I have the following dataset df:
A B C D E
1 2 5 6 9
7 9 10 11 13
6 10 11 23 87
I want to create a seaborn line plot so that for each of the columns I get a different linestyle with the same color, the linestyle which I can choose, however, I am clueless about how to proceed from here
I tried this and I am getting the required result however I want to choose the different linestyle for each of the columns manually:
sns.lineplot(data=df)
Use markers to activate multiple linestyles, and use palette to set all columns to the same color:
sns.lineplot(data=df, markers=True, palette=['blue'] * df.columns.size)
This example uses markers=True which lets seaborn automatically choose the linestyles, but you can also pass a list of matplotlib markers to manually specify your own:
markers : boolean, list, or dictionary
Object determining how to draw the markers for different levels of the style variable. Setting to True will use default markers, or you can pass a list of markers or a dictionary mapping levels of the style variable to markers. Setting to False will draw marker-less lines. Markers are specified as in matplotlib.
Related
I am trying to create a rangeSlider that controls the colorbar and bins of a hvplot quadmesh plot of gridded data. Right now I am using cmap and it is wonderful but I need a way to bin and color the data to a 3 color scheme namely,
(min, rangeSlider[0]) = Green labeled Good
(rangeSlider[0], rangeSlider[1]) = Yellow labeled Caution
(rangeSlider[1], max) = Red labeled Dangerous
So I made a couple of attempts but am not sure how to pass a ListedColormap from Matplotlib.colors as well as labels to a "bining" function of the quadmesh hvplot object.
How can I draw such a heatmap using the "seaborn.heatmap" function?
The color shades are determined by matrix A and the annotation of each grid is determined by matrix B.
For example, if I get a matrix, I want its color to be displayed according to the z-score of this matrix, but the annotation remains the matrix itself.
I know I should resort to the parameter 'annot_kws', but how exactly should I write the code?
Instead of simply setting annot=True, annot= can be set to a dataframe (or 2D numpy array, or a list of lists) with the same number of rows and columns as the data. That way, the coloring will be applied using the data, and the annotation will come from annot. Seaborn will still take care to use white text for the dark cells and black text for the light ones.
annot_kws= is used to change the text properties, typically the fontsize. But you also could change the font itself, or the alignment if you'd used multiline text.
Here is an example using numbers 1 to 36 as annotation, but the numbers modulo 10 for the coloring. The annot_kws are used to enlarge and rotate the text. (Note that when the annotation are strings, you also need to set the format, e.g. fmt='').
import seaborn as sns
import numpy as np
a = pd.DataFrame({'count': [1, 2, 3]})
matrix_B = np.arange(1, 37).reshape(6, 6) # used for annotations
matrix_A = (matrix_B % 10) # used for coloring
sns.heatmap(data=matrix_A, annot=matrix_B,
annot_kws={'size': 20, 'rotation': 45},
square=True, cbar_kws={'label': 'last digit'})
For example, I have a data frame like this
x1
x2
class
0.1
0.2
1
0.3
0.4
2
...
...
...
How can I use boxplot to create a chart like this
To achieve a scatter plot, I seperate the dataframe into 2, based on class and plot them separately,on the same plot. But how to achieve something like the image above with boxplot?
A box plot uses boxes and lines to depict the distributions of one or more groups of numeric data. This means the one axis must be categorical and the other numerical.
Refer to this link for more info: https://chartio.com/learn/charts/box-plot-complete-guide/
If still, you want to achieve this then you can bin one column.
Refer This: https://pandas.pydata.org/docs/reference/api/pandas.cut.html
I'm using the "PyPlot" package in Julia, and I want to add gridlines at specified locations. I'm not familiar enough with Python/Matlab to use their documentation pages to help - the commands differ in Julia. I want a basic plot, with gridlines on both axes at intervals of 1:
using PyPlot
fig=figure("Name")
grid("on")
scatter([1,2,3,4],[4,5,6,7])
Help appreciated...
PyPlot is just an interface to Matplotlib, so the commands
to customize the grid are Matplotlib's commands.
One way to configure the gridlines on both axes at intervals of 1 (for the given data) is:
using PyPlot
fig=figure(figsize=[6,3])
ax1=subplot(1,1,1) # creates a subplot with just one graphic
ax1[:xaxis][:set_ticks](collect(1:4)) # configure x ticks from 1 to 4
ax1[:yaxis][:set_ticks](collect(4:7)) # configure y ticks from 4 to 7
grid("on")
scatter([1,2,3,4],[4,5,6,7])
This code was tested inside an IJulia's notebook, and produces the following output:
Take a look at Various Julia plotting examples using PyPlot.
tested with Julia Version 0.4.3
The values where grid lines are drawn can be controlled by passing an array to the xticks() and yticks() functions.
A simple example:
using PyPlot
fig=figure("Name")
grid("on")
xticks(0:5)
yticks(3:8)
scatter([1,2,3,4],[4,5,6,7])
If you want it to be more flexible you can figure out the limits based on your data and set the tick interval to something else.
One little more dynamic way to configure the x-axis of the grid could be:
x_data = [1,2,3,4]
x_tick_interval = 2;
x_tick_start = minimum(xdata)
x_tick_end = maximum(xdata)
xticks(x_tick_start:x_tick_interval:x_tick_end)
I am currently trying to plot a large amount of data on a single plot. I have structured my representation using repeated colors and symbols. However, when plotting the final results, the legend appears slightly off because I cannot control the number of rows within it. Thus, instead of getting 5 repeated green, then 5 repeated red, 5 repeated blue then 2 other, I get 5 -4 -4 -4 (where I would have prefered 5 - 5 - 5 - 2)
You can clearly see this in attached image.
Right now I use these options for the legend:
axp.legend(loc="lower right",ncol=4)
I also had this problem a couple of times and use this workaround by adding dummy items to the legend to fill the last column, if there are more elegant methods available I would also be very interested to hear about them.
import numpy as np
import matplotlib.pylab as pl
pl.figure()
pl.plot(np.arange(10), np.random.random([10,5]), color='r', label='red')
pl.plot(np.arange(10), np.random.random([10,5]), color='g', label='green')
pl.plot(np.arange(10), np.random.random([10,5]), color='b', label='blue')
pl.plot(np.arange(10), np.random.random([10,2]), color='k', label='black')
# Add empty dummy legend items
pl.plot(np.zeros(1), np.zeros([1,3]), color='w', alpha=0, label=' ')
pl.legend(ncol=4)