A home for orphaned spreadsheets. A fast, powerful, flexible and easy to use open source data analysis and manipulation tool built on the Python Programming Language. Start your journey to becoming a valuable contributor to your organization's decision making process.
pandas is a Python package that furnishes an enhanced library for handling data structures designed to be expressive. pandas assists in carrying out practical, real world data analysis in Python with powerful and flexible open source analytics / manipulation tools. pandas is a relatively new Python library developed for data manipulation. Pandas takes data and creates a Python object with rows and columns referred to as being a data frame that looks very similar to Excel style tables. These data structures permit operations necessary for numerical manipulation of tables and time series. pandas is short for "panel data", an econometrics term commonly applied to data sets spanning multiple time periods for the same attribute. ongitudinal data, Some times referred to as longitudinal data that contains observations relating to different cross sections across time. According to Wikipedia, Wes McKinney started building what would become pandas at AQR Capital while he was a researcher there from 2007 to 2010. pandas share a number of similarities with R. The R programming language provides the data.frame data structure and multiple packages, such as tidyverse use and extend data.frame for convenient data handling functionalities similar to pandas. Likewise, the column and row organisation characteristic of Excel transfers readily to pandas. Typical applications would include opening: a local file e.g. delimited text file, Excel file or CSV file / accessing a database or website through a URL / accessing a SQL table / database / converting a Numpy array to a Pandas data frame
This dedicated Python library for data analysis and manipulation can be used to communicate with a small datasets or larger databases somewhat like dplyr from R tidyverse and can operate on tables stored locally or in the cloud. pandas and dplyr can be made to operate on tabular data (i.e. tables that consist of rows and columns) and implement queries in a similar style. To make a direct comparison with R for Data Science it is worth considering Python for Data Science. The pandas package is a natural way to engage in routine, real life data analysis in a Python environment. Google Colab below, is set up with some examples from chapter 5 from Python for Data Science. pandas is well suited for many different kinds of data: (1) Tabular data with heterogeneously-typed columns, as in an Excel spreadsheets and googlesheets (2) Ordered and unordered (not necessarily fixed-frequency) time series data (3) Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels and (4) Any other form of observational / statistical data sets. The data need not be labeled at all to be placed into a pandas data structure. To compare pandas and dplyr further it might be worth checking out this article. Also, for a complete expose of relevant verbs - please see here.
Below, we set out step-by-step instruction introducing the pandas verbs from chapter 5 of Python for Data Science . The approach here is designed to shadow chapter 5 R for Data Science where previously we had learned to create some new variables or summaries, rename variables, and reorder observations for the data to be easier to manage. Here, we import the nycflights13 dataset using R and then apply pandas in a combined R and Python google colab notebook. This largely relates to transforming your data using the pandas package and making use of the nycflights13 dataset.
