Develop Reference

Why is hist2d plotting UNIFORM density map?

I am plotting density map of ~40k points but hist2d returns a uniform density map. This is my code
hist2d(x, y, bins=(1000, 1000), cmap=plt.cm.jet)
Here is the scatter plot
Here is the histogram
I was expecting that there is a red horizontal portion in the center and the gradually turns blue towards higher/lower y values
EDIT:
#bb1 suggested decrease the number of bins but by setting it to bins=(100, 1000), I get this result

I think you are specifying too many bins. By setting bins=(1000,000) you get 1,000,000 bins. With 40,000 points, most of the bins will be empty and they overwhelm the image.
You may also consider using seaborn kdeplot() function instead of plt.hist2d(). It will visualize the density of data without subdividing data into bins:
import seaborn as sns
sns.kdeplot(x=x, y=y, levels = 100, fill=True, cmap="mako", thresh=0)

Seaborn distplot() won't display frequency in the y-axis

I am trying to display the weighted frequency in the y-axis of a seaborn.distplot() graph, but it keeps displaying the density (which is the default in distplot())
I read the documentation and also many similar questions here in Stack.
The common answer is to set norm_hist=False and also to assign the weights in a bumpy array as in a standard histogram. However, it keeps showing the density and not the probability/frequency of each bin.
My code is
plt.figure(figsize=(10, 4))
plt.xlim(-0.145,0.145)
plt.axvline(0, color='grey')
data = df['col1']
x = np.random.normal(data.mean(), scale=data.std(), size=(100000))
normal_dist =sns.distplot(x, hist=False,color="red",label="Gaussian")
data_viz = sns.distplot(data,color="blue", bins=31,label="data", norm_hist=False)
# I also tried adding the weights inside the argument
#hist_kws={'weights': np.ones(len(data))/len(data)})
plt.legend(bbox_to_anchor=(1, 1), loc=1)
And I keep receiving this output:
Does anyone have an idea of what could be the problem here?
Thanks!
[EDIT]: The problem is that the y-axis is showing the kdevalues and not those from the weighted histogram. If I set kde=False then I can display the frequency in the y-axis. However, I still want to keep the kde, so I am not considering that option.

Keeping the kde and the frequency/count in one y-axis in one plot will not work because they have different scales. So it might be better to create a plot with 2 axis with each showing the kde and histogram separately.
From documentation norm_hist If True, the histogram height shows a density rather than a count. **This is implied if a KDE or fitted density is plotted**.
versusnja in https://github.com/mwaskom/seaborn/issues/479 has a workaround:
# Plot hist without kde.
# Create another Y axis.
# Plot kde without hist on the second Y axis.
# Remove Y ticks from the second axis.
first_ax = sns.distplot(data, kde=False)
second_ax = ax.twinx()
sns.distplot(data, ax=second_ax, kde=True, hist=False)
second_ax.set_yticks([])
If you need this just for visualization it should be good enough.

pyplot - How to set a specific range in x axis

I have a CDF plot with data of wifi usage in MB. For better understanding I would like to present the usage starting in KB and finishing in TB. I would like to know how to set a specific range for x axis to replace the produce by plt.plot() and show the axis x, per example, as [1KB 10KB 1MB 10MB 1TB 10TB], even the space between bins not representing the real values.
My code for now:
wifi = np.sort(matrix[matrix['wifi_total_mb']>0]['wifi_total_mb'].values)
g = sns.distplot(wifi, kde_kws=dict(cumulative=True))
plt.show()
Thanks
EDIT 1
I know that I can use plt.xticks and i already tried it: plt.xticks([0.00098, 0.00977, 1, 10, 1024, 10240, 1048576, 10485760, 24117248]). These are values in MB that represents the sample range I specified before. But the plot is still wrong.
The result expected
In excel it is pretty easy makes what I want to. Look the image, with the same range I get the plot I wanted.
Thanks

It may be better to calculate the data to plot manually, instead of relying on some seaborn helper function like distplot. This also makes it easier to understand the underlying issue of histogramming with very unequal bin sizes.
Calculating histogram
The histogram of the data can be calculated by using np.histogram(). It can take the desired bins as argument.
In order to get the cummulative histogram, np.cumsum does the job.
Now there are two options here: (a) plotting the real data or (b) plotting the data enumerated by bin.
(a) Plotting the real data:
Because the bin sizes are pretty unequal, a logarithmic scaling seems adequate, which can be done by semilogx(x,y). The bin edges can be shown as xticks using set_xticks (and since the semilogx plot will not automatically set the labels correctly, we also need to set them to the bin edges' values).
(b) Plotting data enumerated by bin:
The second option is to plot the histogram values bin by bin, independent of the actual bin size. Is is very close to the Excel solution from the question. In this case the x values of the plot are simply values from 0 to number of bins and the xticklabels are the bin edges.
Here is the complete code:
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
#use the bin from the question
bins = [0, 0.00098, 0.00977, 1, 10, 1024, 10240, 1048576, 10485760, 24117248]
# invent some data
data = np.random.lognormal(2,4,10000)
# calculate histogram of the data into the given bins
hist, _bins = np.histogram(data, bins=bins)
# make histogram cumulative
cum_hist=np.cumsum(hist)
# normalize data to 1
norm_cum_hist = cum_hist/float(cum_hist.max())
fig, (ax, ax2) = plt.subplots(nrows=2)
plt.subplots_adjust(hspace=0.5, bottom=0.17)
# First option plots the actual data, i.e. the bin width is reflected
# by the spacing between values on x-axis.
ax.set_title("Plotting actual data")
ax.semilogx(bins[1:],norm_cum_hist, marker="s")
ax.set_xticks(bins[1:])
ax.set_xticklabels(bins[1:] ,rotation=45, horizontalalignment="right")
# Second option plots the data bin by bin, i.e. every bin has the same width,
# independent of it's actual value.
ax2.set_title("Plotting bin by bin")
ax2.plot(range(len(bins[1:])),norm_cum_hist, marker="s")
ax2.set_xticks(range(len(bins[1:])))
ax2.set_xticklabels(bins[1:] ,rotation=45, horizontalalignment="right")
for axes in [ax, ax2]:
axes.set_ylim([0,1.05])
plt.show()

Matplotlib - Boxplot calculated on log10 values but shown in logarithmic scale

I think this is a simple question, but I just still can't seem to think of a simple solution. I have a set of data of molecular abundances, with values ranging many orders of magnitude. I want to represent these abundances with boxplots (box-and-whiskers plots), and I want the boxes to be calculated on log scale because of the wide range of values.
I know I can just calculate the log10 of the data and send it to matplotlib's boxplot, but this does not retain the logarithmic scale in plots later.
So my question is basically this:
When I have calculated a boxplot based on the log10 of my values, how do I convert the plot afterward to be shown on a logarithmic scale instead of linear with the log10 values?
I can change tick labels to partly fix this, but I have no clue how I get logarithmic scales back to the plot.
Or is there another more direct way to plotting this. A different package maybe that has this options already included?
Many thanks for the help.

I'd advice against doing the boxplot on the raw values and setting the y-axis to logarithmic, because the boxplot function is not designed to work across orders of magnitudes and you may get too many outliers (depends on your data, of course).
Instead, you can plot the logarithm of the data and manually adjust the y-labels.
Here is a very crude example:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import MultipleLocator, FormatStrFormatter
np.random.seed(42)
values = 10 ** np.random.uniform(-3, 3, size=100)
fig = plt.figure(figsize=(9, 3))
ax = plt.subplot(1, 3, 1)
ax.boxplot(np.log10(values))
ax.set_yticks(np.arange(-3, 4))
ax.set_yticklabels(10.0**np.arange(-3, 4))
ax.set_title('log')
ax = plt.subplot(1, 3, 2)
ax.boxplot(values)
ax.set_yscale('log')
ax.set_title('raw')
ax = plt.subplot(1, 3, 3)
ax.boxplot(values, whis=[5, 95])
ax.set_yscale('log')
ax.set_title('5%')
plt.show()
The right figure shows the box plot on the raw values. This leads to many outliers, because the maximum whisker length is computed as a multiple (default: 1.5) of the interquartile range (the box height), which does not scale across orders of magnitude.
Alternatively, you could specify to draw the whiskers for a given percentile range:
ax.boxplot(values, whis=[5, 95])
In this case you get a fixed amount of outlires (5%) above and below.

You can use plt.yscale:
plt.boxplot(data); plt.yscale('log')

Plotting histogram with matplotlib

i try to plot data in a histogram or bar in python. The data size (array size) is between 0-10000. The data itself (each entry of the array) depends on the input and has a range between 0 and e+20 (mostly the data is in th same range). So i want to do a hist plot with matplotlib. I want to plot how often a data is in some intervall (to illustrate the mean and deviation). Sometimes it works like this:
hist1.
But sometimes there is a problem with the intevall size like this:
hist2.
In this plot i need more bars at point 0-100 etc.
Can anyone help me with this?
The plots are just made with:
from numpy.linalg import *
import matplotlib.pyplot as plt
plt.hist(numbers,bins=100)
plt.show()

By default, hist produces a plot with an x range that covers the full range of your data.
If you have one outsider at very high x in comparison with the other values, then you will see this image with a 'compressed' figure.
I you want to have always the same view you can fix the limits with xlim.
Alternatively, if you want to see your distribution always centered and as nicer as possible, you can calculate the mean and the standard deviation of your data and fix the x range accordingly (p.e. for mean +/- 5 stdev)