Pandas Overview
1. Introduction to Pandas
What is Pandas: Pandas is a Python library used for data manipulation and analysis, providing data
structures like DataFrame and Series for handling structured data.
History of Pandas: Pandas was developed by Wes McKinney in 2008, designed to offer flexible data
manipulation tools for Python.
Key Features: Pandas offers powerful data alignment, indexing, reshaping, and merging operations
for working with tabular and time-series data.
2. Pandas Data Structures
Series: A Pandas Series is a one-dimensional array-like object, similar to a list or NumPy array, but
with labeled indices.
DataFrame: A DataFrame is a two-dimensional, tabular data structure with labeled rows and
columns, resembling a table or SQL table.
Panel (Deprecated): The Panel was a three-dimensional data structure in Pandas, now deprecated
in favor of using multi-indexed DataFrames.
3. Data Import and Export in Pandas
Reading Data: Pandas can read data from various file formats like CSV, Excel, SQL databases,
JSON, and more using functions like read_csv(), read_excel(), and read_sql().
Writing Data: Data can be written to files using functions like to_csv(), to_excel(), and to_json(),
making it easy to export DataFrames to different formats.
Handling Missing Data: Pandas provides functions like isnull() and dropna() for detecting and
handling missing values in datasets.
4. Data Manipulation with Pandas
Filtering and Subsetting: Pandas allows for subsetting data using conditions, boolean indexing, and
loc[] or iloc[] for accessing rows and columns.
Adding and Removing Columns: New columns can be added by assigning values to a DataFrame,
1. Introduction to Pandas
What is Pandas: Pandas is a Python library used for data manipulation and analysis, providing data
structures like DataFrame and Series for handling structured data.
History of Pandas: Pandas was developed by Wes McKinney in 2008, designed to offer flexible data
manipulation tools for Python.
Key Features: Pandas offers powerful data alignment, indexing, reshaping, and merging operations
for working with tabular and time-series data.
2. Pandas Data Structures
Series: A Pandas Series is a one-dimensional array-like object, similar to a list or NumPy array, but
with labeled indices.
DataFrame: A DataFrame is a two-dimensional, tabular data structure with labeled rows and
columns, resembling a table or SQL table.
Panel (Deprecated): The Panel was a three-dimensional data structure in Pandas, now deprecated
in favor of using multi-indexed DataFrames.
3. Data Import and Export in Pandas
Reading Data: Pandas can read data from various file formats like CSV, Excel, SQL databases,
JSON, and more using functions like read_csv(), read_excel(), and read_sql().
Writing Data: Data can be written to files using functions like to_csv(), to_excel(), and to_json(),
making it easy to export DataFrames to different formats.
Handling Missing Data: Pandas provides functions like isnull() and dropna() for detecting and
handling missing values in datasets.
4. Data Manipulation with Pandas
Filtering and Subsetting: Pandas allows for subsetting data using conditions, boolean indexing, and
loc[] or iloc[] for accessing rows and columns.
Adding and Removing Columns: New columns can be added by assigning values to a DataFrame,
