Quick Python Tips to Explore Data

Simplest and quickest ways to do Exploratory Data Analysis with Pandas. I have taken Nobel Laureate dataset from Kaggle to do this exploratory data analysis, you can find the dataset here.

Simplest and quickest ways to do Exploratory Data Analysis with Pandas

“Sometimes not much is just enough.” ― John O’Callaghan

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I

am new to python and used to struggle often with minor data exploratory commands, there are numerous ways to explore datasets and do basic calculations, minor editing to research about data.

Initially, when we are new to python we come across several errors for simple commands, however these can overcome by simple user friendly tips and tricks. I came across some of these and thought of sharing with larger group, especially with new python users. I hope they like it.

I have taken Nobel Laureate dataset from Kaggle to do this exploratory data analysis, you can find the dataset here.

First of all import Pandas library and read data:

Now, let’s check the details about this dataset, this is bit old dataset as last couple of years winners name not included.

How do we get the number of rows or columns, here are some of the quickest ways:

len(df)

len(df.columns)

df.columns

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Let’s uncover practical details of Pandas’s Series, DataFrame, and Panel. Pandas is a column-oriented data analysis API. It’s a great tool for handling and analyzing input data.