I have a series of data that I'm reading in from a tutorial site.
I've managed to plot the distribution of the TV column in that data, however I also want to overlay a normal distribution curve with StdDev ticks on a second x-axis (so I can compare the two curves). I'm struggling to work out how to do it..
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as stats
import matplotlib.mlab as mlab
import math
# read data into a DataFrame
data = pd.read_csv('http://www-bcf.usc.edu/~gareth/ISL/Advertising.csv', index_col=0)
# draw distribution curve
h = sorted(data.TV)
hmean = np.mean(h)
hstd = np.std(h)
pdf = stats.norm.pdf(h, hmean, hstd)
plt.plot(h, pdf)
Here is a diagram close to what I'm after, where x is the StdDeviations. All this example needs is a second x axis to show the values of data.TV
Not sure what you really want, but you could probably use second axis like this
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as stats
import matplotlib.mlab as mlab
import math
# read data into a DataFrame
data = pd.read_csv('Advertising.csv', index_col=0)
fig, ax1 = plt.subplots()
# draw distribution curve
h = sorted(data.TV)
ax1.plot(h,'b-')
ax1.set_xlabel('TV')
ax1.set_ylabel('Count', color='b')
for tl in ax1.get_yticklabels():
tl.set_color('b')
hmean = np.mean(h)
hstd = np.std(h)
pdf = stats.norm.pdf(h, hmean, hstd)
ax2 = ax1.twinx()
ax2.plot(h, pdf, 'r.')
ax2.set_ylabel('pdf', color='r')
for tl in ax2.get_yticklabels():
tl.set_color('r')
plt.show()
Ok, assuming that you want to plot the distribution of your data, the fitted normal distribution with two x-axes, one way to achieve this is as follows.
Plot the normalized data together with the standard normal distribution. Then use matplotlib's twiny() to add a second x-axis to the plot. Use the same tick positions as the original x-axis on the second axis, but scale the labels so that you get the corresponding original TV values. The result looks like this:
Code
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as stats
import matplotlib.mlab as mlab
import math
# read data into a DataFrame
data = pd.read_csv('http://www-bcf.usc.edu/~gareth/ISL/Advertising.csv', index_col=0)
h = sorted(data.TV)
hmean = np.mean(h)
hstd = np.std(h)
h_n = (h - hmean) / hstd
pdf = stats.norm.pdf( h_n )
# plot data
f,ax1 = plt.subplots()
ax1.hist( h_n, 20, normed=1 )
ax1.plot( h_n , pdf, lw=3, c='r')
ax1.set_xlim( [h_n.min(), h_n.max()] )
ax1.set_xlabel( r'TV $[\sigma]$' )
ax1.set_ylabel( r'Relative Frequency')
ax2 = ax1.twiny()
ax2.grid( False )
ax2.set_xlim( ax1.get_xlim() )
ax2.set_ylim( ax1.get_ylim() )
ax2.set_xlabel( r'TV' )
ticklocs = ax2.xaxis.get_ticklocs()
ticklocs = [ round( t*hstd + hmean, 2) for t in ticklocs ]
ax2.xaxis.set_ticklabels( map( str, ticklocs ) )
Related
I have the following Plot
I need to add percentage inside the bars, it should be like this:
My code is the following:
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sb
pkmn = pd.read_csv('data/Pokemon.csv')
pkmn.head()
order = pkmn['Generation'].value_counts().index
order
pkmngen = pkmn['Generation'].value_counts()
plt.figure(figsize=(6,4))
sb.countplot(data=pkmn, y='Generation', color = sb.color_palette()[4], order=order, )
plt.xticks(rotation=90)
plt.show()
I would like create an plot with to display the last value on line. But i can not create the plot with the last value on chart. Do you have an idea for to resolve my problem, thanks you !
Input :
DataFrame
Plot
Output :
Cross = Last Value In columns
Output Final
# import eikon as ek
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
import os
import seaborn as sns; sns.set()
import pylab
from scipy import *
from pylab import *
fichier = "P:/GESTION_RPSE/GES - Gestion Epargne Salariale/Dvp Python/Florian/Absolute
Performance/PLOT.csv"
df = pd.read_csv(fichier)
df = df.drop(columns=['Unnamed: 0'])
# sns.set()
plt.figure(figsize=(16, 10))
df = df.melt('Date', var_name='Company', value_name='Value')
#palette = sns.color_palette("husl",12)
ax = sns.lineplot(x="Date", y="Value", hue='Company', data=df).set_title("LaLaLa")
plt.show()
Do you just want to put an 'X' at the end of your lines?
If so, you could pass markerevery=[-1] to the call to lineplot(). However there are a few caveats:
You have to use style= instead of hue= otherwise, there are no markers drawn
Filled markers work better than unfilled markers (like "x"). You can just use markers=True to use the default markers, or pass a list markers=['s','d','o',etc...]
code:
fmri = sns.load_dataset("fmri")
fig, ax = plt.subplots()
ax = sns.lineplot(x="timepoint", y="signal",
style="event", data=fmri, ci=None, markers=True, markevery=[-1], markersize=10)
I can plot multiple histograms in a single plot using pandas but there are few things missing:
How to give the label.
I can only plot one figure, how to change it to layout=(3,1) or something else.
Also, in figure 1, all the bins are filled with solid colors, and its kind of difficult to know which is which, how to fill then with different markers (eg. crosses,slashes,etc)?
Here is the MWE:
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
df = sns.load_dataset('iris')
df.groupby('species')['sepal_length'].hist(alpha=0.7,label='species')
plt.legend()
Output:
To change layout I can use by keyword, but can't give them colors
HOW TO GIVE DIFFERENT COLORS?
df.hist('sepal_length',by='species',layout=(3,1))
plt.tight_layout()
Gives:
You can resolve to groupby:
fig,ax = plt.subplots()
hatches = ('\\', '//', '..') # fill pattern
for (i, d),hatch in zip(df.groupby('species'), hatches):
d['sepal_length'].hist(alpha=0.7, ax=ax, label=i, hatch=hatch)
ax.legend()
Output:
In pandas version 1.1.0 you can simply set the legend keyword to true.
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
df = sns.load_dataset('iris')
df.groupby('species')['sepal_length'].hist(alpha=0.7, legend = True)
output image
It's more code, but using pure matplotlib will always give you more control over the plots. For your second case:
import matplotlib.pyplot as plt
import numpy as np
from itertools import zip_longest
# Dictionary of color for each species
color_d = dict(zip_longest(df.species.unique(),
plt.rcParams['axes.prop_cycle'].by_key()['color']))
# Use the same bins for each
xmin = df.sepal_length.min()
xmax = df.sepal_length.max()
bins = np.linspace(xmin, xmax, 20)
# Set up correct number of subplots, space them out.
fig, ax = plt.subplots(nrows=df.species.nunique(), figsize=(4,8))
plt.subplots_adjust(hspace=0.4)
for i, (lab, gp) in enumerate(df.groupby('species')):
ax[i].hist(gp.sepal_length, ec='k', bins=bins, color=color_d[lab])
ax[i].set_title(lab)
# same xlim for each so we can see differences
ax[i].set_xlim(xmin, xmax)
How does matplotlib ensure that a dataset can be within plot with specified size.
How do i from a plot stored as numpy, How do i read the color of the pixels illustration a datapoint (0,4) - in the plot.
example:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
from PIL import Image
import librosa
import librosa.display
from matplotlib import cm
fig = plt.figure(figsize=(12,4))
min = -1.828067
max = 22.70058
data = np.random.uniform(low=min, high=max, size=(474,40))
librosa.display.specshow(data.T,sr=16000,x_axis='frames',y_axis='mel',hop_length=160,cmap=cm.jet)
plt.show()
raw_input("sadas")
convert = plt.get_cmap(cm.jet)
numpy_output_static = convert(data.T)
plt.imshow(numpy_output_static, aspect = 'auto')
plt.show()
raw_input("asds")
First plot being :
Second plot being:
so the first has been resized to plot size 12,4 where the last basically plots the same data but just using the data shape as size... how do i change that?
Librosa just performs pcolormesh according to the GitHub source code
You need to define another figure with its own size for the second figure.
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
from PIL import Image
import librosa
import librosa.display
from matplotlib import cm
fig = plt.figure(figsize=(12,4))
min = -1.828067
max = 22.70058
data = np.random.uniform(low=min, high=max, size=(474,40))
librosa.display.specshow(data.T,sr=16000,x_axis='frames',y_axis='mel',hop_length=160,cmap=cm.jet)
plt.show()
raw_input("sadas")
convert = plt.get_cmap(cm.jet)
numpy_output_static = convert(data.T)
fig = plt.figure(figsize=(12,4))
plt.imshow(numpy_output_static, aspect = 'auto')
plt.show()
raw_input("asds")
I am trying to do a Kernel Density Estimation (KDE) plot with seaborn and locate the median. The code looks something like this:
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
sns.set_palette("hls", 1)
data = np.random.randn(30)
sns.kdeplot(data, shade=True)
# x_median, y_median = magic_function()
# plt.vlines(x_median, 0, y_median)
plt.show()
As you can see I need a magic_function() to fetch the median x and y values from the kdeplot. Then I would like to plot them with e.g. vlines. However, I can't figure out how to do that. The result should look something like this (obviously the black median bar is wrong here):
I guess my question is not strictly related to seaborn and also applies to other kinds of matplotlib plots. Any ideas are greatly appreciated.
You need to:
Extract the data of the kde line
Integrate it to calculate the cumulative distribution function (CDF)
Find the value that makes CDF equal 1/2, that is the median
import numpy as np
import scipy
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_palette("hls", 1)
data = np.random.randn(30)
p=sns.kdeplot(data, shade=True)
x,y = p.get_lines()[0].get_data()
#care with the order, it is first y
#initial fills a 0 so the result has same length than x
cdf = scipy.integrate.cumtrapz(y, x, initial=0)
nearest_05 = np.abs(cdf-0.5).argmin()
x_median = x[nearest_05]
y_median = y[nearest_05]
plt.vlines(x_median, 0, y_median)
plt.show()