x = ['01-02', '02-02', '03-02', '04-02', '05-02']
y = [2, 2, 3, 7, 2]
fig, ax = plt.subplots(1, 1)
ax.bar(range(len(y)), y, width=0.3,align='center',color='skyblue')
plt.xticks(range(len(y)), x, size='small')
plt.savefig('/home/user/graphimages/foo2.png')
plt.close()
I want to draw grid lines (of x & y) behind the bar graph.
To add a grid you simply need to add
ax.grid()
If you want the grid to be behind the bars then add
ax.grid(zorder=0)
ax.bar(range(len(y)), y, width=0.3, align='center', color='skyblue', zorder=3)
The important part is that the zorder of the bars is greater than grid. Experimenting it seems zorder=3 is the lowest value that actually gives the desired effect. I have no idea why zorder=1 isn't sufficient.
EDIT:
I have noticed this question has already been answered here using a different method although it suffers some link rot. Both methods yield the same result as far as I can see but andrew cooke's answer is more elegant.
I am suggesting another solution since the most voted answer did not work for me. You can use the following code to set the gridlines behind the plot.
ax.set_axisbelow(True)
ax.grid(color='gray', linestyle='dashed')
I got this code from this answer.
plt.grid(True, color = "grey", linewidth = "1.4", linestyle = "-.")
This worked for me, the grid lines will be in grey border color,if you want can change border design to linestyle = ".."
Like this
plt.grid(True, color = "grey", linewidth = "1.4", linestyle = "..")
Summing up entire code block:
fig, ax = plt.subplots(1, 1)
ax.bar(range(len(y)), y, width=0.3,align='center',color='skyblue')
plt.xticks(range(len(y)), x, size='small')
plt.grid(True, color = "grey", linewidth = "1.4", linestyle = "-.")
plt.savefig('/home/user/graphimages/foo2.png')
plt.close()
use .grid() it makes the order go to 0 (back)
ax.grid(zorder=0)
ax.grid(zorder=0) Woud work. But First Place the Bar and then Place the Grid.Not the orther way.
ax = df.plot.bar(x='Index', y='Values', rot=90)
ax.grid(zorder=0)
I took some currency Correlation with Year and Sorted it as my Data Frame df, and below is the result of the code run.
Related
I do not really understand why this code is not working
fig, ax = plt.subplots()
ax.plot(Vg_vec, sigma_i)
ax.set_xlabel('$V_\mathrm{g}$ ($\mathrm{V}}$)')
ax.set_ylabel('$\sigma_\mathrm{i}$ ($\mathrm{C/m^2}$)')
peaks, _ = find_peaks(sigma_i, height=0.006415)
plt.axvline(Vg_vec[peaks], color='red')
ax2 = ax.twiny()
ax2.set_xticks(Vg_vec[peaks])
ax2.tick_params(axis='x', colors='red')
My result
There should be a red tick in the top x axis.
Thanks
the issue with your code is twofold:
By using twiny instead of secondary_axis, the upper x-axis will be different to the bottom one, and I assume you want them to be the same. That's extra work to fix, so I used secondary_axis in my example.
This is something I don't know why it happens, but it has happened to me before. When supplying the tick values, the first one is always "ignored", so you have to supply two or more values. I used 0, but you can use anything.
Here's my code:
fig, ax = plt.subplots()
ax.plot(Vg_vec, sigma_i)
ax.set_xlabel('$V_\mathrm{g}$ ($\mathrm{V}}$)')
ax.set_ylabel('$\sigma_\mathrm{i}$ ($\mathrm{C/m^2}$)')
peaks, _ = find_peaks(sigma_i, height=None)
plt.axvline(Vg_vec[peaks], color='red')
ax2 = ax.secondary_xaxis('top')
ax2.tick_params(axis='x', color='red')
ax2.set_xticks([0, *Vg_vec[peaks]], minor=False)
And the resulting plot:
I am trying to plot two separate quantities on the same graph using twiny as follows:
fig = figure()
ax = fig.add_subplot(111)
ax.plot(T, r, 'b-', T, R, 'r-', T, r_geo, 'g-')
ax.set_yscale('log')
ax.annotate('Approx. sea level', xy=(Planet.T_day*1.3,(Planet.R)/1000), xytext=(Planet.T_day*1.3, Planet.R/1000))
ax.annotate('Geostat. orbit', xy=(Planet.T_day*1.3, r_geo[0]), xytext=(Planet.T_day*1.3, r_geo[0]))
ax.set_xlabel('Rotational period (hrs)')
ax.set_ylabel('Orbital radius (km), logarithmic')
ax.set_title('Orbital charts for ' + Planet.N, horizontalalignment='center', verticalalignment='top')
ax2 = ax.twiny()
ax2.plot(v,r,'k-')
ax2.set_xlabel('Linear speed (ms-1)')
show()
and the data is presented fine, but I am having the problem that the figure title is overlapping with the axes labels on the secondary x axis so that it's barely legible (I wanted to post a picture example here, but I don't have a high enough rep yet).
I'd like to know if there's a straightforward way to just shift the title directly up a few tens of pixels, so that the chart looks prettier.
I'm not sure whether it is a new feature in later versions of matplotlib, but at least for 1.3.1, this is simply:
plt.title(figure_title, y=1.08)
This also works for plt.suptitle(), but not (yet) for plt.xlabel(), etc.
Forget using plt.title and place the text directly with plt.text. An over-exaggerated example is given below:
import pylab as plt
fig = plt.figure(figsize=(5,10))
figure_title = "Normal title"
ax1 = plt.subplot(1,2,1)
plt.title(figure_title, fontsize = 20)
plt.plot([1,2,3],[1,4,9])
figure_title = "Raised title"
ax2 = plt.subplot(1,2,2)
plt.text(0.5, 1.08, figure_title,
horizontalalignment='center',
fontsize=20,
transform = ax2.transAxes)
plt.plot([1,2,3],[1,4,9])
plt.show()
I was having an issue with the x-label overlapping a subplot title; this worked for me:
import matplotlib.pyplot as plt
fig, ax = plt.subplots(2, 1)
ax[0].scatter(...)
ax[1].scatter(...)
plt.tight_layout()
.
.
.
plt.show()
before
after
reference:
https://matplotlib.org/users/tight_layout_guide.html
ax.set_title('My Title\n', fontsize="15", color="red")
plt.imshow(myfile, origin="upper")
If you put '\n' right after your title string, the plot is drawn just below the title. That might be a fast solution too.
You can use pad for this case:
ax.set_title("whatever", pad=20)
Just use plt.tight_layout() before plt.show(). It works well.
A temporary solution if you don't want to get into the x, y position of your title.
Following worked for me.
plt.title('Capital Expenditure\n') # Add a next line after your title
kudos.
Using the plt.tight_layout() before the plt.show() works for me well.
you can even make it better and visible by adding a padding
ax.set_title("title", pad=15)
I'm attempting to plot two bar charts using matplotlib.pyplot.subplots. I've created subplots within a function, but when I output the subplots they are too long in height and not long enough in width.
Here's the function that I wrote:
def corr_bar(data1, data2, method='pearson'):
# Basic configuration.
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(7, 7))
ax1, ax2 = axes
corr_matrix1 = data1.corr(method=method)
corr_matrix2 = data2.corr(method=method)
cmap = cm.get_cmap('coolwarm')
major_ticks = np.arange(0, 1.1, 0.1)
minor_ticks = np.arange(0, 1.1, 0.05)
# Values for plotting.
x1 = corr_matrix1['price'].sort_values(ascending=False).index
x2 = corr_matrix2['price'].sort_values(ascending=False).index
values1 = corr_matrix1['price'].sort_values(ascending=False).values
values2 = corr_matrix2['price'].sort_values(ascending=False).values
im1 = ax1.bar(x1, values1, color=cmap(values1))
im2 = ax2.bar(x2, values2, color=cmap(values2))
# Formatting for plot 1.
ax1.set_yticks(major_ticks)
ax1.set_yticks(minor_ticks, minor=True)
plt.setp(ax1.get_xticklabels(), rotation=45, ha='right', rotation_mode='anchor')
ax1.grid(which='both')
ax1.grid(which='minor', alpha=0.4)
ax1.grid(which='major', alpha=0.7)
ax1.xaxis.grid(False)
# Formatting for plot 2.
ax2.set_yticks(major_ticks)
ax2.set_yticks(minor_ticks, minor=True)
plt.setp(ax2.get_xticklabels(), rotation=45, ha='right', rotation_mode='anchor')
ax2.grid(which='both')
ax2.grid(which='minor', alpha=0.4)
ax2.grid(which='major', alpha=0.7)
ax2.xaxis.grid(False)
fig.tight_layout()
plt.show()
This function (when run with two Pandas DataFrames) outputs an image like the following:
I purposely captured the blank right side of the image as well in an attempt to better depict my predicament. What I want is for the bar charts to be appropriately sized in height and width as to take up the entire space, rather than be elongated and pushed to the left.
I've tried to use the ax.set(aspect='equal') method but it "scrunches up" the bar chart. Would anybody happen to know what I could do to solve this issue?
Thank you.
When you define figsize=(7,7) you are setting the size of the entire figure and not the subplots. So your entire figure must be a square in this case. You should change it to figsize=(14,7) or use a number larger than 14 to get a little bit of extra space.
I am trying to plot two separate quantities on the same graph using twiny as follows:
fig = figure()
ax = fig.add_subplot(111)
ax.plot(T, r, 'b-', T, R, 'r-', T, r_geo, 'g-')
ax.set_yscale('log')
ax.annotate('Approx. sea level', xy=(Planet.T_day*1.3,(Planet.R)/1000), xytext=(Planet.T_day*1.3, Planet.R/1000))
ax.annotate('Geostat. orbit', xy=(Planet.T_day*1.3, r_geo[0]), xytext=(Planet.T_day*1.3, r_geo[0]))
ax.set_xlabel('Rotational period (hrs)')
ax.set_ylabel('Orbital radius (km), logarithmic')
ax.set_title('Orbital charts for ' + Planet.N, horizontalalignment='center', verticalalignment='top')
ax2 = ax.twiny()
ax2.plot(v,r,'k-')
ax2.set_xlabel('Linear speed (ms-1)')
show()
and the data is presented fine, but I am having the problem that the figure title is overlapping with the axes labels on the secondary x axis so that it's barely legible (I wanted to post a picture example here, but I don't have a high enough rep yet).
I'd like to know if there's a straightforward way to just shift the title directly up a few tens of pixels, so that the chart looks prettier.
I'm not sure whether it is a new feature in later versions of matplotlib, but at least for 1.3.1, this is simply:
plt.title(figure_title, y=1.08)
This also works for plt.suptitle(), but not (yet) for plt.xlabel(), etc.
Forget using plt.title and place the text directly with plt.text. An over-exaggerated example is given below:
import pylab as plt
fig = plt.figure(figsize=(5,10))
figure_title = "Normal title"
ax1 = plt.subplot(1,2,1)
plt.title(figure_title, fontsize = 20)
plt.plot([1,2,3],[1,4,9])
figure_title = "Raised title"
ax2 = plt.subplot(1,2,2)
plt.text(0.5, 1.08, figure_title,
horizontalalignment='center',
fontsize=20,
transform = ax2.transAxes)
plt.plot([1,2,3],[1,4,9])
plt.show()
I was having an issue with the x-label overlapping a subplot title; this worked for me:
import matplotlib.pyplot as plt
fig, ax = plt.subplots(2, 1)
ax[0].scatter(...)
ax[1].scatter(...)
plt.tight_layout()
.
.
.
plt.show()
before
after
reference:
https://matplotlib.org/users/tight_layout_guide.html
ax.set_title('My Title\n', fontsize="15", color="red")
plt.imshow(myfile, origin="upper")
If you put '\n' right after your title string, the plot is drawn just below the title. That might be a fast solution too.
You can use pad for this case:
ax.set_title("whatever", pad=20)
Just use plt.tight_layout() before plt.show(). It works well.
A temporary solution if you don't want to get into the x, y position of your title.
Following worked for me.
plt.title('Capital Expenditure\n') # Add a next line after your title
kudos.
Using the plt.tight_layout() before the plt.show() works for me well.
you can even make it better and visible by adding a padding
ax.set_title("title", pad=15)
This is a kind of follow-up question to this post, where the coloring of axes, ticks and labels was discussed. I hope it is alright to open a new, extended question for this.
Changing the color of a complete frame (ticks and axes) around a double-plot (via add_subplot) with axes [ax1, ax2] results in a lot of code. This snippet changes the color of the frame of the upper plot:
ax1.spines['bottom'].set_color('green')
ax1.spines['top'].set_color('green')
ax1.spines['left'].set_color('green')
ax1.spines['right'].set_color('green')
for t in ax1.xaxis.get_ticklines(): t.set_color('green')
for t in ax1.yaxis.get_ticklines(): t.set_color('green')
for t in ax2.xaxis.get_ticklines(): t.set_color('green')
for t in ax2.yaxis.get_ticklines(): t.set_color('green')
So for changing the frame color of two plots with two y-axes each, I would need 16(!) lines of code... This is how it looks like:
Other methods I dug up so far:
matplotlib.rc: discussed here; changes globally, not locally. I want to have some other plots in different colors. Please, no discussions about too many colors in plots... :-)
matplotlib.rc('axes',edgecolor='green')
dig out the spines of the axis, then change it: also discussed here; not really elegant, I think.
for child in ax.get_children():
if isinstance(child, matplotlib.spines.Spine):
child.set_color('#dddddd')
Is there an elegant way of condensing the above block, something
more "pythonic"?
I'm using python 2.6.5 with matplotlib 0.99.1.1 under ubuntu.
Assuming you're using a reasonably up-to-date version of matplotlib (>= 1.0), perhaps try something like this:
import matplotlib.pyplot as plt
# Make the plot...
fig, axes = plt.subplots(nrows=2)
axes[0].plot(range(10), 'r-')
axes[1].plot(range(10), 'bo-')
# Set the borders to a given color...
for ax in axes:
ax.tick_params(color='green', labelcolor='green')
for spine in ax.spines.values():
spine.set_edgecolor('green')
plt.show()
Refactoring your code above:
import matplotlib.pyplot as plt
for ax, color in zip([ax1, ax2, ax3, ax4], ['green', 'green', 'blue', 'blue']):
plt.setp(ax.spines.values(), color=color)
plt.setp([ax.get_xticklines(), ax.get_yticklines()], color=color)
Maybe it is a bit crude to answer my own question, but I would like to share what I could find so far. This version can color two subplots with axes [ax1, ax2] and [ax3, ax4] in two different colors. It is much shorter than the 16 lines I stated in my question above. It is inspired by Joe Kington's answer here and in twinx kills tick label color.
import matplotlib.pyplot as plt
import numpy as np
# Generate some data
num = 200
x = np.linspace(501, 1200, num)
yellow_data, green_data , blue_data= np.random.random((3,num))
green_data += np.linspace(0, 3, yellow_data.size)/2
blue_data += np.linspace(0, 3, yellow_data.size)/2
fig = plt.figure()
plt.subplot(211) # Upper Plot
ax1 = fig.add_subplot(211)
ax1.fill_between(x, 0, yellow_data, color='yellow')
ax2 = ax1.twinx()
ax2.plot(x, green_data, 'green')
plt.setp(plt.gca(), xticklabels=[])
plt.subplot(212) # Lower Plot
ax3 = fig.add_subplot(212)
ax3.fill_between(x, 0, yellow_data, color='yellow')
ax4 = ax3.twinx()
ax4.plot(x, blue_data, 'blue')
# Start coloring
for ax, color in zip([ax1, ax2, ax3, ax4], ['green', 'green', 'blue', 'blue']):
for ticks in ax.xaxis.get_ticklines() + ax.yaxis.get_ticklines():
ticks.set_color(color)
for pos in ['top', 'bottom', 'right', 'left']:
ax.spines[pos].set_edgecolor(color)
# End coloring
plt.show()
I marked this as accepted since it's the most compact solution that I could find so far. Still, I am open for other, maybe more elegant ways to solve it.