This is a guide on Python List comprehension. List comprehension is a great way to write simple and easy to read Python codes. You can use it dictionaries and sets as well. Credits to Corey Schafer , creator of Python course materials. lets create a list In [115]:
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num = range(10) num |
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[0, 1, 2, 3, 4, 5, 6, 7, 8, 9] |
lets grab all the…
Group By FunctionThis is a quick look at Python groupby function. Very powerful and useful function. We will take a simple look at it here. Credits to Data School , creator of Python course materials. lets import sample dataset In [18]:
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import pandas as pd |
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drinks = pd.read_csv('http://bit.ly/drinksbycountry') |
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#lets check the head drinks.head() |
Out[20]: country beer_servings spirit_servings wine_servings total_litres_of_pure_alcohol continent…
Pandas iloc, loc, and ix functions are very powerful ways to quickly select data from your dataframe. Today , we take a quick look at these 3 functions. Credits to Data School, you can check him out in Youtube In [1]:
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import pandas as pd |
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#lets get this public dataset and play with it ufo = pd.read_csv('http://bit.ly/uforeports') |
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#lets examine the head ufo.head() |
Out[3]: City Colors Reported Shape Reported State…
In this guide, we take a look at Natural Language Processing, NLP in Python. Natural Language is a very extensive topic and very powerful machine learning algorithm you can build. This is very useful in many areas of most industries. Credits to Jose Portilla, creator of Learning Python for Data Analysis and Visualization course on Udemy …
When you have a script running and you have logs set , you might also want to send yourself or colleagues, email alerts in case things go wrong or simply to let you know that your scripts ran to completion without any problems. In this short notes, we take a look at sending email…
After writing your script, you might want to schedule it to run periodically, let’s say, Daily, Weekly or Any Time interval you will decide Before you schedule that you want to simply create a batch file and schedule it run with a Windows Scheduler. Here are simple steps to do that. First of all, if…
If you have some Python scripts which rely on some packages that need a different version other than the version your Python Spyder uses, you can simply use Python Virtualenv to run Spyder IDE . This will save you a lot of headache and you can simply continue with your development projects. This tutorial will…
In this notes, we are looking at a simple but yet useful and powerful logging in Python In [77]:
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import logging #levels of logging are #1 debug - detailed info #2 info - confirmation that things went according to plan #3 warning - something unexpected happened #4 error - some function failed #5 critical - something failed and application must close from imp import reload reload(logging) # this will help our logging to be saved to file logging.basicConfig(filename='logFile3.log',format='%(asctime)s %(message)s', level=logging.DEBUG) # the logging will only log the level you set it to and any #other levels ABOVE it but NOT below it |
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#lets define a simple function def main(): try: #mathFail = 1/0 # this will give an error which can be catched if 1<2: logging.debug('Entered into one of the if statements') print('we are in the IF STATEMENT') #this error will not break our entire script so we will catch the error and give some info try: urllib2.urlopen('http://www.google.com').read() logging.info('Opened url fine') except Exception as e: logging.error('urllib2 url visit failed and the reason was %s' % str(e)) else: logging.debug('Entered into the else statement') print('We are in the ELSE STATEMENT') except Exception as e: print(str(e)) logging.critical(e) |
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main() |
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we are in the IF STATEMENT Could not open url |
This is some quick notes about getting stock data from Yahoo and plotting it using Matplotlib . The Python version used is Python 3.5 Credits to sentdex. You can check him out on Youtube. In [11]:
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import matplotlib.pyplot as plt import numpy as np import urllib import matplotlib.dates as mdate import urllib.request %matplotlib inline #%matplotlib qt #%matplotlib notebook # interactive popout window plot |
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#getting data from the internet #define our converter function def bytespdate2num(fmt, encoding='utf-8'): strconverter = mdate.strpdate2num(fmt) def bytesconverter(b): s = b.decode(encoding) return strconverter(s) return bytesconverter |
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#define a function which will take the stock data and plot the graph def graph_data(stock): stock_data_url = 'http://chartapi.finance.yahoo.com/instrument/1.0/'+stock+'/chartdata;type=quote;range=10y/csv' source_code = urllib.request.urlopen(stock_data_url).read().decode() stock_data =[] split_source = source_code.split('\n') #split the code based on next line #lets grab only the relevant stock data for line in split_source: split_line = line.split(',') if(len(split_line))==6: if 'values' not in line and 'labels' not in line: stock_data.append(line) date, closep, highp, lowp, openp, volume = np.loadtxt(stock_data, delimiter=',',unpack=True, converters={0: bytespdate2num('%Y%m%d')}) plt.plot_date(date,closep,'-', label='Price') plt.xlabel('Date') plt.ylabel('Price') plt.title('Interesting stock graph from yahoo') plt.legend() |
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#lets plot TSLA stock prices. graph_data('TSLA') |
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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