Python Libraries and Tools for Data Exploration and Analysis

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Data exploration is a crucial step in understanding and preparing your data for analysis. In Python, you can perform data exploration using various libraries and tools. Here’s a list of common libraries and techniques you can use for data exploration:

1. Import Data:

pandas: Use the read_csv(), read_excel(), or other data reader functions to import your data.

2. Overview of Data:

• head(): View the first few rows of your dataset.
• info(): Display the data types and missing values.
• describe(): Get summary statistics for numerical columns.

3. Data Cleaning:

• isnull(): to see if there is any null and missing values
• dropna(): Remove rows with missing values.
• fillna(): Fill missing values with specified values.
• duplicated(): Check for and remove duplicate rows.
• drop(): drop duplicates

4. Data Visualization:

matplotlib, seaborn, plotly, or bokeh: Create a variety of plots and charts, including histograms, bar plots, scatter plots, and heatmaps to visualize your data.