I have a population dataframe. How to plot a gamma distribution diagram?
I am using python and I can plot a diagram using seaborn or plot bar. But I need a gamma distribution diagram. I dont know how to fit my parameters into gamma functions.
plot = sns.distplot(df["Percent"])
fig = plot.get_figure()
plot2 = rows['state'].value_counts().plot.bar()
fig2 = plot2.get_figure()
It only shows me two general diagrams. Not sure how to get gamma distribution.
Related
I am analyzing a time series dataset and I used seasonal_decompose function in statsmodel library to obtain trend and seasonal behavior. I obtained the autocorrelation plot and the decomposition of the time-series provided should provide a “remainder” component that should be uncorrelated. By observing the autocorrelation plot how do we say that auto-correlation function indicate that the remainder is indeed uncorrelated?
I am attaching the code I used to obtain autocorrelation plot and the plot obtained.
fig, ax = plt.subplots(figsize=(20, 5))
plot_acf(data, ax=ax)
plt.show()
Autocorrelation_plot
if the results of auto correlation are close to zero then the features not not correlated. I use lag of 40, but you will need to adjust this value dependant on your data.
plt.clf()
fig,ax = plt.subplots(figsize=(12,4))
plt.style.use('seaborn-pastel')
fig = tsaplots.plot_acf(df['value'], lags=40,ax=ax)
plt.show()
print('values close to 1 are showing strong positive correlation. The blue regions are showing areas of uncertainty')
I am looking to plot some density maps from some grid-like data:
X,Y,Z = np.mgrids[-5:5:50j, -5:5:50j, -5:5:50j]
rho = np.random.rand(50,50,50) #for the sake of argument
I am interested in producing an interpolated density plot as shown below, from Mathematica here, using Python.
Is there any solution in Matplotlib or another plotting suite for this sort of plot?
To be clear, I do not want a scatterplot of coloured points, which is not suitable the plot I am trying to make. I would like a 3D interpolated density plot, as shown below.
Plotly
Plotly Approach from https://plotly.com/python/3d-volume-plots/ uses np.mgrid
import plotly.graph_objects as go
import numpy as np
X, Y, Z = np.mgrid[-8:8:40j, -8:8:40j, -8:8:40j]
values = np.sin(X*Y*Z) / (X*Y*Z)
fig = go.Figure(data=go.Volume(
x=X.flatten(),
y=Y.flatten(),
z=Z.flatten(),
value=values.flatten(),
isomin=0.1,
isomax=0.8,
opacity=0.1, # needs to be small to see through all surfaces
surface_count=17, # needs to be a large number for good volume rendering
))
fig.show()
Pyvista
Volume Rendering example:
https://docs.pyvista.org/examples/02-plot/volume.html#sphx-glr-examples-02-plot-volume-py
3D-interpolation code you might need with pyvista:
interpolate 3D volume with numpy and or scipy
I have data in the CSV file. I am trying to plot a histogram using matplotlib.
Here is the code that I am trying.
data.hist(bins=10)
plt.ylabel('Frequency')
plt.xlabel('Data')
plt.show()
This is the plot that I get.
Now using the same code, I need to create a normalized histogram that shows the probability distribution of the data. But now on the y-axis, instead of plotting the number of data points that fall in each bin, you will plot the number of data points in that data bin divided by the total number of data points.
How should I do it?
Pandas' histogram adds some functionality to the underlying pyplot.hist(). Many of the parameters are passed through. One of them is density=.
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
data = pd.DataFrame(np.random.uniform(258.1, 262.3, 20))
data.hist(bins=10, density=True)
plt.ylabel('Density')
plt.xlabel('Data')
plt.show()
A related library, seaborn, has a command to create a density histogram together with a kde curve as an approximation of the probability distribution.
import seaborn as sns
sns.distplot(data, bins=10)
In Java, I usually rely on the org.apache.commons.math3.random.EmpiricalDistribution class to do the following:
Derive a probability distribution from observed data.
Generate random values from this distribution.
Is there any Python library that provides the same functionality? It seems like scipy.stats.gaussian_kde.resample does something similar, but I'm not sure if it implements the same procedure as the Java type I'm familiar with.
import numpy as np
import scipy.stats
import matplotlib.pyplot as plt
# This represents the original "empirical" sample -- I fake it by
# sampling from a normal distribution
orig_sample_data = np.random.normal(size=10000)
# Generate a KDE from the empirical sample
sample_pdf = scipy.stats.gaussian_kde(orig_sample_data)
# Sample new datapoints from the KDE
new_sample_data = sample_pdf.resample(10000).T[:,0]
# Histogram of initial empirical sample
cnts, bins, p = plt.hist(orig_sample_data, label='original sample', bins=100,
histtype='step', linewidth=1.5, density=True)
# Histogram of datapoints sampled from KDE
plt.hist(new_sample_data, label='sample from KDE', bins=bins,
histtype='step', linewidth=1.5, density=True)
# Visualize the kde itself
y_kde = sample_pdf(bins)
plt.plot(bins, y_kde, label='KDE')
plt.legend()
plt.show(block=False)
new_sample_data should be drawn from roughly the same distribution as the original data (to the degree that the KDE is a good approximation to the original distribution).
I have created a script to plot a histogram of a NO2 vs Temperature residuals in a dataframe called nighttime.
The histogram shows the normal distribution of the residuals from a regression line somewhere else in the python script.
I am struggling to find a way to plot a bell curve over the histogram like this example :
Plot Normal distribution with Matplotlib
How can I get a fitting normal distribution for my residual histogram?
plt.suptitle('NO2 and Temperature Residuals night-time', fontsize=20)
WSx_rm = nighttime['Temperature']
WSx_rm = sm.add_constant(WSx_rm)
NO2_WS_RM_mod = sm.OLS(nighttime.NO2, WSx_rm, missing = 'drop').fit()
NO2_WS_RM_mod_sr = (NO2_WS_RM_mod.resid / np.std(NO2_WS_RM_mod.resid))
#Histogram of residuals
ax = plt.hist(NO2_WS_RM_mod.resid)
plt.xlim(-40,50)
plt.xlabel('Residuals')
plt.show
You can exploit the methods from seaborn library for plotting the distribution with the bell curve. The residual variable is not clear to me in the example you have provided. You may see the code snippet below just for your reference.
# y here is an arbitrary target variable for explaining this example
residuals = y_actual - y_predicted
import seaborn as sns
sns.distplot(residuals, bins = 10) # you may select the no. of bins
plt.title('Error Terms', fontsize=20)
plt.xlabel('Residuals', fontsize = 15)
plt.show()
Does the following work for you? (using some adapted code from the link you gave)
import scipy.stats as stats
plt.suptitle('NO2 and Temperature Residuals night-time', fontsize=20)
WSx_rm = nighttime['Temperature']
WSx_rm = sm.add_constant(WSx_rm)
NO2_WS_RM_mod = sm.OLS(nighttime.NO2, WSx_rm, missing = 'drop').fit()
NO2_WS_RM_mod_sr = (NO2_WS_RM_mod.resid / np.std(NO2_WS_RM_mod.resid))
#Histogram of residuals
ax = plt.hist(NO2_WS_RM_mod.resid)
plt.xlim(-40,50)
plt.xlabel('Residuals')
# New Code: Draw fitted normal distribution
residuals = sorted(NO2_WS_RM_mod.resid) # Just in case it isn't sorted
normal_distribution = stats.norm.pdf(residuals, np.mean(residuals), np.std(residuals))
plt.plot(residuals, normal_distribution)
plt.show