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[null,"1.","Record Nr.",null,"UNINA9910888063603321"]
[null,null,"Autore",null,"Lotz Walther"]
[null,null,"Titolo",null,"Finanzwissenschaftliche Untersuchungen. : Wissenschaftliche Gutachten. Erster Teil. (Schriften des Vereins für Sozialpolitik 174-I)"]
[null,null,"Pubbl/distr/stampa",null,"Berlin : , : Duncker & Humblot, , 2022"]
[{"$nulls":4},"©1928"]
[null,null,"ISBN",null,"9783428575084"]
[{"$nulls":4},"3428575083"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (65 pages)"]
[null,null,"Collana",null,"Duncker & Humblot reprints"]
[null,null,"Soggetti",null,"Finance, Public"]
[{"$nulls":4},"Fiscal policy"]
[null,null,"Lingua di pubblicazione",null,"Tedesco"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Vorbemerkung des Herausgebers -- 1. Steuerverwendung und Interessenpolitik. Von Rudolf Goldscheid --   Inhalt --   Vorbemerkung --   Einleitung --   I. Steuergerechtigkeit, Steuerüberwälzung und Steuerzweck --   II. Das Ganze der Wirtschaft als Synthese von Güterokonomie und Menschenökonomie --   III. Die wirtschaftliche Tüchtigkeit des armen und des reichen Gemeinwesens --   IV. Die Erfahrungen an der Finanzpolitik der Gemeinde Wien als Beweis für die Bedeutung der Finanzsoziologie --   V. Die Entstehung der öffentlichen Ausgaben --   VI. Der Wandel in der sozialen Funktion der Steuern --   VII. Die Steuerverwendungslehre und das Problem des Staates --   VIII. Das Verhältnis von Mensch und Staat und der produktive Arbeitsstaat --   IX. Die Wechselbeziehungen zwischen Steuerdruck und Wirtschaftsdruck --   X. Das Prinzip der Selbsthilfe in soziologischer Betrachtung --   XI. Die Rationalisierung der Wirtschaft, die Rationalisierung des öffentlichen Haushalts und die sozialen Lasten --   XII. Die finanzsoziologischen und menschenökonomischen Lehren der Wirtschaftlichkeitswissenschaft -- 2. Wie nimmt der Generalagent für deutsche Reparationszahlungen zu den Tatsachen des Finanzausgleichs Stellung? Von Dr. Constantin Miller --   Inhalt --   Die Bedeutung des Memorandums vom 20. Oktober 1927 für die Reparationsdebatte und die Grundzüge der in ihm enthaltenen Kritik --   Die Zitatkorrektur der Reichsregierung und die Frage der Möglichkeit einer Kritik der Reparationsberichte --   Die Stellungnahme des "]
[{"$nulls":4},"Reparationsagenten zum Finanzausgleich im Berichte vom 80. November 1926 und der ihr zugrunde liegende Widerspruch --   Die Finanzausgleichskritik im Bericht vom 10. Juni 1927 und ihre Lücken --   Die grundsätzliche Stellungnahme des Reparationsagenten zum Finanzausgleich und ihr Verhältnis zu der von ihm tatsächlich geübten Kritik --   Sonstiges"]
[null,null,null,"Sommario/riassunto","This book, edited by Walther Lotz and featuring contributions from Rudolf Goldscheid and Constantin Miller, explores the intricacies of public finance and economic policy. It delves into the theoretical and practical aspects of taxation and public spending, emphasizing the relationship between state objectives and fiscal policies. The text critiques the traditional approach to public finance, advocating for a more socially equitable distribution of tax burdens and public expenditures. It highlights the importance of aligning fiscal policies with social priorities and the need for a comprehensive understanding of the socio-economic impacts of taxation. Aimed at economists and policymakers, the book seeks to offer a critical analysis of public finance practices and propose reforms for a more efficient and just economic system."]
[null,"2.","Record Nr.",null,"UNINA9911004803403321"]
[null,null,"Autore",null,"Campesato Oswald"]
[null,null,"Titolo",null,"Python Tools for Data Scientists Pocket Primer"]
[null,null,"Pubbl/distr/stampa",null,"Bloomfield : , : Mercury Learning & Information, , 2022"]
[{"$nulls":4},"©2022"]
[null,null,"ISBN",null,"9781683928218"]
[{"$nulls":4},"1683928210"]
[{"$nulls":4},"9781683928225"]
[{"$nulls":4},"1683928229"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (323 pages)"]
[null,null,"Collana",null,"Computing"]
[null,null,"Disciplina",null,"001.642"]
[null,null,"Soggetti",null,"Computer programming"]
[{"$nulls":4},"Electronic data processing"]
[{"$nulls":4},"Python (Computer program language)"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"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 "]
[{"$nulls":4},"Dates -- Listing 1.9: String2Date.py -- Exception Handling in Python -- Listing 1.10: Exception1.py -- Handling User Input -- Listing 1.11: UserInput1.py -- Listing 1.12: UserInput2.py -- Listing 1.13: UserInput3.py -- Command-Line Arguments -- Listing 1.14: Hello.py -- Summary -- Chapter 2: Introduction to NumPy -- What is NumPy? -- Useful NumPy Features -- What are NumPy Arrays? -- Listing 2.1: nparray1.py -- Working with Loops."]
[{"$nulls":4},"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)."]
[{"$nulls":4},"Listing 2.24: plain_linreg2.py -- Google Colaboratory -- Uploading CSV Files in Google Colaboratory -- Listing 2.25: upload_csv_file.ipynb -- Summary -- Chapter 3: Introduction to Pandas -- What is Pandas? -- Pandas Options and Settings -- Pandas Data Frames -- Data Frames and Data Cleaning Tasks -- Alternatives to Pandas -- A Pandas Data Frame with a NumPy Example -- Listing 3.1: pandas_df.py -- Describing a Pandas Data Frame -- Listing 3.2: pandas_df_describe.py -- Pandas Boolean Data Frames -- Listing 3.3: pandas_boolean_df.py -- Transposing a Pandas Data Frame -- Pandas Data Frames and Random Numbers -- Listing 3.4: pandas_random_df.py -- Listing 3.5: pandas_combine_df.py -- Reading CSV Files in Pandas -- Listing 3.6: sometext.txt -- Listing 3.7: read_csv_file.py -- The loc() and iloc() Methods in Pandas -- Converting Categorical Data to Numeric Data -- Listing 3.8: cat2numeric.py -- Listing 3.9: shirts.csv -- Listing 3.10: shirts.py -- Matching and Splitting Strings in Pandas -- Listing 3.11: shirts_str.py -- Converting Strings to Dates in Pandas -- Listing 3.12: string2date.py -- Merging and Splitting Columns in Pandas -- Listing 3.13: employees.csv -- Listing 3.14: emp_merge_split.py -- Combining Pandas Data Frames -- Listing 3.15: concat_frames.py -- Data Manipulation with Pandas Data Frames (1) -- Listing 3.16: pandas_quarterly_df1.py -- Data Manipulation with Pandas Data Frames (2) -- Listing 3.17: pandas_quarterly_df2.py -- Data Manipulation with Pandas Data Frames (3) -- Listing 3.18: "]
[{"$nulls":4},"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."]
[{"$nulls":4},"Listing 3.23: scale_columns.py -- Managing Rows in Pandas -- Selecting a Range of Rows in Pandas -- Listing 3.24: duplicates.csv -- Listing 3.25: row_range.py -- Finding Duplicate Rows in Pandas -- Listing 3.26: duplicates.py -- Listing 3.27: drop_duplicates.py -- Inserting New Rows in Pandas -- Listing 3.28: emp_ages.csv -- Listing 3.29: insert_row.py -- Handling Missing Data in Pandas -- Listing 3.30: employees2.csv -- Listing 3.31: missing_values.py -- Multiple Types of Missing Values -- Listing 3.32: employees3.csv -- Listing 3.33: missing_multiple_types.py -- Test for Numeric Values in a Column -- Listing 3.34: test_for_numeric.py -- Replacing NaN Values in Pandas -- Listing 3.35: missing_fill_drop.py -- Sorting Data Frames in Pandas -- Listing 3.36: sort_df.py -- Working with groupby() in Pandas -- Listing 3.37: groupby1.py -- Working with apply() and mapapply() in Pandas -- Listing 3.38: apply1.py -- Listing 3.39: apply2.py -- Listing 3.40: mapapply1.py -- Listing 3.41: mapapply2.py -- Handling Outliers in Pandas -- Listing 3.42: outliers_zscores.py -- Pandas Data Frames and Scatterplots -- Listing 3.43: pandas_scatter_df.py -- Pandas Data Frames and Simple Statistics -- Listing 3.44: housing.csv -- Listing 3.45: housing_stats.py -- Aggregate Operations in Pandas Data Frames -- Listing 3.46: aggregate1.py -- Aggregate Operations with the titanic.csv Dataset -- Listing 3.47: aggregate2.py -- Save Data Frames as CSV Files and Zip Files -- Listing 3.48: save2csv.py -- Pandas Data Frames and Excel Spreadsheets -- Listing 3.49: write_people_xlsx.py -- Listing 3.50: read_people_xslx.py -- Working with JSON-based Data -- Python Dictionary and JSON -- Listing 3.51: dict2json.py -- Python, Pandas, and JSON -- Listing 3.52: pd_python_json.py -- Useful One-line Commands in Pandas -- What is Method Chaining? -- Pandas and Method Chaining."]
[{"$nulls":4},"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 "]
[{"$nulls":4},"-- What is Data Cleaning? -- Data Cleaning for Personal Titles -- Data Cleaning in SQL -- Replace NULL with 0 -- Replace NULL Values with the Average Value -- Listing 5.1: replace_null_values.sql."]
[{"$nulls":4},"Replace Multiple Values with a Single Value."]
[null,null,null,"Sommario/riassunto","\"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\"--"]