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    Data Analysis with Pandas and Python

    BY
    Udemy

    With the help of python's robust Pandas library and database, learn the skills and techniques for performing various big data analysis tasks.

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    Mode

    Online

    Fees

    ₹ 599 4099

    Quick Facts

    particular details
    Medium of instructions English
    Mode of learning Self study
    Mode of Delivery Video and Text Based

    Course overview

    Data Analysis with Pandas and Python online course is developed by Boris Paskhaver, Software Engineer, Consultant, Author, and Instructor and offered by Udemy Inc., a US-based online learning platform to help beginners learn new skills and professionals to polish their existing skills. 

    Data Analysis with Pandas and Python online certification will make you familiar with the famous Pandas library, which is based on the Python programming language. Pandas is a comprehensive tool that allows individuals to perform tasks with big data sets, including analysing, organising, sorting, filtering, pivoting, aggregating, munging, cleaning, and calculating.

    Data Analysis with Pandas and Python online training will take you through Pandas stepwise, from installation to visualisation through 21.5 hours of pre-recorded lectures, articles, downloadable materials, coding exercises, etc. where hundreds of methods, characteristics, features, and capabilities are crammed into a fantastic package for an outstanding understanding of all the topics. Interested candidates can enrol themselves in the course by making a one-time payment.

    The highlights

    • Certificate of completion
    • Self-paced course
    • Online course
    • Multi-language subtitles
    • 19.5 hours of pre-recorded video content
    • 30-day money-back guarantee
    • Unlimited access
    • Accessible on mobile devices and TV

    Program offerings

    • Certificate of completion
    • Self-paced course
    • English videos
    • Multi-language subtitles
    • 30-day money-back guarantee
    • Unlimited access
    • 32 coding exercises
    • 19.5 hours of pre-recorded video content
    • 4 articles
    • 2 downloadable resources

    Course and certificate fees

    Fees information
    ₹ 599  ₹4,099
    certificate availability

    Yes

    certificate providing authority

    Udemy

    What you will learn

    Knowledge of python

    After completing the Data Analysis with Pandas and Python, learners will be able to use Python's popular "pandas" package to perform a variety of data operations such as grouping, pivoting, and joining, learn about hundreds of methods and characteristics which are available across a variety of pandas objects, have a good understanding of how to manipulate 1D, 2D, and 3D data sets, and fixing frequent problems with data sets that are broken or incomplete.

    The syllabus

    Installation and Setup

    • Introduction to Data Analysis with Pandas And Python
    • Completed Course Files
    • About Me
    • macOS - Download the Anaconda Distribution, our Python development environment
    • macOS - Install Anaconda Distribution
    • macOS - Access the Terminal Application
    • macOS - Create conda Environment and Install pandas and Jupyter Notebook
    • macOS - Unpack Course Materials + The Start and Shutdown Process
    • Windows - Find Out if Your System is 32-bit or 64-bit
    • Windows - Download and Install the Anaconda Distribution
    • Windows - Create conda Environment and Install pandas and Jupyter Notebook
    • Windows - Unpack Course Materials + The Startdown and Shutdown Process
    • Troubleshooting Issues with Jupyter Notebook
    • Intro to the Jupyter Notebook Interface
    • Cell Types and Cell Modes in Jupyter Notebook
    • Code Cell Execution in Jupyter Notebook
    • Popular Keyboard Shortcuts in Jupyter Notebook
    • Import Libraries into Jupyter Notebook
    • Setup & Installation

    Bonus : Python Crash Course

    • Intro to the Python Crash Course
    • Comments
    • Basic Data Types
    • Operators
    • Variables
    • Built-in Functions
    • Custom Functions
    • String Methods
    • Lists
    • Index Positions and Slicing
    • Dictionaries

    Series

    • Create Jupyter Notebook for the Series Module
    • Create A Series Object from a Python List
    • Create A Series Object from a Python Dictionary
    • Create a Series Object
    • Coding Exercise SOLUTION: Create a Series Object
    • Intro to Attributes on a Series Object
    • Intro to Methods on a Series Object
    • Parameters and Arguments
    • Create Series from Dataset with the pd.read_csv Method
    • Import Series with the read_csv Function
    • Coding Exercise SOLUTION: Import Series with the read_csv Function
    • Use the head and tail Methods to Return Rows from Beginning and End of Dataset
    • Passing pandas Objects to Python Built-In Functions
    • Accessing More Series Attributes
    • Use the Sort Values Method to sort a Series in Ascending and Descending order
    • Use the inplace Parameter to permanently mutate a pandas data structure
    • Use the sort_index Method to Sort the Index of a pandas Series object
    • The sort_values and sort_index Methods
    • Coding Exercise SOLUTION: The sort_values and sort_index Methods
    • Use Python's in Keyword to Check for Inclusion in Series values or index
    • Extract Series Values by Index Positiox
    • Extract Series Values by Index Label
    • Extract Series Values by Index Position or Index Label
    • Coding Exercise SOLUTION: Extract Series Values by Index Position or Index Label
    • Use the get Method to Retrieve a Value for an index label in a Series
    • Math Methods on Series Objects
    • Use the idxmax and idxmin Methods to Find Index of Greatest or Smallest Value
    • Use the value_counts Method to See Counts of Unique Values within a Series
    • Use the apply Method to Invoke a Function on Every Series Values
    • The Series#map Method
    • A Review of the Series Module

    Data Frames I : Introduction

    • Intro to DataFrames I Module
    • Shared Methods and Attributes between Series and DataFrames
    • Differences between Shared Methods
    • Select One Column from a DataFrame
    • Select One Column from a DataFrame
    • Coding Exercise SOLUTION: Select One Column from a DataFrame
    • Select Two or More Columns from a DataFrame
    • Select Two or More Columns from a DataFrame
    • Coding Exercise SOLUTION: Select Two or More Columns from a DataFrame
    • Add New Column to DataFrame
    • Broadcasting Operations on DataFrames
    • A Review of the value_counts Method
    • Drop DataFrame Rows with Null Values with the dropna Method
    • Delete DataFrame Rows with Missing Values
    • Coding Exercise SOLUTION: Delete DataFrame Rows with Missing Values
    • Fill in Null DataFrame Values with the fillna Method
    • Convert DataFrame Column Types with the astype Method
    • Sort a DataFrame with sort values Method Part I
    • Sort a DataFrame with sort values Method Part II
    • The sort_values Method on a DataFrame
    • Coding Exercise SOLUTION: The sort_values Method on a DataFrame
    • Sort DataFrame Indexwith the sort_index Method
    • Rank Series Values with the rank Method

    Data Frames II : Filtering Data

    • This Module's Dataset + Memory Optimization
    • Filter a DataFrame Based on A Condition
    • Filter DataFrame with More than One Condition (AND - &)
    • Filter DataFrame with More than One Condition (OR - |)
    • Check for Inclusion with the isin Method
    • Check for Null and Present DataFrame Values with the isnull and not-null Methods
    • Check For Inclusion Within a Range of Values with the between Method
    • Check for Duplicate DataFrame Rows with the duplicated Method
    • Delete Duplicate DataFrame Rows with the drop_duplicates Method
    • Identify and Count Unique Values with the unique and nunique Methods

    Data Frames III : Data Extraction

    • Intro to the DataFrames III Module + Import Dataset
    • Use the set_index and reset_index methods to define a new DataFrame index
    • Retrieve Rows by Index Label with loc Accessor
    • Retrieve Rows by Index Position with iloc Accessor
    • Passing second arguments to the loc and iloc Accessors
    • Set New Value for a Specific Cell or Cells In a Row
    • Set Multiple Values in a DataFrame
    • Rename Index Labels or Columns in a DataFrame
    • Delete Rows or Columns from a DataFrame
    • Create Random Sample with the sample Method
    • Use the nsmallest / nlargest methods to get rows with smallest / largest values.
    • Filter A DataFrame with the where method
    • Filter A DataFrame with the query method
    • A Review of the apply Method on a pandas Series Object
    • Apply a Function to every DataFrame Row with the apply Method
    • Create a Copy of a DataFrame with the copy Method

    Working with Text Data

    • Intro to the Working with Text Data Section
    • Common String Methods - Lower, Upper, Title and len
    • Use the str.replace method to replace all occurrences of character with another
    • Filter a DataFrame's Rows with String Methods
    • More DataFrame String Methods - strip, lstrip, and rstrip
    • Invoke String Methods on DataFrame Index and Columns
    • Split Strings by Characters with the str.split Method
    • More Practice with the str.split method on a Series
    • Exploring the expand and n Parameters of the str.split Method

    MultiIndex

    • Intro to the MultiIndex Module
    • Create a MultiIndex on a DataFrame with the set_index Method
    • Extract Index Level Values with the get_level_values Method
    • Change Index Level Name with the set_names Method
    • The sort_index Method on a MultiIndex DataFrame
    • Extract Rows from a MultiIndex DataFrame
    • The transpose Method on a MultiIndex DataFrame
    • The .swaplevel() Method
    • The .stack() Method
    • The .unstack() Method, Part 1
    • The .unstack() Method, Part 2
    • The .unstack() Method, Part 3
    • The pivot Method
    • Use the pivot_table method to create an aggregate summary of a DataFrame
    • Use the pd.melt method to create a narrow dataset from a wide one

    The GroupBy Object

    • Intro to the Groupby Module
    • First Operations with groupby Object
    • Retrieve a group from a GroupBy object with the get_group Method
    • Methods on the Groupby Object and DataFrame Columns
    • Grouping by Multiple Columns
    • The .agg() Method
    • Iterating through Groups

    Merging, Joining and Concatenating DataFrames

    • Intro to the Merging, Joining, and Concatenating Section
    • The pd.concat Method, Part 1
    • The pd.concat Method, Part 2
    • Inner Joins, Part 1
    • Inner Joins, Part 2
    • Outer Joins
    • Left Joins
    • The left_on and right_on Parameters
    • Merging by Indexes with the left_index and right_index Parameters
    • The .join() Method
    • The pd.merge() Method

    Working in Dates and Times in Datasets

    • Intro to the Working with Dates and Times Module
    • Review of Python's datetime Module
    • The pandas Timestamp Object
    • The pandas DateTimeIndex Object
    • The pd.to_datetime() Method
    • Create range of Dates with the pd.date_range() Method, Part 1
    • Create Range of Dates with the pd.date_range() Method, Part 2
    • Create Range of Dates with the pd.date_range() Method, Part 3
    • The .dt Accessor
    • Install pandas-datareader Library
    • Import Financial Data Set with pandas_datareader Library
    • Selecting Rows from a DataFrame with a DateTimeIndex
    • Timestamp Object Attributes and Methods
    • The pd.DateOffset Object
    • Timeseries Offsets
    • The Timedelta Object
    • Timedeltas in a Dataset

    Input and Output in Pandas

    • Intro to the Input and Output Section
    • Pass a URL to the pd.read_scv Method
    • Quick Object Conversions
    • Export CSV File with the to_csv Method
    • Install xlrd and openpyxl Libraries to Read and Write Excel Files
    • Import Excel File into pandas with the read_excel Method
    • Export Excel File with the to_excel Method
    • Input and Output

    Visualization

    • Intro to Visualization Section
    • Use the plot Method to Render a Line Chart
    • Modifying Plot Aesthetics with matplotlib Templates
    • Creating Bar Graphs to Show Counts
    • Creating Pie Charts to Represent Proportions
    • Visualization

    Options And Settings in Pandas

    • Introduction to the Options and Settings Module
    • Changing pandas Options with Attributes and Dot Syntax
    • Changing pandas Options with Methods
    • The precision Option

    Conclusion

    • Conclusion
    • Bonus!

    Instructors

    Boris Paskhaver
    Software Engineer
    Udemy

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