Learning python data visualization pdf

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Beginning Python Visualization: Crafting Visual Transformation Scripts sense : using Python effectively for data analysis and visualization and not just learning Python plotlib supports several file formats including PDF, PNG, and PS. Master how to build dynamic HTML5-ready SVG charts using Python and the pygal library. Data Analysis and Visualization Using Python - Dr. Ossama This book is for people who want to learn Python for the data science field in order.

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records Learning and Image Recognition purposes. . Python Data Visualization Cookbook is for developers who already know about Python programming in general. If .. Adams C. Learning Python Data Visualization. Файл формата pdf; размером 5, 58 МБ. Добавлен пользователем Anatol ; Отредактирован. Python Data Visualization Cookbook - All IT eBooks. Pages · Learning Selenium Testing Tools with Python - All IT eBooks. Pages··

The free book has numerous code examples to go along with each topic. Therefore, you can read their elements using a positive index or negative backward index, as shown in Listing In [23]: Line Plot In inline plotting, you should determine the x- and y-axes, and then you can add more features such as a title, a legend, and more see Listing Regular Expressions In [54]: The rename method is used to rename a file; it takes two arguments:

Write and run a Python script. Also, you can save, cut, and move lines up and down. The following steps demonstrate how to set up and use Spyder. Python Spyder IDE On the left side, you can write Python scripts, and on the right side you can see the executed script in the console.

Adams C. Learning Python Data Visualization

Python is a case-sensitive programming language. Thus, Manpower and manpower are two different identifiers in Python. The following are the rules for naming Python identifiers: All other identifiers start with a lowercase letter. The help? Listing Getting Help from Python In [3]: Type help for interactive help, or help object for help about object.

Define the builtin 'help'. This is a wrapper around pydoc. Calling help at the Python prompt starts an interactive help session. Calling help thing prints help for the python object 'thing'.

The smallest unit inside a given Python script is known as a token, which represents punctuation marks, reserved words, and each individual word in a statement, which could be keywords, identifiers, literals, and operators. Table lists the reserved words in Python. Table Therefore, a code segment block is denoted by line indentation, which is rigidly enforced, as shown in Listing Line Indentation Syntax Error In [4]: Otherwise, a syntax error will occur. Multiline Statements In [5]: In [6]: Statements with Quotations In [7]: However, triple quotes are used to span the string across multiple lines, as shown in Listing Quotation Marks in Python In [8]: In addition, the semicolon ; allows multiple statements on a single line if neither statement starts a new code block, as shown in Listing Reading Data from the User In [10]: Once you assign a value to a variable, Python considers the variable according to the assigned value.

If the assigned value is a string, then the variable is considered a string. If the assigned value is a real, then Python considers the variable as a double variable.

Therefore, Python does not restrict you to declaring variables before using them in the application. It allows you to create variables at the required time.

Python has five standard data types that are used to define the operations possible on them and the storage method for each of them. Assign Operator In [11]: You can assign a single value to multiple variables or assign multiple values to multiple variables, as shown in Listing Multiple Assigns In [13]: There are many rules and restrictions for variable names.

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The following example shows incorrect naming: The following shows correct naming for a variable: A program contains a sequence of statements, each of which has a specific purpose during program execution. The expression is a combination of values, variables, and operators that are evaluated by the interpreter to do a specific task, as shown in Listing Expression and Statement Forms In [16]: Like different programming languages, Python supports the following operators: It can be one line of code or thousands of code segments written to perform a specific task by a computer.

Therefore, Python is an interpreted language that executes line per line. This differs from other languages such as C , which is a compiled language that needs to handle the entire program. Python Comments There are two types of comments in Python: The symbol is used for single-line comments. Multiline comments can be given inside triple quotes, as shown in Listing Python Comment Forms In [18]: Python single line comment In [19]: This operator takes two operands, which are a formatted string and a value.

The following example shows that you pass a string and the 3. It should be clear that the value can be a single value, a tuple of values, or a dictionary of values. In [20]: You can use multiple conversions within the same string, for example, to convert into double, float, and so on. In [1]: In the following example, you can use a tuple of different data types and inject the output in a string message: In [21]: In [23]: If an index is provided, it is the index of the list of arguments provided in the field.

In [24]: In the following example, you will create a class called A , which has a single variable called x that is assigned the value 9. Then you create an instance object called w from the class A. In [34]: You can retrieve the current date and time and manipulate the date and time using the built-in methods. The example in Listing imports the time package and calls its. Time Methods In [42]: Fri Aug 17 It receives a string String,format of date and a format.

Built-in Calendar Module Functions Methods Description prcal year prints the whole calendar of the year. It is by default 0, which specifies Monday. In [45]: Selection Statements The if statement is used to execute a specific statement or set of statements when the given condition is true. An action or set of actions is executed if the outcome is true or false otherwise.

Any nonzero and non- null values are considered true in Python, while either zero or null values are considered false. Selection statement structure Listing demonstrates two examples of a selection statement, remember the indentation is important in the Python structure.

The first block shows that the value of x is equal to 5; hence, the condition is testing whether x equals 5 or not. Therefore, the output implements the statement when the condition is true. The if-else Statement Structure In [13]: In Listing , the if statement condition is false, and hence the outer print statement is the only executed statement.

Indentation of Execution In [12]: In other words, a nested if statement is an if statement inside another if statement, as shown in Listing Nested Selection Statements In [2]: The for loop is one of these structures; it is used to iterate the elements of a collection in the order that they appear.

In general, statements are executed sequentially, where the first statement in a function is executed first, followed by the second, and so on. There may be a situation when you need to execute a block of code several numbers of times.

The Python programming language provides different types of loop statements to handle iteration requirements. Iteration Statement Structure 1 for loop executes a sequence of statements multiple times and abbreviates the code that manages the loop variable.

It tests the condition before executing the loop body. It tests the condition after executing the loop body. Python provides various support methods for iteration statements where it allows you to terminate the iteration, skip a specific iteration, or pass if you do not want any command or code to execute.

Loop Control Statements 1 Break statement terminates the loop statement and transfers execution to the statement immediately following the loop. The range statement is used with for loop statements where you can specify one value. For example, if you specify 4, the loop statement starts from 1 and ends with 3, which is n Also, you can specify the start and end values. The following examples demonstrate loop statements. Using the range Method In [24]: None Loop 30 30 Loop 45 45 Loop 90 90 Largest 90 In the previous examples, the first and second iterations used the for loop with a range statement.

In the last example, iteration goes through a list of elements and stops once it reaches the last element of the iterated list. A break statement is used to jump statements and transfer the execution control.

It breaks the current execution, and in the case of an inner loop, the inner loop terminates immediately. However, a continue statement is a jump statement that skips execution of current iteration. After skipping, the loop continues with the next iteration. The pass keyword is used to execute nothing.

The following examples demonstrate how and when to employ each statement. Break, Continue, and Pass Statements In [44]: You stop iteration once you find the condition, which is the letter o.

The pass statement is a null operation; nothing happens when it executes. For instance, if you have a string variable named var1, which maintains the word PYTHON, then var1[1] will return the character Y, while var1[-2] will return the character O.

Python considers strings by enclosing text in single as well as double quotes.

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String Special Operators Table lists the operators used in string processing. In addition, the len method is a built-in function that returns the number of characters in a string.

Concatenation enables you to join more than one string together to form another string. The operator [n: If you omit the first index before the colon , the slice starts at the beginning of the string.

In addition, if you omit the second index, the slice goes to the end of the string. String Slicing and Concatenation In [3]: W var2[1: String Conversion and Format Symbols In [14]: Omar Enter your age: Omar Age: A lot of computations involve processing a string one character at a time.

String processing can start at the beginning, select each character in turn, do something to it, and continue until the end. This pattern of processing is called a traversal. One way to write a traversal is with a while loop, as shown in Listing Iterations Through Strings In [30]: In [32]: Iterating and Slicing a String In [2]: Listing shows how to use built-in methods to remove white space from a string, count specific letters within a string, check whether the string contains another string, and so on.

Implementing String Methods In [29]: The in Method in String Processing In [43]: The atpost variable is used to maintain a returned index of the substring as it appears in the Maindata string variable. Parsing and Extracting Strings In [39]: It can be unstructured data, semistructured data, or structured data.

Python provides different structures to maintain data and to manipulate it such as variables, lists, dictionaries, tuples, series, panels, and data frames. Tabular data can be easily represented in Python using lists of tuples representing the records of the data set in a data frame structure.

Though easy to create, these kinds of representations typically do not enable important tabular data manipulations, such as efficient column selection, matrix mathematics, or spreadsheet-style operations.

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Tabular is a package of Python modules for working with tabular data. You can put data into a tabarray object for more flexible and powerful data processing.

The Pandas library also provides rich data structures and functions designed to make working with structured data fast, easy, and expressive. In addition, it provides a powerful and productive data analysis environment. A Pandas data frame can be created using the following constructor: DataFrame data, index, columns, dtype, copy A Pandas data frame can be created using various input forms such as the following: Python Pandas Data Science Library Pandas is an open source Python library providing high-performance data manipulation and analysis tools via its powerful data structures.

The following are the key features of the Pandas library: A lightweight alternative is to install Numpy using popular the Python package installer pip. The Pandas library is used to create and process series, data frames, and panels.

A Pandas Series A series is a one-dimensional labeled array capable of holding data of any type integer, string, float, Python objects, etc. Listing shows how to create a series using the Pandas library.

In other words, data is aligned in a tabular fashion in rows and columns. In the following table, you have two columns and three rows of data. Listing shows how to create a data frame using the Pandas library.

In [40]: Name Age 1 Ali 17 2 Omar 25 You can create a data frame using a dictionary. In [41]: In [42]: Age Name 35 Ahmed 17 Ali You can select only the name column in a data frame. In [43]: Name, dtype: DataFrame np. Panel data print p['Temperature Day1'] 0 1 2 0 1.

These functions are throwaway functions; they are just needed where they have been created. The lambda feature is useful mainly for Lisp programmers. Lambda functions are used in combination with the functions filter , map , and reduce. A lambda is created without using the def keyword; it takes any number of arguments and returns an evaluated expression, as shown in Listing Anonymous Function In [34]: The map function has two arguments.

It returns a new list with the elements changed by func, as shown in Listing Using the map Function In [65]: For instance, the function filter func, list1 needs a function called func as its first argument.

This function will be applied to every element of the list list1. Only if func returns true will the element of the list be included in the result list. The filter function in Listing is used to return only even values. Using the filter Function In [79]: The reduce function is used to find the max value in a sequence of integers, as shown in Listing Using the reduce Function In [81]: The Numpy library is used to apply the following operations: Example of the Numpy Function In [83]: Developing an accurate model and getting accurate predictions from the applied model depend on the missing values treatment.

Therefore, handling missing data is important to make models more accurate and valid. Numerous techniques and approaches are used to handle missing data such as the following: In [31]: It differs from lists and dictionaries. An easy way to visualize this is as two columns of data. The first is the special index, a lot like the dictionary keys, while the second is your actual data. Also, Python can automatically retrieve data based on the passed value. If you pass an object, then Python considers that you want to use the index label—based loc.

However, if you pass an index integer parameter, then Python considers the iloc attribute, as indicated in Listing Series Structure and Query In [6]: Series animals Out[6]: In [5]: Series marks Out[5]: In [11]: Ahmed 75 Omar 84 Salwa 70 dtype: You can query a series using a series label or the lock attribute. In [13]: In [19]: In [25]: Series [70,90,65,25, 99] s Out[25]: In [28]: In [29]: Series [99,55,66,88] s.

Python is an efficient tool for implementing numerous statistical data analysis operations such as the following: A nonlinear relationship where the exponent of any variable is not equal to 1 creates a curve shape.

The function in Seaborn to find the linear regression relationship is regplot. Regression analysis Correlation refers to some statistical relationship involving dependence between two data sets, such as the correlation between the price of a product and its sales volume. Standard deviation is the square root of variance.

In other words, it is the average of the squared difference of values in a data set from the mean value. In Python, you can calculate this value by using the function std from the Pandas library. In [58]: Series [34,26,25,27,30,54,23,43,40,30,28,46] , 'Height': Series [ DataFrame d Create a DataFrame print df. In [59]: It gives you an idea of the average value of the data in the data set and an indication of how widely the values are spread in the data set. The following example finds the mean, median, and mode values of the previously created data frame: In [60]: Write a Python script to prompt users to enter two values; then perform the basic arithmetical operations of addition, subtraction, multiplication, and division on the values.

In [2]: Store input numbers: Write a Python script to prompt users to enter the lengths of a triangle sides. Then calculate the semiperimeters. Calculate the triangle area and display the result to the user. In [3]: Write a Python script to prompt users to enter the first and last values and generate some random values between the two entered values. In [7]: Write a Python program to prompt users to enter a distance in kilometers; then convert kilometers to miles, where 1 kilometer is equal to 0.

Display the result. In [9]: Write a program to prompt users to enter their working hours and rate per hour to calculate gross pay. The program should give the employee 1. Write a program to prompt users to enter a value; then check whether the entered value is positive or negative value and display a proper message.

Write a program to prompt users to enter a value; then check whether the entered value is odd or even and display a proper message. In [4]: Write a program to prompt users to enter an age; then check whether each person is a child, a teenager, an adult, or a senior. Display a proper message. Write a program to prompt users to enter a Fibonacci sequence.

The Fibonacci sequence is the series of numbers 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, The next number is found by adding the two numbers before it. In [14]: Currently, we are living in the era of big data, where data has been described as a raw material for business. The volume of data used in businesses, industries, research organizations, and technological development is massive, and it is rapidly growing every day.

The more data we collect and analyze, the more capable we can be in making critical business decisions. However, with the enormous growth of data, it has become harder for businesses to extract crucial information from the available data.

That is where the importance of data visualization becomes clear. Data visualization helps people understand the significance of data by summarizing and presenting a huge amount of data in a simple and easy-to-understand format in order to communicate the information clearly and effectively.

Ossama Embarak 85 O. Business intelligence has become more mainstream; hence, vendors are beginning to focus on both ends of the pipeline and improve the quality of data input. There is also a strong focus on ensuring that the output is well-structured and clearly presented. This focus on output has largely been driven by the demands of consumers, who have been enticed by what visualization can offer. A BI dashboard can be a great way to compile several different data visualizations to provide an at-a-glance overview of business performance and areas for improvement.

Why Is Data Visualization Important? A picture is worth a thousand words, as they say. Humans just understand data better through pictures rather than by reading numbers in rows and columns. Accordingly, if the data is presented in a graphical format, people are more able to effectively find correlations and raise important questions. Data visualization helps the business to achieve numerous goals. With the huge volume of data collected about business activities using different means, business leaders need proper techniques to easily drill down into the data to see where they can improve operational processes and grow their business.

Data visualization brings business intelligence to reality. Data visualization is needed by modern businesses for these reasons: Data science technologies impose the need to move from relatively simple graphs to multifaceted relational maps. Multidimensional visualizations will boost the role that data visualizations can play in the Internet of Things, network and complexity theories, nanoscience, social science research, education systems, conative science, space, and much more.

Data visualization will play a vital role, now and in the future, in applying many concepts such as network theory, Internet of Things, complexity theory, and more.

Data visualization will be used intensively to analyze and visualize data streams collected from billions of interconnected devices, from smart appliances and wearables to automobile sensors and environmental and smart cities monitors. In this context, data visualization will improve safety levels, drive operational efficiencies, help to better understand several worldwide phenomena, and improve and customize provided intercontinental services.

How Data Visualization Is Used for Business Decision-Making Data visualization is a real asset for any business to help make real- time business decisions. It visualizes extracted information into logical and meaningful parts and helps users avoid information overload by keeping things simple, relevant, and clear.

There are many ways in which visualizations help a business to improve its decision-making. Therefore, data visualization gives the full picture of the scoped parameters and simplifies the data by enabling decision-makers to cherry-pick the relevant data they need and dive into a detailed view wherever is needed. Easier Pattern Visualization Data visualization provides easier approaches to identifying upcoming trends and patterns within data sets and hence enables businesses to make efficient decisions and prepare strategies in advance.

Team Involvement Data visualizations process not only historical data but also real-time data. Different organization units gain the benefit of having direct access to the extracted information displayed by data visualization tools. This increases the levels of collaboration between departments to help them achieve strategic goals. Unify Interpretation Data visualizations can produce charts and graphics that lead to the same interpretations by all who use the extracted information for decision- making.

The R Language vs. Users scholars and researchers. Integration runs locally. Well-integrated with app. Database size handles huge size. IDE examples rstudio. Important packages tydiverse, ggplot2, pandas, numpy, scipy, scikit- and libraries Caret, zoo. Jupyter notebook. Introducing Data Visualization Techniques Data visualization aims to understand data by extracting and graphing information to show patterns, spot trends, and identify outliers. There are two basic types of data visualization.

There are many types of 2D data visualizations, such as temporal, multidimensional, hierarchical, and network. In the following section, we demonstrate numerous data visualization techniques provided by the Python programming language. For instance, you can install Matplotlib using pip as follows: Installed or Upgraded Packages In [5]: Once you load any library to your Python script, then you can call the package functions and attributes.

Data visualization libraries Each of these libraries has its own features. Some of these libraries may be adopted for implementation and dependent on other libraries. For example, Seaborn is a statistical data visualization library that uses Matplotlib. In addition, it needs Pandas and maybe NumPy for statistical processing before visualizing data.

Matplotlib Matplotlib is a Python 2D plotting library for data visualization built on Numpy arrays and designed to work with the broader SciPy stack. It produces publication-quality figures in a variety of formats and interactive environments across platforms.

There are two options for embedding graphics directly in a notebook. Listing plots fixed data using Matplotlib and adjusts the plot attributes. Importing and Using the Matplotlib Library In [12]: Visualizing data using Matplotlib Listing plots fixed data using Matplotlib and adjusts the plot attributes.

Determining the adapted function sin and cos by Matplotlib All altered attributes are applied to the same graph as shown above. There are many different plotting formats generated by the Matplotlib package; some of these formats will be discussed in Chapter 7. Importing and Using the Seaborn Library In [34]: In [36]: In [37]: In [38]: A hexagonally based histogram graph You can also visualize multidimensional relationships among the samples by calling sns.

Multidimensional relationships graph There are many different plotting formats generated by the Seaborn package; some of these formats will be discussed in Chapter 7. Plotly The Plotly Python graphing library makes interactive, publication-quality graphs online. Different dynamic graphs formats can be generated online or offline. Importing and Using the Plotly Library In [67]: Dynamic heatmap graph Use plotly.

In [90]: Importing and Using the Plotly Package In [64]: Geoplotlib Geoplotlib is a toolbox for creating a variety of map types and plotting geographical data. Geoplotlib needs Pyglet as an object-oriented programming interface. This type of plotting is not covered in this book. Pandas Pandas is a Python library written for data manipulation and analysis.

You can use Python with Pandas in a variety of academic and commercial domains, including finance, economics, statistics, advertising, web analytics, and much more. Pandas is covered in Chapter 6. The following are examples of direct plotting. Listing implements a basic plotting plot. Running Basic Plotting In []: Direct Plotting In []: Create a stacked bar plot In []: Stacked bar plot graph To get horizontal bar plots, use the barh method, as shown in Listing Bar Plots In []: Multiple Histograms per Column In []: Creating a Box Plot In []: Creating an Area Plot In []: Creating a Scatter Plot In []: Scatter plot graph See Chapter 7 for more graphing formats.

Summary This chapter demonstrated how to implement data visualization in modern business. The next chapter will cover data collection structure and much more. What is meant by data visualization? Data visualization is the process of interpreting the data in the form of pictorial or graphical format.

Why is data visualization important? Data Visualization helps business to achieve numerous goals through the following. Why do modern businesses need data visualization?

Learning Python Data Visualization | PACKT Books

Data visualization is needed by the modern business to support the following areas. How is data visualization used for business decision-making? There are many ways in which visualization help the business to improve decision making. Faster Times Response: It becomes incredibly useful to put useful interpretation of the collected data into the hands of managers and decision makers enabling them to quickly identify issues and improve response times.

Easier Pattern Visualization: Team Involvement: Write a Python script to create a data frame for the following table: Create a bar plot of the sales volume. Create a pie chart of item sales. Pie chart of sales C. Create a box plot of item sales. In [8]: Box plot of sales D. Create an area plot of item sales. Area plot of sales E.

Create a stacked bar plot of item sales. This chapter will demonstrate these various structures in detail with practical examples. Lists A list is a sequence of values of any data type that can be accessed forward or backward.

Each value is called an element or a list item. Elements are stored in the given order. Various operations can be conducted on lists such as insertion, sort, and deletion. A list can be created by storing a sequence of different types of values separated by commas.

A Python list is enclosed between a square brackets [] , and elements are stored in the index based on a starting index of 0. Ossama Embarak O. Listing shows how to create a list. Creating Lists In [1]: For instance, in Listing , list2 [3: You can also access a list element backward using negative indices.

For example, list3[-3] will return the third element in the backward sequence n-3, i. Accessing Lists In [9]: Adding and Updating List Elements In [50]: You can also update an element in the list by using the list name and the element index. Deleting List Elements To remove a list element, either you can delete it using the del statement in the element index, or you can remove the element using the remove method via the element value in the list.

If you use the remove method to remove an element that is repeated more than one time in the list, it removes only the first occurrence of that element inside the list. Also, you can use the pop method to remove a specific element by its index value, as shown in Listing Deleting an Element from a List In [48]: List Operations In [46]: Therefore, you can read their elements using a positive index or negative backward index, as shown in Listing Indexing and Slicing List Elements In [9]: List Functions Sr.

Function Description 1 cmp list1, list2 Compares elements of both lists 2 len list1 Gives the total length of the list 3 max list1 Returns an item from the list with max value 4 min list1 Returns an item from the list with min value 5 list seq Converts a tuple into list List Functions Built-in functions facilitate list processing.

The following tables show functions and methods that can be used to manipulate lists. You can find the list size using the len method.

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In addition, you can find the minimum and maximum values in a list using the min and max methods, respectively. See Listing for an example.

The pop and remove methods are used to remove elements from a list. Built-in List Methods Sr. Methods Description 1 list. You can create a list from a sequence; in addition, you can sort and traverse list elements for processing using iteration statements, as shown in Listing List Sorting and Traversing In [58]: In addition, you can split a string into a list of words using the split method.

The default delimiter for the split method is a white space. However, you can specify which characters to use as the word boundaries. For example, you can use a hyphen as a delimiter, as in Listing It takes a list of strings and concatenates the elements. You have to specify the delimiter that the join method will add between the list elements to form a string. Using the join Method In [73]: The association of a variable with an object is called a reference.

Listing demonstrates the use of the assign operator. Say you have a list called a. True Out[]: False Dictionaries A dictionary is an unordered set of key-value pair; each key is separated from its value by a colon: In fact, an empty dictionary is written only with curly braces:. Dictionary keys should be unique and should be of an immutable data type such as string, integer, etc. Creating Dictionaries You can create a dictionary and assign a key-value pair directly. In addition, you can create an empty dictionary and then assign values to each generated key, as shown in Listing Creating Dictionaries In [36]: Also, you can update the existing value of the key Salwa Ahmed.

Accessing Dictionary Elements In [2]: In each iteration, you print the key name and the returned net salary value. Also, you can delete the entire dictionary in a single operation using a del keyword, as shown in Listing Alter a Dictionary In [40]: But starting with Python 3, the cmp function is not available anymore, and you cannot define it.

See also Listing If the passed variable is a dictionary, then it would return a dictionary type.

Learning Python Data Visualization

Implementing Dictionary Functions In [43]: Implementing Dictionary Methods In [89]: Sorting a Dictionary In [96]: The differences between tuples and lists are that the tuples cannot be altered; also, tuples use parentheses, whereas lists use square brackets.

You can access an element in the tuple by index, as shown in Listing Creating and Displaying Tuples In [1]: Altering a Tuple for Editing In [3]: You can sort a list of tuples. Listing shows how to sort tuple elements in place as well as how to create another sorted tuple.

Sorting a Tuple In [1]: Concatenating Tuples As mentioned, tuples are immutable, which means you cannot update or change the values of tuple elements. You can take portions of existing tuples to create new tuples, as Listing demonstrates. Concatenating Tuples In [5]: Accessing Values in a Tuple In [4]: Deleting a Tuple In [5]: You can access a tuple element forward and backward; in addition, you can slice values from a tuple using indices.

Listing shows that you can slice in a forward manner where MarksCIS[1: Slicing Tuple Values In [6]: See Listing and Listing Series np.

Slicing Data from a Series In [10]: Sample Operations in a Series In [11]: Otherwise, the series index will be the lexically ordered list of the dictionary keys. Creating a Series from a Dictionary In [12]: The value will be repeated to match the length of index. See Listing Series 8.

Thus, you can write computations without giving consideration to whether the series involved have the same labels. If labels are not matches, it gives a missing value NaN. Vectorizing Operations on a Series In [16]: Using a Series Name Attribute In [17]: A data frame can be created from numerous data collections such as the following: If there is an index without a value, it gives a NaN value, as shown in Listing Series [1.

DataFrame dict1 df Out[5]: Control the labels appearance of the DataFrame pd. Also, the passed index should be of the same length as the arrays. If no index is passed, the result will be range n , where n is the array length. Creating a Data Frame from an Ndarray In [11]: DataFrame ndarrdict Out[11]: Assign index pd. DataFrame data Out[18]: C A B 0 b'Hello' 1 2. DataFrame data2 Out[19]: Listing demonstrates how to alter a data frame and its related operations.

To delete a column, you can use the del or pop method, as shown in Listing However, you can use the insert function to insert at a particular location in the columns, as shown previously. Also, you can change values of specific columns by altering the columns and making the necessary operations, as in column A in Listing Operations on Data Frames In [69]: Transposing a Data Frame In [78]: For example, you can implement matrix multiplication as in Listing Matrix Multiplications In [78]: A panel creation has three main attributes.

Creating a Panel from a 3D Ndarray In [3]: Panel np. Panel data P2 In [5]: Creating a Panel from a Data Frame In [26]: Slicing and Selecting Items from a Panel In [33]: Write a program to create a list of names; then define a function to display all the elements in the received list.

Call the function to execute its statements and display all names in the list. In []: Write a program to read text file data and create a dictionary of all keywords in the text file. The program should count how many times each word is repeated inside the text file and then find the keyword with a highest repeated number. The program should display both the keywords dictionary and the most repeated word. Write a program to compare tuples of integers and tuples of strings. Name GPA Omar 2. Display the final data frame.

Course1 90 Course2 50 Course3 89 Name: Omar, dtype: You can get input from the screen and output data to the screen as well as read data from files and store data in files. Data Input and Output You can read data from a user using the input function.

Received data by default is in text format. Hence, you should use conversion functions to convert the data into numeric values if required, as shown in Listing Osama Hashim Out[2]: Open File Modes No. Opened File Attributes No.

Attribute Description 1 file. Opened File Attributes In [41]: False Opening mode: Closing Files In [40]: False Closed or not: Writing to a File In [39]: The rename method is used to rename a file; it takes two arguments: Also, the remove method can be used to delete files by supplying the name of the file to be deleted as an argument. By the end of this book, you will be able to represent complex sets of data using Python. Chad R. He lives in Raymore, Missouri, with his wife, Heather, and son, Leo.

In the past, Chad worked as a web developer for large websites, such as MSN. You can contact Chad on LinkedIn https: Sign up to our emails for regular updates, bespoke offers, exclusive discounts and great free content.

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Table of Contents Chapter 1: Setting Up Your Development Environment. Chapter 2: Python Refresher. Chapter 3: Getting Started with pygal. Chapter 4: Advanced Charts. Chapter 5: Tweaking pygal. Chapter 6: Importing Dynamic Data. Chapter 7: Putting It All Together.