Learn Python Libraries For Data Analysis & Data Manipulation
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
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