Presenting your report in an interactive and user-friendly way is a very effective communicative tool which can help you convey message to your audience easily. R is a very powerful analytic tool which helps you to do that with the “shiny” package. Here, I simply look at an app about the Mining Fields Evaluation which was…
Superset (formerly called Caravel ) BI Tool by http://airbnb.io/superset is a free Data Visualization tool which you can harness its powers for your visualization analytic needs. If you are finding problems installing on your windows PC or you simply want your own online version you can simply sign up FREE with Heroku (at least choosing…
If you installed your Superset (Caravel) BI Tool with Heroku and if you are finding it difficult to initially populate your MySQL database tables with Data and you will be using MySQL the default database that come with your installation, then you can follow these steps to get data into Superset. (By the way, you…
Sometimes, you need some charts to explore your data and many times when your analysis is done and you want to report, you would most likely want to convey your message using charts and graphs. This is a quick look at some charts which are available and their names: We use this raw data in…
There are a couple Free Open Source Data Visualization Tools and Services which you can make use of for your work data exploration and visualization purposes. Below , I list a few ones in no order of importance. Caravel: It is a Python based data exploration and visualization software by Airbnb. It is powerful and…
This is some quick notes about graphing or plotting graphs with Matplotlib in Python. Credits to sentdex. You can check him out on Youtube.
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import matplotlib.pyplot as plt %matplotlib inline # simple syntax is matplotlib plots as plt.plot(x, y) plt.plot([1,2,3], [5,6,7]) plt.show() |
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# plotting with variables and basic labelling of our plot x = [1,2,3] y = [5,6,7] ##adding a legend to the plot #add another line vectors first x2 = [4,6,8] y2= [10,14,18] plt.plot(x, y, label='First Line') # you have to give the plots a label before you can call the legends on them plt.plot(x2, y2, label='Second Line') plt.xlabel('Plot number') plt.ylabel('Important var') plt.title('Plotting\nNice Graph') #\nGives you a sub title plt.legend() #call the legend plt.show() |
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#plotting barcharts and histograms x3 = [2,4,6,8,10] y3 = [3,5,7,9,11] #second set of bars x4 = [1,3,5,7,9] y4 = [2,6,6,12,4] #plot barchart plt.bar(x3, y3, label='Bar1', color='r') plt.bar(x4,y4, label='Bar2', color ='c') plt.xlabel('x3') plt.ylabel('y3') plt.title('Bar Chart') plt.legend() plt.show() |
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#plotting histogram. population_dist = [24,45,5,6,42,65,34,75,75,34,75,7,24,26,87,98,98,67,86, 65,63, 87,98,76,53,35] #ids = [x for x in range(len(population_dist))] for bar chart plotting #plot barchart #plt.bar(ids, population_dist, label='hist') #histogram works with bins so we will have to create bin 'containers' for our distribution bins = [10,20,30,40,50,60,70,80,90,100] plt.hist(population_dist, bins, histtype='bar', rwidth=0.5) plt.xlabel('x3') plt.ylabel('y3') plt.title('Histogram') plt.legend() plt.show() |
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## scatter plot. #basic skeletion x = [1,2,3,4,5,6,7,8,9] y = [4,5,8,3,6,9,5,4,8] plt.scatter(x,y, label='ScatterShow', color='c', marker='^' , s=60) # s = size of the marker plt.xlabel('x3') plt.ylabel('y3') plt.title('Scatter') plt.legend() plt.show() |
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#stacked plot days = [1, 2, 3, 4, 5] sleeping = [6,8,7,3,8] eating = [6,9,7,3,6] working = [9,8,5,7,8] #working around legends for stackplots plt.plot([],[], color='red', label='eating', linewidth=2) plt.plot([],[], color='blue', label='sleeping', linewidth=2) plt.plot([],[], color='yellow', label='working', linewidth=2) plt.stackplot(days, eating, sleeping, working, color=['red','blue','yellow']) plt.xlabel('x3') plt.ylabel('y3') plt.title('Stacked Plot') plt.legend() plt.show() |
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#pie charts days = [1, 2, 3, 4, 5] sleeping = [6,8,7,3,8] eating = [6,9,7,3,6] working = [9,8,5,7,8] slices = [2,4,6,8,1] colors = ['red','blue','green','brown','pink'] activities = ['sleeping','eating','working','dancing','travelling'] plt.pie(slices, labels=activities, colors=colors, startangle=90, shadow=True, explode=(0,0.1,0,0,0), autopct='%1.1f%%') |
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([<matplotlib.patches.Wedge at 0x814e588>, <matplotlib.patches.Wedge at 0x7fb7080>, <matplotlib.patches.Wedge at 0x96f9ba8>, <matplotlib.patches.Wedge at 0x82853c8>, <matplotlib.patches.Wedge at 0x785a080>], [<matplotlib.text.Text at 0x82ac4a8>, <matplotlib.text.Text at 0x96f9438>, <matplotlib.text.Text at 0x8026390>, <matplotlib.text.Text at 0x94ac400>, <matplotlib.text.Text at 0x785ada0>], [<matplotlib.text.Text at 0x7fb7fd0>, <matplotlib.text.Text at 0x96f97b8>, <matplotlib.text.Text at 0x8285f98>, <matplotlib.text.Text at 0x94aca58>, <matplotlib.text.Text at 0x65bf860>]) |
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#modifying the matplotlib #plotting barchart. x3 = [2,4,6,8,10] y3 = [3,5,7,9,11] #second set of bars x4 = [1,3,5,7,9] y4 = [2,6,6,12,4] fig = plt.figure(facecolor='#f0f0f0') #set facecolour or background colour which will be maintained once we save our graph ax1 = plt.subplot2grid((1,1),(0,0)) #tilt the xlabels for labels in ax1.xaxis.get_ticklabels(): labels.set_rotation(45) #plot barchart ax1.bar(x3, y3, label='Bar1', color='r') ax1.bar(x4,y4, label='Bar2', color ='c') ax1.grid(True) #add a grid plt.xlabel('x3') plt.ylabel('y3') plt.title('Bar Chart') plt.legend() #customise the legend leg = ax1.legend(loc=2,ncol=6, prop={'size':10}) leg.get_frame().set_alpha(0.4) ##define spacing around the ticks and labels plt.subplots_adjust(left = 0.09, bottom=0.20, right=0.94, top= 0.93, wspace = 0.2, hspace = 0) plt.show() #lets save the graph fig.savefig('matplotlib.png', facecolor=fig.get_facecolor()) |
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