Python Bootcamp: Data Exploration and Matplotlib Plots

[LeeAndro]

Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 678 MB | Duration: 2h 3m

Initial Analysis of the data using methods or attributes like: Shape, info( ), describe( )etc.

What you'll learn

Sort values, unique values, index, mean, reset index, rename column

Data type change for a column/series, Subsetting

Logical OR, Logical AND, isin( ), not isin( )

String methods: copy( ), upper( ), lower( ), title( ), replace( ), split( )

Handling of missing values: isna( ), dropna( ), isna( )

Summary statistics of a numerical column: describe( ), std( ), var( ), boxplot( )

Linspace( )

Slicing using loc and iloc

groupby, query( ), division of values of a column/series by values in another column/series

Matplotlib: plotting basic graphs, customizations (marker size, line width, xlabel, ylabel, title)

Subplopts, subplots with same Y-axis, subplots with same X-axis

Matplotlib: histogram, bargraph, boxplot, scatter plot

Matplotlib: style sheets, saving plot/figures

Description

This course will help you to learn and understand Data exploration in python and basic plots of Matplotlib for better visualization of the dataset.

The python version used in this course is 3.8.5

This course is designed for students who have zero to basic knowledge of Python. All the methods, attributes, plots, graphs, customizations are explained from scratch.

The first part of the course mainly focuses on data analysis and will help you to understand functions/methods that you may need to use to explore your dataset in its entirety.

Further, some part focuses on cleansing as well like handling duplicate and missing values.

The structure for the Data exploration (first part) is:-

Sort values, unique values, index, mean, reset index, rename column

Data type change for a column/series, Subsetting

Logical OR, Logical AND, isin( ), not isin( )

String methods: copy( ), upper( ), lower( ), title( ), replace( ), split( )

Handling of missing values: isna( ), dropna( ), isna( )

Summary statistics of a numerical column: describe( ), std( ), var( ), boxplot( )

Linspace( )

Slicing using loc and iloc

groupby, query( ), division of values of a column/series by values in another column/series

The second part mainly focuses on plots/graphs of Matplotlib:-

Matplotlib: plotting basic graphs, customizations (marker size, line width, xlabel, ylabel, title)

Subplopts, subplots with same Y-axis, subplots with same X-axis

Matplotlib: histogram, bargraph, boxplot, scatter plot

Matplotlib: style sheets, saving plot/figures




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