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دانلود کتاب Pandas Cookbook: Recipes for Scientific Computing, Time Series Analysis and Data Visualization using Python
دانلود کتاب کتاب آشپزی پانداها: دستور العمل هایی برای محاسبات علمی، تجزیه و تحلیل سری های زمانی و تجسم داده ها با استفاده از پایتون
مشخصات کتاب
Pandas Cookbook: Recipes for Scientific Computing, Time Series Analysis and Data Visualization using Python
ویرایش:
نویسندگان: Theodore Petrou
سری:
ISBN (شابک) : 9781784393878
ناشر: Packt Publishing
سال نشر: 2017
تعداد صفحات: 0
زبان: English
فرمت فایل : EPUB (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود)
حجم فایل: 28 مگابایت
قیمت کتاب (تومان) : 54,000
در صورت تبدیل فایل کتاب Pandas Cookbook: Recipes for Scientific Computing, Time Series Analysis and Data Visualization using Python به فرمت های PDF، EPUB، AZW3، MOBI و یا DJVU می توانید به پشتیبان اطلاع دهید تا فایل مورد نظر را تبدیل نمایند.
توجه داشته باشید کتاب کتاب آشپزی پانداها: دستور العمل هایی برای محاسبات علمی، تجزیه و تحلیل سری های زمانی و تجسم داده ها با استفاده از پایتون نسخه زبان اصلی می باشد و کتاب ترجمه شده به فارسی نمی باشد. وبسایت اینترنشنال لایبرری ارائه دهنده کتاب های زبان اصلی می باشد و هیچ گونه کتاب ترجمه شده یا نوشته شده به فارسی را ارائه نمی دهد.
توضیحاتی در مورد کتاب کتاب آشپزی پانداها: دستور العمل هایی برای محاسبات علمی، تجزیه و تحلیل سری های زمانی و تجسم داده ها با استفاده از پایتون
بیش از 95 دستور العمل عملی برای استفاده از قدرت پانداها برای محاسبات علمی کارآمد و تجزیه و تحلیل داده درباره این کتاب* از قدرت پانداها برای حل ساده ترین مشکلات محاسباتی علمی استفاده کنید* از ساختارهای داده سریع و قوی در پانداها برای به دست آوردن بینش مفید استفاده کنید. داده‌های شما* دستور العمل‌های کاربردی و آسان برای پیاده‌سازی راه‌حل‌های سریع برای مشکلات رایج در داده‌ها با استفاده از پانداها. . دستور العمل های موجود در این کتاب هم برای کاربران مبتدی و هم برای کاربران حرفه ای مناسب است و در صورت لزوم حاوی نکات، ترفندها و هشدارهای مفیدی است. درک کمی از پانداها مفید خواهد بود، اما اجباری نیست. آنچه خواهید آموخت* تسلط بر اصول اولیه پانداها برای شروع سریع کاوش هر مجموعه داده*ایزوله کردن هر زیرمجموعه ای از داده ها با انتخاب صحیح و پرس و جو داده ها* قبل از اعمال تجمیع، داده ها را به گروه های مستقل تقسیم کنید. و تبدیل به هر گروه * ساختار داده‌ها را به شکل مرتبی تغییر دهید تا تجزیه و تحلیل داده‌ها و تجسم آسان‌تر شود * مجموعه داده‌های آشفته در دنیای واقعی برای یادگیری ماشینی آماده کنید * داده‌ها را از منابع مختلف از طریق عملیات SQL مانند پانداها ترکیب و ادغام کنید * از عملکرد سری زمانی بی‌نظیر پانداها استفاده کنید* از طریق قلاب‌های مستقیم پانداها به Matplotlib و SeabornIn Detailاین کتاب، تجسم‌های زیبا و روشن‌فکرانه‌ای ایجاد کنید. برخی از دستور العمل ها بر دستیابی به درک عمیق تر از اصول اساسی، یا مقایسه و تضاد دو عملیات مشابه تمرکز دارند. دستور العمل های دیگر عمیقاً در یک مجموعه داده خاص فرو می روند و بینش های جدید و غیرمنتظره ای را در طول مسیر آشکار می کنند. کتابخانه پانداها بسیار بزرگ است و معمولاً کاربران مکرر از بسیاری از ویژگی های چشمگیرتر آن بی اطلاع هستند. مستندات رسمی پانداها، اگرچه کامل است، اما شامل مثال‌های مفید زیادی از نحوه کنار هم قرار دادن چندین فرمان مانند یکی در طول یک تجزیه و تحلیل واقعی نیست. این کتاب شما را راهنمایی می کند، گویی که از روی شانه یک متخصص، در موقعیت های عملی که به احتمال زیاد با آنها روبرو خواهید شد. تجربه وسیع آموزش پانداها در یک محیط حرفه ای برای ارائه توضیحات بسیار دقیق برای هر خط کد در همه دستور العمل ها. تمام توضیحات کد و مجموعه داده در نوت بوک های Jupyter وجود دارد، یک رابط عالی برای کاوش داده ها.
توضیحاتی درمورد کتاب به خارجی
Over 95 hands-on recipes to leverage the power of pandas for efficient scientific computation and data analysisAbout This Book* Use the power of pandas to solve most complex scientific computing problems with ease* Leverage fast, robust data structures in pandas to gain useful insights from your data* Practical, easy to implement recipes for quick solutions to common problems in data using pandasWho This Book Is ForThis book is for data scientists, analysts and Python developers who wish to explore data analysis and scientific computing in a practical, hands-on manner. The recipes included in this book are suitable for both novice and advanced users, and contain helpful tips, tricks and caveats wherever necessary. Some understanding of pandas will be helpful, but not mandatory.What You Will Learn* Master the fundamentals of pandas to quickly begin exploring any dataset* Isolate any subset of data by properly selecting and querying the data* Split data into independent groups before applying aggregations and transformations to each group* Restructure data into tidy form to make data analysis and visualization easier* Prepare real-world messy datasets for machine learning* Combine and merge data from different sources through pandas SQL-like operations* Utilize pandas unparalleled time series functionality* Create beautiful and insightful visualizations through pandas direct hooks to Matplotlib and SeabornIn DetailThis book will provide you with unique, idiomatic, and fun recipes for both fundamental and advanced data manipulation tasks with pandas. Some recipes focus on achieving a deeper understanding of basic principles, or comparing and contrasting two similar operations. Other recipes will dive deep into a particular dataset, uncovering 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 like one would do during an actual analysis. This book 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 recipes combine several different features across the pandas library to generate results.Style and approachThe author relies on his vast experience teaching pandas in a professional setting to deliver very detailed explanations for each line of code in all of the recipes. All code and dataset explanations exist in Jupyter Notebooks, an excellent interface for exploring data.
فهرست مطالب
Cover Copyright Credits About the Author Acknowledgements About the Reviewers www.PacktPub.com Customer Feedback Table of Contents Preface Chapter 1: Pandas Foundations Introduction Dissecting the anatomy of a DataFrame Getting ready How to do it... How it works... There's more... See also Accessing the main DataFrame components Getting ready How to do it... How it works... There's more... See also Understanding data types Getting ready How to do it... How it works... There's more... See also Selecting a single column of data as a Series Getting ready How to do it... How it works... There's more... See also Calling Series methods Getting ready How to do it... How it works... There's more... See also Working with operators on a Series Getting ready How to do it... How it works... There's more... See also Chaining Series methods together Getting ready How to do it... How it works... There's more... Making the index meaningful Getting ready How to do it... How it works... There's more... See also Renaming row and column names Getting ready How to do it... How it works... There's more... Creating and deleting columns Getting ready How to do it... How it works... There's more... See also Essential Chapter 2: DataFrame Operations Introduction Selecting multiple DataFrame columns Getting ready How to do it... How it works... There's more... Selecting columns with methods Getting ready How it works... How it works... There's more... See also Ordering column names sensibly Getting ready How to do it... How it works... There's more... See also Operating on the entire DataFrame Getting ready How to do it... How it works... There's more... Chaining DataFrame methods together Getting ready How to do it... How it works... There's more... See also Working with operators on a DataFrame Getting ready How to do it... How it works... There's more... See also Comparing missing values Getting ready How to do it... How it works... There's more... Transposing the direction of a DataFrame operation Getting ready How to do it... How it works... There's more... See also Determining college campus diversity Getting ready How to do it... How it works... There's more... See also Chapter 3: Beginning Data Analysis Introduction Developing a data analysis routine Getting ready How to do it... How it works... There's more... Data dictionaries See also Reducing memory by changing data types Getting ready How to do it... How it works... There's more... See also Selecting the smallest of the largest Getting ready How to do it... How it works... There's more... Selecting the largest of each group by sorting Getting ready How to do it... How it works... There's more... Replicating nlargest with sort_values Getting ready How to do it... How it works... There's more... Calculating a trailing stop order price Getting ready How to do it... How it works... There's more... See also Chapter 4: Selecting Subsets of Data Introduction Selecting Series data Getting ready How to do it... How it works... There's more... See also Selecting DataFrame rows Getting ready How to do it... How it works... There's more... See also Selecting DataFrame rows and columns simultaneously Getting ready How to do it... How it works... There's more... Selecting data with both integers and labels Getting ready How to do it... How it works... There's more... See also Speeding up scalar selection Getting ready How to do it... How it works... There's more... Slicing rows lazily Getting ready How to do it... How it works... There's more... Slicing lexicographically Getting ready How to do it... How it works... There's more... Chapter 5: Boolean Indexing Introduction Calculating boolean statistics Getting ready How to do it... How it works... There's more... See also Constructing multiple boolean conditions Getting ready How to do it... How it works... There's more... See also Filtering with boolean indexing Getting ready How to do it... How it works... There's more... See also Replicating boolean indexing with index selection Getting ready How to do it... How it works... There's more... Selecting with unique and sorted indexes Getting ready How to do it... How it works... There's more... See also Gaining perspective on stock prices Getting ready How to do it... How it works... There's more... See also Translating SQL WHERE clauses Getting ready How to do it... How it works... There's more... See also Determining the normality of stock market returns Getting ready How to do it... How it works... There's more... See also Improving readability of boolean indexing with the query method Getting ready How to do it... How it works... There's more... See also Preserving Series with the where method Getting ready How to do it... How it works... There's more... See also Masking DataFrame rows Getting ready How to do it... How it works... There's more... See also Selecting with booleans, integer location, and labels Getting ready How to do it... How it works... There's more... See also Chapter 6: Index Alignment Introduction Examining the Index object Getting ready How to do it... How it works... There's more... See also Producing Cartesian products Getting ready How to do it... How it works... There's more... See also Exploding indexes Getting ready How to do it... How it works... There's more... Filling values with unequal indexes Getting ready How to do it... How it works... There's more... Appending columns from different DataFrames Getting ready How to do it... How it works... There's more... See also Highlighting the maximum value from each column Getting ready How to do it... How it works... There's more... See also Replicating idxmax with method chaining Getting ready How to do it... How it works... There's more... Finding the most common maximum Getting ready How to do it... How it works... There's more... Chapter 7: Grouping for Aggregation, Filtration, and Transformation Introduction Defining an aggregation Getting ready How to do it... How it works... There's more... See also Grouping and aggregating with multiple columns and functions Getting ready How to do it... How it works... There's more... Removing the MultiIndex after grouping Getting ready How to do it... How it works... There's more... Customizing an aggregation function Getting ready How to do it... How it works... There's more... Customizing aggregating functions with *args and **kwargs Getting ready How to do it... How it works... There's more... See also Examining the groupby object Getting ready How to do it... How it works... There's more... See also Filtering for states with a minority majority Getting ready How to do it... How it works... There's more... See also Transforming through a weight loss bet Getting ready How to do it... How it works... There's more... See also Calculating weighted mean SAT scores per state with apply Getting ready How to do it... How it works... There's more... See also Grouping by continuous variables Getting ready How to do it... How it works... There's more... See also Counting the total number of flights between cities Getting ready How to do it... How it works... There's more... See also Finding the longest streak of on-time flights Getting ready How to do it... How it works... There's more... See also Chapter 8: Restructuring Data into a Tidy Form Introduction Tidying variable values as column names with stack Getting ready How to do it... How it works... There's more... See also Tidying variable values as column names with melt Getting ready How to do it... How it works... There's more... See also Stacking multiple groups of variables simultaneously Getting ready How to do it... How it works... There's more... See also Inverting stacked data Getting ready How to do it... How it works... There's more... See also Unstacking after a groupby aggregation Getting ready How to do it... How it works... There's more... See also Replicating pivot_table with a groupby aggregation Getting ready How to do it... How it works... There's more... Renaming axis levels for easy reshaping Getting ready How to do it... How it works... There's more... Tidying when multiple variables are stored as column names Getting ready... How to do it... How it works... There's more... See also Tidying when multiple variables are stored as column values Getting ready How to do it... How it works... There's more... See also Tidying when two or more values are stored in the same cell Getting ready... How to do it.. How it works... There's more... Tidying when variables are stored in column names and values Getting ready How to do it... How it works... There's more... Tidying when multiple observational units are stored in the same table Getting ready How to do it... How it works... There's more... See also Chapter 9: Combining Pandas Objects Introduction Appending new rows to DataFrames Getting ready How to do it... How it works... There's more... Concatenating multiple DataFrames together Getting ready How to do it... How it works... There's more... Comparing President Trump's and Obama's approval ratings Getting ready How to do it... How it works... There's more... See also Understanding the differences between concat, join, and merge Getting ready How to do it... How it works... There's more... See also Connecting to SQL databases Getting ready How to do it... How it works... There's more... See also Chapter 10: Time Series Analysis Introduction Understanding the difference between Python and pandas date tools Getting ready How to do it... How it works... There's more... See also Slicing time series intelligently Getting ready How to do it... How it works... There's more... See also Using methods that only work with a DatetimeIndex Getting ready How to do it... How it works... There's more... See also Counting the number of weekly crimes Getting ready How to do it... How it works... There's more... See also Aggregating weekly crime and traffic accidents separately Getting ready How to do it... How it works... There's more... Measuring crime by weekday and year Getting ready How to do it... How it works... There's more... See also Grouping with anonymous functions with a DatetimeIndex Getting ready How to do it... How it works... There's more... See also Grouping by a Timestamp and another column Getting ready How to do it... How it works... There's more... Finding the last time crime was 20% lower with merge_asof Getting ready How to do it... How it works... There's more... Chapter 11: Visualization with Matplotlib, Pandas, and Seaborn Introduction Getting started with matplotlib Getting ready Object-oriented guide to matplotlib How to do it... How it works... There's more... See also Visualizing data with matplotlib Getting ready How to do it... How it works... There's more... See also Plotting basics with pandas Getting ready How to do it.. How it works... There's more... See also Visualizing the flights dataset Getting ready How to do it... How it works... See also Stacking area charts to discover emerging trends Getting ready How to do it... How it works... There's more... Understanding the differences between seaborn and pandas Getting ready How to do it... How it works... See also Doing multivariate analysis with seaborn Grids Getting ready How to do it... How it works... There's more... Uncovering Simpson's paradox in the diamonds dataset with seaborn How to do it... How it works... There's more... Index
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