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Pandas - 2.2
- pd.Series()
- pd.DataFrame()
- pd.read_pickle()
- pd.read_table()
- pd.read_csv()
- pd.read_fwf()
- pd.read_clipboard()
- pd.read_excel()
- pd.ExcelFile.parse()
- pd.read_json()
- pd.json_normalize()
- pd.read_html()
- pd.read_xml()
- pd.read_hdf()
- pd.read_feather()
- pd.read_parquet()
- pd.read_orc()
- pd.read_sql_table()
- pd.read_sql_query()
- pd.read_sql()
Pandas | Getting Started
A library in Python used for working with structured data such as tables, spreadsheets, or time-series data.
Provides Series and DataFrames, flexible tools for data manipulation and analysis.
Highly efficient for handling, cleaning, and transforming large datasets.
What is Pandas?
Series: A one-dimensional labeled array capable of holding data of any type (e.g., integers, strings, floats).
DataFrame: A two-dimensional labeled data structure, similar to a table in a database or an Excel spreadsheet.
Interoperability: It works well with other libraries like NumPy (for numerical computations),, Matplotlib (for plotting and visualization), and SciPy (for additional scientific computing tools).
Why use Pandas?
Data Handling: Pandas makes it easy to load, clean, transform, and analyze data from various sources like CSV, Excel, SQL databases, and JSON.
Label-Based Indexing: You can access and modify data using row/column labels or positions, making operations intuitive and efficient.
Data Transformation: Its methods allow for reshaping, filtering, and aggregating data in just a few lines of code.