Sometimes, you might need some packages which do not come with the standard Python libraries and you would want to install them separately.
You will notice how frustrating it is when you get stuck because of a package you are missing.
Sentdex on Youtube covers that accurately and you can watch him here:
Other relevant links to go with the video:
Hope this helps you.
Want more information like this?
What is population size: In simple terms, let’s us say population is the WHOLE SET and What is sample size: let us say it is the SUBSET (which is within the whole set) The norm Normally subsets are within the whole set and are smaller than the whole set Can they be equal ? Yes…
This is a quick to create a dictionary object , write it to a JSON file and then read back the file and convert it to a dictionary and access the items in the dictionary. Credit to : codebasics. You can check him out on Youtube In [3]:
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 |
# lets create a dictionary of books book = {} book['Tom'] = { 'name':'Tom', 'address':'1 red street, NY', 'phone':98988988 } book['Bob'] = { 'name':'Bob', 'address':'1 green street, NY', 'phone':252525885 } |
In [6]:
|
1 2 3 4 |
#import json and pass the dictionary to it import json s = json.dumps(book) s |
Out[6]:
|
1 |
'{"Bob": {"phone": 252525885, "name": "Bob", "address": "1 green street, NY"}, "Tom": {"phone": 98988988, "name": "Tom", "address": "1 red street, NY"}}' |
In [14]:…
This is some quick notes about graphing or plotting graphs with Matplotlib in Python. Credits to sentdex. You can check him out on Youtube.
|
1 2 3 4 5 |
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() |
In [4]:
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 |
# 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() |
In [10]:
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 |
#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() |
In [14]:
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 |
#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() |
In [20]:
|
1 2 3 4 5 6 7 8 9 10 11 |
## 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() |
In [23]:
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 |
#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() |
In [30]:
|
1 2 3 4 5 6 7 8 9 10 |
#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%%') |
Out[30]:
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 |
([<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>]) |
In [48]:
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 |
#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()) |
Want more information like this?
Summarizing Data This week we are looking at five most common aggregate functions used to summarize information stored in tables: COUNT and SUM very frequently. COUNT is the only aggregate function that can work on any type of variable. The other four aggregate functions are only appropriate for numerical data. All aggregate functions require you…
Bokeh is a powerful framework for data visualization in Python. Here I take a look at straightforward plotting and visualization using this powerful library. Credit to PythonHow. You can check him out on Youtube bokeh.models #It is a low level interface which involves a lot of work bokeh.plotting # It is a middle level interface bokeh.chart #…
An experienced DevOps and Cloud Training Company to meet your DevOps and Cloud needs
You can see how this popup was set up in our step-by-step guide: https://wppopupmaker.com/guides/auto-opening-announcement-popups/
