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Annotate bars with values on Pandas bar plots
(4 answers)
Closed 1 year ago.
I would like to create an annotation to a bar chart that compares the value of the bar to two reference values. An overlay such as shown in the picture, a kind of staff gauge, is possible, but I'm open to more elegant solutions.
The bar chart is generated with the pandas API to matplotlib (e.g. data.plot(kind="bar")), so a plus would be if the solution is playing nicely with that.
You may use smaller bars for the target and benchmark indicators. Pandas cannot annotate bars automatically, but you can simply loop over the values and use matplotlib's pyplot.annotate instead.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
a = np.random.randint(5,15, size=5)
t = (a+np.random.normal(size=len(a))*2).round(2)
b = (a+np.random.normal(size=len(a))*2).round(2)
df = pd.DataFrame({"a":a, "t":t, "b":b})
fig, ax = plt.subplots()
df["a"].plot(kind='bar', ax=ax, legend=True)
df["b"].plot(kind='bar', position=0., width=0.1, color="lightblue",legend=True, ax=ax)
df["t"].plot(kind='bar', position=1., width=0.1, color="purple", legend=True, ax=ax)
for i, rows in df.iterrows():
plt.annotate(rows["a"], xy=(i, rows["a"]), rotation=0, color="C0")
plt.annotate(rows["b"], xy=(i+0.1, rows["b"]), color="lightblue", rotation=+20, ha="left")
plt.annotate(rows["t"], xy=(i-0.1, rows["t"]), color="purple", rotation=-20, ha="right")
ax.set_xlim(-1,len(df))
plt.show()
There's no direct way to annotate a bar plot (as far as I am aware) Some time ago I needed to annotate one so I wrote this, perhaps you can adapt it to your needs.
import matplotlib.pyplot as plt
import numpy as np
ax = plt.subplot(111)
ax.set_xlim(-0.2, 3.2)
ax.grid(b=True, which='major', color='k', linestyle=':', lw=.5, zorder=1)
# x,y data
x = np.arange(4)
y = np.array([5, 12, 3, 7])
# Define upper y limit leaving space for the text above the bars.
up = max(y) * .03
ax.set_ylim(0, max(y) + 3 * up)
ax.bar(x, y, align='center', width=0.2, color='g', zorder=4)
# Add text to bars
for xi, yi, l in zip(*[x, y, list(map(str, y))]):
ax.text(xi - len(l) * .02, yi + up, l,
bbox=dict(facecolor='w', edgecolor='w', alpha=.5))
ax.set_xticks(x)
ax.set_xticklabels(['text1', 'text2', 'text3', 'text4'])
ax.tick_params(axis='x', which='major', labelsize=12)
plt.show()
import numpy as np
import matplotlib.pyplot as plt
data = np.loadtxt('Lab6.csv', dtype='float', delimiter=',', unpack=True)
t = (data[:1])
yt = np.cos(4 * np.pi * t )
y = data[1:2]
plt.figure()
plt.plot(t,yt,t,y,'o')
plt.xlabel('Time, s')
plt.ylabel('Voltage, V')
plt.legend(('Signal 1', 'Signal 1'))
plt.show()
I imported my data from a cvs file. I am looking to have to waveforms with lines between each data point and two separate colors for each wave
Output from code
I am trying to make a scatter plot for my data using matplotlib.pyplot (which is imported as plt) and using the following command:
fig = plt.figure(1, figsize = (10, 6))
plt.figure()
plt.scatter(X_train_reduced[:, 0], X_train_reduced[:, 1], c = y_train, cmap = plt.cm.Paired , linewidths=10)
but it gives this error:
ValueError: 'c' argument must be a mpl color, a sequence of mpl colors or a sequence of numbers, not sample group
when I remove this argument:
c = y_train
it returns the plot but the same color for all groups (I have 2 groups).
do you know how to fix it?
I've got the following data:
I'm interested in fitting a line on the 'middle bit' (intercept 0). How do I do that? It would be useful to get a figure for the gradient as well.
(FYI These are a list of cash transactions, in and out. The gradient would be the profit or loss).
Here's some of the data:
https://gist.github.com/chrism2671/1081c13b6760878b457a112d2041622f
You can use numpy.polyfit and numpy.poly1d to achieve that:
import matplotlib.pyplot as plt
import numpy as np
# Create data
ls = np.linspace(0, 100)
s = np.random.rand(len(ls))*100 + ls
# Fit the data
z = np.polyfit(ls, s, deg=1)
p = np.poly1d(z)
# Plotting
plt.figure(figsize=(16,4.5))
plt.plot(ls, s,
alpha=.75, label='signal')
plt.plot(ls, p(ls),
linewidth=1, linestyle='--', color='r', label='polyfit')
plt.legend(ncol=2)
Using the data you provided:
I need to draw many such rows (for a0 .. a128) in a single window. I've searched in FacetGrid, PairGrid and all over around but couldn't find. Only regplot has similar argument ax but it doesn't plot histograms. My data is 128 real valued features with label column [0, 1]. I need the graphs to be shown from my Python code as a separate application on Linux.
Also, it there a way to scale this histogram to show relative values on Y such that the right curve is not skewed?
g = sns.FacetGrid(df, col="Result")
g.map(plt.hist, "a0", bins=20)
plt.show()
Just a simple example using matplotlib. The code is not optimized (ugly, but simple plot-indexing):
import numpy as np
import matplotlib.pyplot as plt
N = 5
data = np.random.normal(size=(N*N, 1000))
f, axarr = plt.subplots(N, N) # maybe you want sharex=True, sharey=True
pi = [0,0]
for i in range(data.shape[0]):
if pi[1] == N:
pi[0] += 1 # next row
pi[1] = 0 # first column again
axarr[pi[0], pi[1]].hist(data[i], normed=True) # i was wrong with density;
# normed=True should be used
pi[1] += 1
plt.show()
Output: