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If you want a fancier look of the dataframe, you would want to use the Jupyter notebook to write and run your Python code. That’s how it looks on a basic Python shell. That code would generate the following output in Python: Loading a CSV file in Python with pandas import pandas as pdĭf1 = pd.read_csv("C:/PythonHow/income_data.csv") See how easy it is to create a pandas dataframe out of this CSV file. Once you have the dataframe loaded in Python, you can apply various data analysis and visualization functions to the dataframe and basically turn the dataframe data into valuable information. You can create data frames out of various input data formats such as CSV, JSON, Python dictionaries, etc. A dataframe is basically a 2D numpy array with rows and columns, that also has labels for columns and rows. The pandas main object is called a dataframe. Pandas is a powerful data analysis Python library that is built on top of numpy which is yet another library that lets you create 2D and even 3D arrays of data in Python. That’s definitely the synonym of “Python for data analysis”. Loading CSV data in Python with pandas Interactive Course