Knowing how analyze data is necessary skill for activities and with this course you learn the tools that Excel provides you to face the challenges related to planning, forecasting, budgeting, decision making, modelling and all the tasks that can be done using data analysis. Instructor has more than 10 years of experience working with MS Excel. If, like me, your primary exposure to visualization tools is Excel, then this mindset is a bit foreign. As I work with seaborn, I sometimes fight with it when I try to treat it like creating an Excel chart. However, once I started to produce some impressive plots with seaborn, I started to “get it.” There is no doubt I am still learning. A Word About Seaborn Before getting too deep into the article, I think it is important to give a quick word about seaborn. The gives more details, including this section: Seaborn aims to make visualization a central part of exploring and understanding data. The plotting functions operate on dataframes and arrays containing a whole dataset and internally perform the necessary aggregation and statistical model-fitting to produce informative plots. Seaborn’s goals are similar to those of R’s ggplot, but it takes a different approach with an imperative and object-oriented style that tries to make it straightforward to construct sophisticated plots. If matplotlib “tries to make easy things easy and hard things possible”, seaborn aims to make a well-defined set of hard things easy too. If, like me, your primary exposure to visualization tools is Excel, then this mindset is a bit foreign. As I work with seaborn, I sometimes fight with it when I try to treat it like creating an Excel chart. However, once I started to produce some impressive plots with seaborn, I started to “get it.” There is no doubt I am still learning. One thing I have found, though, is that if you are in a business setting where everyone sees the normal (boring) Excel charts, they will think you’re a genius once you show them some of the output from seaborn! ![]() The rest of this article will discuss how to visualize the survey results with seaborn and use the complex visualization to gain insights into the data. Import gspread from oauth2client.client import SignedJwtAssertionCredentials import pandas as pd import json import matplotlib.pyplot as plt import seaborn as sns SCOPE = [ '] SECRETS_FILE = 'Pbpython-key.json' SPREADSHEET = 'PBPython User Survey (Responses)' # Based on docs here - # Load in the secret JSON key (must be a service account) json_key = json. Load ( open ( SECRETS_FILE )) # Authenticate using the signed key credentials = SignedJwtAssertionCredentials ( json_key [ 'client_email' ], json_key [ 'private_key' ], SCOPE ) gc = gspread. Authorize ( credentials ) # Open up the workbook based on the spreadsheet name workbook = gc. Open ( SPREADSHEET ) # Get the first sheet sheet = workbook. ![]() Sheet1 # Extract all data into a dataframe results = pd. DataFrame ( sheet. Get_all_records ()) Please refer to the for some more details on what the data looks like. Since the column names are so long, let’s clean those up and and convert the timestamp to a date time. Mac os android. For index, row in suggestions. Iteritems (): display ( row ) A bit more coverage on how to make presentations - which in a lot of corporations just means powerpoint slides with python, from a business analyst perspective, of course Add some other authors to the website which can publish equally relevant content. Would be nice to see more frequent updates if possible, keep up the good work! How to produce graphics using Python, Google Forms. Awesome site - keep up the good work Great job on the site. Nice to see someone writing about actual Python use cases. So much writing is done elsewhere about software development without the connection to actual business work. Drop the suggestions. We won’t use them any more. Running_results = total_results. Groupby ( pd. TimeGrouper ( 'D' ))[ 'count' ]. Cumsum () running_results timestamp 2015-06-09 1 2015-06--06--06--06--06--06--06--06--06--06--06--06--06--06--06--06--06-26 53 Freq: D, Name: count, dtype: int64 To label the x-axis we need to define our time range as a series from 0 to the max number of days. All_counts = [] for tech in [ 'freq-py', 'freq-sql', 'freq-r', 'freq-ruby', 'freq-js', 'freq-vba' ]: all_counts.
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