MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + .srt | Duration: 9 lectures (4h 5m) | Size: 1.28 GB

When working with tabular data, such as data stored in spreadsheets or databases, pandas is the right tool for you.

The Ultimate Pandas Tutorial for Data Science Bners

You will learn the basics of Pandas Library

You will have clarity on Pandas Data structures - Series & Dataframes

You will Play with Dataframes, Selecting columns & rows from a dataframe

You will understand Subsetting of dataframes - df[start_index:end_index]

You will get insights on Indexing

You will get clarity on Dataframes meg and concatenating

Basic experience with the Python programming language

Strong knowledge of data types (strings, integers, floating points, booleans) etc

Pandas Background:

pandas will help you to explore, clean and process your data. In pandas, a data table is called a DataFrame. Pandas supports the integration with many file formats or data sources out of the box (csv, excel, sql, json, parquet,. . . ). Importing data from each of these data sources is provided by function with the prefix read_*. Similarly, the to_* methods are used to store data.

Selecting or filtering specific rows and/or columns? Filtering the data on a condition? Methods for slicing, selecting, and extracting the data you need are available in pandas. There is no need to loop over all rows of your data table to do calculations. Data manipulations on a column work elementwise. Adding a column to a DataFrame based on existing data in other columns is straightforward.

Pandas has great support for series and has an extensive set of tools for working with dates, s, and indexed data. Data sets do not only contain numerical data. pandas provides a wide range of functions to cleaning textual data and extract useful information from it.

In this course we cover:

Basics of Pandas Library

Pandas Data structures - Series & Dataframes

Playing with Dataframes, Selecting columns & rows from a dataframe

Subsetting of dataframes - df[start_index:end_index]

Indexing

Dataframes meg and concatenating

Python programming has become one of the most sought after programming languages in the world, with its extensive amount of features and the sheer amount of productivity it provides. Therefore, being able to code Pandas in Python, enables you to tap into the power of the various other features and libraries which will use with Python. Some of these libraries are NumPy, SciPy, MatPlotLib, etc.

Data analysts and business analysts

Excel users looking to learn a more powerful software for data analysis



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