In an excel spreadsheet with four sheets (this is an A/B test, Sheet 1 is a_group_flights, Sheet 2 is b_group_flights, Sheet 3 is a_group_hotels, Sheet 4 is b_group_hotels), I'm interested in plotting two of the columns "budget_price" and "total_spend" over a time period shown by "budget_datetime" and have those two lines (budget_price and total_spend) overlap to show the difference between what your budget is and how much you're actually spending over time on trips.
This is what the spreadsheet looks like: https://ibb.co/JHCf12z
I am using Dash Plotly and want to read the excel spreadsheet with Pandas, and then plot the data on a graph.
xlsx = pd.ExcelFile('data.xlsx')
Read the excel instead with
xlsx = pd.read_excel('your_excel_file.xls')
To make a simple line plot just make
df = xlsx data = [go.Scatter( x=df['X_axis_column'], y=df['first_y_data', 'second_y_data'] )]
If your excel has more than one sheet, just make
df1 = pd.read_excel('exel_file.xls', 'Sheet1') df2 = pd.read_excel('exel_file.xls', 'Sheet2')
Then you should check here to see which kind of plot you want
If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition
When you get introduced to machine learning, the first step is to learn Python and the basic step of learning Python is to learn pandas library. We can install pandas library by pip install pandas. After installing we have to import pandas each time of the running session. The data used for example is from the UCI repository “https://archive.ics.uci.edu/ml/datasets/Heart+failure+clinical+records ”
2. Head and Tail
3. Shape, Size and Info
#pandas: most used functions in data science #pandas #data science #function #used python data #most used functions in data science
The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.
This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.
As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).
This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.
#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management
As data mesh advocates come to suggest that the data mesh should replace the monolithic, centralized data lake, I wanted to check in with Dipti Borkar, co-founder and Chief Product Officer at Ahana. Dipti has been a tremendous resource for me over the years as she has held leadership positions at Couchbase, Kinetica, and Alluxio.
According to Dipti, while data lakes and data mesh both have use cases they work well for, data mesh can’t replace the data lake unless all data sources are created equal — and for many, that’s not the case.
All data sources are not equal. There are different dimensions of data:
Each data source has its purpose. Some are built for fast access for small amounts of data, some are meant for real transactions, some are meant for data that applications need, and some are meant for getting insights on large amounts of data.
Things changed when AWS commoditized the storage layer with the AWS S3 object-store 15 years ago. Given the ubiquity and affordability of S3 and other cloud storage, companies are moving most of this data to cloud object stores and building data lakes, where it can be analyzed in many different ways.
Because of the low cost, enterprises can store all of their data — enterprise, third-party, IoT, and streaming — into an S3 data lake. However, the data cannot be processed there. You need engines on top like Hive, Presto, and Spark to process it. Hadoop tried to do this with limited success. Presto and Spark have solved the SQL in S3 query problem.
#big data #big data analytics #data lake #data lake and data mesh #data lake #data mesh
whenever it comes to visualizations for data plotly comes out as a standout package to choose among multiple options especially when it comes to interactivity also plotly comes with Dash which is really a handy framework for creating powerful Dashboards. when there are times when you have live data and that too with multiple figures.
And well its sometimes easy to set an interval for each figure and make Dash update the figure in that interval but consider if the data you are using is shared between all the figures. so you have to process the data in each callback, yep!.. you have to do the same computation multiple times and you can not update the data outside the callback because than it won’t be updated also you can’t use global variables because it will break your app especially when you are having user inputs.
now the one way is to create a hidden div and process and store the data in that and then use it as an input for your callbacks you can find this example in here
But what if this is the case
that is for each figure you have to process the data from the main live data. In this case, you have to process read the data from JSON string in each callback which is again computationally expensive and you might get bugs.
thus a better way to do is to use multiple outputs
#data-visualization #livedata #plotly #dashboard #dash #data analysis