Pandas

Pandas is a powerful Python library used for data manipulation and analysis, providing flexible data structures like DataFrames and Series to handle and analyze structured data efficiently.

Getting started with Pandas involves installing the library, importing it with an alias, and using its data structures like DataFrames and Series for data manipulation and analysis.

Pandas is commonly imported as pd in Python to provide a shorter alias for easier reference in data manipulation and analysis tasks.

A Pandas Series is a one-dimensional array-like object that can hold any data type and is labeled with an index.

In Pandas, a Series is a key/value object where the keys are the labels (index) and the values are the data elements.

You can create a DataFrame by combining two Series:

Named indexes in Pandas allow you to assign custom labels to the rows or columns of a DataFrame or Series for easier data reference and manipulation.

Pandas read_csv() function is used to load data from a CSV file into a DataFrame for analysis and manipulation.

Pandas read_json() function is used to load data from a JSON file or string into a DataFrame for analysis and manipulation.

Pandas provides powerful tools for analyzing data, allowing you to manipulate, aggregate, and summarize datasets to extract meaningful insights.

The info() method in Pandas provides a concise summary of a DataFrame, including the number of non-null entries, data types, and memory usage.

Cleaning data involves identifying and handling missing, incorrect, or inconsistent values to improve the quality and accuracy of a dataset for analysis.

Cleaning empty cells involves handling missing values by removing them or filling them with appropriate data to maintain dataset integrity.

Replacing using mean, median, or mode involves filling empty or missing values with the average (mean), middle value (median), or most frequent value (mode) of the respective column.

Cleaning data of the wrong format involves converting or correcting data types to ensure consistency and proper analysis.

Cleaning wrong data involves identifying and correcting or removing inaccurate, inconsistent, or invalid entries in a dataset.

Pandas provides the drop_duplicates() method to remove duplicate rows from a DataFrame based on specified columns.

Pandas provides the corr() method to calculate the correlation between numerical columns in a DataFrame, measuring their relationship.

Pandas provides built-in plotting capabilities via the plot() method, allowing easy visualization of data from DataFrames and Series.

A scatter plot is a graphical representation of data points on a two-dimensional axis, showing the relationship between two numerical variables.

A histogram is a graphical representation of the distribution of numerical data, showing the frequency of data points within specified ranges (bins).