Seaborn bar plot - different y axis values? - python

I am very new to coding and just really stuck with a graph I am trying to produce for a Uni assignment
This is what it looks like
I am pretty happy with the styling my concern is with the y axis. I understand that because I have one value much higher than the rest it is difficult to see the true values of the values further down the scale.
Is there anyway to change this?
Or can anyone recommend a different grah type that may show this data mor clearly?
Thanks!


You can try using a combination of ScalarFormatter on the y-axis and MultipleLocator to specify the tick-frequency of the y-axis values. You can read more about customising tricks for data-visualisations here Customising tricks for visualising data in Python
import numpy as np
import seaborn.apionly as sns
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
ax_data = sns.barplot(x= PoliceForce, y = TotalNRMReferrals) # change as per how you are plotting, just for an example
ax_data.yaxis.set_major_locator(ticker.MultipleLocator(40)) # it would have a tick frequency of 40, change 40 to the tick-frequency you want.
ax_data.yaxis.set_major_formatter(ticker.ScalarFormatter())
plt.show()
Based on your current graph, I would suggest lowering the tick-frequency (try with values lower than 100, say 50 for instance). This would present the graph in a more readable fashion. I hope this helps answer your question.

Related

In a pairplot, how can I not show confidence intervals but display grid lines instead? [duplicate]

I'm plotting two data series with Pandas with seaborn imported. Ideally I would like the horizontal grid lines shared between both the left and the right y-axis, but I'm under the impression that this is hard to do.
As a compromise I would like to remove the grid lines all together. The following code however produces the horizontal gridlines for the secondary y-axis.
import pandas as pd
import numpy as np
import seaborn as sns
data = pd.DataFrame(np.cumsum(np.random.normal(size=(100,2)),axis=0),columns=['A','B'])
data.plot(secondary_y=['B'],grid=False)

You can take the Axes object out after plotting and perform .grid(False) on both axes.
# Gets the axes object out after plotting
ax = data.plot(...)
# Turns off grid on the left Axis.
ax.grid(False)
# Turns off grid on the secondary (right) Axis.
ax.right_ax.grid(False)

sns.set_style("whitegrid", {'axes.grid' : False})
Note that the style can be whichever valid one that you choose.
For a nice article on this, refer to this site.

The problem is with using the default pandas formatting (or whatever formatting you chose). Not sure how things work behind the scenes, but these parameters are trumping the formatting that you pass as in the plot function. You can see a list of them here in the mpl_style dictionary
In order to get around it, you can do this:
import pandas as pd
pd.options.display.mpl_style = 'default'
new_style = {'grid': False}
matplotlib.rc('axes', **new_style)
data = pd.DataFrame(np.cumsum(np.random.normal(size=(100,2)),axis=0),columns=['A','B'])
data.plot(secondary_y=['B'])

This feels like buggy behavior in Pandas, with not all of the keyword arguments getting passed to both Axes. But if you want to have the grid off by default in seaborn, you just need to call sns.set_style("dark"). You can also use sns.axes_style in a with statement if you only want to change the default for one figure.

You can just set:
sns.set_style("ticks")
It goes back to normal.

Grouped bar chart of multiindex

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.

How can I loop through a list of elements and create time series plots in Python

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))

Python x-labels evenly spreaded

I am trying to get my labels turned up correctly.
I want it to show 10 values on the list, but I have no idea how.
The usual way to show it doesn't work and when there are a lot of values, it is hard to read, see the picture.
Here you see that it is impossible to read the data.
Do you have an idea to make it work properly?
I have tried autoDateLocator, but that didn't work. The axis values were wrong.
I also tried to do that manually, but the same result happened.
Thanks in advance!

I have kinda 'solved this issue'. I asked around and didn't really find an answer that suited my needs, as every tick always gets shown.
However, when I keep the xtick_labels in the datetime-format, it gets sorted out by matplotlib itself, if there are too many values to show. This according to the dataset Armamatita provided
import matplotlib.pyplot as plt
import datetime
import numpy as np
x = np.array([datetime.datetime(i,1,1) for i in range(1700,2017)])
y = np.random.randint(0,100,len(x))
fig, ax = plt.subplots()
ax.plot(x,y)
plt.show()
I when the days I want to see is more than 14, I just let matplotlib pick te xtick_labels. When equal or less than 14, I add it myself using:
alldays = DayLocator()
weekFormatter = DateFormatter('%a %b %d %Y')
and this right before returning the fig:
ax.xaxis.set_major_locator(alldays)
ax.xaxis.set_major_formatter(weekFormatter)
It isn't the most neat pythonic code, but it does the trick.

How to get rid of grid lines when plotting with Seaborn + Pandas with secondary_y

I'm plotting two data series with Pandas with seaborn imported. Ideally I would like the horizontal grid lines shared between both the left and the right y-axis, but I'm under the impression that this is hard to do.
As a compromise I would like to remove the grid lines all together. The following code however produces the horizontal gridlines for the secondary y-axis.
import pandas as pd
import numpy as np
import seaborn as sns
data = pd.DataFrame(np.cumsum(np.random.normal(size=(100,2)),axis=0),columns=['A','B'])
data.plot(secondary_y=['B'],grid=False)

You can take the Axes object out after plotting and perform .grid(False) on both axes.
# Gets the axes object out after plotting
ax = data.plot(...)
# Turns off grid on the left Axis.
ax.grid(False)
# Turns off grid on the secondary (right) Axis.
ax.right_ax.grid(False)

sns.set_style("whitegrid", {'axes.grid' : False})
Note that the style can be whichever valid one that you choose.
For a nice article on this, refer to this site.

The problem is with using the default pandas formatting (or whatever formatting you chose). Not sure how things work behind the scenes, but these parameters are trumping the formatting that you pass as in the plot function. You can see a list of them here in the mpl_style dictionary
In order to get around it, you can do this:
import pandas as pd
pd.options.display.mpl_style = 'default'
new_style = {'grid': False}
matplotlib.rc('axes', **new_style)
data = pd.DataFrame(np.cumsum(np.random.normal(size=(100,2)),axis=0),columns=['A','B'])
data.plot(secondary_y=['B'])

This feels like buggy behavior in Pandas, with not all of the keyword arguments getting passed to both Axes. But if you want to have the grid off by default in seaborn, you just need to call sns.set_style("dark"). You can also use sns.axes_style in a with statement if you only want to change the default for one figure.

You can just set:
sns.set_style("ticks")
It goes back to normal.

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