PYTHON TOOLS FOR DATA SCIENTISTS POCKET PRIMER

"This book contains a fast-paced introduction to as much relevant information about Python tools for data scientists as possible that can be reasonably included in a book of this size. If you are a novice, this book will give you a starting point from which you can decide which Python technolog...

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Bibliographic Details
Main Author: CAMPESATO, OSWALD.
Format: Electronic
Language: English
Published: [S.l.] : MERCURY LEARNING & INFORM, 2022.
Subjects:
Computer programming.
Electronic data processing.
Python (Computer program language)
elektronické knihy
electronic books
ISBN: 9781683928225
1683928229
1683928237
9781683928232
Physical Description: 1 online resource

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Table of contents

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040 |a YDX  |b eng  |c YDX  |d QGK  |d SFB  |d OCLCF  |d OCLCO 
020 |a 9781683928225  |q (electronic bk.) 
020 |a 1683928229  |q (electronic bk.) 
020 |z 1683928237 
020 |z 9781683928232 
035 |a (OCoLC)1348922887 
100 1 |a CAMPESATO, OSWALD. 
245 1 0 |a PYTHON TOOLS FOR DATA SCIENTISTS POCKET PRIMER  |h [electronic resource]. 
260 |a [S.l.] :  |b MERCURY LEARNING & INFORM,  |c 2022. 
300 |a 1 online resource 
505 0 |a Cover -- Half-Title -- Title -- Copyright -- Dedication -- Contents -- Preface -- Chapter 1: Introduction to Python -- Tools for Python -- easy_install and pip -- virtualenv -- Python Installation -- Setting the PATH Environment Variable (Windows Only) -- Launching Python on Your Machine -- The Python Interactive Interpreter -- Python Identifiers -- Lines, Indentations, and Multi-Lines -- Quotation and Comments in Python -- Saving Your Code in a Module -- Some Standard Modules in Python -- The help() and dir() Functions -- Compile Time and Runtime Code Checking -- Simple Data Types in Python -- Working with Numbers -- Working with Other Bases -- The chr() Function -- The round() Function in Python -- Formatting Numbers in Python -- Unicode and UTF-8 -- Working with Unicode -- Listing 1.1: Unicode1.py -- Working with Strings -- Comparing Strings -- Listing 1.2: Compare.py -- Formatting Strings in Python -- Uninitialized Variables and the Value None in Python -- Slicing and Splicing Strings -- Testing for Digits and Alphabetic Characters -- Listing 1.3: CharTypes.py -- Search and Replace a String in Other Strings -- Listing 1.4: FindPos1.py -- Listing 1.5: Replace1.py -- Remove Leading and Trailing Characters -- Listing 1.6: Remove1.py -- Printing Text without NewLine Characters -- Text Alignment -- Working with Dates -- Listing 1.7: Datetime2.py -- Listing 1.8: datetime2.out -- Converting Strings to Dates -- Listing 1.9: String2Date.py -- Exception Handling in Python -- Listing 1.10: Exception1.py -- Handling User Input -- Listing 1.11: UserInput1.py -- Listing 1.12: UserInput2.py -- Listing 1.13: UserInput3.py -- Command-Line Arguments -- Listing 1.14: Hello.py -- Summary -- Chapter 2: Introduction to NumPy -- What is NumPy? -- Useful NumPy Features -- What are NumPy Arrays? -- Listing 2.1: nparray1.py -- Working with Loops. 
505 8 |a Listing 2.2: loop1.py -- Appending Elements to Arrays (1) -- Listing 2.3: append1.py -- Appending Elements to Arrays (2) -- Listing 2.4: append2.py -- Multiplying Lists and Arrays -- Listing 2.5: multiply1.py -- Doubling the Elements in a List -- Listing 2.6: double_list1.py -- Lists and Exponents -- Listing 2.7: exponent_list1.py -- Arrays and Exponents -- Listing 2.8: exponent_array1.py -- Math Operations and Arrays -- Listing 2.9: mathops_array1.py -- Working with "−1" Sub-ranges With Vectors -- Listing 2.10: npsubarray2.py -- Working with "−1" Sub-ranges with Arrays -- Listing 2.11: np2darray2.py -- Other Useful NumPy Methods -- Arrays and Vector Operations -- Listing 2.12: array_vector.py -- NumPy and Dot Products (1) -- Listing 2.13: dotproduct1.py -- NumPy and Dot Products (2) -- Listing 2.14: dotproduct2.py -- NumPy and the Length of Vectors -- Listing 2.15: array_norm.py -- NumPy and Other Operations -- Listing 2.16: otherops.py -- NumPy and the reshape() Method -- Listing 2.17: numpy_reshape.py -- Calculating the Mean and Standard Deviation -- Listing 2.18: sample_mean_std.py -- Code Sample with Mean and Standard Deviation -- Listing 2.19: stat_values.py -- Trimmed Mean and Weighted Mean -- Working with Lines in the Plane (Optional) -- Plotting Randomized Points with NumPy and Matplotlib -- Listing 2.20: np_plot.py -- Plotting a Quadratic with NumPy and Matplotlib -- Listing 2.21: np_plot_quadratic.py -- What is Linear Regression? -- What is Multivariate Analysis? -- What about Non-Linear Datasets? -- The MSE (Mean Squared Error) Formula -- Other Error Types -- Non-Linear Least Squares -- Calculating the MSE Manually -- Find the Best-Fitting Line in NumPy -- Listing 2.22: find_best_fit.py -- Calculating MSE by Successive Approximation (1) -- Listing 2.23: plain_linreg1.py -- Calculating MSE by Successive Approximation (2). 
505 8 |a Listing 2.24: plain_linreg2.py -- Google Colaboratory -- Uploading CSV Files in Google Colaboratory -- Listing 2.25: upload_csv_file.ipynb -- Summary -- Chapter 3: Introduction to Pandas -- What is Pandas? -- Pandas Options and Settings -- Pandas Data Frames -- Data Frames and Data Cleaning Tasks -- Alternatives to Pandas -- A Pandas Data Frame with a NumPy Example -- Listing 3.1: pandas_df.py -- Describing a Pandas Data Frame -- Listing 3.2: pandas_df_describe.py -- Pandas Boolean Data Frames -- Listing 3.3: pandas_boolean_df.py -- Transposing a Pandas Data Frame -- Pandas Data Frames and Random Numbers -- Listing 3.4: pandas_random_df.py -- Listing 3.5: pandas_combine_df.py -- Reading CSV Files in Pandas -- Listing 3.6: sometext.txt -- Listing 3.7: read_csv_file.py -- The loc() and iloc() Methods in Pandas -- Converting Categorical Data to Numeric Data -- Listing 3.8: cat2numeric.py -- Listing 3.9: shirts.csv -- Listing 3.10: shirts.py -- Matching and Splitting Strings in Pandas -- Listing 3.11: shirts_str.py -- Converting Strings to Dates in Pandas -- Listing 3.12: string2date.py -- Merging and Splitting Columns in Pandas -- Listing 3.13: employees.csv -- Listing 3.14: emp_merge_split.py -- Combining Pandas Data Frames -- Listing 3.15: concat_frames.py -- Data Manipulation with Pandas Data Frames (1) -- Listing 3.16: pandas_quarterly_df1.py -- Data Manipulation with Pandas Data Frames (2) -- Listing 3.17: pandas_quarterly_df2.py -- Data Manipulation with Pandas Data Frames (3) -- Listing 3.18: pandas_quarterly_df3.py -- Pandas Data Frames and CSV Files -- Listing 3.19: weather_data.py -- Listing 3.20: people.csv -- Listing 3.21: people_pandas.py -- Managing Columns in Data Frames -- Switching Columns -- Appending Columns -- Deleting Columns -- Inserting Columns -- Scaling Numeric Columns -- Listing 3.22: numbers.csv. 
505 8 |a Listing 3.23: scale_columns.py -- Managing Rows in Pandas -- Selecting a Range of Rows in Pandas -- Listing 3.24: duplicates.csv -- Listing 3.25: row_range.py -- Finding Duplicate Rows in Pandas -- Listing 3.26: duplicates.py -- Listing 3.27: drop_duplicates.py -- Inserting New Rows in Pandas -- Listing 3.28: emp_ages.csv -- Listing 3.29: insert_row.py -- Handling Missing Data in Pandas -- Listing 3.30: employees2.csv -- Listing 3.31: missing_values.py -- Multiple Types of Missing Values -- Listing 3.32: employees3.csv -- Listing 3.33: missing_multiple_types.py -- Test for Numeric Values in a Column -- Listing 3.34: test_for_numeric.py -- Replacing NaN Values in Pandas -- Listing 3.35: missing_fill_drop.py -- Sorting Data Frames in Pandas -- Listing 3.36: sort_df.py -- Working with groupby() in Pandas -- Listing 3.37: groupby1.py -- Working with apply() and mapapply() in Pandas -- Listing 3.38: apply1.py -- Listing 3.39: apply2.py -- Listing 3.40: mapapply1.py -- Listing 3.41: mapapply2.py -- Handling Outliers in Pandas -- Listing 3.42: outliers_zscores.py -- Pandas Data Frames and Scatterplots -- Listing 3.43: pandas_scatter_df.py -- Pandas Data Frames and Simple Statistics -- Listing 3.44: housing.csv -- Listing 3.45: housing_stats.py -- Aggregate Operations in Pandas Data Frames -- Listing 3.46: aggregate1.py -- Aggregate Operations with the titanic.csv Dataset -- Listing 3.47: aggregate2.py -- Save Data Frames as CSV Files and Zip Files -- Listing 3.48: save2csv.py -- Pandas Data Frames and Excel Spreadsheets -- Listing 3.49: write_people_xlsx.py -- Listing 3.50: read_people_xslx.py -- Working with JSON-based Data -- Python Dictionary and JSON -- Listing 3.51: dict2json.py -- Python, Pandas, and JSON -- Listing 3.52: pd_python_json.py -- Useful One-line Commands in Pandas -- What is Method Chaining? -- Pandas and Method Chaining. 
505 8 |a Pandas Profiling -- Listing 3.53: titanic.csv -- Listing 3.54: profile_titanic.py -- Summary -- Chapter 4: Working with Sklearn and Scipy -- What is Sklearn? -- Sklearn Features -- The Digits Dataset in Sklearn -- Listing 4.1: load_digits1.py -- Listing 4.2: load_digits2.py -- Listing 4.3: sklearn_digits.py -- The train_test_split() Class in Sklearn -- Selecting Columns for X and y -- What is Feature Engineering? -- The Iris Dataset in Sklearn (1) -- Listing 4.4: sklearn_iris1.py -- Sklearn, Pandas, and the Iris Dataset -- Listing 4.5: pandas_iris.py -- The Iris Dataset in Sklearn (2) -- Listing 4.6: sklearn_iris2.py -- The Faces Dataset in Sklearn (Optional) -- Listing 4.7: sklearn_faces.py -- What is SciPy? -- Installing SciPy -- Permutations and Combinations in SciPy -- Listing 4.8: scipy_perms.py -- Listing 4.9: scipy_combinatorics.py -- Calculating Log Sums -- Listing 4.10: scipy_matrix_inv.py -- Calculating Polynomial Values -- Listing 4.11: scipy_poly.py -- Calculating the Determinant of a Square Matrix -- Listing 4.12: scipy_determinant.py -- Calculating the Inverse of a Matrix -- Listing 4.13: scipy_matrix_inv.py -- Calculating Eigenvalues and Eigenvectors -- Listing 4.14: scipy_eigen.py -- Calculating Integrals (Calculus) -- Listing 4.15: scipy_integrate.py -- Calculating Fourier Transforms -- Listing 4.16: scipy_fourier.py -- Flipping Images in SciPy -- Listing 4.17: scipy_flip_image.py -- Rotating Images in SciPy -- Listing 4.18: scipy_rotate_image.py -- Google Colaboratory -- Uploading CSV Files in Google Colaboratory -- Listing 4.19: upload_csv_file.ipynb -- Summary -- Chapter 5: Data Cleaning Tasks -- What is Data Cleaning? -- Data Cleaning for Personal Titles -- Data Cleaning in SQL -- Replace NULL with 0 -- Replace NULL Values with the Average Value -- Listing 5.1: replace_null_values.sql. 
506 |a Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty 
520 |a "This book contains a fast-paced introduction to as much relevant information about Python tools for data scientists as possible that can be reasonably included in a book of this size. If you are a novice, this book will give you a starting point from which you can decide which Python technologies that you want to explore in greater detail. You will be exposed to features of Numpy and Pandas, how to write regular expressions, and how to perform data cleaning tasks. Some topics are presented in a cursory manner, which is for two main reasons. First, it's important that you be exposed to these concepts. In some cases you will find topics that might pique your interest, and hence motivate you to learn more about them through self-study; in other cases you will probably be satisfied with a brief introduction. In other words, you will decide whether or not to delve into more detail regarding each of the topics in this book"--  |c Provided by publisher. 
590 |a Knovel  |b Knovel (All titles) 
650 0 |a Computer programming. 
650 0 |a Electronic data processing. 
650 0 |a Python (Computer program language) 
655 7 |a elektronické knihy  |7 fd186907  |2 czenas 
655 9 |a electronic books  |2 eczenas 
776 0 8 |c Original  |z 1683928237  |z 9781683928232  |w (OCoLC)1347365096 
856 4 0 |u https://proxy.k.utb.cz/login?url=https://app.knovel.com/hotlink/toc/id:kpPTDSPP06/python-tools-for?kpromoter=marc  |y Full text 

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