What is Pandas in Python -2023?

Pandas is one of the most popular and essential libraries in the Python ecosystem, especially when it comes to data analysis and manipulation. With its expressive syntax, versatile functionality, and ability to handle vast amounts of data, Pandas has become the go-to tool for data scientists, analysts, and developers alike. It is an absolute MUST for any Data Analyst

Overview of Python Pandas

Pandas, short for “Python Data Analysis Library,” was developed by Wes McKinney in 2008. He was aiming to bring powerful data analysis tools, which were then mainly available in the R language, to the Python programming world. The result was a library that not only achieved this but also introduced an efficient way to manage and manipulate structured data.

Core Components

Pandas primarily is composed of two core data structures: the DataFrame and the Series.

  1. DataFrame: A two-dimensional, size-mutable, and heterogeneous tabular data structure with labeled axes (rows and columns). You can think of it like an Excel spreadsheet or a SQL table in Python.
  2. Series: A one-dimensional labeled array that can hold any data type, similar to a column in a spreadsheet or a list in Python.

Why Use Pandas?

  • Flexibility: Whether you’re working with data from an Excel file, a database, or a web API, Pandas can ingest and output data in a multitude of formats.
  • Efficiency: Built on top of the NumPy library, Pandas operations are fast, allowing for efficient data manipulation.
  • Rich Functionality: With built-in functions for aggregating, filtering, transforming, and visualizing data, Pandas can cater to a wide range of data analysis needs.
  • Community Support: A vast community of users and contributors ensures that Pandas remains updated, with new features added regularly and ample resources available for learning and troubleshooting.

Top Ten Most Used Functions in Pandas:

  1. read_csv(): Reads a comma-separated values (csv) file into a DataFrame. It’s one of the most common ways to import data into Pandas.
  2. head(): Returns the first n rows of a DataFrame or Series. By default, it shows the first five rows.
  3. describe(): Provides a statistical summary of a DataFrame’s numeric columns, including count, mean, standard deviation, and more.
  4. groupby(): Groups a DataFrame using a particular column or set of columns, allowing for aggregate operations on the grouped data.
  5. merge(): Combines rows of two DataFrames based on one or more keys, similar to SQL JOIN operations.
  6. fillna(): Fills missing values in a DataFrame or Series. It can replace NaNs with a specified value or a method like forward fill or backward fill.
  7. drop(): Removes columns or rows from a DataFrame.
  8. loc[] and iloc[]: Used for label-based and integer-based indexing, respectively. They allow for selecting rows and columns from a DataFrame.
  9. value_counts(): Returns a Series representing counts of unique values, useful for understanding the distribution of categorical data.
  10. pivot_table(): Creates a spreadsheet-style pivot table as a DataFrame. It can aggregate data with complex transformations.

Gaelim Holland

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