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 tutorial is just to illustrate how to save Python Pandas dataframe into one excel work SHEET . You can save it column-wise, that is side by side or row-wise, that is downwards, one dataframe after the other. In [110]:
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import sqlalchemy import pyodbc from pandas import DataFrame from bokeh.plotting import figure, output_file, show import pandas as pd |
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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") |
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connected |
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#lets look at the head of the dataframe df.head() |
Out[112]: First Name Last Name…
Untitled This is a quick script on how to save Python pivot_table in an excel file. Credit to pbpython In [1]:
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import sqlalchemy import pyodbc from pandas import DataFrame import pandas as pd |
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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 * 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") |
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connected |
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df.head() |
Out[8]: First Name Last Name Sex Phone Fax Email Address Booking Date Departure Date Arrival Date Address1 … Number of Passengers Total Cost Currency Numeric Code…
pivot table for year on year This is a quick example of how to use pivot_table, to calculate year on year percentage sales . In [162]:
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import sqlalchemy import pyodbc import numpy as np from pandas import DataFrame from bokeh.plotting import figure, output_file, show import pandas as pd |
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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 * 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") |
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connected |
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#lets check the head df.head() |
Out[164]: First Name Last Name Sex Phone Fax Email Address Booking Date Departure Date Arrival Date Address1 … Number of Passengers…
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]:
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import sqlalchemy import pyodbc from pandas import DataFrame from bokeh.plotting import figure, output_file, show import pandas as pd |
In [50]:
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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") |
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connected |
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#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…
Sometimes you want to get some numbers and also percentages for quick analysis. This is a quick simple way to get quick overviews
This is just some quick scrap notes on the basics of Python GUI building using PyQt. In [1]:
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import sys from PyQt4 import QtGui, QtCore # to help add buttons #app = QtGui.QApplication(sys.argv) # #window = QtGui.QWidget() # ##set the posstion and size of the window #window.setGeometry(50, 50, 500, 500) # ##lets set the title #window.setWindowTitle('Date Incrementor') #window.show() class Window(QtGui.QMainWindow): def __init__(self): super(Window, self).__init__() self.setGeometry(50,50, 500, 500) self.setWindowTitle("Date incrementor") # self.setWindowIcon(QtGui.QIcon('logo.png')) #self.show() self.home() def home(self): #create and name a button btn = QtGui.QPushButton("Quit", self) #define what we will do when the button is clicked #btn.clicked.connect(QtCore.QCoreApplication.instance().quit) btn.clicked.connect(self.closeapplication) #lets resisze the button btn.resize(100,100) #lets position the button btn.move(100,100) #lets show the button self.show() #lets create custom methods def closeapplication(self): print('Application closed') sys.exit() |
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the ‘init‘ statements runs anytime we call call the Window class we have created and it runs all the code in the Window class the _init__ method calls the super constructor and then sets…
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 #…
The key advice for any Data Analysis project is to never start any analysis without first having a structured plan. You should set your goals and they should be S.M.A.R.T Below are the two charts to help with Analysis. This is part of Data Visualization and Communication with Tableau by Duke University on Coursera. Set your…
This is a short scrap notes from the Machine Learning Course taught by Associate Professor, Andrew Ng, Stanford University on Coursera. hypothesis = hØ(x) + Øo + Ø1X Parameters = Øo , Ø1 Our Goal = minimise Ø0 , Ø1 that is the cost function J(Ø0, Ø1) GRADIENT Descent. An algorithm wich minimises the…
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