Pandas Data Operations Mastery
Become an expert in Data Handling & Data Analysis using Pandas and Python
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
Outer Joins (without Intersection) with merge()
Left & Right Joins (without Intersection) with merge()
Joining on different Column Names / Indexes
Joining on more than one Column
pd.merge() and join()
6
GroupBy Operations
Understanding the GroupBy Object
Splitting with many Keys
split-apply-combine explained
split-apply-combine applied
Advanced aggregation with agg()
GroupBy Aggregation with Relabeling (NEW - Pandas Version 0.25)
Transformation with transform()
Replacing NA Values by group-specific Values
Generalizing split-apply-combine with apply()
Hierarchical Indexing with Groupby
stack() and unstack()
7
Reshaping and Pivoting DataFrames
Transposing Rows and Columns
Pivoting DataFrames with pivot()
Limits of pivot()
pivot_table()
pd.crosstab()
melting DataFrames with melt()
8
Data Preparation and Feature Creation
Arithmetic Operations
Transformation/Mapping with map()
Conditional Transformation
Discretization and Binning with pd.cut()
Discretization and Binning with pd.qcut()
Floors and Caps
Scaling / Standardization
Creating Dummy Variables
String Operations
- 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
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()
00:19:00Cleaning 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
00:08:10Time 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()
00:07:00Importing 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
00:08:28Merging, 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
Outer Joins (without Intersection) with merge()
Left & Right Joins (without Intersection) with merge()
Joining on different Column Names / Indexes
Joining on more than one Column
pd.merge() and join()
GroupBy Operations
Understanding the GroupBy Object
Splitting with many Keys
split-apply-combine explained
split-apply-combine applied
Advanced aggregation with agg()
GroupBy Aggregation with Relabeling (NEW - Pandas Version 0.25)
Transformation with transform()
Replacing NA Values by group-specific Values
Generalizing split-apply-combine with apply()
Hierarchical Indexing with Groupby
stack() and unstack()
Reshaping and Pivoting DataFrames
Transposing Rows and Columns
Pivoting DataFrames with pivot()
Limits of pivot()
pivot_table()
pd.crosstab()
melting DataFrames with melt()
Data Preparation and Feature Creation
Arithmetic Operations
Transformation/Mapping with map()
Conditional Transformation
Discretization and Binning with pd.cut()
Discretization and Binning with pd.qcut()
Floors and Caps
Scaling / Standardization
Creating Dummy Variables
String Operations
Jobaaj Learnings
Jobaaj Learnings offer the best in class industry-focused programs to students.
Our trainers have several years of experience in the respective field and work with leading companies.
We are the pioneer of a personalised micro-learning education model in India.
Why micro-learning?
A rise in digital devices, fast internet, and a rapidly changing environment has made micro-learning the demand of an hour.
We consider it the most effective method of consuming learning content & are on a mission of making professionals habitual of upskilling by stealing little time daily from their busy lifestyle.
