Does anyone know how can i draw more lines with matplotlib.pyplot.plot but forcing them to use its own axes?
eg I have data in lists a b c
a is the base of the others (time), so I would like to draw how b and c changes
but b contains big numbers and c contains small numbers, so when i draw both then i can see only b
thanks
You just have to add a secondary axis to your plot.
As an example, this code...
from matplotlib.pyplot import *
#creating some data
a = range(10)
b = [2*x for x in a]
c = [x**10 for x in a]
fig = figure()
ax1 = fig.add_subplot(111)
ax1.set_ylabel('$y = 2 . x$')
ax1.plot(a, b, 'yo')
ax2 = ax1.twinx() #create a twin of Axes for generating a plot
# with a share x-axis but independent y axis
ax2.set_ylabel('$y = x^{10}$')
ax2.plot(a,c,'b-')
show()
...will generate this figure:
Related
I've two plots generated using matplotlib. The first represents my backround and the second a group of points which I want to show. Is there a way to overlap the two plots?
background:
import matplotlib.pyplot as plt
fig, ax = plt.subplots(figsize = (10,10))
grid_duomo = gpd.read_file('/content/Griglia_2m-SS.shp')
grid_duomo.to_crs(epsg=32632).plot(ax=ax, color='lightgrey')
points:
fig = plt.figure(figsize=(10, 10))
ids = traj_collection_df_new_app['id'].unique()
for id_ in ids:
self_id = traj_collection_df_new_app[traj_collection_df_new_app['id'] == id_]
plt.plot(
self_id['lon'],
self_id['lat'],
# markers= 'o',
# markersize=12
)
plt.plot() will always take the most recent axis found by matplotlib and use it for plotting.
Its practically the same as plt.gca().plot() where plt.gca() stands for "get current axis".
To get full control over which axis is used, you should do something like this:
(the zorder argument is used to set the "vertical stacking" of the artists, e.g. zorder=2 will be plotted on top of zorder=1)
f = plt.figure() # create a figure
ax = f.add_subplot( ... ) # create an axis in the figure f
ax.plot(..., zorder=0)
grid_duomo.plot(ax=ax, ..., zorder=1)
# you can then continue to add more axes to the same figure using
# f.add_subplot() or f.add_axes()
(if this is unclear, maybe check the quick_start guide of matplotlib? )
I have a subplot that plots a line (x,y) and a particular point (xx,yy). I want to highligh (xx,yy), so I've plotted it with scatter. However, even if I order it after the original plot, the new point still shows up behind the original line. How can I fix this? MWE below.
x = 1:10
y = 1:10
xx = 5
yy = 5
fig, ax = subplots()
ax[:plot](x,y)
ax[:scatter](xx,yy, color="red", label="h_star", s=100)
legend()
xlabel("x")
ylabel("y")
title("test")
grid("on")
You can change which plots are displayed on top of each other with the argument zorder. The matplotlib example shown here gives a brief explanation:
The default drawing order for axes is patches, lines, text. This
order is determined by the zorder attribute. The following defaults
are set
Artist Z-order
Patch / PatchCollection 1
Line2D / LineCollection 2
Text 3
You can change the order for individual artists by setting the zorder.
Any individual plot() call can set a value for the zorder of that
particular item.
A full example based on the code in the question, using python is shown below:
import matplotlib.pyplot as plt
x = range(1,10)
y = range(1,10)
xx = 5
yy = 5
fig, ax = plt.subplots()
ax.plot(x,y)
# could set zorder very high, say 10, to "make sure" it will be on the top
ax.scatter(xx,yy, color="red", label="h_star", s=100, zorder=3)
plt.legend()
plt.xlabel("x")
plt.ylabel("y")
plt.title("test")
plt.grid("on")
plt.show()
Which gives:
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:
I would like to annotate the data points with their values next to the points on the plot. The examples I found only deal with x and y as vectors. However, I would like to do this for a pandas DataFrame that contains multiple columns.
ax = plt.figure().add_subplot(1, 1, 1)
df.plot(ax = ax)
plt.show()
What is the best way to annotate all the points for a multi-column DataFrame?
Here's a (very) slightly slicker version of Dan Allan's answer:
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import string
df = pd.DataFrame({'x':np.random.rand(10), 'y':np.random.rand(10)},
index=list(string.ascii_lowercase[:10]))
Which gives:
x y
a 0.541974 0.042185
b 0.036188 0.775425
c 0.950099 0.888305
d 0.739367 0.638368
e 0.739910 0.596037
f 0.974529 0.111819
g 0.640637 0.161805
h 0.554600 0.172221
i 0.718941 0.192932
j 0.447242 0.172469
And then:
fig, ax = plt.subplots()
df.plot('x', 'y', kind='scatter', ax=ax)
for k, v in df.iterrows():
ax.annotate(k, v)
Finally, if you're in interactive mode you might need to refresh the plot:
fig.canvas.draw()
Which produces:
Or, since that looks incredibly ugly, you can beautify things a bit pretty easily:
from matplotlib import cm
cmap = cm.get_cmap('Spectral')
df.plot('x', 'y', kind='scatter', ax=ax, s=120, linewidth=0,
c=range(len(df)), colormap=cmap)
for k, v in df.iterrows():
ax.annotate(k, v,
xytext=(10,-5), textcoords='offset points',
family='sans-serif', fontsize=18, color='darkslategrey')
Which looks a lot nicer:
Do you want to use one of the other columns as the text of the annotation? This is something I did recently.
Starting with some example data
In [1]: df
Out[1]:
x y val
0 -1.015235 0.840049 a
1 -0.427016 0.880745 b
2 0.744470 -0.401485 c
3 1.334952 -0.708141 d
4 0.127634 -1.335107 e
Plot the points. I plot y against x, in this example.
ax = df.set_index('x')['y'].plot(style='o')
Write a function that loops over x, y, and the value to annotate beside the point.
def label_point(x, y, val, ax):
a = pd.concat({'x': x, 'y': y, 'val': val}, axis=1)
for i, point in a.iterrows():
ax.text(point['x'], point['y'], str(point['val']))
label_point(df.x, df.y, df.val, ax)
draw()
Let's assume your df has multiple columns, and three of which are x, y, and lbl. To annotate your (x,y) scatter plot with lbl, simply:
ax = df.plot(kind='scatter',x='x',y='y')
df[['x','y','lbl']].apply(lambda row: ax.text(*row),axis=1);
I found the previous answers quite helpful, especially LondonRob's example that improved the layout a bit.
The only thing that bothered me is that I don't like pulling data out of DataFrames to then loop over them. Seems a waste of the DataFrame.
Here was an alternative that avoids the loop using .apply(), and includes the nicer-looking annotations (I thought the color scale was a bit overkill and couldn't get the colorbar to go away):
ax = df.plot('x', 'y', kind='scatter', s=50 )
def annotate_df(row):
ax.annotate(row.name, row.values,
xytext=(10,-5),
textcoords='offset points',
size=18,
color='darkslategrey')
_ = df.apply(annotate_df, axis=1)
Edit Notes
I edited my code example recently. Originally it used the same:
fig, ax = plt.subplots()
as the other posts to expose the axes, however this is unnecessary and makes the:
import matplotlib.pyplot as plt
line also unnecessary.
Also note:
If you are trying to reproduce this example and your plots don't have the points in the same place as any of ours, it may be because the DataFrame was using random values. It probably would have been less confusing if we'd used a fixed data table or a random seed.
Depending on the points, you may have to play with the xytext values to get better placements.
I would like to annotate the data points with their values next to the points on the plot. The examples I found only deal with x and y as vectors. However, I would like to do this for a pandas DataFrame that contains multiple columns.
ax = plt.figure().add_subplot(1, 1, 1)
df.plot(ax = ax)
plt.show()
What is the best way to annotate all the points for a multi-column DataFrame?
Here's a (very) slightly slicker version of Dan Allan's answer:
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import string
df = pd.DataFrame({'x':np.random.rand(10), 'y':np.random.rand(10)},
index=list(string.ascii_lowercase[:10]))
Which gives:
x y
a 0.541974 0.042185
b 0.036188 0.775425
c 0.950099 0.888305
d 0.739367 0.638368
e 0.739910 0.596037
f 0.974529 0.111819
g 0.640637 0.161805
h 0.554600 0.172221
i 0.718941 0.192932
j 0.447242 0.172469
And then:
fig, ax = plt.subplots()
df.plot('x', 'y', kind='scatter', ax=ax)
for k, v in df.iterrows():
ax.annotate(k, v)
Finally, if you're in interactive mode you might need to refresh the plot:
fig.canvas.draw()
Which produces:
Or, since that looks incredibly ugly, you can beautify things a bit pretty easily:
from matplotlib import cm
cmap = cm.get_cmap('Spectral')
df.plot('x', 'y', kind='scatter', ax=ax, s=120, linewidth=0,
c=range(len(df)), colormap=cmap)
for k, v in df.iterrows():
ax.annotate(k, v,
xytext=(10,-5), textcoords='offset points',
family='sans-serif', fontsize=18, color='darkslategrey')
Which looks a lot nicer:
Do you want to use one of the other columns as the text of the annotation? This is something I did recently.
Starting with some example data
In [1]: df
Out[1]:
x y val
0 -1.015235 0.840049 a
1 -0.427016 0.880745 b
2 0.744470 -0.401485 c
3 1.334952 -0.708141 d
4 0.127634 -1.335107 e
Plot the points. I plot y against x, in this example.
ax = df.set_index('x')['y'].plot(style='o')
Write a function that loops over x, y, and the value to annotate beside the point.
def label_point(x, y, val, ax):
a = pd.concat({'x': x, 'y': y, 'val': val}, axis=1)
for i, point in a.iterrows():
ax.text(point['x'], point['y'], str(point['val']))
label_point(df.x, df.y, df.val, ax)
draw()
Let's assume your df has multiple columns, and three of which are x, y, and lbl. To annotate your (x,y) scatter plot with lbl, simply:
ax = df.plot(kind='scatter',x='x',y='y')
df[['x','y','lbl']].apply(lambda row: ax.text(*row),axis=1);
I found the previous answers quite helpful, especially LondonRob's example that improved the layout a bit.
The only thing that bothered me is that I don't like pulling data out of DataFrames to then loop over them. Seems a waste of the DataFrame.
Here was an alternative that avoids the loop using .apply(), and includes the nicer-looking annotations (I thought the color scale was a bit overkill and couldn't get the colorbar to go away):
ax = df.plot('x', 'y', kind='scatter', s=50 )
def annotate_df(row):
ax.annotate(row.name, row.values,
xytext=(10,-5),
textcoords='offset points',
size=18,
color='darkslategrey')
_ = df.apply(annotate_df, axis=1)
Edit Notes
I edited my code example recently. Originally it used the same:
fig, ax = plt.subplots()
as the other posts to expose the axes, however this is unnecessary and makes the:
import matplotlib.pyplot as plt
line also unnecessary.
Also note:
If you are trying to reproduce this example and your plots don't have the points in the same place as any of ours, it may be because the DataFrame was using random values. It probably would have been less confusing if we'd used a fixed data table or a random seed.
Depending on the points, you may have to play with the xytext values to get better placements.