This question already has answers here:
How can I change the x axis in matplotlib so there is no white space?
(2 answers)
Closed 3 years ago.
Whenever I plot figures using matplotlib or seaborn, there is always some whitespace remaining at the sides of the plot and the top and bottom of the plot. The (x_0,y_0) is not in the bottom left corner, x_0 is offset a little bit to the right and y_0 is offset a little bit upwards for some reason? I will demonstrate a quick example below with the 'ggplot' style so it is clear what I mean:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('ggplot')
fig = plt.figure()
x = np.linspace(0,5,11)
ax = fig.add_axes([0.1,0.1,1,1])
ax.plot(x,x**2)
How do I get (0,0) to the bottom left corner and how do I get rid of the unnecessary space where y > 25, x >5?
Thank you.
The "whitespace" is caused by the plot margins. A better way to get rid of them without changing the axes limits explicitly is to set 0-margins
plt.style.use('ggplot')
fig = plt.figure()
x = np.linspace(0,5,11)
ax = fig.add_axes([0.1,0.1,1,1])
ax.margins(x=0,y=0)
ax.plot(x,x**2)
Alternatively:
x = np.linspace(0,5,11)
plt.xlim((0,5))
plt.ylim((0,25))
plt.plot(x,x**2);
To not have borders you can use set_xlim and set_ylim:
ax.set_xlim([0, 5])
ax.set_ylim([0, 25])
Full code:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('ggplot')
fig = plt.figure()
x = np.linspace(0,5,11)
ax = fig.add_axes([0.1,0.1,1,1])
ax.plot(x,x**2)
ax.set_xlim([0, 5])
ax.set_ylim([0, 25])
plt.show()
Related
This question already has an answer here:
PyPlot move alternative y axis to background
(1 answer)
Closed 3 years ago.
I'm using matplotlib.axes.Axes.twinx to have a shared x-axis in matplotlib for both . I am being unable to order the plots using zorder. What I want is to plot the line graphs with ax1 to be on the front and bar grpah with ax2 to be behind it.
I know there is a related question to it, but it actually didn't help much. Related Question
Link of data set
%matplotlib inline
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
dataFrame=pd.read_csv("NEM.csv",sep=',')
dataFrame['ratio']=dataFrame['Expert']/dataFrame['Novice']
fig, ax1 = plt.subplots(figsize=(9, 6))
ax1.set_title('N-E Analysis')
xticklabels=dataFrame['Task'].tolist()
ax1.plot('Novice', data=dataFrame, marker='', color='dodgerblue', linewidth=2,label='Novice',zorder=3)
ax1.plot('Expert', data=dataFrame, marker='', color='darkorange', linewidth=2,label='Expert',zorder=2)
plt.ylim(0,120)
ax2 = ax1.twinx()
ax2.bar('Task','ratio', data=dataFrame, color='gray',width=0.35,label='NE',zorder=0)
ax1.spines['top'].set_visible(False)
ax1.spines['right'].set_visible(False)
ax1.spines['left'].set_visible(False)
ax2.spines['top'].set_visible(False)
ax2.spines['right'].set_visible(False)
ax2.spines['left'].set_visible(False)
ax1.set_xticklabels(xticklabels, rotation = 45, ha="right")
ax1.yaxis.grid()
ax1.tick_params(left='off',bottom='off')
ax2.tick_params(right='off')
plt.ylim(0,12)
h1, l1 = ax1.get_legend_handles_labels()
h2, l2 = ax2.get_legend_handles_labels()
p=ax1.legend(h2+h1, l2+l1, loc=2,frameon=False)
fig.tight_layout()
plt.show()
You can try the following.
ax1.set_zorder(2)
ax2.set_zorder(1)
ax1.patch.set_visible(False)
So currently learning how to import data and work with it in matplotlib and I am having trouble even tho I have the exact code from the book.
This is what the plot looks like, but my question is how can I get it where there is no white space between the start and the end of the x-axis.
Here is the code:
import csv
from matplotlib import pyplot as plt
from datetime import datetime
# Get dates and high temperatures from file.
filename = 'sitka_weather_07-2014.csv'
with open(filename) as f:
reader = csv.reader(f)
header_row = next(reader)
#for index, column_header in enumerate(header_row):
#print(index, column_header)
dates, highs = [], []
for row in reader:
current_date = datetime.strptime(row[0], "%Y-%m-%d")
dates.append(current_date)
high = int(row[1])
highs.append(high)
# Plot data.
fig = plt.figure(dpi=128, figsize=(10,6))
plt.plot(dates, highs, c='red')
# Format plot.
plt.title("Daily high temperatures, July 2014", fontsize=24)
plt.xlabel('', fontsize=16)
fig.autofmt_xdate()
plt.ylabel("Temperature (F)", fontsize=16)
plt.tick_params(axis='both', which='major', labelsize=16)
plt.show()
There is an automatic margin set at the edges, which ensures the data to be nicely fitting within the axis spines. In this case such a margin is probably desired on the y axis. By default it is set to 0.05 in units of axis span.
To set the margin to 0 on the x axis, use
plt.margins(x=0)
or
ax.margins(x=0)
depending on the context. Also see the documentation.
In case you want to get rid of the margin in the whole script, you can use
plt.rcParams['axes.xmargin'] = 0
at the beginning of your script (same for y of course). If you want to get rid of the margin entirely and forever, you might want to change the according line in the matplotlib rc file:
axes.xmargin : 0
axes.ymargin : 0
Example
import seaborn as sns
import matplotlib.pyplot as plt
tips = sns.load_dataset('tips')
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4))
tips.plot(ax=ax1, title='Default Margin')
tips.plot(ax=ax2, title='Margins: x=0')
ax2.margins(x=0)
Alternatively, use plt.xlim(..) or ax.set_xlim(..) to manually set the limits of the axes such that there is no white space left.
If you only want to remove the margin on one side but not the other, e.g. remove the margin from the right but not from the left, you can use set_xlim() on a matplotlib axes object.
import seaborn as sns
import matplotlib.pyplot as plt
import math
max_x_value = 100
x_values = [i for i in range (1, max_x_value + 1)]
y_values = [math.log(i) for i in x_values]
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4))
sn.lineplot(ax=ax1, x=x_values, y=y_values)
sn.lineplot(ax=ax2, x=x_values, y=y_values)
ax2.set_xlim(-5, max_x_value) # tune the -5 to your needs
I can generate an error-bar plot using the code below. The graph produced by the code shows vertical lines that represent the errors in y. I would like to have horizontal lines at the tips of these errors ("error bars") and am not sure how to do so.
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(1, 10, 10, dtype=int)
y = 2**x
yerr = np.sqrt(y)*10
fig, ax = plt.subplots()
ax.errorbar(x, y, yerr, solid_capstyle='projecting')
ax.grid(alpha=0.5, linestyle=':')
plt.show()
plt.close(fig)
The code generates the figure below. I've played with the solid_capstyle kwarg. Is there a specific kwarg that does what I am trying to do?
And as an example of what I'd like, the figure below:
In case it's relevant, I am using matplotlib 2.2.2
The argument you are looking for is capsize= in ax.errorbar(). The default is None so the length of the cap will default to the value of matplotlib.rcParams["errorbar.capsize"]. The number you give will be the length of the cap in points:
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(1, 10, 10, dtype=int)
y = 2**x
yerr = np.sqrt(y)*10
fig, ax = plt.subplots()
ax.errorbar(x, y, yerr, solid_capstyle='projecting', capsize=5)
ax.grid(alpha=0.5, linestyle=':')
plt.show()
This question already has answers here:
multiple axis in matplotlib with different scales [duplicate]
(3 answers)
Closed 5 years ago.
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
d = ['d1','d2','d3','d4','d5','d6']
value = [111111, 222222, 333333, 444444, 555555, 666666]
y_cumsum = np.cumsum(value)
sns.barplot(d, value)
sns.pointplot(d, y_cumsum)
plt.show()
I'm trying to make pareto diagram with barplot and pointplot. But I can't print percentages to the right side ytick. By the way, if I manuplate yticks it overlaps itself.
plt.yticks([1,2,3,4,5])
overlaps like in the image.
Edit: I mean that I want to quarter percentages (0, 25%, 50%, 75%, 100%) on the right hand side of the graphic, as well.
From what I understood, you want to show the percentages on the right hand side of your figure. To do that, we can create a second y axis using twinx(). All we need to do then is to set the limits of this second axis appropriately, and set some custom labels:
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
d = ['d1','d2','d3','d4','d5','d6']
value = [111111, 222222, 333333, 444444, 555555, 666666]
fig, ax = plt.subplots()
ax2 = ax.twinx() # create a second y axis
y_cumsum = np.cumsum(value)
sns.barplot(d, value, ax=ax)
sns.pointplot(d, y_cumsum, ax=ax)
y_max = y_cumsum.max() # maximum of the array
# find the percentages of the max y values.
# This will be where the "0%, 25%" labels will be placed
ticks = [0, 0.25*y_max, 0.5*y_max, 0.75*y_max, y_max]
ax2.set_ylim(ax.get_ylim()) # set second y axis to have the same limits as the first y axis
ax2.set_yticks(ticks)
ax2.set_yticklabels(["0%", "25%","50%","75%","100%"]) # set the labels
ax2.grid("off")
plt.show()
This produces the following figure:
Given the shape file available here: I know can produce the basic map that I need with county labels and even some points on the map (see below). The issue I'm having is that I cannot seem to control the size of the figure with figsize.
Here's what I have:
import geopandas as gpd
import matplotlib.pyplot as plt
%matplotlib inline
figsize=5,5
fig = plt.figure(figsize=(figsize),dpi=300)
shpfileshpfile=r'Y:\HQ\TH\Groups\NR\PSPD\Input\US_Counties\cb_2015_us_county_20m.shp'
c=gpd.read_file(shpfile)
c=c.loc[c['GEOID'].isin(['26161','26093','26049','26091','26075','26125','26163','26099','26115','26065'])]
c['coords'] = c['geometry'].apply(lambda x: x.representative_point().coords[:])
c['coords'] = [coords[0] for coords in c['coords']]
ax=c.plot()
#Control some attributes regarding the axis (for the plot above)
ax.spines['top'].set_visible(False);ax.spines['bottom'].set_visible(False);ax.spines['left'].set_visible(False);ax.spines['right'].set_visible(False)
ax.tick_params(axis='y',which='both',left='off',right='off',color='none',labelcolor='none')
ax.tick_params(axis='x',which='both',top='off',bottom='off',color='none',labelcolor='none')
for idx, row in c.iterrows():
ax.annotate(s=row['NAME'], xy=row['coords'],
horizontalalignment='center')
lat2=[42.5,42.3]
lon2=[-84,-83.5]
#Add another plot...
ax.plot(lon2,lat2,alpha=1,marker='o',linestyle='none',markeredgecolor='none',markersize=15,color='white')
plt.show()
As you can see, I opted to call the plots by the axis name because I need to control attributes of the axis, such as tick_params. I'm not sure if there is a better approach. This seems like a "no-brainer" but I can't seem to figure out why I can't control the figure size.
Thanks in advance!
I just had to do the following:
Use fig, ax = plt.subplots(1, 1, figsize = (figsize))
2.use the ax=ax argument in c.plot()
import geopandas as gpd
import matplotlib.pyplot as plt
%matplotlib inline
figsize=5,5
#fig = plt.figure(figsize=(figsize),dpi=300)
#ax = fig.add_subplot(111)
fig, ax = plt.subplots(1, 1, figsize = (figsize))
shpfileshpfile=r'Y:\HQ\TH\Groups\NR\PSPD\Input\US_Counties\cb_2015_us_county_20m.shp'
c=gpd.read_file(shpfile)
c=c.loc[c['GEOID'].isin(['26161','26093','26049','26091','26075','26125','26163','26099','26115','26065'])]
c['coords'] = c['geometry'].apply(lambda x: x.representative_point().coords[:])
c['coords'] = [coords[0] for coords in c['coords']]
c.plot(ax=ax)
ax.spines['top'].set_visible(False);ax.spines['bottom'].set_visible(False);ax.spines['left'].set_visible(False);ax.spines['right'].set_visible(False)
ax.tick_params(axis='y',which='both',left='off',right='off',color='none',labelcolor='none')
ax.tick_params(axis='x',which='both',top='off',bottom='off',color='none',labelcolor='none')
for idx, row in c.iterrows():
ax.annotate(s=row['NAME'], xy=row['coords'],
horizontalalignment='center')
lat2=[42.5,42.3]
lon2=[-84,-83.5]
ax.plot(lon2,lat2,alpha=1,marker='o',linestyle='none',markeredgecolor='none',markersize=15,color='white')