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Reserve Your Spot Now!Pandas Data Operations Mastery
Become an expert in Data Handling & Data Analysis using Pandas and Python
7 Hours 56 Mins
₹4,999/-₹9,999/-
Join the course
Created by
Jobaaj Learnings
Course description
The Data Analysis with Pandas and Python video tutorial offers in-depth tutorials on the most powerful data analysis toolkit available today. Among the topics covered are:
- installing
- sorting
- filtering
- grouping
- aggregating
- de-duplicating
- pivoting
- munging
- deleting
- merging
- visualizing
and more!
What makes pandas so interesting?
This course is designed for people who have experience with spreadsheet software such as Microsoft Excel, Apple Numbers, or Google Sheets and want to take their data analysis skills to the next level.
Python programming language is used to develop the Pandas library, which is a popular data analysis library.
Using Pandas, you can analyze, organize, sort, filter, pivot, aggregate, munge, clean, calculate, and more with colossal data sets.
Prerequisite
- Knowledge Of Python
What you will learn
- Get hands-on practice with all relevant Pandas methods and workflows using real-world data
- The import, cleaning, and merging of messy data, as well as the preparation of data for machine learning
Lesson List
1
Importing Data Using Pandas
Using pd.read_csv to import csv files
00:15:34Using pd.read_csv to import messy csv-files
00:10:00Importing Data from Excel with pd.read_excel()
00:05:30Importing messy Data from Excel with pd.read_excel()
00:08:00Importing Data from the Web with pd.read_html()
00:08:00Creating IPL Dataset using pd.read_html()
00:19:002
Cleaning Data Using Pandas
First Steps of Data Cleaning - Data Inspection
00:19:00Different ways of formatting and Cleaning string Data
00:15:10Typecasting - Changing datatypes using astype()
00:10:12What are null, NA, NaN values?
00:06:25How to detect the null values in large datasets?
00:11:35Removing null values without affecting the dataframe
00:14:30How null values can be replaced?
00:07:50Intro and detection of Duplicates
00:06:05Handling the duplicate data in a dataframe
00:08:46What are outliers? How to find an Outlier?
00:10:43Best ways of handling an Outlier?
00:09:04Compressing data size using categorical dtype
00:08:103
Time Series in Pandas
Getting started with the Time Series Dataset
00:07:29Types of converting str to datetime object
00:09:59Analysis and Visualization of Timeseries Data
00:09:17Using Indexing and Slicing in Time Series Data
00:11:36fixed frequency DatetimeIndex using pd.date_range()
00:22:00Using resample() to Downsampling Time Series
00:12:00Time Series Using yfinance and resample()
00:07:004
Importing Financial Data from Yahoo Finance
Getting Data of TESLA Stock by YFinance
00:09:29Customising the Stock Data by YFinance
00:14:24Stock Split and Dividends by YFinance
00:08:09Exporting to CSV/ Excel File by YFinance
00:04:57Importing multiples stocks and Financial Indexes Data by YFinance
00:10:56Importing Currency Exchange & CryptoCurrency Data by YFinance
00:07:03Importing ETFs and MF Data by YFinance
00:08:16Stock Fundamentals, Meta Info and Performance Metrics by YFinance
00:12:05Financials (Balancesheet, Cashflows, P&L) by YFinance
00:07:55Put and Call Options by YFinance
00:04:02Stream Real Time data from YFinance
00:08:285
Merging, Joining, and Concatenating Data
Combining Datasets using Pandas : Concat and Append
00:17:16Arithmetic operations with Pandas Objects
00:06:06Using merge() for inner and outer joins
00:14:24Outer joins excluding intersection
00:07:13Left & Right Joins excluding intersection
00:06:51Using different columns / indexes to join
00:09:44Joining using multiple columns
00:07:39pd.concat() vs merge() vs join()
00:13:216
