Pandas Data Operations Mastery

Become an expert in Data Handling & Data Analysis using Pandas and Python

(3 reviews)
7 Hours 56 Mins
₹4,999/-₹9,999/-
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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
00:15:34
Using pd.read_csv to import messy csv-files
00:10:00
Importing Data from Excel with pd.read_excel()
00:05:30
Importing messy Data from Excel with pd.read_excel()
00:08:00
Importing Data from the Web with pd.read_html()
00:08:00
Creating IPL Dataset using pd.read_html()
00:19:00
2

Cleaning Data Using Pandas

First Steps of Data Cleaning - Data Inspection
00:19:00
Different ways of formatting and Cleaning string Data
00:15:10
Typecasting - Changing datatypes using astype()
00:10:12
What are null, NA, NaN values?
00:06:25
How to detect the null values in large datasets?
00:11:35
Removing null values without affecting the dataframe
00:14:30
How null values can be replaced?
00:07:50
Intro and detection of Duplicates
00:06:05
Handling the duplicate data in a dataframe
00:08:46
What are outliers? How to find an Outlier?
00:10:43
Best ways of handling an Outlier?
00:09:04
Compressing data size using categorical dtype
00:08:10
3

Time Series in Pandas

Getting started with the Time Series Dataset
00:07:29
Types of converting str to datetime object
00:09:59
Analysis and Visualization of Timeseries Data
00:09:17
Using Indexing and Slicing in Time Series Data
00:11:36
fixed frequency DatetimeIndex using pd.date_range()
00:22:00
Using resample() to Downsampling Time Series
00:12:00
Time Series Using yfinance and resample()
00:07:00
4

Importing Financial Data from Yahoo Finance

Getting Data of TESLA Stock by YFinance
00:09:29
Customising the Stock Data by YFinance
00:14:24
Stock Split and Dividends by YFinance
00:08:09
Exporting to CSV/ Excel File by YFinance
00:04:57
Importing multiples stocks and Financial Indexes Data by YFinance
00:10:56
Importing Currency Exchange & CryptoCurrency Data by YFinance
00:07:03
Importing ETFs and MF Data by YFinance
00:08:16
Stock Fundamentals, Meta Info and Performance Metrics by YFinance
00:12:05
Financials (Balancesheet, Cashflows, P&L) by YFinance
00:07:55
Put and Call Options by YFinance
00:04:02
Stream Real Time data from YFinance
00:08:28
5

Merging, Joining, and Concatenating Data

Combining Datasets using Pandas : Concat and Append
00:17:16
Arithmetic operations with Pandas Objects
00:06:06
Using merge() for inner and outer joins
00:14:24
Outer joins excluding intersection
00:07:13
Left & Right Joins excluding intersection
00:06:51
Using different columns / indexes to join
00:09:44
Joining using multiple columns
00:07:39
pd.concat() vs merge() vs join()
00:13:21
6

GroupBy Operations

Getting to know GroupBy Objects
00:11:48
Apply Groupby with multiple keys
00:06:04
How split-apply-combine works
00:07:28

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