Learn Python Libraries For Data Analysis & Data Manipulation

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Requirements

• Basic Knowledge of Python
• Knows how to install applications on computer

Description

• 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 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
• Section 5:Handling Missing Data
• Lecture 17:Handdling Missing Data in Dataframes – Fillna Method
• Lecture 18:Handdling Missing Data in Dataframes – Fillna Method Continued
• Lecture 19:Interpolation in Dataframes – Handling Missing Data
• Lecture 20:Replace Methodd in Dataframes – Handling Missing Data
• Lecture 21:Groupby in Python Pandas on Columns with repeating values
• Lecture 22:Concatenate Dataframes and visualize them
• Section 6:Connecting Pandas Dataframe with MySQL Server Database
• Lecture 23:How to Connect Pandas With MySQL Server Database
• Lecture 24:Use of Merge Method in Python Pandas
• Section 7:Reshaping DataFrames in Pandas
• Lecture 25:Pivot and Pivot_Table Methods in Python Pandas
• Lecture 26:Stack and Unstack Methods in Python Pandas
• Lecture 27:Melt Method for Data Manipulation in Pandas
• Lecture 28:Crosstab method in Python Pandas
• Section 8:Working with Time Series Data in Pandas
• Lecture 29:DatetimeIndex in Python Pandas – Time Series
• Lecture 30:date_range() method in Python Pandas – Time Series
• Lecture 31:to_datetime() Method in Python Pandas
• Section 9:Working with JSON Data Using JSON Module and Pandas Module
• Lecture 32:What is JSON
• Lecture 33:What is an API ?
• Lecture 34:JSON API Weather Data Analysis Project Using Python Pandas and Matplotlib
• Lecture 35:Stock Price Data From JSON API Analysis using Python Libraries
• Section 10:EDA on Titanic Dataset from Scratch
• Lecture 36:Exploratory Data Analysis on Titanic Dataset – Pie Chart and Drop
• Lecture 37:Correlation Matrix or Heatmap using Seaborn EDA on Titanic Dataset
• Lecture 38:Analysis of Parch and Sibsp Columns in Titanic Dataset – 3 Graphs Side By Side
• Lecture 39:Histogram Plot and Kernel Density Estimation Using Python
• Section 11:Restaurant Tips Dataset
• Lecture 40:Scatter Plot using Python Libraries on Tips Dataset

Who this course is for:

• Beginner Python Developers curious about Data Science
• College or School Students who want to Learn Data Analysis

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