Learn Python Libraries For Data Analysis & Data Manipulation

Last updated 6/2020
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 7.92 GB | Duration: 14h 51m
Learn Python Pandas, Matplotlib & Seaborn. Read CSV, Excel, SQL, JSON, HTML etc. Datasets.

What you’ll learn
Python Pandas Library and Its Methods
Reading Data from Sources like CSV, Excel, Html, Json, Json API, Dictionary, etc, using Python Pandas
Handling Missing Data in Datasets
Working with TIme Series Datasets
Use of Matplotlib Library For Plotting Graphs like Line Graph, Bar Graph, Histogram, Pie Chart etc.
Use of Seaborn Library For Plotting Graphs like Line , Bar, Distplot, Catplot, Swarmplot etc.
Exploratory Data Analysis on Titanic Dataset
Exploratory Data Analysis on GOT Dataset
Exploratory Data Analysis on Historial Stock Data ( From JSON API)
Exploratory Data Analysis on Restaruant Tips Dataset
Requirements
Basic Knowledge of Python
Knows how to install applications on computer
Description
Lecture 2:Introduction to Python PandasLecture 3:How to Install Python Pandas on ComputerLecture 4:Data Structures in Python Pandas Section 2:Pandas SeriesLecture 5:How to Create Pandas Series from ScratchLecture 6:How to Create Pandas Series Using Ndarray and Dictionary Section 3:Pandas DataframesLecture 7:Creating Your First DataframeLecture 8:Creating a Datafram Using Python ListsLecture 9:Create an indexed DataFrame using arraysLecture 10:Getting Data of a Row or Multiple Rows in Pandas DataframeLecture 11:Basic Operations on Pandas Dataframes – Using Some Methods and AttributesLecture 12:Setting and Resetting Index of a DataframeLecture 13:How to Locate Values On the basis of Index Name Section 4:Reading CSV Files – With Exploratory Data Analysis on DatasetLecture 14:Reading CSV Files EDA On GOT Dataset Part 1Lecture 15:Reading CSV Files EDA On GOT Dataset Part 2Lecture 16:Read Excel OR Csv File and Write to an Excel Or CSV File Section 5:Handling Missing DataLecture 17:Handdling Missing Data in Dataframes – Fillna MethodLecture 18:Handdling Missing Data in Dataframes – Fillna Method ContinuedLecture 19:Interpolation in Dataframes – Handling Missing DataLecture 20:Replace Methodd in Dataframes – Handling Missing DataLecture 21:Groupby in Python Pandas on Columns with repeating valuesLecture 22:Concatenate Dataframes and visualize them Section 6:Connecting Pandas Dataframe with MySQL Server DatabaseLecture 23:How to Connect Pandas With MySQL Server DatabaseLecture 24:Use of Merge Method in Python Pandas Section 7:Reshaping DataFrames in PandasLecture 25:Pivot and Pivot_Table Methods in Python PandasLecture 26:Stack and Unstack Methods in Python PandasLecture 27:Melt Method for Data Manipulation in PandasLecture 28:Crosstab method in Python Pandas Section 8:Working with Time Series Data in PandasLecture 29:DatetimeIndex in Python Pandas – Time SeriesLecture 30:date_range() method in Python Pandas – Time SeriesLecture 31:to_datetime() Method in Python Pandas Section 9:Working with JSON Data Using JSON Module and Pandas ModuleLecture 32:What is JSONLecture 33:What is an API ?Lecture 34:JSON API Weather Data Analysis Project Using Python Pandas and MatplotlibLecture 35:Stock Price Data From JSON API Analysis using Python Libraries Section 10:EDA on Titanic Dataset from ScratchLecture 36:Exploratory Data Analysis on Titanic Dataset – Pie Chart and DropLecture 37:Correlation Matrix or Heatmap using Seaborn EDA on Titanic DatasetLecture 38:Analysis of Parch and Sibsp Columns in Titanic Dataset – 3 Graphs Side By SideLecture 39:Histogram Plot and Kernel Density Estimation Using Python Section 11:Restaurant Tips DatasetLecture 40:Scatter Plot using Python Libraries on Tips Dataset
Overview
Section 1: Introduction
Lecture 1 Introduction
Lecture 2 Introduction to Python Pandas
Lecture 3 How to Install Python Pandas on Computer
Lecture 4 Data Structures in Python Pandas
Section 2: Pandas Series
Lecture 5 How to Create Pandas Series from Scratch
Lecture 6 How to Create Pandas Series Using Ndarray and Dictionary
Section 3: Pandas Dataframes
Lecture 7 Creating Your First Dataframe
Lecture 8 Creating a Datafram Using Python Lists
Lecture 9 Create an indexed DataFrame using arrays
Lecture 10 Getting Data of a Row or Multiple Rows in Pandas Dataframe
Lecture 11 Basic Operations on Pandas Dataframes – Using Some Methods and Attributes
Lecture 12 Setting and Resetting Index of a Dataframe
Lecture 13 How to Locate Values On the basis of Index Name
Section 4: Reading CSV Files – With Exploratory Data Analysis on GOT Dataset
Lecture 14 Reading CSV Files EDA On GOT Dataset Part 1
Lecture 15 Reading CSV Files EDA On GOT Dataset Part 2
Lecture 16 Read Excel OR Csv File and Write to an Excel Or CSV File
Lecture 17 EDA on GOT Data Part 3 – Grouped Bar Chart
Lecture 18 What is a Box Plot or Box Whisker Plot ?
Section 5: Handling Missing Data
Lecture 19 Handdling Missing Data in Dataframes – Fillna Method
Lecture 20 Handdling Missing Data in Dataframes – Fillna Method Continued
Lecture 21 Interpolation in Dataframes – Handling Missing Data
Lecture 22 Replace Methodd in Dataframes – Handling Missing Data
Lecture 23 Groupby in Python Pandas on Columns with repeating values
Lecture 24 Concatenate Dataframes and visualize them
Section 6: Connecting Pandas Dataframe with MySQL Server Database
Lecture 25 How to Connect Pandas With MySQL Server Database
Lecture 26 Use of Merge Method in Python Pandas
Section 7: Reshaping DataFrames in Pandas
Lecture 27 Pivot and Pivot_Table Methods in Python Pandas
Lecture 28 Stack and Unstack Methods in Python Pandas
Lecture 29 Melt Method for Data Manipulation in Pandas
Lecture 30 Crosstab method in Python Pandas
Section 8: Working with Time Series Data in Pandas
Lecture 31 DatetimeIndex in Python Pandas – Time Series
Lecture 32 date_range() method in Python Pandas – Time Series
Lecture 33 to_datetime() Method in Python Pandas
Section 9: Working with JSON Data Using JSON Module and Pandas Module
Lecture 34 What is JSON
Lecture 35 What is an API ?
Lecture 36 JSON API Weather Data Analysis Project Using Python Pandas and Matplotlib
Lecture 37 Stock Price Data From JSON API Analysis using Python Libraries
Section 10: EDA on Titanic Dataset from Scratch
Lecture 38 Exploratory Data Analysis on Titanic Dataset – Pie Chart and Drop
Lecture 39 Correlation Matrix or Heatmap using Seaborn EDA on Titanic Dataset
Lecture 40 Analysis of Parch and Sibsp Columns in Titanic Dataset – 3 Graphs Side By Side
Lecture 41 Histogram Plot and Kernel Density Estimation Using Python
Section 11: Restaurant Tips Dataset
Lecture 42 Scatter Plot using Python Libraries on Tips Dataset
Lecture 43 FacetGrid Plot in Seaborn Library on Tips Dataset
Lecture 44 3D Plot Using MatplotLib in Python – Introduction
Section 12: Graphical Analysis on Misc Datasets
Lecture 45 Stacked Bar Chart Plot Using Python Matplotlib on Cricket Series Data
Lecture 46 Code For Grouped Bar Chart Using Matplotlib
Lecture 47 Lmplot – Regression and Scatter – On Flights Dataset
Section 13: Read_html() Method in Pandas
Lecture 48 Read_html in Python Pandas Reading Table from a website
Lecture 49 Creating HTML Table and Reading Data from Local Html File
Section 14: Iris Dataset
Lecture 50 Seaborn Pairplot Example on Iris Dataset
Beginner Python Developers curious about Data Science,College or School Students who want to Learn Data Analysis

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