Interesting facts about pandas - The best data manipulation library provided by Python. pandas is a fast, powerful, flexible, and easy-to-use open-source data analysis and manipulation tool, built on top of the Python programming language.
pandas is a popular Python programming language package for Data Science. It provides efficient data structures and data manipulation tools for handling large datasets.
pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It is the most powerful and flexible open-source data analysis/manipulation tool.
Interesting Facts About Pandas
- pandas is the fundamental high-level building block for doing practical, real-world data analysis in Python.
- pandas is best suitable for tabular data analysis, such as spreadsheets, databases, etc. pandas also processes following type of data efficiently, like ordered and unordered data, arbitrary matrix data with row and column labels, observational/statistical data sets, etc.
- The two primary data structures of pandas, Series (1-dimensional) and DataFrame (2-dimensional), handle the vast majority of typical use cases in finance, statistics, social science, and many areas of engineering.
- pandas' "Series" data structure is 1D labeled homogeneously typed array.
- pandas' "DataFrame" data structure is a general 2D labeled, size-mutable tabular structure with the potentially heterogeneously-typed columns.
- pandas data structures are the flexible containers for lower dimensional data.
- pandas is the right tool to explore, clean, and process large amounts of data.
- In pandas, a data table is called a DataFrame.
- pandas support many different data formats namely, CSV, Excel, SQL, JSON, parquet, hdf5, gbq out of the box.
- pandas provide robust IO tools for loading data from flat files (CSV and delimited), Excel files, databases, and saving/loading data from the ultrafast HDF5 format.
- Importing data from CSV, Excel, JSON, parquet, SQL, gbq, hdf5 is piece of cake with the help of read_* prefixed methods.
- Exporting or storing data to CSV, Excel, SQL, JSON, parquet, gbq, hdf5 is piece of cake with the help of to_* prefixed methods.
- In pandas, you can easily select or filter specific rows and columns based on conditions. There are readymade methods for the selection, extraction of data.
- pandas has provided a powerful functionality to present the complex data with the help of plots using the Matplotlib library.
- You can visualize data using pandas with appropriate plotting functions for scatter, bar, boxplot based on the data available.
- It's extremely easy to add new columns based on the existing DataFrame in pandas. There is no need to loop over all the data rows present in DataFrame to perform the mathematical operations or calculations.
- pandas is quite intelligent with label-based slicing, fancy indexing, and subsetting of large data sets, intuitive merging and joining data sets.
- pandas provide an easy interface to calculate the basic statistics, like mean, median, min, max, counts. These aggregation functions can be applied to the whole dataset within a fraction of time. It's also possible to apply such functions on the sliding window of the data or grouped by categories with the help of the split-apply-combine approach.
- pandas provide easy-to-use handy functionality to restructure or reshape the data tables in multiple ways. It is possible to melt() the data from wide to long or tidy form or pivot() from long to wide format. With aggregations built in, a pivot table is created with a single command.
- pandas provide a quick approach to merge or combine the data from multiple sources, both column or row-wise. There are database merge/join-like operations available to combine multiple tables of data.
- Easy handling of missing data (represented as NaN) in floating point as well as non-floating point data
- pandas support time series manipulation tools equipped with dates, times, and time-indexed data support.
- pandas also support size mutability. Columns can be inserted and deleted from DataFrame and higher dimensional objects.
- pandas provide a wide range of text manipulation functions. Because data can not only be of numerical type. Text type data processing is now a common thing. pandas provide a lot of functions to clean and extract useful information from textual data.
- pandas is fast. Many of the low-level algorithmic bits have been extensively tweaked in Cython code.
- pandas is a dependency of statsmodels, making it an important part of the statistical computing ecosystem in Python.
- pandas is an extremely powerful tool and have been used extensively in financial data analysis.
- To sum things up pandas provides functionality for: Object creation, Viewing data, Selection, Missing data, Operations, Merge, Grouping, Reshaping, Time series, Categoricals, Plotting, Getting data in/out, and much more.
- pandas - Python Data Analysis Library
- pandas - PyPI
- 10 minutes to pandas
- Pandas Tutorial - W3Schools
- Learn Pandas Tutorials | Kaggle
- pandas (software) - Wikipedia
- Repository - github.com/pandas-dev/pandas
- Pandas Tutorial: DataFrames in Python