How to import CSV files using Pandas DataFrame error-free

How to import CSV files using Pandas DataFrame error-free

EmptyDataError. Sounds Familiar? Then read stick with me for some tips to avoid any form of error when loading your CSV files using Pandas… After reading, you will definitely not get this error anymore.

The first step of data cleaning/wrangling is loading the file and then establishing a connection via the path of a file. There are different types of delimited files like tab-separated file, comma-separated file, multi-character delimited file etc. The delimitations indicate how the data is to be separated within columns whether through comma, tab or semicolon etc. The most commonly used files are tab-separated and comma-separated files.

Data wrangling and cleaning accounts for about 50 to 70% of the Data analytics professionals’ time within the whole ML pipeline. The first step is to import the file to a Pandas DataFrame. However, this step constitutes the most encountered errors. People often get stuck in this particular step and come across errors like

EmptyDataError: No columns to parse from file

The common errors occur, mainly, due to :

· Wrong file delimiters mentioned.

· File path not formed properly.

· Wrong syntax or separator used to specify the file path.

· Wrong file directory mentioned.

· File Connection not formed.

Data analytics professionals cannot afford more time being drained into an already time-consuming step. While loading the file, certain important steps must be followed which will save time and cut through the hassle of scouring through a plethora of information to find the solution to your specific problem. Therefore, I have laid out some steps to avoid any error while importing and loading a data file using pandas DataFrame.

Reading and importing the CSV file is not so simple as one may surmise. Here are some tips which must be kept in mind once you start loading your file to build your Machine Learning model.

python data-science machine-learning pandas-dataframe csv-file

What is Geek Coin

What is GeekCash, Geek Token

Best Visual Studio Code Themes of 2021

Bootstrap 5 Tutorial - Bootstrap 5 Crash Course for Beginners

Nest.JS Tutorial for Beginners

Hello Vue 3: A First Look at Vue 3 and the Composition API

Data Science With Python Training | Python Data Science Course | Intellipaat

🔵 Intellipaat Data Science with Python course: https://intellipaat.com/python-for-data-science-training/In this Data Science With Python Training video, you...

6 Best Python IDEs for Data Science & Machine Learning [2021]

6 Best Python IDEs for Data Science & Machine Learning [2021] - An IDE (Integrated Development Environment) is used for software development. An IDE may have a compiler, debugger, and all the other requirements needed for software development. IDEs help in consolidating different aspects of a computer program

6 Best Python IDEs for Data Science & Machine Learning [2021] | upGrad blog

Introduction  An IDE (Integrated Development Environment) is used for software development. An IDE may have a compiler, debugger, and all the other requirements needed for software development. IDEs help in consolidating different aspects of a computer program. 6 Best Python IDEs for Data Science & Machine Learning [2021]

How I'd Learn Data Science If I Were To Start All Over Again

A couple of days ago I started thinking if I had to start learning machine learning and data science all over again where would I start?

Applied Data Analysis in Python Machine Learning and Data Science | Scikit-Learn

Applied Data Analysis in Python Machine learning and Data science, we will investigate the use of scikit-learn for machine learning to discover things about whatever data may come across your desk.