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-
Free ChapterInstallation and Setup
- Introduction to Data Analysis with Pandas and Python
- 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 - Download the Anaconda Distribution
- Windows - Install Anaconda Distribution
- Windows - Create Conda Environment and Install Pandas and Jupyter Notebook
- Windows - Unpack Course Materials + The Start and Shutdown Process
- Introduction 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
-
Bonus: Python Crash Course
-
Series
- Create Jupyter Notebook for the Series Module
- Create a Series Object from a Python List
- Create a Series Object from a Python Dictionary
- Introduction to Attributes on a Series Object
- Introduction to Methods on a Series Object
- Parameters and Arguments
- Create Series from a Dataset with the pd.read_csv Method
- Use the Head and Tail Methods to Return Rows from the Beginning and End of a Dataset
- Passing Pandas Objects to Python Built-In Functions
- Accessing More Series Attributes
- Use the sort_values Method to Sort a Series in Ascending or 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
- Use Python's in Keyword to Check for Inclusion in Series Values or Index
- Extract Series Values by Index Position
- Extract Series Values by 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
-
DataFrames I: Introduction
- Introduction to DataFrames I Module
- Shared Methods and Attributes Between Series and DataFrames
- Differences Between Shared Methods
- Select One Column from a DataFrame
- Select Two or More Columns from a DataFrame
- Add a 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
- Fill in Null DataFrame Values with the fillna Method
- Convert DataFrame Column Types with the astype Method
- Sort a DataFrame with the sort_values Method, Part I
- Sort a DataFrame with the sort_values Method, Part II
- Sort DataFrame Index with the sort_index() Method
- Rank Series Values with the rank Method
-
DataFrames 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 notnull 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
-
DataFrames III: Data Extraction
- Introduction 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
- Introduction 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 a 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
- Introduction 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
-
Merging, Joining, and Concatenating DataFrames
- Introduction to the Merging, Joining, and Concatenating Section
- The pd.concat Method, Part 1
- The pd.concat Method, Part 2
- The append Method on a DataFrame
- 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 with Dates and Times in Datasets
- Introduction 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 Dataset 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
- Introduction to the Input and Output Section
- Pass a URL to the pd.read_csv 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
-
Visualization
-
Options and Settings in Pandas
-
Thank You
About this video
Data Analysis with Pandas and Python introduces you to the popular Pandas library built on top of the Python programming language.
Pandas is a powerhouse tool that allows you to do anything and everything with colossal datasets—analyzing, organizing, sorting, filtering, pivoting, aggregating, munging, cleaning, calculating, and more! Hence, we call it “Excel on steroids”!
Over the course of more than 19 hours, we will go step-by-step through Pandas, from installation to visualization! We will cover hundreds of different methods, attributes, features, and functionalities packed away inside this awesome library. We will dive into tons of different datasets, short and long, broken, and pristine, to demonstrate the incredible versatility and efficiency of this package.
This course is bundled with dozens of datasets for you to use and improve your skills. Dive right in and follow along with the lessons to see how easy it is to get started with Pandas!
By the end of this course, you will be able to gain deeper insights into your data that would be impractical in Excel but is now possible with Pandas.
All resources for this course are available at https://github.com/PacktPublishing/Data-Analysis-with-Pandas-and-Python
- Publication date:
- October 2017
- Publisher
- Packt
- Duration
- 20 hours 40 minutes
- ISBN
- 9781788622394
Download code from GitHub
Latest Reviews
(6 reviews total)
Very good pandas video, it shows all the steps of running pandas.
5
Videokurssilla annetaan perusteelliset tiedot sovelluksen käyttämiseksi.
5
I got the required content at very nominal
5