I have 3 lists to plot as curves. But every time I run the same plt lines, even with the ax.legend(loc='lower right', handles=[line1, line2, line3]), these 3 lists jumps randomly in the legend like below. Is it possible to fix their sequences and the colors for the legend as well as the curves in the plot?
EDIT:
My code is as below:
def plot_with_fixed_list(n, **kwargs):
np.random.seed(0)
fig, ax1 = plt.subplots()
my_handles = []
for key, values in kwargs.items():
value_name = key
temp, = ax1.plot(np.arange(1, n+ 1, 1).tolist(), values, label=value_name)
my_handles.append(temp)
ax1.legend(loc='lower right', handles=my_handles)
ax1.grid(True, which='both')
plt.show()
plot_with_fixed_list(300, FA_Hybrid=fa, BP=bp, Ssym_Hybrid=ssym)
This nondeterminism bug resides with python==3.5, matplotlib==3.0.0. After I updated to python==3.6, matplotlib==3.3.2, problem solved.
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'm plotting a kde distribution of 2 dataframes on the same axis, and I need to set a legend saying which line is which dataframe. Now, this is my code:
fig, ax = plt.subplots(figsize=(15,10))
for label, df in dataframe1.groupby('ID'):
dataframe1.Value.plot(kind="kde", ax=ax,color='r')
for label, df in dataframe2.groupby('ID'):
dataframe2.Value.plot(kind='kde', ax=ax, color='b')
plt.legend()
plt.title('title here', fontsize=20)
plt.axvline(x=np.pi,color='gray',linestyle='--')
plt.xlabel('mmHg', fontsize=16)
plt.show()
But the result is this:
How can I show the legends inside the graph as 'values from df1' and 'results from df2'?
Edit:
with the following code I correctly have the question's result. But in some dataframes I get the following results:
fig, ax = plt.subplots(figsize=(15,10))
sns.kdeplot(akiPEEP['Value'], color="r", label='type 1', ax=ax)
sns.kdeplot(noAkiPEEP['Value'], color="b",label='type 2', ax=ax)
plt.legend()
plt.title('d', fontsize=20)
plt.axvline(x=np.pi,color='gray',linestyle='--')
plt.xlabel('value', fontsize=16)
plt.show()
A distribution I'm plotting now:
How do I fix this? Also, is it good to also plot the rolling means over this distribution or it becomes too heavy?
I'm not sure I understand your question, but from your code, it looks like you are trying to plot one KDE per ID value in your dataframes. In which case you would have to do:
for label, df in dataframe1.groupby('ID'):
df.Value.plot(kind="kde", ax=ax,color='r', label=label)
notice that I replaced dataframe1 by df in the body of the for-loop. df correspond to the sub-dataframe where all the elements in the column ID have value label
Trying to plot linear regression-plot with Seaborn and I am ending up having this:
and under it these empty plots:
I don't need the last 3 small subplots, or at least how to get them plotted correctly, with the main first 3 subplots above?
Here is the code I used:
fig, axes = plt.subplots(3, 1, figsize=(12, 15))
for col, ax in zip(['gross_sqft_thousands','land_sqft_thousands','total_units'], axes.flatten()):
ax.tick_params(axis='x', rotation=85)
ax.set_ylabel(col, fontsize=15)
sns.jointplot(x="sale_price_millions", y=col, data=clean_df, kind='reg', joint_kws={'line_kws':{'color':'cyan'}}, ax=ax)
fig.suptitle('Sale Price vs Continuous Variables', position=(.5,1.02), fontsize=20)
fig.tight_layout()
fig.show()
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 have 2 dataframes. I want to plot a histogram based on a column 'rate' for each, side by side. How to do it?
I tried this:
import matplotlib.pyplot as plt
plt.subplot(1,2,1)
dflux.hist('rate' , bins=100)
plt.subplot(1,2,2)
dflux2.hist('rate' , bins=100)
plt.tight_layout()
plt.show()
It did not have the desired effect. It showed two blank charts then one populated chart.
Use subplots to define a figure with two axes. Then specify the axis to plot to within hist using the ax parameter.
fig, axes = plt.subplots(1, 2)
dflux.hist('rate', bins=100, ax=axes[0])
dflux2.hist('rate', bins=100, ax=axes[1])
Demo
dflux = pd.DataFrame(dict(rate=np.random.randn(10000)))
dflux2 = pd.DataFrame(dict(rate=np.random.randn(10000)))
fig, axes = plt.subplots(1, 2)
dflux.hist('rate', bins=100, ax=axes[0])
dflux2.hist('rate', bins=100, ax=axes[1])