How Is Pandas Used In Data Science? | Python Tutorial | Part 1 |

Pandas is a powerful and open-source Python library for data manipulation and analysis, providing data structures and functions for efficient operations. In this Python tutorial, Mr. Rohit Mande ( Ex. Chief Data Scientist at Barclays) will explain us about how to read and access data by row, columns, how to Filter data frame and how to drop duplicates from the data.

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 visualisation packages
– scikit-learn for machine learning

What is pandas used for?
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.
– Visualise datasets and uncover insights.

Video Timestamps:
00:00 Introduction
02:00 Read and access data by row,columns
07:18 how to Filter data frame
12:43 how to drop duplicates from the data

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About Us:
Rohit Mande is Founder and CEO of He has 10+ years of professional experience as a Data Scientist. In his previous role as ‘Chief Data Scientist at Barclays’ he was leading a team of Data Scientists. He has done his Masters from Technical University of Darmstadt, Germany in 2013-2015. He is also having published patent applications listed on Google Patents. He is passionate about helping people in transitioning to Data Science role.
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