دانلود کتاب Pandas in 7 Days: Utilize Python to Manipulate Data, Conduct Scientific Computing, Time Series Analysis, and Exploratory Data Analysis (English Edition)

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Cover Page\nTitle Page\nCopyright Page\nDedication Page\nAbout the Author\nAbout the Reviewers\nAcknowledgements\nPreface\nErrata\nTable of Contents\n1. Pandas, the Python Library\n Structure\n Objective\n A bit of history\n Why use Pandas (and Python) for data analysis?\n A trust gained over the years\n A flexible language that adapts to any context\n Automation, reproducibility, and interaction\n Data Analysis\n What is data analysis?\n The data scientist\n The analysis process\n Tabular form of data\n Tabular form of data\n Spreadsheets\n SQL tables from databases\n Pandas and Dataframes\n Conclusion\n References\n2. Setting up a Data Analysis Environment\n Structure\n Objective\n The basic procedure\n Installing Python\n Python 3.x vs Python 2.7\n Installing Pandas and the other libraries\n Installing Jupyter Notebooks\n Considerations\n Anaconda\n The Anaconda distribution\n Installing Anaconda\n Installing Anaconda on Linux systems\n Conda and the Anaconda command line shell\n Creating and managing environments with Conda\n Conda – Create an environment with a different Python version\n Conda – Export the environments\n Creating and managing packages (and libraries) with Conda\n Jupyter Notebook\n Creating a Notebook on Jupyter\n First steps on a new Jupyter Notebook\n Working online on development environments: Replit\n Replit\n Creating a Python workspace for data analysis on Replit\n Conclusion\n Questions\n Multiple Choice Questions\n Answers\n Key Terms\n References/Further Readings\n3. Day 1 - Data Structures in Pandas library\n Structure\n Objective\n Introduction to the Series\n A Series as a sequence of data\n The Index() constructor\n The name attribute of Series\n Various ways to build Series\n The Series() constructor\n Creating a Series from a NumPy array\n Creating a Series from a scalar value\n Creating a Series from a dictionary\n Creating a Series from a list of tuples\n Creating a Series from a 2-element array list\n Introduction to DataFrames\n The DataFrame as an indexed data table\n The DataFrame() constructor\n Viewing DataFrames in Jupyter Notebook\n Other constructor arguments\n Access the various elements of the DataFrame with iloc[ ] and loc[ ]\n Various ways to build DataFrames\n Creating a DataFrame from a Numpy ndarray\n Creating a DataFrame from a Series dictionary\n Creating a DataFrame from a ndarray dictionary\n Pass-by-copy with Dictionaries\n Creating a DataFrame from a structured array or a record array\n Creating a DataFrame from a list of dictionaries\n Creating a DataFrame from a list of Data Classes\n Creating a DataFrame from a list of Named Tuples\n MultiIndex DataFrame\n Creating a MultiIndex DataFrame from a dictionary of tuples\n Conclusion\n Questions\n Multiple Choice Questions\n Answers\n Key Terms\n References\n4. Day 2 - Working within a DataFrame, Basic Functionalities\n Structure\n Objective\n Viewing Data\n Direct printing of the DataFrame values\n The head() and tail() methods\n Selection\n Selection by Subsetting\n Subsetting by index or by position\n Scalars and Series as return values\n Indexing Operators and Indexers\n loc[ ] – Selection by Labels\n iloc[ ] – Selection by location\n Indexer ix[ ]\n at[] and iat[] – Selection by value\n Selection on dtype\n Filtering\n The Boolean condition\n Filtering on the lines\n Filtering on columns\n Application of multiple conditions\n Boolean reductions\n Filtering with isin()\n Editing\n Adding, inserting, and deleting a column in a DataFrame\n Adding, inserting, and deleting a row in a DataFrame\n Adding new columns with assign()\n Descriptive Statistics\n The describe() method\n Calculation of individual statistics\n The Skipna option\n The standardization of data\n Transposition, Sorting, and Reindexing\n Transposition\n Sorting\n Sorting by label\n Sorting by values\n Reindexing\n Reindexing using another DataFrame as a reference\n Conclusion\n Questions\n MCQs\n Solution:\n Key Terms\n References/Further Readings\n5. Day 3 - Working within a DataFrame, Advanced Functionalities\n Structure\n Objective\n Shifting\n The shift() method\n Reshape\n The stack() and unstack() methods\n Pivoting\n Iteration\n Iteration with for loops\n Methods of iteration\n Row iterations in DataFrames\n Application of Functions on a Dataframe\n The levels of operability of the functions\n Applying NumPy functions directly on DataFrame\n Applying functions with the apply () method\n Applications of functions with the pipe() method\n Chaining pipe() and apply() methods\n Applying functions with arguments with the pipe() method\n The applymap() and map() methods\n Transforming\n The transform() method\n Reducing a MultiIndex DataFrame to a DataFrame\n Transform() does not aggregate elements\n Transform() works with a single column at a time\n Aggregation\n The agg() method\n Grouping\n The groupby() method\n The groupby() method for indexes\n Grouping with Transformation\n Grouping with Aggregation\n Apply() with groupby()\n Categorization\n Specifying a category column\n Conclusion\n Questions\n MCQs\n Solution:\n Key Terms\n References/Further Readings\n6. Day 4 - Working with Two or More DataFrames\n Structure\n Objective\n Add data to a DataFrame\n The append() method\n Appending multiple Series\n The concat() function\n Concatenations between DataFrame and Series that are not homogeneous\n Multiple concatenations\n Joining between two DataFrames\n The merge() method\n The LEFT Join\n The RIGHT Join\n The OUTER Join\n The INNER Join\n Arithmetic with DataFrames\n Two homogeneous DataFrames as operands\n Two non-homogeneous DataFrames as operands\n Operations between a DataFrame and a Series\n Flexible binary arithmetic methods\n Flexible binary methods and arithmetic operators\n Differences between arithmetic operators and flexible binary methods\n Arithmetic operations between subsets of the same DataFrame\n Arithmetic operations between subsets of different DataFrames\n The relative binary methods\n Boolean operations\n Boolean operations between two DataFrames\n Flexible Boolean binary methods\n Aligning on DataFrames\n The align() method\n Aligning with the Join modalities\n Conclusion\n Questions\n Multiple choice questions\n Solution\n Key Terms\n References/Further Readings\n7. Day 5 - Working with Data Sources and Real-World Datasets\n Structure\n Objective\n Files as external data sources\n Read and write data from files in CSV format\n Graphical display of DataFrames in Jupyter Notebook\n Preview of the content being written to file\n Some optional to_csv() parameters\n Read and Write Microsoft Excel File\n Read data from HTML pages on the web\n Read and write data from XML files\n Read and Write data in JSON\n Write and read data in binary format\n Final thoughts on saving data to file\n Interact with databases for data exchange\n SQLite installation\n Creation of test data on SQLite\n Other data sources\n Working with Time Data\n Date Times\n Time Deltas\n Create arrays of time values\n Import Date Time values from file\n Working with Text Data\n The methods of correcting the characters of the string\n Methods for splitting strings\n Methods for concatenating columns of text\n Separate alphanumeric from numeric characters in a column\n Adding characters to the rows of a column\n Recognition methods between alphanumeric and numeric characters\n Searching methods in strings\n Conclusion\n Questions\n Multiple choice questions\n Solution\n Key Terms\n References/Further Readings\n8. Day 6 - Troubleshooting Challenges with Real Datasets\n Structure\n Objective\n Handling Missing Data\n How NaNs are formed\n Other operations that generate NaN\n Working with Missing values\n Find and count the missing values\n Drop missing values\n Filling in of Missing Values\n Fill Forward or Backward\n Interpolate\n Conversion of data types in the presence of NaN\n Other common operations for correcting newly imported DataFrame\n Data Replacing\n Data Duplicated\n Renaming Axis Indexes\n Sparse DataFrames\n Tidy Data\n Methods for inversion of Stacked Data\n Example with the stack() method\n Example with the melt() method\n Columns that contain values, not variables\n Several variables in the same column\n Columns that contain multiple variables\n Variables in both rows and columns\n Groups of Variables in the columns\n Names of columns with multiple variables\n Conclusion\n Questions\n Multiple choice questions\n Solution\n Key Terms\n References/Further Readings\n9. Day 7 – Data Visualization and Reporting\n Structure\n Objective\n Matplotlib\n Making a Line Plot with Matplotlib\n Making a Scatter Plot\n Making a Bar Chart\n Making a Pie Chart\n Making a Box Plot\n Using the data in a DataFrame with Matplotlib\n Introduction to Seaborn\n Reporting\n Data Profiling and Reporting\n Reporting in HTML and PDF\n Conclusion\n Questions\n Multiple Choice Questions\n Answers\n Key Terms\n References/Further Readings\n10. Beyond Pandas\n Structure\n Objective\n Where to learn more about Pandas?\n The official Pandas documentation\n Participate in the development of the Pandas library\n What can you learn after Pandas?\n The Data Scientist\n The knowledge of the Data Scientist\n The Task of a Data Scientist\n The goals of a Data Scientist\n Data Scientist and R\n Statistical analysis with StatsModels\n Machine Learning with Scikit-learn\n Image Processing and Deep Learning\n Big Data\n The Data Engineer, a new professional figure\n Conclusion\n Questions\n Multiple Choice Questions\n Answers\n Key Terms\n References/Further Readings\nIndex