I would like to have a step-wise area plot in matplotib with pandas. I adjusted the code for a step-wise line plot but I get an error message. Here is the current code:
import pandas as pd
from matplotlib import pyplot as plt
%matplotlib inline
columns = ['Conventional control', 'Optimized control']
power_values = [[0.73,1.28],
[0.21, 0.21],
[0.18, 0.18],
[0.16, 1.00],
[0.57, 0.76],
[1.63, 1.62],
[3.28, 2.77],
[3.92, 0.47],
[3.29, 0.51],
[2.01, 3.64],
[1.72, 4.45],
[2.2, 0.59],
[2.33, 4.34],
[2.01, 2.05],
[1.39, 1.68],
[2.06, 0.55],
[3.07, 0.61],
[4.07, 0.61],
[3.66, 0.59],
[2.67, 0.59] ,
[1.54, 1.65],
[1.37, 1.55],
[1.36, 0.95],
[1.1, 1.70],
[0,0]]
wind_data = pd.DataFrame(power_values, index=range(0, 25), columns=columns)
fig = plt.figure(linewidth=1, figsize=(9, 5))
ax = wind_data.plot.area(ax=plt.gca(), color =["saddlebrown", "limegreen"], stacked=False, drawstyle="steps-post" )
ax.set_facecolor("white")
ax.set_xlabel("Time of day", fontsize = 14, labelpad=8)
ax.set_ylabel("Electrical power in kW", fontsize = 14,labelpad=8)
ax.set_xlim(0, 24)
ax.set_ylim(0, 5)
plt.xticks(wind_data.index, labels=[f'{h:02d}:00' for h in wind_data.index], rotation=90)
plt.grid(axis='y', alpha=.4)
plt.tight_layout()
hours = list(range(25)) # [0, 1, 2, ... 22, 23, 24]
labels = [f'{h:02d}:00' for h in hours] # ["00:00", "01:00", ... "23:00", "24:00"]
ax.tick_params(axis='both', which='major', labelsize=14)
ax.legend(loc='center left', bbox_to_anchor=(0.15, 1.07), fontsize = 14, ncol=3)
plt.savefig('CS_Cost_PerTimeslot.png', edgecolor='black', dpi=400, bbox_inches='tight')
plt.show()
If I do not use the argument "drawstyle="steps-post" I get just an normal area plot. But I would like to have a step-wise area plot. When using this attribut (as with the line plot) I get the error message:"
AttributeError: 'PolyCollection' object has no property 'drawstyle'
". I'd be very happy if someone could help me on that. Maybe there is also another way how to tell matpoltlib not to linearly interpolate the lines between the data points.
I think the simplest way to solve your problem is to use the pyplot fill_between command directly. That way you get superb control over all the plotting elements you might want. Slightly less user friendly than the DataFrame.plot api, but still good.
Replace the line
ax = wind_data.plot.area(ax=plt.gca(), color =["saddlebrown", "limegreen"], stacked=False,drawstyle="steps-post")
with
ax=plt.gca()
for column,color in zip(wind_data.columns,['saddlebrown','limegreen']):
ax.fill_between(
x=wind_data.index,
y1=wind_data[column],
y2=0,
label=column,
color=color,
alpha=.5,
step='post',
linewidth=2,
)
and you're good.
Related
I have the following code for a Matplotlib plot:
import pandas as pd
from matplotlib import pyplot as plt
columns = ['Price']
price_values = [[4.2],
[4.1],
[4],
[3.8],
[3.9],
[4.2],
[4.5],
[4.8],
[5.2],
[5.2],
[5.2],
[5.6],
[5.2],
[5.1],
[5.3],
[6],
[6.2],
[6.3],
[6.2],
[6],
[5.5] ,
[5.2],
[4.8],
[4.6]]
price_data = pd.DataFrame(price_values, index=range(0, 24), columns=columns)
fig = plt.figure(linewidth=1, figsize=(9, 5))
ax=plt.gca()
for column,color in zip(price_data.columns,['gold']):
ax.fill_between(
x=price_data.index,
y1=price_data[column],
y2=0,
label=column,
color=color,
alpha=.5,
step='post',
linewidth=5,
)
ax.set_facecolor("white")
ax.set_xlabel("Time of day", fontsize = 14, labelpad=8)
ax.set_ylabel("Price [Cent/kWh]", fontsize = 14,labelpad=8)
ax.set_xlim(0, 23)
ax.set_ylim(0, 8)
plt.xticks(price_data.index, labels=[f'{h:02d}:00' for h in price_data.index], rotation=90)
plt.tight_layout()
hours = list(range(25))
labels = [f'{h:02d}:00' for h in hours]
ax.tick_params(axis='both', which='major', labelsize=14)
ax.legend(loc='center left', bbox_to_anchor=(0.15, 1.07), fontsize = 14, ncol=3)
plt.savefig('Diagramm.png', edgecolor='black', dpi=400, bbox_inches='tight')
plt.show()
Now I would like to remove the area under the curve, sucht that I can only see the curve. I tried to use
plt.bar(fill=False)
but I get the error "TypeError: bar() missing 2 required positional arguments: 'x' and 'height'". Any suggestions how I can do that
Using fill_between and later remove the area under the curve seems like a pretty convoluted way to plot your data. But you could just set y2=price_data[column]:
price_data = pd.DataFrame(price_values, index=range(0, 24), columns=columns)
fig = plt.figure(linewidth=1, figsize=(9, 5))
ax=plt.gca()
for column,color in zip(price_data.columns,['gold']):
ax.fill_between(
x=price_data.index,
y1=price_data[column],
y2=price_data[column],
label=column,
color=color,
alpha=.5,
step='post',
linewidth=5,
)
ax.set_facecolor("white")
ax.set_xlabel("Time of day", fontsize = 14, labelpad=8)
ax.set_ylabel("Price [Cent/kWh]", fontsize = 14,labelpad=8)
ax.set_xlim(0, 23)
ax.set_ylim(0, 8)
plt.xticks(price_data.index, labels=[f'{h:02d}:00' for h in price_data.index], rotation=90)
plt.tight_layout()
hours = list(range(25))
labels = [f'{h:02d}:00' for h in hours]
ax.tick_params(axis='both', which='major', labelsize=14)
ax.legend(loc='center left', bbox_to_anchor=(0.15, 1.07), fontsize = 14, ncol=3)
plt.savefig('Diagramm.png', edgecolor='black', dpi=400, bbox_inches='tight')
plt.show()
Output:
Edit: #JohanC rightfully noted that the last value barely appears on the plot. One way to avoid this would be to replace your loop with the following:
price_data.plot(ax=ax, color="gold", drawstyle="steps-mid", linewidth=2)
Note that your solution is missing the last price value, the one between 23 and 24 h. You'll need to repeat the last value for this to work. To draw a step plot, the easiest way is ax.step.
The following example code changes the values for the first and the last value to make them stand out more.
from matplotlib import pyplot as plt
import pandas as pd
columns = ['Price']
price_values = [[1.2], [4.1], [4], [3.8], [3.9], [4.2], [4.5], [4.8], [5.2], [5.2], [5.2], [5.6], [5.2], [5.1], [5.3], [6], [6.2], [6.3], [6.2], [6], [5.5], [5.2], [4.8], [1.6]]
price_data = pd.DataFrame(price_values, index=range(0, 24), columns=columns)
fig, ax = plt.subplots(figsize=(9, 5))
for column, color in zip(price_data.columns, ['gold']):
ax.step(x=range(len(price_data) + 1), y=list(price_data[column]) + list(price_data[column][-1:]),
where='post', color=color, linewidth=5, label=column)
ax.set_xlabel("Time of day", fontsize=14, labelpad=8)
ax.set_ylabel("Price [Cent/kWh]", fontsize=14, labelpad=8)
ax.set_xlim(0, 24)
ax.set_ylim(0, 8)
xs = range(len(price_data) + 1)
ax.set_xticks(xs, labels=[f'{h:02d}:00' for h in xs], rotation=90)
ax.tick_params(axis='both', which='major', labelsize=14)
ax.legend(loc='lower left', bbox_to_anchor=(0.15, 1.01), fontsize=14, ncol=3)
plt.tight_layout()
plt.savefig('Diagramm.png', edgecolor='black', dpi=400, bbox_inches='tight')
plt.show()
Alternatively, you could use Seaborn's histplot, which has a step option (element='step', fill=False), but that works easiest if you'd let seaborn do the counting for the histogram. You could use sns.histplot's weights= parameter to fill in the values, e.g.
sns.histplot(x=price_data.index, weights=price_data[column].values, bins=len(price_data), binrange=(0, 24),
element='step', fill=False, color=color, linewidth=5, label=column, ax=ax)
I'm plotting a bar graphic and horizontal spans with this code:
fig = plt.figure('Graphic', figsize=(20,15), dpi=400)
ax1 = fig.add_axes([0.1, 0.1, 0.85, 0.75])
data.plot('DATE',["PP"],kind='bar',color='black', fontsize = 15.0,ax=ax1,alpha=1)
data.plot('DATE',['PP'],kind='line',marker='*',style=['--'],linewidth=1,color='gray', ms=5,ax=ax1)
ax1.axhspan(0, 1, facecolor='lightyellow', alpha=1)
ax1.axhspan(1, 1.5, facecolor='yellow', alpha=1)
ax1.axhspan(1.5, 2, facecolor='lime', alpha=1)
ax1.axhspan(2, 3.5, facecolor='green', alpha=1)
ax1.axhspan(0, -1, facecolor='bisque', alpha=1)
ax1.axhspan(-1, -1.5, facecolor='orange', alpha=1)
ax1.axhspan(-1.5, -2, facecolor='pink', alpha=1)
ax1.axhspan(-2, -3.5, facecolor='red', alpha=1)
The issue is that spans are hiding the Bar graphic. I would like to be able to visualize the spans with the bar graphs. Both with alpha=1. I don't want to reduce the alpha values.
Is this possible?
Thanks in advance.
I am displaying the image with axhspans with alpha=1 covering the bar charts.
I noticed two things that needed to change.
When you use pandas line and bar plots with X-axis being dates, there was/is a bug. Refer to this link. The workaround used here is what was there. Using matplotlib plot instead of pandas helped resolve this.
Refer to zorder. You can specify the order of the various components (line plot, bar, spans) to tell it what will come on top of what. Higher the zorder, the higher the plot will be. I have used 1 for the spans zorder, 2 for the bar plot zorder and 2 for line plot.
Updated code is below. See if this helps.
fig = plt.figure('Graphic', figsize=(20,15), dpi=400)
ax1 = fig.add_axes([0.1, 0.1, 0.85, 0.75])
data.plot('DATE',["PP"],kind='bar',color='black', fontsize = 15.0,ax=ax1,alpha=1, zorder=2) ## Added zorder
# Changed to matplotlib, increased linewidth to 3 so you can see it and zorder=3
ax1.plot(data[['PP']], marker='*',ls='--',linewidth=3,color='gray', ms=5, zorder=3)
## All zorder = 0
ax1.axhspan(0, 1, facecolor='lightyellow', alpha=1, zorder=1)
ax1.axhspan(1, 1.5, facecolor='yellow', alpha=1, zorder=1)
ax1.axhspan(1.5, 2, facecolor='lime', alpha=1, zorder=1)
ax1.axhspan(2, 3.5, facecolor='green', alpha=1, zorder=1)
ax1.axhspan(0, -1, facecolor='bisque', alpha=1, zorder=1)
ax1.axhspan(-1, -1.5, facecolor='orange', alpha=1, zorder=1)
ax1.axhspan(-1.5, -2, facecolor='pink', alpha=1, zorder=1)
ax1.axhspan(-2, -3.5, facecolor='red', alpha=1, zorder=1)
Plot
The order of display in the pandas plot is not adjustable, so I guess we have to deal with it in matplotlib. ax is set up with a line chart and horizontal fill, and a bar chart is added as a second axis. Then I get the order of the line chart, add 1 to the value of the line chart, and set the display order to the bar chart. Since no data was provided, stock price data was used as a sample.
import yfinance as yf
import pandas as pd
data = yf.download("AAPL", start="2022-06-01", end="2022-09-01")
data.index = pd.to_datetime(data.index)
import matplotlib.pyplot as plt
fig = plt.figure('Graphic', figsize=(10,7.5), dpi=100)
ax1 = fig.add_axes([0.1, 0.1, 0.85, 0.75])
ax1.plot(data.index, data['Close'], marker='*', linestyle='--', linewidth=1, color='gray', ms=5)
ax1.axhspan(170, 180, facecolor='lightyellow', alpha=1)
ax1.axhspan(160, 170, facecolor='yellow', alpha=1)
ax1.axhspan(150, 160, facecolor='lime', alpha=1)
ax1.axhspan(145, 150, facecolor='green', alpha=1)
ax1.axhspan(140, 145, facecolor='bisque', alpha=1)
ax1.axhspan(135, 140, facecolor='orange', alpha=1)
ax1.axhspan(130, 135, facecolor='pink', alpha=1)
ax1.axhspan(120, 130, facecolor='red', alpha=1)
ax2 = ax1.twinx()
ax2.bar(x=data.index, height=data['Volume'], color='black')
ax2.set_zorder(ax1.get_zorder()+1)
ax2.set_frame_on(False)
ax1.set_ylim(120, 180)
plt.show()
I have a plot with both a colorbar and a legend. I want to place the legend outside of the plot to the right of the colorbar. To accomplish this, I use bbox_to_anchor argument, but this causes the legend to get cut off:
import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import norm
_, ax = plt.subplots()
extent = np.r_[0, 1, 0, 1]
space = np.linspace(0, 1)
probs = np.array([[norm.cdf(x + y) for x in space] for y in space])
colormap = ax.imshow(probs, aspect="auto", origin="lower", extent=extent, alpha=0.5)
colorbar = plt.colorbar(colormap, ax=ax)
colorbar.set_label(f"Probability")
ax.scatter(
[0.2, 0.4, 0.6], [0.8, 0.6, 0.4], color="r", label="Labeled Points",
)
plt.legend(loc="center left", bbox_to_anchor=(1.3, 0.5))
plt.title
plt.show()
Plot with legend cut off
To fix the legend, I insert a call to plt.tight_layout() before plt.show(), but this causes the aspect ratio to get distorted:
Plot with distorted aspect ratio
How can I show the entire legend and preserve the aspect ratio of the axes?
You can manage the ratio between axis height and width with matplotlib.axes.Axes.set_aspect. Since you want them to be equal:
ax.set_aspect(1)
Then you can use matplotlib.pyplot.tight_layout to fit the legend within the figure.
If you want to adjust margins too, you can use matplotlib.pyplot.subplots_adjust.
Complete Code
import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import norm
_, ax = plt.subplots()
extent = np.r_[0, 1, 0, 1]
space = np.linspace(0, 1)
probs = np.array([[norm.cdf(x + y) for x in space] for y in space])
colormap = ax.imshow(probs, aspect="auto", origin="lower", extent=extent, alpha=0.5)
colorbar = plt.colorbar(colormap, ax=ax)
colorbar.set_label(f"Probability")
ax.scatter([0.2, 0.4, 0.6], [0.8, 0.6, 0.4], color="r", label="Labeled Points",)
plt.legend(loc="center left", bbox_to_anchor=(1.3, 0.5))
ax.set_aspect(1)
plt.tight_layout()
plt.subplots_adjust(left = 0.1)
plt.show()
I am trying to draw a traffic flow map.
The code I used is as below:
def map_draw(transport_gdf, map_path=map_path):
fig, ax = plt.subplots(figsize=(16, 16), facecolor=(0.37, 0.39, 0.41))
plt.axis("off")
plt.setp(ax.get_yticklabels(), visible=False)
plt.setp(ax.get_xticklabels(), visible=False)
plt.xlim((110, 125))
plt.ylim((32, 42))
CHN_adm = CHN_map(map_path)
CHN_adm.plot(ax=ax, color="black", edgecolor=(0.37, 0.39, 0.41), linewidth=2.5)
transport_gdf.plot(
column=np.log(transport_gdf.iloc[:, -1]),
ax=ax,
cmap="autumn",
linewidth=transport_gdf.iloc[:, -1] / 100000,
)
where transport_gdf is a GeoDataFrame object like this.
And what I got is like this:
Here, I want to normalize the colormap I have applied. Is there any way I can do this?
I went through Stackoverflow and only found an answer for a scatter plot, which does not apply to my situation.
I am creating a confusion matrix plot for my data. Next to the plot, I am placing a colorbar and want to change the font size of the colorbar tick labels. I search on the internet for a while but could not figure out how I can change the font size of the ticks of my colorbar since I am creating the colorbar using imshow. This could be because creating the colorbar this way is not the usual way as done/suggested in most places on the web (e.g. here and here). So I need your help for this. Here's how I'm creating my plot and add the colorbar next to it:
data=np.array([[0.83, 0.6, 0.76],[0.59, 0.46, 0.52],[0.62, 0.58, 0.88]])
xTicksMajor, yTicksMajor = [0.5, 1.5, 2.5], [0.5, 1.5, 2.5]
xTicksMinor, yTicksMinor = [0, 1, 2], [0, 1, 2]
fig, ax = plt.subplots()
cmapProp = {'drawedges': True, 'boundaries': np.linspace(0, 1, 13, endpoint=True).round(2)}
m = ax.imshow(data, cmap=plt.cm.get_cmap('Oranges'))
m.set_clim(0, 1)
ax.figure.colorbar(m, ax=ax, **cmapProp)
ax.set_xticks(xTicksMajor)
ax.set_yticks(yTicksMajor)
ax.set_xticks(xTicksMinor, minor=True)
ax.set_yticks(yTicksMinor, minor=True)
ax.yaxis.grid(True, color='black', linestyle='-', linewidth=0.5)
ax.xaxis.grid(True, color='black', linestyle='-', linewidth=0.5)
thresh = data.max() / 1.4
for i, j in itertools.product(range(data.shape[0]), range(data.shape[1])):
ax.text(j, i, format(data[i, j], '.2f'),
horizontalalignment="center",
verticalalignment='center',
color="black" if data[i, j] > thresh else "dimgrey",
fontsize=26)
fig.savefig('temp.png', dpi=200)
plt.close()
I tried changing the font size of the ticks as follow:
cmapProp = {'drawedges': True, 'boundaries': np.linspace(0, 1, 13, endpoint=True).round(2), 'fontsize': 14}
But this gives me the following error:
TypeError: init() got an unexpected keyword argument 'fontsize'
I wonder, how can I change the font size of the tick labels next to the colorbar? Feel free to make suggestions like creating the colorbar in a different way so that it is easy to change the fontsize.
Also, the above code results in the plot show below:
How about this:
...
fig, ax = plt.subplots()
cmapProp = {'drawedges': True, 'boundaries': np.linspace(0, 1, 13, endpoint=True).round(2)}
m = ax.imshow(data, cmap=plt.cm.get_cmap('Oranges'))
m.set_clim(0, 1)
# And here try this:
cbar = ax.figure.colorbar(m, ax=ax, **cmapProp)
cbar.ax.tick_params(labelsize=25) # set your label size here
...
Out:
bold labels:
...
cbar = ax.figure.colorbar(m, ax=ax, **cmapProp)
cbar.ax.tick_params(labelsize=25)
for tick in cbar.ax.yaxis.get_major_ticks():
tick.label2.set_fontweight('bold')
...
Out: