first of all: I'm completely new to python.
I'm trying to visualize some measured data. Each entry has a quadrant, number and sector. The original data lies in a .xlsx file. I've managed to use a .pivot_table to sort the data according to its sector. Due to overlapping, number and quadrant also have to be indexed. Now I want to plot it as a bar chart, where the bars are grouped by sector and the colors represent the quadrant.
But because number also has to be indexed, it shows up in the bar chart as a separate group. There should only be three groups, 0, i and a.
MWE:
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
d = {'quadrant': ["0","0","0","0","0","0","I","I","I","I","I","I","I","I","I","I","I","I","II","II","II","II","II","II","II","II","II","II","II","II","III","III","III","III","III","III","III","III","III","III","III","III","IV","IV","IV","IV","IV","IV","IV","IV","IV","IV","IV","IV"], 'sector': [0,"0","0","0","0","0","a","a","a","a","a","a","i","i","i","i","i","i","a","a","a","a","a","a","i","i","i","i","i","i","a","a","a","a","a","a","i","i","i","i","i","i","a","a","a","a","a","a","i","i","i","i","i","i"], 'number': [1,2,3,4,5,6,1,2,3,4,5,6,1,2,3,4,5,6,1,2,3,4,5,6,1,2,3,4,5,6,1,2,3,4,5,6,1,2,3,4,5,6,1,2,3,4,5,6,1,2,3,4,5,6], 'Rz_m': [67.90,44.17,44.30,63.43,49.87,39.33,61.17,69.37,66.20,44.20,64.77,39.93,44.33,50.97,55.90,51.33,58.23,44.53,50.03,47.40,58.67,71.57,57.60,70.77,63.93,47.37,46.90,34.73,41.27,48.23,58.30,47.07,50.53,51.20,32.67,50.37,37.50,55.50,41.20,48.07,56.80,49.77,40.87,44.43,44.00,60.03,63.73,72.80,51.60,45.53,60.27,71.00,59.63,48.70]}
df = pd.DataFrame(data=d)
B = df.pivot_table(index=['sector','number', 'quadrant'])
B.unstack().plot.bar(y='Rz_m')
The data viz ecosystem in Python is pretty diverse and there are multiple libraries you can use to produce the same chart. Matplotlib is a very powerful library, but it's also quite low-level, meaning you often have to do a lot of preparatory work before getting to the chart, so usually you'll find people use seaborn for static visualisations, especially if there is a scientific element to them (it has built-in support for things like error bars, etc.)
Out of the box, it has a lot of chart types to support exploratory data analysis and is built on top of matplotlib. For your example, if I understood it right, it would be as simple as:
import seaborn as sns
sns.catplot(x="sector", y="Rz_m", hue="quadrant", data=df, ci=None,
height=6, kind="bar", palette="muted")
And the output would look like this:
Note that in your example, you missed out "" for one of the zeroes and 0 and "0" are plotted as separate columns. If you're using seaborn, you don't need to pivot the data, just feed it the df as you've defined it.
For interactive visualisations (with tooltips, zoom, pan, etc.), you can also check out bokeh.
There is an interesting wrinkle to this - how to center the nested bars on the label. By default the bars are drawn with center alignment which works fine for an odd number of columns. However, for an even number, you'd want them to be centered on the right edge. You can make a small alteration in the source code categorical.py, lines beginning 1642 like so:
# Draw the bars
offpos = barpos + self.hue_offsets[j]
barfunc(offpos, self.statistic[:, j], -self.nested_width,
color=self.colors[j], align="edge",
label=hue_level, **kws)
Save the .png and then change it back, but it's not ideal. Probably worth flagging up to the library maintainers.
Here is a sample of the data I'm working with WellAnalyticalData I'd like to loop through each well name and create a time series chart for each parameter with sample date on the x-axis and the value on the y-axis. I don't think I want subplots, I'm just looking for individual plots of each analyte for each well. I've used pandas to try grouping by well name and then attempting to plot, but that doesn't seem to be the way to go. I'm fairly new to python and I think I'm also having trouble figuring out how to construct the loop statement. I'm running python 3.x and am using the matplotlib library to generate the plots.
so if I understand your question correctly you want one plot for each combination of Well and Parameter. No subplots, just a new plot for each combination. Each plot should have SampleDate on the x-axis and Value on the y-axis. I've written a loop here that does just that, although you'll see that since in your data has just one date per well per parameter, the plots are just a single dot.
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
df = pd.DataFrame({'WellName':['A','A','A','A','B','B','C','C','C'],
'SampleDate':['2018-02-15','2018-03-31','2018-06-07','2018-11-14','2018-02-15','2018-11-14','2018-02-15','2018-03-31','2018-11-14'],
'Parameter':['Arsenic','Lead','Iron','Magnesium','Arsenic','Iron','Arsenic','Lead','Magnesium'],
'Value':[0.2,1.6,0.05,3,0.3,0.79,0.3,2.7,2.8]
})
for well in df.WellName.unique():
temp1 = df[df.WellName==well]
for param in temp1.Parameter.unique():
fig = plt.figure()
temp2 = temp1[temp1.Parameter==param]
plt.scatter(temp2.SampleDate,temp2.Value)
plt.title('Well {} and Parameter {}'.format(well,param))
I'm currently trying to get an overview of plots of data of different dates. To get a good feeling of the data I would like to plot relevant plots next to each other. This means I want to use the same plot multiple times in the gridplot command. However what I noticed is that when i use the same chart multiple times it will only show it once in the final .html file. My first attempt at solving this was to use a copy.deepcopy for the charts, but this gave the following error:
RuntimeError: Cannot get a property value 'label' from a LineGlyph instance before HasProps.__init__
My approach has been as follows:
from bokeh.charts import Line, output_file, show, gridplot
import pandas as pd
output_file('test.html')
plots = []
df = pd.DataFrame([[1,2], [3,1], [2,2]])
print(df)
df.columns = ['x', 'y']
for i in range(10):
plots.append(Line(df, x='x', y='y', title='Forecast: ' + str(i),
plot_width=250, plot_height=250))
plot_matrix = []
for i in range(len(plots)-1, 2, -1):
plot_matrix.append([plots[i-3], plots[i-2], plots[i]])
p = gridplot(plot_matrix)
show(p)
The results of which is a an html page with a grid plot with a lot of missing graphs. Each graph is exactly shown once (instead of the 3 times required), which leads me to think that the gridplot does not like me using the same object multiple times. An obvious solve is to simply create every graph 3 times as a different object, which I will do for now, but not only is this inefficient, it also hurts my eyes when looking at my code. I'm hoping somebody has a more elegant solution for my problem.
EDIT: made code runable
This is not possible. Bokeh plots (or Bokeh objects in general) may not be re-used in layouts.