#data cleansing in python

Data scientists spend a large amount of their time cleaning datasets and getting them down to a form with which they can work.  Lot of data scientists argue that the initial steps of obtaining and cleaning data constitute 80% of the job. In our python data science project training covering how to deal with Pandas and NumPy libraries to clean data. If you are new bees, beginner or experienced professional but doesn't know much about python and wants to enter into the real life of Data Scientist. Our experts are here for you to teach from the very basic to the advanced concepts.

We are stepping ahead with this concern and planning to create a small introduction video for our learners. It is important to be able to deal with messy data, how to do Data Cleansing using Python, whether that means missing values, inconsistent formatting, malformed records, or nonsensical outliers. We have seen how to setup a database, prepare a table in model files and migration along with uploading in our previous video tutorial. Today we are discussing how to save a csv file into a folder, extraction of data, cleaning and massaging with the help of Pandas and NumPy also saving of data inside the table i.e. importing a csv. If you missed out our previous video tutorial, please subscribe our channel for more live updates and go through the below link to watch previous video.

We need to follow few basic steps to perform the desired operations and result.

Step_1: Upload a csv

Step_2: Extract the csv. data in the form of data frame performing cleaning and massaging of data

Step_3: Save the same csv. file with the same name

Step_4: Upload the cleaned data csv into the data table.

Precautions:

Table attribute sequence and csv sequence must be same.

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With our Comprehensive Project-based data science courses covering all concepts and fundamentals of the Data Science process from Data Integration, Data Manipulation, Descriptive Analytics and Visualization and rendering chart with chartJS, Data Cleansing using Python, Statistical Analysis, Predictive Analytics and Machine Learning models, Data Cleansing using Python that can enable you to master in elements of Data Science. The Most demanded Industry pattern for Project with Data Science Certification Course by BISP.

In today's scenario we have explained one case study with the help of this video tutorials for our learners in which we took financial data to perform operations for desired results. The topic is "Data Wrangling" for Remove punctuation. We are performing data massaging, data transformations. First, we have received data from various sources like ERP, CRM, SQL Server etc. We need to remove punctuation like full stop, comma, parenthesis and all before doing advance analysis on data. There are multiple ways and methods to perform this task in python like NLP toolkit but we are using string function to achieve the desired results.

We have done various projects and a long-lasting esteemed client list, we are sharing our real time knowledge and experience with our candidates and a live project working with our online training of Advanced Data Science certification Training Course where we teach you how to build a project from scratch with Data Science skills and practice mode helps you sharpen them. There are many methodologies and conceptual knowledge we are sharing with our candidates like...

Why to learn Python?

Why python is necessary for Data Science?

How data is very important for companies?

How to play with data?

Various Issues are associated with data at the very beginning when we receive from various sources, later how to set up a python project? Hurdle’s that came up as the training progresses further, we teach Data Cleansing using Python, Data massaging, Data Transformation, Data Wrangling, Algorithms, Calculations and many more. We know how to build candidate confidence so they can face and solve real time challenges and issues.