Dictionary for Pandas

Attributes

Properties associated with an object in Python. In the context of the pandas library, attributes provide access to specific information or properties of pandas objects like DataFrames and Series.

Example: df.shape df.columns df.index

CSV

CSV stands for Comma-Separated Values. It is a plain text file format used to store tabular data, where each line of the file represents a row in the table, and each value in the row is separated by a comma.

DataFrame

A 2-dimensional labeled data structure with columns of potentially different types. Is often called df in the examples, but can be given any name.

Example:

df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})

Index

In pandas, an index is a label or set of labels used to identify and access elements in data structures like Series and DataFrames. It provides a way to reference rows and columns by name or number.

NaN

NaN stands for “Not a Number.” It is a special floating-point value used to represent missing or undefined data in numerical computations. It is often used in datasets to indicate that a value is missing.

pd

Aliasing pandas as pd is a widely adopted convention that simplifies the syntax for accessing its functionalities.
After this statement, you can use pd to access all the functionalities provided by the pandas library.

Example:

import pandas as pd

Series

A Series in Pandas is like a list of values in a single column, where each value has a unique label called an index. It is a simple way to store and manage a sequence of data. It is a 1-dimensional labeled array capable of holding any data type.

Example:

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

Subset

In Pandas, a subset refers to a selection or extraction of a portion of a DataFrame or Series based on specific criteria. This can involve selecting particular rows, columns, or both from the original data structure.

Transpose

transpose() is a method for swapping rows and colums in a DataFrame. Transposing is useful for reshaping data, making it easier to compare rows or apply certain operations that are typically column-based.

df.transpose()