Details
| Title | Pandas 1.x cookbook: practical recipes for scientific computing, time series analysis and exploratory data analysis using Python. — Second edition. |
|---|---|
| Creators | Harrison Matt |
| Other creators | Petrou Theodore |
| Collection | Электронные книги зарубежных издательств ; Общая коллекция |
| Subjects | Python (Computer program language) ; Programming languages (Electronic computers) ; Data mining. ; Science — Mathematics — Computer programs. ; EBSCO eBooks |
| Document type | Other |
| File type | |
| Language | English |
| Rights | Доступ по паролю из сети Интернет (чтение, печать, копирование) |
| Record key | on1147864211 |
| Record create date | 2/26/2020 |
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- Cover
- Copyright
- Packt Page
- Contributors
- Table of Contents
- Preface
- Chapter 1: Pandas Foundations
- Importing pandas
- Introduction
- The pandas DataFrame
- DataFrame attributes
- Understanding data types
- Selecting a column
- Calling Series methods
- Series operations
- Chaining Series methods
- Renaming column names
- Creating and deleting columns
- Chapter 2: Essential DataFrame Operations
- Introduction
- Selecting multiple DataFrame columns
- Selecting columns with methods
- Ordering column names
- Summarizing a DataFrame
- Chaining DataFrame methods
- DataFrame operations
- Comparing missing values
- Transposing the direction of a DataFrame operation
- Determining college campus diversity
- Chapter 3: Creating and Persisting DataFrames
- Introduction
- Creating DataFrames from scratch
- Writing CSV
- Reading large CSV files
- Using Excel files
- Working with zip files
- Working with databases
- Reading JSON
- Reading HTML tables
- Chapter 4: Beginning Data Analysis
- Introduction
- Developing a data analysis routine
- Data dictionaries
- Reducing memory by changing data types
- Selecting the smallest of the largest
- Selecting the largest of each group by sorting
- Replicating nlargest with sort_values
- Calculating a trailing stop order price
- Chapter 5: Exploratory Data Analysis
- Introduction
- Summary statistics
- Column types
- Categorical data
- Continuous data
- Comparing continuous values across categories
- Comparing two continuous columns
- Comparing categorical and categorical values
- Using the pandas profiling library
- Chapter 6: Selecting Subsets of Data
- Introduction
- Selecting Series data
- Selecting DataFrame rows
- Selecting DataFrame rows and columns simultaneously
- Selecting data with both integers and labels
- Slicing lexicographically
- Chapter 7: Filtering Rows
- Introduction
- Calculating Boolean statistics
- Constructing multiple Boolean conditions
- Filtering with Boolean arrays
- Comparing row filtering and index filtering
- Selecting with unique and sorted indexes
- Translating SQL WHERE clauses
- Improving the readability of Boolean indexing with the query method
- Preserving Series size with the .where method
- Masking DataFrame rows
- Selecting with Booleans, integer location, and labels
- Chapter 8: Index Alignment
- Introduction
- Examining the Index object
- Producing Cartesian products
- Exploding indexes
- Filling values with unequal indexes
- Adding columns from different DataFrames
- Highlighting the maximum value from each column
- Replicating idxmax with method chaining
- Finding the most common maximum of columns
- Chapter 9: Grouping for Aggregation, Filtration, and Transformation
- Introduction
- Defining an aggregation
- Grouping and aggregating with multiple columns and functions
- Removing the MultiIndex after grouping
- Grouping with a custom aggregation function
- Customizing aggregating functions with *args and **kwargs
- Examining the groupby object
- Filtering for states with a minority majority
- Transforming through a weight loss bet
- Calculating weighted mean SAT scores per state with apply
- Grouping by continuous variables
- Counting the total number of flights between cities
- Finding the longest streak of on-time flights
- Chapter 10: Restructuring Data into a Tidy Form
- Introduction
- Tidying variable values as column names with stack
- Tidying variable values as column names with melt
- Stacking multiple groups of variables simultaneously
- Inverting stacked data
- Unstacking after a groupby aggregation
- Replicating pivot_table with a groupby aggregation
- Renaming axis levels for easy reshaping
- Tidying when multiple variables are stored as column names
- Tidying when multiple variables are stored as a single column
- Tidying when two or more values are stored in the same cell
- Tidying when variables are stored in column names and values
- Chapter 11: Combining Pandas Objects
- Introduction
- Appending new rows to DataFrames
- Concatenating multiple DataFrames together
- Understanding the differences between concat, join, and merge
- Connecting to SQL databases
- Chapter 12: Time Series Analysis
- Introduction
- Understanding the difference between Python and pandas date tools
- Slicing time series intelligently
- Filtering columns with time data
- Using methods that only work with a DatetimeIndex
- Counting the number of weekly crimes
- Aggregating weekly crime and traffic accidents separately
- Measuring crime by weekday and year
- Grouping with anonymous functions with a DatetimeIndex
- Grouping by a Timestamp and another column
- Chapter 13: Visualization with Matplotlib, Pandas, and Seaborn
- Introduction
- Getting started with matplotlib
- Object-oriented guide to matplotlib
- Visualizing data with matplotlib
- Plotting basics with pandas
- Visualizing the flights dataset
- Stacking area charts to discover emerging trends
- Understanding the differences between seaborn and pandas
- Multivariate analysis with seaborn Grids
- Uncovering Simpson's Paradox in the diamonds dataset with seaborn
- Chapter 14: Debugging and Testing Pandas
- Code to transform data
- Apply performance
- Improving apply performance with Dask, Pandarell, Swifter, and more
- Inspecting code
- Debugging in Jupyter
- Managing data integrity with Great Expectations
- Using pytest with pandas
- Generating tests with Hypothesis
- Other Books You May Enjoy
- Index
