Python Data Analytics Free PDF Download helps the programmer to understand from basic to advanced that help in your future.
Python Data analytics is very interesting to learn and now it’s high in demand. So, you learn Python Data Analytics helps a lot.
Here, in the article on Python Data analytics, You get 11 Chapters and each Chapter contains several topics for better understanding.
Explanation of 11 Chapters of Python Data Analytics-
Chapter 1- An Introduction to Data Analysis
It is a technique or process that works to perform, transmit, collect, and organize data for the prediction of the future. It is used in Python because it provides a maximum range of libraries to perform the tasks.
The topics covered in Chapter 1 are-Knowledge Domains of the Data Analyst, Understanding the Nature of the Data, The Data Analysis Process, Quantitative and Qualitative Data Analysis, how to open Data, Python and Data Analysis, and a small summary of Chapter 1.
Chapter 2- Introduction to the Python’s World
Python is a high-level programming language that works to perform and make a web application for Software. It works to build, control, manage, and test web applications for software developers. Here, you get a full overview of Python.
Some of the topics names of Chapter 2 are- Python and Data Analysis, Python—The Interpreter, Python 2 and Python 3, How to install Python in our system, Python Distributions, the process of using Python, Writing Python Code, Data Structure, Functional Programming (Only for Python 3.4), IPython, IPython Notebook, The Jupyter Project, PyPI—The Python Package Index, The IDEs for Python, SciPy, and conclusion.
Chapter 3- The NumPy Library
It handles a wide range of hardware and computing platform. It is an open-source Python library that is almost used in every field of technology and science. It works in numerical values and is easy to apply in mathematical functions.
NumPy: A Little History, The NumPy Installation, Ndarray: The Heart of the Library, Basic Operations, Indexing, Slicing, and Iterating, Conditions and Boolean Arrays, Shape Manipulation, Array Manipulation, General Concepts, Structured Arrays, Reading and Writing Array Data on Files, and Short conclusion.
Chapter 4- The Pandas Library—An Introduction
It is mostly used for data analysis to perform tasks in Python. It is a very easy, powerful, and flexible tool for manipulating data analysis.
Pandas: The Python Data Analysis Library, Installation process, Test Your Panda’s Installation, Getting Started with Pandas, Introduction to Pandas Data Structures, Defining Series from NumPy Arrays and Other Series, Operations and Mathematical Functions, The DataFrame, DataFrame from Nested dict, The Index Objects, Other Functionalities on Indexes, Arithmetic, and Data Alignment, Operations between Data Structures, Function Application, and Mapping, Sorting, and Ranking, Correlation and Covariance, Hierarchical Indexing, and Leveling, and conclusion.
Chapter 5- pandas: Reading and Writing Data
Here, we learn how to read and write Pandas in Python program. You get the full ideas about Pandas and how to use them.
/O API Tools, CSV and Textual Files, Reading Data in CSV or Text Files, Using RegExp for Parsing TXT Files, Reading TXT Files into Parts or Partially, Reading and Writing HTML Files, Reading Data from XML, Reading and Writing Data on Microsoft Excel Files, JSON Data, The Format HDF5, Pickle—Python Object Serialization, Interacting with Databases, and Reading and Writing Data with a NoSQL Database: MongoDB.
Chapter 6- pandas in Depth: Data Manipulation
It is a process that works to extract information and apply logic to it to make a new set of data.
Data Preparation, Concatenating, Pivoting with Hierarchical Indexing, Data Transformation, Discretization, Binning, Permutation, String Manipulation, Data Aggregation, Group Iteration, and Advanced Data Aggregation.
Chapter 7- Data Visualization with Matplotlib
It is mainly used for graphics and visualization, Matplotlib is a multi-platform for data visualization.
The matplotlib Library, Installation, IPython, and IPython QtConsole, matplotlib Architecture, pyplot, Using the kwargs, Adding Further Elements to the Chart, Saving Your Charts, Handling Date Values, Chart Typology, Histogram, Advanced Charts, mplot3d, and Multi-Panel Plots.
Chapter 8- Machine Learning with Scikit-learn
It is open-source for Python in Machine Learning. It provides a selection of sufficient tools for machine learning.
The scikit-learn Library, Machine Learning, Supervised Learning with scikit-learn, The Iris Flower Dataset, K-Nearest Neighbors Classifier, Diabetes Dataset, Linear Regression: The Least Square Regression, Support Vector Machines (SVMs), and Plotting Different SVM Classifiers Using the Iris Dataset.
Chapter 9- An Example—Meteorological Data
It is very easy to find on the system. It is very useful for checking and finding data, air temperature, amount of water vapour, etc.
A Hypothesis to Be Tested: The Influence of the Proximity of the Sea, Data Source, Data Source, The RoseWind, and Calculating the Distribution of the Wind Speed Means.
Chapter 10- Embedding the JavaScript D3 Library in IPython Notebook
Here, you see how the graphical representation is embedded in your IPython Notebooks even the matplotlib library cannot do it.
The Open Data Source for Demographics, The JavaScript D3 Library, Drawing a Clustered Bar Chart, The Choropleth Maps, and The Choropleth Map of the US Population in 2014.
Chapter 11- Recognizing Handwritten Digits
It is a technique and a process that has the ability to machine recognize the digit of a human.
Handwriting Recognition, Recognizing Handwritten Digits with scikit-learn, The Digits Dataset, Learning, and Predicting.
Conclusion-
This article is very helpful for understanding Python Data Analytics in a very easy and deep way. The topics that come under the Chapters are explained and each Chapter has its own topics. Here, only the topic’s name is mentioned, if you want more details about each topic then visit the pdf of that article.
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