Python Tools for Data Scientists Pocket Primer.

Python Tools for Data Scientists Pocket Primer.

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Bibliographic Details
Main Author: Campesato, Oswald
Format: Ebook
Language:English
Published: Bielefeld : Mercury Learning & Information, 2022.
Edition:1st ed.
Series:Computing Ser.
Subjects:
Electronic books.
Online Access:Click here to view this book
Table of Contents:
  • 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.
  • 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).
  • 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.
  • 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.
  • 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.
  • Replace Multiple Values with a Single Value.
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