Easy Data Science with Python session 469 скачать в хорошем качестве

Easy Data Science with Python session 469

Description of Python beginning course tutorial: This video is part 469 of Python full beginning course tutorials. And focus of this video is on filtering Pandas Data Frame by column values in Python programming. Section 1 Installation of Anaconda and set up Python environment Section 2 Variables and simple data types String variables Numbers Section 3 Working with lists Introducing lists Changing, Appending,Removing items of Lists Organizing lists Looping through an entire list Making Numerical Lists List comprehension and Working with Part of a List Tuples Section 4 Conditional test and If statements Conditional Tests if Statements Using if Statements with Lists Section 5 Dictionaries Working with Dictionaries Looping Through a Dictionary Nesting dictionaries Python Dictionary get() Method Removing from dictionaries – the pop() method and the del statement Section 6 User input and while loop User input - A User input - B Introducing while loops Using break and continue in while loops Using a while Loop with Lists Using a while Loop with Dictionaries User input Section 7 Functions Defining a function Passing arguments to function Functions: Return a simple value Functions: Return a dictionary Using a Function with a while Loop Passing a List to function Passing an Arbitrary Number of Arguments to function Storing Your Functions in Modules Map function and Lambda expression in Python to replace characters pass multiple arguments to map function Partial functions Section 8 Classes Creating and Using a Class Working with Classes and Instances Inheritance of Classes Working with Attributes and Methods for the Child Class Importing Classes Section 9 Files and Exceptions Reading from a File Writing to a File Introducing try-except Blocks Exception Handling the FileNotFoundError Exception Using Try, Except, else,pass and Finally in Python Storing Data using json() module Refactoring Section 10 The NumPy library Introducing NumPy library Creation of Array in NumPy Basic operations of Numpy ndarray Indexing, Slicing, and Iterating, (Conditions and Boolean) of NumPy arrays Joining, Splitting and Shape Manipulation of NumPy arrays Copies and Views, difference between NumPy arrays and Python lists Broadcasting of NumPy arrays Random Number Generation with Python and NumPy Reading and Writing NumPy Array Data on Files Section 11 Introducing The pandas library Getting Started with Pandas in Python Introduction of Pandas Data Structures: The Series Pandas Series Operations Introduction of Pandas Data Structures: The DataFrame Basic manipulation of Pandas DataFrame Working with Index of Pandas Data Structures Operations and Functions of Pandas Data Structures Statistics Functions of Pandas Data Structures Sorting and Ranking of Pandas Data Structures Handling "Not a Number" Data with Pandas Data Structures Hierarchical Indexing and Leveling of Pandas Data Structures Accessing Rows and Columns of DataFrame Ways to filter Pandas DataFrame by column values Section 12 Reading and Writing Data with Pandas library() Reading Data in CSV or Text Files with Pandas Using Regular Expressions to Parse TXT Files with Pandas Writing Data to CSV Files with Pandas Reading and Writing Data on Microsoft Excel Files with Pandas Reading and Writing HTML Files with Pandas Reading Data from XML with Pandas Reading and Writing JSON Data with Pandas Section 13 Pandas in Depth: Data Manipulation() Merging Datasets with Pandas Concatenating and Combining Datasets with Numpy and Pandas Pivoting,Stacking,Unstacking,Long and Wide forms of Datasets with Pandas Removing, Mapping Operations with Pandas Rename Indexes of Axes with Pandas Detecting and Filtering Outliers with Pandas Discretization and Binning of Datasets with Pandas Permutation,Random Sampling with Pandas Data Aggregation,Grouping with Pandas Reshape Wide long form pandas #python #numpy #pandas