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...
Clasificación: | Libro Electrónico |
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Autor principal: | |
Formato: | Electrónico eBook |
Idioma: | Inglés |
Publicado: |
[S.l.] :
MERCURY LEARNING & INFORM,
2022.
|
Temas: | |
Acceso en línea: | Texto completo |
Tabla de Contenidos:
- 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.