I have the following dataframes:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
df_One = pd.DataFrame({'Category': ['1024Sen', '1024Act', '2089Eng', '2089Sen'],
'Qtd_Instrumentation': [18, 5, 25, 10]})
df_Two = pd.DataFrame({'Category': ['1024Sen', '1024Act', '2089Eng', '2089Sen'],
'Qtd_Instrumentation': [14, 1, 22, 10]})
I would like to build a bar graph that contains the information from the two data frames, that is, the blue bars indicate the datadrame_One and the vertical red bars indicate the information of the dataframe_Two.
I tried to implement it as follows:
fig, ax = plt.subplots()
n_group = len(df_One['Category'])
index = np.arange(n_group)
bar_width = 0.35
opacity = 0.8
rects1 = df_One.plot.bar(x='Category', y='Qtd_Instrumentation', color='r', label = 'Station
One')
rects2 = df_Two.plot.bar(x='Category', y='Qtd_Instrumentation', color='b', label = 'Station
Two')
plt.xlabel('Category Instrumentation')
plt.ylabel('Qtd Instrumentation')
plt.show()
However, this code is wrong, as it designs two bar graphs instead of building just one graph with the two representations.
Does anyone know how I can build this described chart? Tks
This is one way to do it by choosing the align='edge' option and then using positive width for one bar and negative width for another. This will make them aligned next to each other. Also, you have to call plt.legend() to display the legends
fig, ax = plt.subplots()
index = np.arange(len(df_One['Category']))
bar_width = 0.35
opacity = 0.8
ax.bar(index, df_One['Qtd_Instrumentation'], color='r', align='edge', width=bar_width, label = 'Station One')
ax.bar(index, df_Two['Qtd_Instrumentation'], color='b', align='edge', width=-bar_width,label = 'Station Two')
# Assign the tick labels
ax.set_xticks(index)
ax.set_xticklabels(df_One['Category'], rotation=90)
plt.xlabel('Category Instrumentation')
plt.ylabel('Qtd Instrumentation')
plt.legend()
plt.show()
Alternative method is the following by using the keyword position to place the bars next to each other as shown here
df_One.Qtd_Instrumentation.plot(kind='bar', color='red', ax=ax, width=bar_width, position=1)
df_Two.Qtd_Instrumentation.plot(kind='bar', color='blue', ax=ax, width=bar_width, position=0)
ax.set_xlim(-0.5, 3.5)
ax.set_xticks(index)
ax.set_xticklabels(df_One['Category'])
I'd suggest merging the two dataframes first:
df_c = pd.merge(df_One, df_Two, on='Category')
df_c.plot.bar(x='Category')
gives:
note that you might want to pass how='outer' to merge if you have missing categories.
Related
I want to highlight the max value and 2 other bars. Can anyone help me with this, thank you!!
#Figure size
plt.figure(figsize = (20, 8))
#Group by position and find the mean salary
df.groupby("Position")["Salary"].mean()
#Plot bar graph
ax = df.groupby("Position")["Salary"].mean()
#Highlight max value bar
ax.plot.bar(color=np.where(ax==ax.max(), '#ff9999','cadetblue'))
#Axis label
plt.xlabel("Position",fontsize=12)
plt.ylabel("Salary",fontsize=12)
plt.title("Wages for different job functions", fontweight='bold', fontsize=14)
plt.show()
Get an array of bar graphs and set the color of the desired location. In the following example, besides setting a special color for the largest value in your code, you have also set the fourth to red.
import matplotlib.pyplot as plt
import numpy as np
# Fixing random state for reproducibility
np.random.seed(19680801)
plt.rcdefaults()
fig, ax = plt.subplots()
# Example data
people = ('Tom', 'Dick', 'Harry', 'Slim', 'Jim')
y_pos = np.arange(len(people))
performance = 3 + 10 * np.random.rand(len(people))
error = np.random.rand(len(people))
bars = ax.bar(y_pos, performance, align='center', color=np.where(performance == performance.max(),'#ff9999','cadetblue'))
ax.set_xticks(y_pos)
ax.set_xticklabels(people)
ax.set_ylabel('Salary')
ax.set_title("Wages for different job functions", fontweight='bold', fontsize=14)
bars[3].set_color("red")
plt.show()
much searching has not yielded a working solution to a python matplotlib problem. I'm sure I'm missing something simple...
MWE:
import pandas as pd
import matplotlib.pyplot as plt
#MWE plot
T = [1, 2, 3, 4, 5, 6]
n = len(T)
d1 = list(zip([500]*n, [250]*n))
d2 = list(zip([250]*n, [125]*n))
df1 = pd.DataFrame(data=d1, index=T)
df2 = pd.DataFrame(data=d2, index=T)
fig = plt.figure()
ax = fig.add_subplot(111)
df1.plot(kind='bar', stacked=True, align='edge', width=-0.4, ax=ax)
df2.plot(kind='bar', stacked=True, align='edge', width=0.4, ax=ax)
plt.show()
Generates:
Shifted Plot
No matter what parameters I play around with, that first bar is cut off on the left. If I only plot a single bar (i.e. not clusters of bars), the bars are not cut off and in fact there is nice even white space on both sides.
I hard-coded the data for this MWE; however, I am trying to find a generic way to ensure the correct alignment since I will likely produce a LOT of these plots with varying numbers of items on the x axis and potentially a varying number of bars in each cluster.
How do I shift the bars so that the they are spaced correctly on the x axis with even white space?
It all depends on the width that you put in your plots. Put some xlim.
import pandas as pd
import matplotlib.pyplot as plt
#MWE plot
T = [1, 2, 3, 4, 5, 6]
n = len(T)
d1 = list(zip([500]*n, [250]*n))
d2 = list(zip([250]*n, [125]*n))
df1 = pd.DataFrame(data=d1, index=T)
df2 = pd.DataFrame(data=d2, index=T)
fig = plt.figure()
ax = fig.add_subplot(111)
df1.plot(kind='bar', stacked=True, align='edge', width=-0.4, ax=ax)
df2.plot(kind='bar', stacked=True, align='edge', width=0.4, ax=ax)
plt.xlim(-.4,5.4)
plt.show()
Hope it works!
I'm trying to create a horizontal bar chart, with dual x axes. The 2 axes are very different in scale, 1 set goes from something like -5 to 15 (positive and negative value), the other set is more like 100 to 500 (all positive values).
When I plot this, I'd like to align the 2 axes so zero shows at the same position, and only the negative values are to the left of this. Currently the set with all positive values starts at the far left, and the set with positive and negative starts in the middle of the overall plot.
I found the align_yaxis example, but I'm struggling to align the x axes.
Matplotlib bar charts: Aligning two different y axes to zero
Here is an example of what I'm working on with simple test data. Any ideas/suggestions? thanks
import pandas as pd
import matplotlib.pyplot as plt
d = {'col1':['Test 1','Test 2','Test 3','Test 4'],'col 2':[1.4,-3,1.3,5],'Col3':[900,750,878,920]}
df = pd.DataFrame(data=d)
fig = plt.figure() # Create matplotlib figure
ax = fig.add_subplot(111) # Create matplotlib axes
ax2 = ax.twiny() # Create another axes that shares the same y-axis as ax.
width = 0.4
df['col 2'].plot(kind='barh', color='darkblue', ax=ax, width=width, position=1,fontsize =4, figsize=(3.0, 5.0))
df['Col3'].plot(kind='barh', color='orange', ax=ax2, width=width, position=0, fontsize =4, figsize=(3.0, 5.0))
ax.set_yticklabels(df.col1)
ax.set_xlabel('Positive and Neg',color='darkblue')
ax2.set_xlabel('Positive Only',color='orange')
ax.invert_yaxis()
plt.show()
I followed the link from a question and eventually ended up at this answer : https://stackoverflow.com/a/10482477/5907969
The answer has a function to align the y-axes and I have modified the same to align x-axes as follows:
def align_xaxis(ax1, v1, ax2, v2):
"""adjust ax2 xlimit so that v2 in ax2 is aligned to v1 in ax1"""
x1, _ = ax1.transData.transform((v1, 0))
x2, _ = ax2.transData.transform((v2, 0))
inv = ax2.transData.inverted()
dx, _ = inv.transform((0, 0)) - inv.transform((x1-x2, 0))
minx, maxx = ax2.get_xlim()
ax2.set_xlim(minx+dx, maxx+dx)
And then use it within the code as follows:
import pandas as pd
import matplotlib.pyplot as plt
d = {'col1':['Test 1','Test 2','Test 3','Test 4'],'col 2' [1.4,-3,1.3,5],'Col3':[900,750,878,920]}
df = pd.DataFrame(data=d)
fig = plt.figure() # Create matplotlib figure
ax = fig.add_subplot(111) # Create matplotlib axes
ax2 = ax.twiny() # Create another axes that shares the same y-axis as ax.
width = 0.4
df['col 2'].plot(kind='barh', color='darkblue', ax=ax, width=width, position=1,fontsize =4, figsize=(3.0, 5.0))
df['Col3'].plot(kind='barh', color='orange', ax=ax2, width=width, position=0, fontsize =4, figsize=(3.0, 5.0))
ax.set_yticklabels(df.col1)
ax.set_xlabel('Positive and Neg',color='darkblue')
ax2.set_xlabel('Positive Only',color='orange')
align_xaxis(ax,0,ax2,0)
ax.invert_yaxis()
plt.show()
This will give you what you're looking for
I am trying to add custom xticks to a relatively complicated bar graph plot and I am stuck.
I am plotting from two data frames, merged_90 and merged_15:
merged_15
Volume y_err_x Area_2D y_err_y
TripDate
2015-09-22 1663.016032 199.507503 1581.591701 163.473202
merged_90
Volume y_err_x Area_2D y_err_y
TripDate
1990-06-10 1096.530711 197.377497 1531.651913 205.197493
I want to create a bar graph with two axes (i.e. Area_2D and Volume) where the Area_2D and Volume bars are grouped based on their respective data frame. An example script would look like:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import scipy
fig = plt.figure()
ax1 = fig.add_subplot(111)
merged_90.Volume.plot(ax=ax1, color='orange', kind='bar',position=2.5, yerr=merged_90['y_err_x'] ,use_index=False , width=0.1)
merged_15.Volume.plot(ax=ax1, color='red', kind='bar',position=0.9, yerr=merged_15['y_err_x'] ,use_index=False, width=0.1)
ax2 = ax1.twinx()
merged_90.Area_2D.plot(ax=ax2,color='green', kind='bar',position=3.5, yerr=merged_90['y_err_y'],use_index=False, width=0.1)
merged_15.Area_2D.plot(ax=ax2,color='blue', kind='bar',position=0, yerr=merged_15['y_err_y'],use_index=False, width=0.1)
ax1.set_xlim(-0.5,0.2)
x = scipy.arange(1)
ax2.set_xticks(x)
ax2.set_xticklabels(['2015'])
plt.tight_layout()
plt.show()
The resulting plot is:
One would think I could change:
x = scipy.arange(1)
ax2.set_xticks(x)
ax2.set_xticklabels(['2015'])
to
x = scipy.arange(2)
ax2.set_xticks(x)
ax2.set_xticklabels(['1990','2015'])
but that results in:
I would like to see the ticks ordered in chronological order (i.e. 1990,2015)
Thanks!
Have you considered dropping the second axis and plotting them as follows:
ind = np.array([0,0.3])
width = 0.1
fig, ax = plt.subplots()
Rects1 = ax.bar(ind, [merged_90.Volume.values, merged_15.Volume.values], color=['orange', 'red'] ,width=width)
Rects2 = ax.bar(ind + width, [merged_90.Area_2D.values, merged_15.Area_2D.values], color=['green', 'blue'] ,width=width)
ax.set_xticks([.1,.4])
ax.set_xticklabels(('1990','2015'))
This produces:
I omitted the error and colors but you can easily add them. That would produce a readable graph given your test data. As you mentioned in comments you would still rather have two axes, presumably for different data with proper scales. To do this you could do:
fig = plt.figure()
ax1 = fig.add_subplot(111)
merged_90.Volume.plot(ax=ax, color='orange', kind='bar',position=2.5, use_index=False , width=0.1)
merged_15.Volume.plot(ax=ax, color='red', kind='bar',position=1.0, use_index=False, width=0.1)
ax2 = ax1.twinx()
merged_90.Area_2D.plot(ax=ax,color='green', kind='bar',position=3.5,use_index=False, width=0.1)
merged_15.Area_2D.plot(ax=ax,color='blue', kind='bar',position=0,use_index=False, width=0.1)
ax1.set_xlim([-.45, .2])
ax2.set_xlim(-.45, .2])
ax1.set_xticks([-.35, 0])
ax1.set_xticklabels([1990, 2015])
This produces:
Your problem was with resetting just one axis limit and not the other, they are created as twins but do not necessarily follow the changes made to one another.
I want to plot a bar chart or a histogram using matplotlib. I don't want a stacked bar plot, but a superimposed barplot of two lists of data, for instance I have the following two lists of data with me:
Some code to begin with :
import matplotlib.pyplot as plt
from numpy.random import normal, uniform
highPower = [1184.53,1523.48,1521.05,1517.88,1519.88,1414.98,1419.34,
1415.13,1182.70,1165.17]
lowPower = [1000.95,1233.37, 1198.97,1198.01,1214.29,1130.86,1138.70,
1104.12,1012.95,1000.36]
plt.hist(highPower, bins=10, histtype='stepfilled', normed=True,
color='b', label='Max Power in mW')
plt.hist(lowPower, bins=10, histtype='stepfilled', normed=True,
color='r', alpha=0.5, label='Min Power in mW')
I want to plot these two lists against the number of values in the two lists such that I am able to see the variation per reading.
You can produce a superimposed bar chart using plt.bar() with the alpha keyword as shown below.
The alpha controls the transparency of the bar.
N.B. when you have two overlapping bars, one with an alpha < 1, you will get a mixture of colours. As such the bar will appear purple even though the legend shows it as a light red. To alleviate this I have modified the width of one of the bars, this way even if your powers should change you will still be able to see both bars.
plt.xticks can be used to set the location and format of the x-ticks in your graph.
import matplotlib.pyplot as plt
import numpy as np
width = 0.8
highPower = [1184.53,1523.48,1521.05,1517.88,1519.88,1414.98,
1419.34,1415.13,1182.70,1165.17]
lowPower = [1000.95,1233.37, 1198.97,1198.01,1214.29,1130.86,
1138.70,1104.12,1012.95,1000.36]
indices = np.arange(len(highPower))
plt.bar(indices, highPower, width=width,
color='b', label='Max Power in mW')
plt.bar([i+0.25*width for i in indices], lowPower,
width=0.5*width, color='r', alpha=0.5, label='Min Power in mW')
plt.xticks(indices+width/2.,
['T{}'.format(i) for i in range(len(highPower))] )
plt.legend()
plt.show()
Building on #Ffisegydd's answer, if your data is in a Pandas DataFrame, this should work nicely:
def overlapped_bar(df, show=False, width=0.9, alpha=.5,
title='', xlabel='', ylabel='', **plot_kwargs):
"""Like a stacked bar chart except bars on top of each other with transparency"""
xlabel = xlabel or df.index.name
N = len(df)
M = len(df.columns)
indices = np.arange(N)
colors = ['steelblue', 'firebrick', 'darksage', 'goldenrod', 'gray'] * int(M / 5. + 1)
for i, label, color in zip(range(M), df.columns, colors):
kwargs = plot_kwargs
kwargs.update({'color': color, 'label': label})
plt.bar(indices, df[label], width=width, alpha=alpha if i else 1, **kwargs)
plt.xticks(indices + .5 * width,
['{}'.format(idx) for idx in df.index.values])
plt.legend()
plt.title(title)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
if show:
plt.show()
return plt.gcf()
And then in a python command line:
low = [1000.95, 1233.37, 1198.97, 1198.01, 1214.29, 1130.86, 1138.70, 1104.12, 1012.95, 1000.36]
high = [1184.53, 1523.48, 1521.05, 1517.88, 1519.88, 1414.98, 1419.34, 1415.13, 1182.70, 1165.17]
df = pd.DataFrame(np.matrix([high, low]).T, columns=['High', 'Low'],
index=pd.Index(['T%s' %i for i in range(len(high))],
name='Index'))
overlapped_bar(df, show=False)
It is actually simpler than the answers all over the internet make it appear.
a = range(1,10)
b = range(4,13)
ind = np.arange(len(a))
fig = plt.figure()
ax = fig.add_subplot(111)
ax.bar(x=ind, height=a, width=0.35,align='center')
ax.bar(x=ind, height=b, width=0.35/3, align='center')
plt.xticks(ind, a)
plt.tight_layout()
plt.show()