Google has now made Data Studio accessible and you can use it to create pretty cool data visualizations. You can find out more about the free versions and the premium versions here. Google Data Studio In the meantime, you can view a simple dashboard I created for Amazon Product Sourcing here: Amazon product Sourcing Google Studio…
This is a quick walk through Bokeh data exploration and visualization and also python pivot_tables (credit to pbpython on the pivot_tables).Visualization Dashboard In [49]:
|
1 2 3 4 5 |
import sqlalchemy import pyodbc from pandas import DataFrame from bokeh.plotting import figure, output_file, show import pandas as pd |
In [50]:
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 |
df = DataFrame ## connect to SqlServer Database and get information. try: import urllib params = urllib.quote_plus("DRIVER={SQL Server Native Client 11.0};SERVER=.\MSSQLSERVER_ENT;DATABASE=Antrak;Trusted_Connection=yes;") engine = sqlalchemy.create_engine('mssql+pyodbc:///?odbc_connect=%s"' % params) resoverall = engine.execute("SELECT top 10 * FROM FlightBookingsMain") df = df(resoverall.fetchall()) df.columns = resoverall.keys() except (RuntimeError, TypeError, NameError): print('Error in Conneccting') print(RuntimeError, TypeError, NameError) finally: print("connected") |
|
1 |
connected |
In [51]:
|
1 2 |
#lets look at the head of the dataframe df.head() |
Out[51]: First Name Last Name Sex Phone Fax Email Address Booking Date Departure Date Arrival Date Address1 … Number of Passengers Total Cost Currency…
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 #…
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]:
|
1 2 3 4 5 6 7 8 9 |
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 |
In [12]:
|
1 2 3 4 5 6 7 8 9 |
#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 |
In [13]:
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 |
#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() |
In [14]:
|
1 2 |
#lets plot TSLA stock prices. graph_data('TSLA') |
Background of the Dataset CSV file Used: The background to the Dataset CSV file used has been explained extensively in the week 2’s assignment. Not to bore assessors and readers by repeating everything here again, please simply check the background information from my previous assignment which can be assessed at this link: http://adabadata.tumblr.com/ OR in tumblr, it can be seen as one…
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/
