None) should remove the axis label.
.tick_params(left=False) will remove the ticks.
Similarly, for the x-axis: How to remove or hide x-axis labels from a seaborn / matplotlib plot?
Tested in python 3.11, pandas 1.5.2, matplotlib 3.6.2, seaborn 0.12.1
Example 1
import seaborn as sns
import matplotlib.pyplot as plt
# load data
exercise = sns.load_dataset('exercise')
pen = sns.load_dataset('penguins')
# create figures
fig, ax = plt.subplots(2, 1, figsize=(8, 8))
# plot data
g1 = sns.boxplot(x='time', y='pulse', hue='kind', data=exercise, ax=ax[0])
g2 = sns.boxplot(x='species', y='body_mass_g', hue='sex', data=pen, ax=ax[1])
plt.show()
Remove Labels
fig, ax = plt.subplots(2, 1, figsize=(8, 8))
g1 = sns.boxplot(x='time', y='pulse', hue='kind', data=exercise, ax=ax[0])
g1.set(yticklabels=[]) # remove the tick labels
g1.set(title='Exercise: Pulse by Time for Exercise Type') # add a title
g1.set(ylabel=None) # remove the axis label
g2 = sns.boxplot(x='species', y='body_mass_g', hue='sex', data=pen, ax=ax[1])
g2.set(yticklabels=[])
g2.set(title='Penguins: Body Mass by Species for Gender')
g2.set(ylabel=None) # remove the y-axis label
g2.tick_params(left=False) # remove the ticks
plt.tight_layout()
plt.show()
Example 2
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# sinusoidal sample data
sample_length = range(1, 1+1) # number of columns of frequencies
rads = np.arange(0, 2*np.pi, 0.01)
data = np.array([(np.cos(t*rads)*10**67) + 3*10**67 for t in sample_length])
df = pd.DataFrame(data.T, index=pd.Series(rads.tolist(), name='radians'), columns=[f'freq: {i}x' for i in sample_length])
df.reset_index(inplace=True)
# plot
fig, ax = plt.subplots(figsize=(8, 8))
ax.plot('radians', 'freq: 1x', data=df)
# or skip the previous two lines and plot df directly
# ax = df.plot(x='radians', y='freq: 1x', figsize=(8, 8), legend=False)
Remove Labels
# plot
fig, ax = plt.subplots(figsize=(8, 8))
ax.plot('radians', 'freq: 1x', data=df)
# or skip the previous two lines and plot df directly
# ax = df.plot(x='radians', y='freq: 1x', figsize=(8, 8), legend=False)
ax.set(yticklabels=[]) # remove the tick labels
ax.tick_params(left=False) # remove the ticks
I am trying to generate a plot with x-axis being a geometric sequence while the y axis is a number between 0.0 and 1.0. My code looks like this:
form matplotlib import pyplot as plt
plt.xticks(X)
plt.plot(X,Y)
plt.show()
which generates a plot like this:
As you can see, I am explicitly setting the x-axis ticks to the ones belonging to the geometric sequence.
My question:Is it possible to make x-ticks evenly spaced despite their value, as the initial terms of the sequence are small, and crowded together. Kind of like logarithmic scale, which would be ideal if dealing with powers of a base, but not for a geometric sequence, I think, as is the case here.
You can do it by plotting your variable as a function of the "natural" variable that parametrizes your curve. For example:
n = 12
a = np.arange(n)
x = 2**a
y = np.random.rand(n)
fig = plt.figure(1, figsize=(7,7))
ax1 = fig.add_subplot(211)
ax2 = fig.add_subplot(212)
ax1.plot(x,y)
ax1.xaxis.set_ticks(x)
ax2.plot(a, y) #we plot y as a function of a, which parametrizes x
ax2.xaxis.set_ticks(a) #set the ticks to be a
ax2.xaxis.set_ticklabels(x) # change the ticks' names to x
which produces:
I had the same problem and spent several hours trying to find something appropriate. But it appears to be really easy and you do not need to make any parameterization or play with some x-ticks positions, etc.
The only thing you need to do is just to plot your x-values as str, not int: plot(x.astype('str'), y)
By modifying the code from the previous answer you will get:
n = 12
a = np.arange(n)
x = 2**a
y = np.random.rand(n)
fig = plt.figure(1, figsize=(7,7))
ax1 = fig.add_subplot(211)
ax2 = fig.add_subplot(212)
ax1.plot(x,y)
ax1.xaxis.set_ticks(x)
ax2.plot(x.astype('str'), y)
Seaborn has a bunch of categorical plot handling natively this kind of task.
Such as pointplot:
sns.pointplot(x="x", y="y", data=df, ax=ax)
Exemple
fig, [ax1, ax2] = plt.subplots(2, figsize=(7,7))
sns.lineplot(data=df, x="x", y="y", ax=ax1) #relational plot
sns.pointplot(data=df, x="x", y="y", ax=ax2) #categorical plot
In case of using Pandas Dataframe:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
n = 12
df = pd.DataFrame(dict(
X=2**np.arange(n),
Y=np.random.randint(1, 9, size=n),
)).set_index('X')
# index is reset in order to use as xticks
df.reset_index(inplace=True)
fig = plt.figure()
ax1 = plt.subplot(111)
df['Y'].plot(kind='bar', ax=ax1, figsize=(7, 7), use_index=True)
# set_ticklabels used to place original indexes
ax1.xaxis.set_ticklabels(df['X'])
convert int to str:
X = list(map(str, X))
plt.xticks(X)
plt.plot(X,Y)
plt.show()
Hi I wanted to draw a histogram with a boxplot appearing the top of the histogram showing the Q1,Q2 and Q3 as well as the outliers. Example phone is below. (I am using Python and Pandas)
I have checked several examples using matplotlib.pyplot but hardly came out with a good example. And I also wanted to have the histogram curve appearing like in the image below.
I also tried seaborn and it provided me the shape line along with the histogram but didnt find a way to incorporate with boxpot above it.
can anyone help me with this to have this on matplotlib.pyplot or using pyplot
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(style="ticks")
x = np.random.randn(100)
f, (ax_box, ax_hist) = plt.subplots(2, sharex=True,
gridspec_kw={"height_ratios": (.15, .85)})
sns.boxplot(x, ax=ax_box)
sns.distplot(x, ax=ax_hist)
ax_box.set(yticks=[])
sns.despine(ax=ax_hist)
sns.despine(ax=ax_box, left=True)
From seaborn v0.11.2, sns.distplot is deprecated. Use sns.histplot for axes-level plots instead.
np.random.seed(2022)
x = np.random.randn(100)
f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={"height_ratios": (.15, .85)})
sns.boxplot(x=x, ax=ax_box)
sns.histplot(x=x, bins=12, kde=True, stat='density', ax=ax_hist)
ax_box.set(yticks=[])
sns.despine(ax=ax_hist)
sns.despine(ax=ax_box, left=True)
Solution using only matplotlib, just because:
# start the plot: 2 rows, because we want the boxplot on the first row
# and the hist on the second
fig, ax = plt.subplots(
2, figsize=(7, 5), sharex=True,
gridspec_kw={"height_ratios": (.3, .7)} # the boxplot gets 30% of the vertical space
)
# the boxplot
ax[0].boxplot(data, vert=False)
# removing borders
ax[0].spines['top'].set_visible(False)
ax[0].spines['right'].set_visible(False)
ax[0].spines['left'].set_visible(False)
# the histogram
ax[1].hist(data)
# and we are good to go
plt.show()
Expanding on the answer from #mwaskom, I made a little adaptable function.
import seaborn as sns
def histogram_boxplot(data, xlabel = None, title = None, font_scale=2, figsize=(9,8), bins = None):
""" Boxplot and histogram combined
data: 1-d data array
xlabel: xlabel
title: title
font_scale: the scale of the font (default 2)
figsize: size of fig (default (9,8))
bins: number of bins (default None / auto)
example use: histogram_boxplot(np.random.rand(100), bins = 20, title="Fancy plot")
"""
sns.set(font_scale=font_scale)
f2, (ax_box2, ax_hist2) = plt.subplots(2, sharex=True, gridspec_kw={"height_ratios": (.15, .85)}, figsize=figsize)
sns.boxplot(data, ax=ax_box2)
sns.distplot(data, ax=ax_hist2, bins=bins) if bins else sns.distplot(data, ax=ax_hist2)
if xlabel: ax_hist2.set(xlabel=xlabel)
if title: ax_box2.set(title=title)
plt.show()
histogram_boxplot(np.random.randn(100), bins = 20, title="Fancy plot", xlabel="Some values")
Image
def histogram_boxplot(feature, figsize=(15,10), bins=None):
f,(ax_box,ax_hist)=plt.subplots(nrows=2,sharex=True, gridspec_kw={'height_ratios':(.25,.75)},figsize=figsize)
sns.distplot(feature,kde=False,ax=ax_hist,bins=bins)
sns.boxplot(feature,ax=ax_box, color='Red')
ax_hist.axvline(np.mean(feature),color='g',linestyle='-')
ax_hist.axvline(np.median(feature),color='y',linestyle='--')
I have a very simple question. I need to have a second x-axis on my plot and I want that this axis has a certain number of tics that correspond to certain position of the first axis.
Let's try with an example. Here I am plotting the dark matter mass as a function of the expansion factor, defined as 1/(1+z), that ranges from 0 to 1.
semilogy(1/(1+z),mass_acc_massive,'-',label='DM')
xlim(0,1)
ylim(1e8,5e12)
I would like to have another x-axis, on the top of my plot, showing the corresponding z for some values of the expansion factor. Is that possible? If yes, how can I have xtics ax
I'm taking a cue from the comments in #Dhara's answer, it sounds like you want to set a list of new_tick_locations by a function from the old x-axis to the new x-axis. The tick_function below takes in a numpy array of points, maps them to a new value and formats them:
import numpy as np
import matplotlib.pyplot as plt
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax2 = ax1.twiny()
X = np.linspace(0,1,1000)
Y = np.cos(X*20)
ax1.plot(X,Y)
ax1.set_xlabel(r"Original x-axis: $X$")
new_tick_locations = np.array([.2, .5, .9])
def tick_function(X):
V = 1/(1+X)
return ["%.3f" % z for z in V]
ax2.set_xlim(ax1.get_xlim())
ax2.set_xticks(new_tick_locations)
ax2.set_xticklabels(tick_function(new_tick_locations))
ax2.set_xlabel(r"Modified x-axis: $1/(1+X)$")
plt.show()
You can use twiny to create 2 x-axis scales. For Example:
import numpy as np
import matplotlib.pyplot as plt
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax2 = ax1.twiny()
a = np.cos(2*np.pi*np.linspace(0, 1, 60.))
ax1.plot(range(60), a)
ax2.plot(range(100), np.ones(100)) # Create a dummy plot
ax2.cla()
plt.show()
Ref: http://matplotlib.sourceforge.net/faq/howto_faq.html#multiple-y-axis-scales
Output:
From matplotlib 3.1 onwards you may use ax.secondary_xaxis
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(1,13, num=301)
y = (np.sin(x)+1.01)*3000
# Define function and its inverse
f = lambda x: 1/(1+x)
g = lambda x: 1/x-1
fig, ax = plt.subplots()
ax.semilogy(x, y, label='DM')
ax2 = ax.secondary_xaxis("top", functions=(f,g))
ax2.set_xlabel("1/(x+1)")
ax.set_xlabel("x")
plt.show()
If You want your upper axis to be a function of the lower axis tick-values you can do as below. Please note: sometimes get_xticks() will have a ticks outside of the visible range, which you have to allow for when converting.
import matplotlib.pyplot as plt
fig, ax1 = plt.subplots()
ax1 = fig.add_subplot(111)
ax1.plot(range(5), range(5))
ax1.grid(True)
ax2 = ax1.twiny()
ax2.set_xticks( ax1.get_xticks() )
ax2.set_xbound(ax1.get_xbound())
ax2.set_xticklabels([x * 2 for x in ax1.get_xticks()])
title = ax1.set_title("Upper x-axis ticks are lower x-axis ticks doubled!")
title.set_y(1.1)
fig.subplots_adjust(top=0.85)
fig.savefig("1.png")
Gives:
Answering your question in Dhara's answer comments: "I would like on the second x-axis these tics: (7,8,99) corresponding to the x-axis position 10, 30, 40. Is that possible in some way?"
Yes, it is.
import numpy as np
import matplotlib.pyplot as plt
fig = plt.figure()
ax1 = fig.add_subplot(111)
a = np.cos(2*np.pi*np.linspace(0, 1, 60.))
ax1.plot(range(60), a)
ax1.set_xlim(0, 60)
ax1.set_xlabel("x")
ax1.set_ylabel("y")
ax2 = ax1.twiny()
ax2.set_xlabel("x-transformed")
ax2.set_xlim(0, 60)
ax2.set_xticks([10, 30, 40])
ax2.set_xticklabels(['7','8','99'])
plt.show()
You'll get:
I'm forced to post this as an answer instead of a comment due to low reputation.
I had a similar problem to Matteo. The difference being that I had no map from my first x-axis to my second x-axis, only the x-values themselves. So I wanted to set the data on my second x-axis directly, not the ticks, however, there is no axes.set_xdata. I was able to use Dhara's answer to do this with a modification:
ax2.lines = []
instead of using:
ax2.cla()
When in use also cleared my plot from ax1.