Pandas is a Python library for data manipulation and analysis, e.g. dataframes, multidimensional time series and cross-sectional datasets commonly found in statistics, experimental science results, econometrics, or finance. Pandas is one of the main data science libraries in Python.

Pandas is a Python library for PAN-el DA-ta manipulation and analysis, e.g. multidimensional time series and cross-sectional data sets commonly found in statistics, experimental science results, econometrics, or finance. `pandas`

is implemented primarily using NumPy and Cython; it is intended to be able to integrate very easily with NumPy-based scientific libraries, such as statsmodels.

### To create a reproducible Pandas example:

- How to make good reproducible pandas examples
- How to provide a reproducible copy of your DataFrame with to_clipboard()

### Main Features:

- Data structures: for one- and two-dimensional labeled datasets (respectively
`Series`

and`DataFrames`

). Some of their main features include:- Automatically aligning data and interpolation
- Handling missing observations in calculations
- Convenient slicing and reshaping ("reindexing") functions
- Categorical data types
- Provide 'group by' aggregation or transformation functionality
- Tools for merging and joining together data sets
- Simple Matplotlib integration for plotting and graphing
- Multi-Indexing providing structure to indices that allow for representation of an arbitrary number of dimensions.

- Date tools: objects for expressing date offsets or generating date ranges. Dates can be aligned to a specific time zone and converted or compared at will
- Statistical models: convenient ordinary least squares and panel OLS implementations for in-sample or rolling time series and cross-sectional regressions. These will hopefully be the starting point for implementing models
- Intelligent Cython offloading; complex computations are performed rapidly due to these optimizations.
- Static and moving statistical tools: mean, standard deviation, correlation, and covariance
- Rich User Documentation, using Sphinx

### Asking Questions:

- Before asking the question, make sure you have gone through the 10 Minutes to pandas introduction. It covers all the basic functionality of Pandas.
- See this question on asking good questions: How to make good reproducible pandas examples
- Please provide the version of Pandas, NumPy, and platform details (if appropriate) in your questions

### Answering Questions:

- How can I effectively load data on Stack Overflow questions using Pandas read_clipboard? (useful for copy pasting data from questions into your terminal as DataFrames)
- Copying MultiIndex dataframes with pd.read_clipboard?

### Useful Canonicals:

*How can I pivot a dataframe?**Pandas Merging 101**How to deal with SettingWithCopyWarning in Pandas**What are the 'levels', 'keys', and names arguments for in Pandas' concat function?**Selecting multiple columns in a Pandas dataframe**Delete a column from a Pandas DataFrame**How to iterate over rows in a DataFrame in Pandas*

More FAQs are at this link.

### Resources and Tutorials:

- 10 Minutes to pandas
- Pandas Documentation
- Pandas GitHub
- Pandas Homepage
- Pandas Cookbook
- Real Python: Pandas