Python Tools for Data Scientists Pocket Primer
- Length: 300 pages
- Edition: 1
- Language: English
- Publisher: Mercury Learning and Information
- Publication Date: 2022-10-30
- ISBN-10: 1683928237
- ISBN-13: 9781683928232
- Sales Rank: #0 (See Top 100 Books)
As part of the best-selling Pocket Primer series, this book is designed to provide a thorough introduction to numerous Python tools for data scientists. The book covers features of NumPy and Pandas, how to write regular expressions, and how to perform data cleaning tasks. It includes separate chapters on data visualization and working with Sklearn and SciPy. Companion files with source code are available.
FEATURES:
- Introduces Python, NumPy, Sklearn, SciPy, and awk
- Covers data cleaning tasks and data visualization
- Features numerous code samples throughout
- Includes companion files with source code
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 Listing 5.2: reduce_values.sql Handle Mismatched Attribute Values Listing 5.3: type_mismatch.sql Convert Strings to Date Values Listing 5.4: str_to_date.sql Data Cleaning from the Command Line (optional) Working with the sed Utility Listing 5.5: delimiter1.txt Listing 5.6: delimiter1.sh Working with Variable Column Counts Listing 5.7: variable_columns.csv Listing 5.8: variable_columns.sh Listing 5.9: variable_columns2.sh Truncating Rows in CSV Files Listing 5.10: variable_columns3.sh Generating Rows with Fixed Columns with the awk Utility Listing 5.11: FixedFieldCount1.sh Listing 5.12: employees.txt Listing 5.13: FixedFieldCount2.sh Converting Phone Numbers Listing 5.14: phone_numbers.txt Listing 5.15: phone_numbers.sh Converting Numeric Date Formats Listing 5.16: dates.txt Listing 5.17: dates.sh Listing 5.18: dates2.sh Converting Alphabetic Date Formats Listing 5.19: dates2.txt Listing 5.20: dates3.sh Working with Date and Time Date Formats Listing 5.21: date-times.txt Listing 5.22: date-times-padded.sh Working with Codes, Countries, and Cities Listing 5.23: country_codes.csv Listing 5.24: add_country_codes.sh Listing 5.25: countries_cities.csv Listing 5.26: split_countries_codes.sh Listing 5.27: countries_cities2.csv Listing 5.28: split_countries_codes2.sh Data Cleaning on a Kaggle Dataset Listing 5.29: convert_marketing.sh Summary Chapter 6: Data Visualization What is Data Visualization? Types of Data Visualization What is Matplotlib? Diagonal Lines in Matplotlib Listing 6.1: diagonallines.py A Colored Grid in Matplotlib Listing 6.2: plotgrid2.py Randomized Data Points in Matplotlib Listing 6.3: lin_plot_reg.py A Histogram in Matplotlib Listing 6.4: histogram1.py A Set of Line Segments in Matplotlib Listing 6.5: line_segments.py Plotting Multiple Lines in Matplotlib Listing 6.6: plt_array2.py Trigonometric Functions in Matplotlib Listing 6.7: sincos.py Display IQ Scores in Matplotlib Listing 6.8: iq_scores.py Plot a Best-Fitting Line in Matplotlib Listing 6.9: plot_best_fit.py The Iris Dataset in SkLearn Listing 6.10: sklearn_iris1.py SkLearn, Pandas, and the Iris Dataset Listing 6.11: pandas_iris.py Working with Seaborn Features of Seaborn Seaborn Built-in Datasets Listing 6.12: seaborn_tips.py The Iris Dataset in Seaborn Listing 6.13: seaborn_iris.py The Titanic Dataset in Seaborn Listing 6.14: seaborn_titanic_plot.py Extracting Data from the Titanic Dataset in Seaborn (1) Listing 6.15: seaborn_titanic.py Extracting Data from the Titanic Dataset in Seaborn (2) Listing 6.16: seaborn_titanic2.py Visualizing a Pandas Dataset in Seaborn Listing 6.17: pandas_seaborn.py Data Visualization in Pandas Listing 6.18: pandas_viz1.py What is Bokeh? Listing 6.19: bokeh_trig.py Summary Appendix A: Working with Data What are Datasets? Data Preprocessing Data Types Preparing Datasets Discrete Data vs. Continuous Data “Binning” Continuous Data Scaling Numeric Data via Normalization Scaling Numeric Data via Standardization What to Look for in Categorical Data Mapping Categorical Data to Numeric Values Working with Dates Working with Currency Missing Data, Anomalies, and Outliers Missing Data Anomalies and Outliers Outlier Detection What is Data Drift? What is Imbalanced Classification? What is SMOTE? SMOTE Extensions Analyzing Classifiers (Optional) What is LIME? What is ANOVA? The Bias-Variance Trade-Off Types of Bias in Data Summary Appendix B: Working with awk The awk Command Built-in Variables that Control awk How Does the awk Command Work? Aligning Text with the printf Statement Listing B.1: columns2.txt Listing B.2: AlignColumns1.sh Conditional Logic and Control Statements The while Statement A for loop in awk Listing B.3: Loop.sh A for loop with a break Statement The next and continue Statements Deleting Alternate Lines in Datasets Listing B.4: linepairs.csv Listing B.5: deletelines.sh Merging Lines in Datasets Listing B.6: columns.txt Listing B.7: ColumnCount1.sh Printing File Contents as a Single Line Joining Groups of Lines in a Text File Listing B.8: digits.txt Listing B.9: digits.sh Joining Alternate Lines in a Text File Listing B.10: columns2.txt Listing B.11: JoinLines.sh Listing B.12: JoinLines2.sh Listing B.13: JoinLines2.sh Matching with Meta Characters and Character Sets Listing B.14: Patterns1.sh Listing B.15: columns3.txt Listing B.16: MatchAlpha1.sh Printing Lines Using Conditional Logic Listing B.17: products.txt Splitting Filenames with awk Listing B.18: SplitFilename2.sh Working with Postfix Arithmetic Operators Listing B.19: mixednumbers.txt Listing B.20: AddSubtract1.sh Numeric Functions in awk One Line awk Commands Useful Short awk Scripts Listing B.21: data.txt Printing the Words in a Text String in awk Listing B.22: Fields2.sh Count Occurrences of a String in Specific Rows Listing B.23: data1.csv Listing B.24: data2.csv Listing B.25: checkrows.sh Printing a String in a Fixed Number of Columns Listing B.26: FixedFieldCount1.sh Printing a Dataset in a Fixed Number of Columns Listing B.27: VariableColumns.txt Listing B.28: Fields3.sh Aligning Columns in Datasets Listing B.29: mixed-data.csv Listing B.30: mixed-data.sh Aligning Columns and Multiple Rows in Datasets Listing B.31: mixed-data2.csv Listing B.32: aligned-data2.csv Listing B.33: mixed-data2.sh Removing a Column from a Text File Listing B.34: VariableColumns.txt Listing B.35: RemoveColumn.sh Subsets of Column-aligned Rows in Datasets Listing B.36: sub-rows-cols.txt Listing B.37: sub-rows-cols.sh Counting Word Frequency in Datasets Listing B.38: WordCounts1.sh Listing B.39: WordCounts2.sh Listing B.40: columns4.txt Displaying Only “Pure” Words in a Dataset Listing B.41: onlywords.sh Working with Multi-line Records in awk Listing B.42: employees.txt Listing B.43: employees.sh A Simple Use Case Listing B.44: quotes3.csv Listing B.45 delim1.sh Another Use Case Listing B.46: dates2.csv Listing B.47: string2date2.sh Summary Index
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