i was wondering if and how i can add x axis label to pywaffle.
value1 = new_df['value1'].tolist()
new_list = [i+1 for i in range(len(value1))]
fig = plt.figure(
FigureClass=Waffle,
rows=1,
columns=len(value1), # Either rows or columns could be omitted
values=value1,
title = {"label": name, "loc": "left"},
)
plt.savefig("plot.png", bbox_inches="tight")
my value1 values are [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31]
i will like every column to be labeld
Yes, it is possible to add ticks etc.
A waffle chart with limited number of columns
But it is a bit unclear what your final goal is. By default, a waffle charts draws as many squares as each of the values indicates. So, if the values are [1, 2, 3, 4, 5, 6], and the color ['red', 'orange', 'blue', 'gold', 'green', 'purple'], there would be 1 red square, 2 oranges, 3 blues, 4 yellows, 5 greens and 6 purples.
import matplotlib.pyplot as plt
from pywaffle import Waffle
value1 = [1, 2, 3, 4, 5, 6]
fig = plt.figure(
FigureClass=Waffle,
rows=1,
#columns=sum(value1),
values=value1,
colors=['red','orange','blue','gold','green','purple']
)
If you set the number of rows and columns so their product is smaller than 21, each of the values will be reduced more or less proportionally, but still be an integer. In the current example, the red one goes suppressed, the orange, blue, yellow and green get reduced to 1, and the green gets reduced to 2 squares. This makes it unclear which label you want to put where.
value1 = [1, 2, 3, 4, 5, 6]
fig = plt.figure(
FigureClass=Waffle,
rows=1,
columns=len(value1),
values=value1,
colors=['red','orange','blue','gold','green','purple']
)
Adding x ticks
To add ticks to a waffle chart, you can turn the axes on. To position the ticks, you need to know that the squares have a width of 1, and a default distance of 0.2. So, the first tick comes at 0.5, the next one at 1+0.2+0.5, etc. Optionally, you can remove spines and the dummy y ticks.
import matplotlib.pyplot as plt
from pywaffle import Waffle
value1 = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31]
fig = plt.figure(
FigureClass=Waffle,
rows=1,
columns=len(value1),
values=value1,
title={"label": 'title', "loc": "left"},
figsize=(15,3),
)
plt.axis('on')
plt.yticks([])
plt.xticks([i * 1.2 + 0.5 for i in range(len(value1))], value1)
for sp in ['left', 'right', 'top']:
plt.gca().spines[sp].set_visible(False)
plt.show()
A Seaborn heatmap
Instead of a waffle chart, you could create a heatmap. Then, each square will get a color corresponding to the given values. Optionally, these values (or another string) can be shown as annotation or as x tick label.
import matplotlib.pyplot as plt
import seaborn as sns
value1 = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31]
plt.figure(figsize=(15, 3))
ax = sns.heatmap(data=[value1], xticklabels=value1, yticklabels=False,
annot=True, square=True, linewidths=1.5, cbar=False)
ax.set_title('title', loc='left')
plt.tight_layout()
plt.show()
# Remove borders, ticks, etc.
ax.axis("off")
saw this in pywaffle.py, so i dont think adding axis is possible.
Related
I have data (duration of a certain activity) for two categories (Monday, Tuesday). I would like to generate a bar chart (see 1). Bars above a threshold (different for both categories) should have a different color; e.g. on Mondays data above 10 hours should be blue and on Tuesdays above 12 hours. Any ideas how I could implement this in seaborn or matplotlib?
Thank you very much.
Monday = [5,6,8,12,5,20,4, 8]
Tuesday=[3,5,8,12,4,17]
Goal
You could draw two barplots, using an array of booleans for the coloring (hue):
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
monday = np.array([5, 6, 8, 12, 5, 20, 4, 8])
tuesday = np.array([3, 5, 8, 12, 4, 17])
sns.set_style('whitegrid')
fig, (ax0, ax1) = plt.subplots(ncols=2, figsize=(10, 4), sharey=True)
palette = {False: 'skyblue', True: 'tomato'}
sns.barplot(x=np.arange(len(monday)), y=monday, hue=monday >= 10, palette=palette, dodge=False, ax=ax0)
ax0.set_xlabel('Monday', size=20)
ax0.set_xticks([])
ax0.legend_.remove()
sns.barplot(x=np.arange(len(tuesday)), y=tuesday, hue=tuesday >= 12, palette=palette, dodge=False, ax=ax1)
ax1.set_xlabel('Tuesday', size=20)
ax1.set_xticks([])
ax1.legend_.remove()
sns.despine()
plt.tight_layout()
plt.subplots_adjust(wspace=0)
plt.show()
I am trying to graph a table as a bar graph.
I get my desired outcome using df.plot(kind='bar') structure. But for certain reasons, I now need to graph it using the ax.bar() structure.
Please refer to the example screenshot. I would like to graph the x axis as categorical labels like the df.plot(kind='bar') structure rather than continuous scale, but need to learn to use ax.bar() structure to do the same.
Make the index categorical by setting the type to 'str'
import pandas as pd
import matplotlib.pyplot as plt
data = {'SA': [11, 12, 13, 16, 17, 159, 209, 216],
'ET': [36, 45, 11, 15, 16, 4, 11, 10],
'UT': [11, 26, 10, 11, 16, 7, 2, 2],
'CT': [5, 0.3, 9, 5, 0.2, 0.2, 3, 4]}
df = pd.DataFrame(data)
df['SA'] = df['SA'].astype('str')
df.set_index('SA', inplace=True)
width = 3
fig, ax = plt.subplots(figsize=(12, 8))
p1 = ax.bar(df.index, df.ET, color='b', label='ET')
p2 = ax.bar(df.index, df.UT, bottom=df.ET, color='g', label='UT')
p3 = ax.bar(df.index, df.CT, bottom=df.ET+df.UT, color='r', label='CT')
plt.legend()
plt.show()
If we look at this code and x,y data,
rects1 = plt.bar([0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,1],[1, 2, 4, 10, 5, 9, 1,4, 9, 9],edgecolor='black')
plt.xlabel('Sample Mean')
plt.ylabel('Probability')
this displays the following graph
I can not understand how the x values go beyond 1 and even takes negative values. Also, why do the bars have different widths?
The problem is that your x-values are separated by a spacing of 0.1 and the default bar width is 1 so you see overlapping bars. The solution is to define the bar width. In your case, a bar width smaller than 0.1 will work perfectly fine. For instance, you can use width=0.05 and you will get the following graph.
Why negative?: The bars are by default centered at 0, 1, 2, 3 and so on. So your first bar in the question was drawn centered at 0 and had a width of 1. That's why it was spanning from -0.5 to +0.5.
rects1 = plt.bar([0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,1],
[1, 2, 4, 10, 5, 9, 1,4, 9, 9], width=0.05, edgecolor='black')
plt.xlabel('Sample Mean')
plt.ylabel('Probability')
If you don't want bars at x<0: You can align your bars to the right by passing argument align='edge.
rects1 = plt.bar([0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,1],
[1, 2, 4, 10, 5, 9, 1,4, 9, 9], width=0.05, align='edge',
edgecolor='black')
Similar to this question but for many circles with numbers in them. I don't know why but the figure that is generated comes out blank. I would like a figure with 9 circles (having 1 of 3 colors), with the "job_id" printed in the circle.
import matplotlib.pyplot as plt
import pandas as pd
d = {'job_id': [1, 2, 3, 4, 5, 6, 7, 8, 9],
'hub': ['ZH1', 'ZH1', 'ZH1', 'ZH2', 'ZH2', 'ZH3', 'ZH3', 'ZH3', 'ZH3'],
'alerts': [18, 35, 45, 8, 22, 34, 29, 20, 30],
'color': ['orange', 'orange', 'orange', 'green', 'green', 'lightblue', 'lightblue', 'lightblue', 'lightblue']}
df=pd.DataFrame(data=d)
ax=plt.subplot(111)
for index, row in df.iterrows():
print(row)
ax.text(index,row['alerts'],str(row['job_id']), transform=plt.gcf().transFigure,
bbox={"boxstyle" : "circle", "color":row['color']})
plt.show()
Two problems.
The transform is set to the figure transform. This would take numbers between 0 and 1 in both directions. However your data ranges much above 1. Since it seems you want to show the circles in data coordinates anyways, remove the transform=... part.
Text elements cannot be used to autoscale the axes. You would hence need to set the limits manually.
Complete code:
import matplotlib.pyplot as plt
import pandas as pd
d = {'job_id': [1, 2, 3, 4, 5, 6, 7, 8, 9],
'hub': ['ZH1', 'ZH1', 'ZH1', 'ZH2', 'ZH2', 'ZH3', 'ZH3', 'ZH3', 'ZH3'],
'alerts': [18, 35, 45, 8, 22, 34, 29, 20, 30],
'color': ['orange', 'orange', 'orange', 'green', 'green', 'lightblue', 'lightblue', 'lightblue', 'lightblue']}
df=pd.DataFrame(data=d)
ax=plt.subplot(111)
for index, row in df.iterrows():
ax.text(index, row['alerts'],str(row['job_id']),
bbox={"boxstyle" : "circle", "color":row['color']})
ax.set(xlim=(-1,len(df)), ylim=(df["alerts"].min()-5, df["alerts"].max()+5))
plt.show()
You need to map the x-y coordinates within 0-1 range. To do so I divide the x and y by the maximum value in the DataFrame. Later, I adjust the x- and y-limits accordingly and label the axes to display the actual values.
You also had only two 'green' but four 'lightblue' in your dictionary. I corrected it. I also replaced index bby row['job_id'] because index starts with 0 but you would want to plot the circle 1 at x=1
for index, row in df.iterrows():
ax.text(row['job_id']/max(d['job_id']),row['alerts']/max(d['alerts']),str(row['job_id']),
bbox={"boxstyle" : "circle", "color":row['color']})
plt.xlim(0, 1.1)
plt.ylim(0, 1.1)
plt.xticks(np.linspace(0,1,10), range(10))
plt.yticks(np.linspace(0,1,10), range(0,50,5))
I am generating a heat map for my data.
everything works fine, but I have a little problem. My data (numbers) are from 0 to 10.000.
0 means nothing (no data) and at the moment the field with 0 just take the lowest color of my color scala. My problem is how to make the data with 0 to have a total different color (e.g. black or white)
Just see the Picture to better understand what i mean:
My code (snippet) looks like this:
matplotlib.pyplot.imshow(results, interpolation='none')
matplotlib.pyplot.colorbar();
matplotlib.pyplot.xticks([0, 1, 2, 3, 4, 5, 6, 7, 8], [10, 15, 20, 25, 30, 35, 40, 45, 50]);
matplotlib.pyplot.xlabel('Population')
matplotlib.pyplot.yticks([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10], [1, 2, 3, 4, 5, 10, 15, 20, 25, 30, 'serial']);
matplotlib.pyplot.ylabel('Communication Step');
axis.xaxis.tick_top();
matplotlib.pyplot.savefig('./results_' + optimisationProblem + '_dim' + str(numberOfDimensions) + '_' + statisticType + '.png');
matplotlib.pyplot.close();
If you are not interested in a smooth transition between the values 0 and 0.0001, you can just set every value that equals 0 to NaN. This will result in a white color whereas 0.0001 will still be deep blue-ish.
In the following code I include an example. I generate the data randomly. I therefore select a single element from my array and set it to NaN. This results in the color white. I also included a line in which you can set every data point that equals 0 to NaN.
import numpy
import matplotlib.pyplot as plt
#Random data
data = numpy.random.random((10, 10))
#Set all data points equal to zero to NaN
#data[data == 0.] = float("NaN")
#Set single data value to nan
data[2][2] = float("NaN")
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.imshow(data, interpolation = "nearest")
plt.show()