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Data Analysis with Pandas and Python [Video]
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-
Free ChapterInstallation and Setup
- Introduction to the Course
- macOS - Download and Install the Anaconda Distribution
- Windows - Download and Install the Anaconda Distribution
- Use Anaconda Navigator to Create a New Environment
- Unpack Course Materials + The Startdown and Shutdown Process
- Intro to the Jupyter Lab Interface
- Code Cell Execution
- Import Libraries into Jupyter Lab
-
Python Crash Course
-
Series
- Create a Series Object from a List
- Create a Series Object from a Dictionary
- Intro to Series Methods
- Intro to Attributes
- Parameters and Arguments
- Import Series with the pd.read_csv Function
- The head and tail Methods
- Passing Series to Python Built-In Functions
- Check for Inclusion with Python's in Keyword
- The sort_values Method
- The sort_index Method
- Extract Series Values by Index Position
- Extract Series Values by Index Label
- The get Method
- Overwrite a Series Value
- The copy Method
- Math Methods on Series Objects
- Broadcasting
- The value_counts Method
- The apply Method
- The map Method
-
DataFrames I: Introduction
- Methods and Attributes between Series and DataFrames
- Differences between Shared Methods
- Select One Column from a DataFrame
- Select Multiple Columns from a DataFrame
- Add New Column to DataFrame
- A Review of the value_counts Method
- Drop DataFrame Rows with Missing Values
- Fill in Missing Values with the fillna Method
- The astype Method I
- The astype Method II
- Sort a DataFrame with the sort_values Method I
- Sort a DataFrame with the sort_values Method II
- Sort DataFrame 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 with More than One Condition (AND - &)
- Filter with More than One Condition (OR - |)
- The isin Method
- The isnull and notnull Methods
- The between Method
- The duplicated Method
- The drop_duplicates Method
- The unique and nunique Methods
-
DataFrames III: Data Extraction
- This Module's Dataset
- The set_index and reset_index Methods
- Retrieve Rows by Index Position with iloc Accessor
- Retrieve Rows by Index Label with loc Accessor
- Second Arguments to loc and iloc Accessors
- Overwrite Value in a DataFrame
- Overwrite 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
- The nsmallest and nlargest Methods
- Filtering with the where Method
- The apply Method with DataFrames
-
Working with Text Data
-
MultiIndex
-
GroupBy
-
Merging DataFrames
-
Working with Dates and Times
-
Input and Output
-
Visualization
-
Options and Settings
-
Conclusion
About this
video
This course begins with the essentials, introducing you to Anaconda and Jupyter Lab setup for Python and Pandas. You'll gain foundational knowledge in Python before diving into Pandas for data analysis. The focus then shifts to Series and DataFrame structures, providing you with the skills to manage and manipulate data effectively.
Further, the course covers handling dates and times, and performing various file input and output operations, essential for real-world data analysis tasks. Advanced sections delve into data visualization using Matplotlib, enabling you to create impactful charts and graphs. You'll also explore advanced Pandas options and settings, enhancing your data manipulation capabilities.
By the end of this course, you'll have a comprehensive understanding of data analysis techniques. You'll be equipped to handle complex datasets, perform detailed analysis, and present data visually, opening doors to advanced data analysis and manipulation in professional settings.
Resources: https://github.com/PacktPublishing/Data-Analysis-with-Pandas-and-Python
- Publication date:
- October 2017
- Publisher
- Packt
- Duration
- 19 hours 26 minutes
- ISBN
- 9781788622394
Download code from GitHub
Latest Reviews
(6 reviews total)
Very good, content, author and so on
5
This video series is superb! It is perfect for my job.
5
great learning resource for Pandas
5
![Data Analysis with Pandas and Python [Video]](https://content.packt.com/V09369/cover_image_small.jpg)