What do you know about Pandas in Python?
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Pandas is a data manipulation package in Python for tabular data. That is, data in the form of rows and columns, also known as DataFrames. Intuitively, you can think of a DataFrame as an Excel sheet. Pandas’ functionality includes data transformations, like sorting rows and taking subsets, to calculating summary statistics such as the mean, reshaping DataFrames, and joining DataFrames together. pandas works well with other popular Python data science packages, often called the PyData ecosystem, including
NumPy for numerical computing
Matplotlib, Seaborn, Plotly, and other data visualization packages
scikit-learn for machine learning
pandas is used throughout the data analysis workflow. With pandas, you can:
Import datasets from databases, spreadsheets, comma-separated values (CSV) files, and more.
Clean datasets, for example, by dealing with missing values.
Tidy datasets by reshaping their structure into a suitable format for analysis.
Aggregate data by calculating summary statistics such as the mean of columns, correlation between them, and more.
Visualize datasets and uncover insights.
pandas also contains functionality for time series analysis and analyzing text data.