Project Data Science- Data Cleansing and Transformation by Python Pandas and NumPy
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.