Creating, Reading and Writing

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• Pandas is the most popular Python library for data analysis.
• To start import pandas as pd
• There are two core objects in pandas: the DataFrame and the Series.
• A DataFrame is a table. It contains an array of individual entries, each of which has a certain value. Each entry corresponds to a row (or record) and a column.

pd.DataFrame({'Yes': [50, 21], 'No': [131, 2]})

or

pd.DataFrame({'Bob': ['I liked it.', 'It was awful.'], 'Sue': ['Pretty good.', 'Bland.']})

• We are using the pd.DataFrame() constructor to generate these DataFrame objects. The syntax for declaring a new one is a dictionary whose keys are the column names (Bob and Sue in this example), and whose values are a list of entries. This is the standard way of constructing a new DataFrame, and the one you are most likely to encounter.

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• If a DataFrame is a table, a Series is a list. And in fact you can create one with nothing more than a list:

pd.Series([1, 2, 3, 4, 5])

A Series is, in essence, a single column of a DataFrame. So you can assign column values to the Series the same way as before, using an index parameter. However, a Series does not have a column name, it only has one overall name:

pd.Series([30, 35, 40], index=['2015 Sales', '2016 Sales', '2017 Sales'], name='Product A')

The Series and the DataFrame are intimately related. It's helpful to think of a DataFrame as actually being just a bunch of Series "glued together".