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آموزش کشف و تحلیل داده با Pandas 

دسته بندی ها: آموزش پانداس (Pandas) ، آموزش های Packtpub ، تحلیل داده (Data Analysis)

آیا به دنبال افزایش قابل توجهی در بهره وری خود هستید؟ آیا شما برخی از ترفندهای جالب و سرگرم کننده را برای حل مشکلات داده های خود جستجو می کنید؟ اگر چنین است، این دوره در واقع یک انتخاب عالی برای شماست. در این دوره با راه حل های منحصر به فرد تسک های دستکاری داده با pandas، درک عمیق تر از اصول اساسی، یا مقایسه و تقابل دو عملیات مشابه،  مجموعه داده خاص و غیره آشنا می شوید.

سرفصل:

  • معرفی دوره
  • مبانی Pandas
  • آناتومی DataFrame
  • درک انواع داده
  • ایجاد و حذف ستون ها
  • فراخوانی متد های Series
  • تحلیل داده
  • کاهش حافظه با تغییر انواع داده
  • انتخاب زیر مجموعه داده
  • انتخاب داده Series
  • انتخاب ردیف DataFrame
  • انتخاب همزمان ردیف ها و ستون های DataFrame
  • انتخاب داده ها با هر دو عدد صحیح و برچسب
  • Boolean Indexing
  • ترجمه SQL WHERE Clauses
  • انتخاب با Booleans، موقعیت عدد صحیح و برچسب ها
  • اضافه کردن ستون از DataFrames مختلف
  • گروه بندی برای جمع کردن، فیلتر کردن و تبدیل کردن
  • حذف MultiIndex بعد از گروه بندی
  • ترکیب آبجکت های Pandas
  • درک تفاوت بین concat، پیوستن و ادغام
  • اتصال به پایگاه داده های SQL
  • و غیره
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Data Analysis and Exploration with Pandas [Video] Publisher:Packtpub Author:Theodore Petrou Duration:5 hours and 12 minutes

Get idiomatic solutions to common data problems while working on real-world datasets and get surprising insights from the pandas library
Are you looking for a gigantic boost in your productivity? Are you searching for some interesting and fun tricks to solve your data problems? If so, then this course is indeed a perfect choice for you. This course provides you with unique, idiomatic, and amazing solutions for both fundamental and advanced data manipulation tasks with pandas.
Some solutions focus on achieving a deeper understanding of basic principles, or comparing and contrasting two similar operations. A few others will delve into a particular dataset, and let you uncover new and unexpected insights along the way.
The pandas library is massive, and it's common for frequent users to be unaware of many of its more impressive features. The official pandas documentation, while thorough, does not contain many useful examples of how to piece together multiple commands as one would do during an actual analysis. This course guides you, as if you were looking over the shoulder of an expert, through practical situations that you are highly likely to encounter. Many advanced solutions combine several different features across the pandas library to generate results.
The code bundle for the video course is available at - https://github.com/PacktPublishing/Data-Analysis-and-Exploration-with-Pandas
Style and Approach
This course includes interesting and illustrative examples and delivers very detailed explanations for each line of code in all of the examples. All code and dataset explanations exist in Jupyter Notebooks, an excellent interface for exploring data. In other words, this is an easy guide with a problem/solution approach for real-world datasets.
Released: Tuesday, May 8, 2018
Pandas Foundations
The Course Overview
Dissecting the Anatomy of a DataFrame
Accessing the Main DataFrame Components
Understanding Data Types
Selecting a Single Column of Data as a Series
Calling Series Methods
Working with Operators on a Series
Chaining Series Methods Together
Making the Index Meaningful
Renaming Row and Column Names
Creating and Deleting Columns
Essential DataFrame Operations
Selecting Multiple DataFrame Columns
Selecting Columns with Methods
Ordering Column Names Sensibly
Operating on the Entire DataFrame
Chaining DataFrame Methods Together
Working with Operators on a DataFrame
Comparing Missing Values
Transposing the Direction of a DataFrame
Determining College Campus Diversity
Beginning Data Analysis
Developing a Data Analysis Routine
Reducing Memory by Changing Data Types
Selecting the Smallest of the Largest
Selecting the Largest of Each Group by Sorting
Replicating nlargest with sort_values
Selecting Subsets of Data
Selecting Series Data
Selecting DataFrame Rows
Selecting DataFrame Rows and Columns Simultaneously
Selecting Data with Both Integers and Labels
Speeding Up Scalar Selection
Slicing Rows Lazily
Slicing Lexicographically
Boolean Indexing
Calculating Boolean Statistics
Constructing Multiple Boolean Conditions
Filtering with Boolean Indexing
Replicating Boolean Indexing with Index Selection
Selecting with Unique and Sorted Indexes
Gaining Perspective on Stock Prices
Translating SQL WHERE Clauses
Determining the Normality of Stock Market Returns
Improving Readability of Boolean Indexing with the Query Method
Preserving Series with the WHERE Method
Masking DataFrame Rows
Selecting with Booleans, Integer Location, and Labels
Index Alignment
Examining the Index Object
Producing Cartesian Products
Exploding Indexes
Filling Values with Unequal Indexes
Appending Columns from Different DataFrames
Highlighting the Maximum Value from Each Column
Replicating idxmax with Method Chaining
Finding the Most Common Maximum
Grouping for Aggregation, Filtration, and Transformation
Defining an Aggregation
Grouping and Aggregating with Multiple Columns and Functions
Removing the MultiIndex After Grouping
Customizing an Aggregation Function
Customizing Aggregating Functions with *args and **kwargs
Examining the groupby Object
Filtering for States with a Minority Majority
Transforming through a Weight Loss Bet
Calculating Weighted Mean SAT Scores Per State with Apply
Grouping By Continuous Variables
Counting the Total Number of Flights Between Cities
Finding the Longest Streak of On-Time Flights
Restructuring Data into a Tidy Form
Tidying Variable Values as Column Names with Stack
Tidying Variable Values as Column Names with Melt
Stacking Multiple Groups of Variables Simultaneously
Inverting Stacked Data
Unstacking After a groupby Aggregation
Replicating pivot_table with a groupby Aggregation
Renaming Axis Levels for Easy Reshaping
Tidying When Multiple Variables are Stored as Column Names
Tidying When Multiple Variables are Stored as Column Values
Tidying When Two or More Values are Stored in the Same Cell
Tidying When Variables are Stored in Column Names and Values
Tidying When Multiple Observational Units are Stored in the Same Table
Combining Pandas Objects
Appending New Rows to DataFrames
Concatenating Multiple DataFrames Together
Comparing President Trump's and Obama's Approval Ratings
Understanding the Differences Between concat, join, and merge
Connecting to SQL Databases

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