Pandas 用户指南目录

“用户指南” 按主题划分区域涵盖了几乎所有Pandas的功能。每个小节都介绍了一个主题（例如“处理缺失的数据”），并讨论了Pandas如何解决问题，其中包含许多示例。

刚开始接触Pandas的同学应该从十分钟入门Pandas开始看起。

有关任何特定方法的更多信息，请参阅API参考。

• IO工具（文本，CSV，HDF5，…）
  • CSV & text files
  • JSON
  • HTML
  • Excel files
  • OpenDocument Spreadsheets
  • Clipboard
  • Pickling
  • msgpack
  • HDF5 (PyTables)
  • Feather
  • Parquet
  • SQL queries
  • Google BigQuery
  • Stata format
  • SAS formats
  • Other file formats
  • Performance considerations
• 索引和数据选择器
  • Different choices for indexing
  • Basics
  • Attribute access
  • Slicing ranges
  • Selection by label
  • Selection by position
  • Selection by callable
  • IX indexer is deprecated
  • Indexing with list with missing labels is deprecated
  • Selecting random samples
  • Setting with enlargement
  • Fast scalar value getting and setting
  • Boolean indexing
  • Indexing with isin
  • The where() Method and Masking
  • The query() Method
  • Duplicate data
  • Dictionary-like get() method
  • The lookup() method
  • Index objects
  • Set / reset index
  • Returning a view versus a copy
• 多索引/高级索引
  • Hierarchical indexing (MultiIndex)
  • Advanced indexing with hierarchical index
  • Sorting a MultiIndex
  • Take methods
  • Index types
  • Miscellaneous indexing FAQ
• 合并、联接和连接
  • Concatenating objects
  • Database-style DataFrame or named Series joining/merging
  • Timeseries friendly merging
• 重塑和数据透视表
  • Reshaping by pivoting DataFrame objects
  • Reshaping by stacking and unstacking
  • Reshaping by Melt
  • Combining with stats and GroupBy
  • Pivot tables
  • Cross tabulations
  • Tiling
  • Computing indicator / dummy variables
  • Factorizing values
  • Examples
  • Exploding a list-like column
• 处理文本字符串
  • Splitting and replacing strings
  • Concatenation
  • Indexing with .str
  • Extracting substrings
  • Testing for Strings that match or contain a pattern
  • Creating indicator variables
  • Method summary
• 处理丢失的数据
  • Values considered “missing”
  • Sum/prod of empties/nans
  • NA values in GroupBy
  • Filling missing values: fillna
  • Filling with a PandasObject
  • Dropping axis labels with missing data: dropna
  • Interpolation
  • Replacing generic values
  • String/regular expression replacement
  • Numeric replacement
• 分类数据
  • Object creation
  • CategoricalDtype
  • Description
  • Working with categories
  • Sorting and order
  • Comparisons
  • Operations
  • Data munging
  • Getting data in/out
  • Missing data
  • Differences to R’s factor
  • Gotchas
• Nullable整型数据类型
• 可视化
  • Basic plotting: plot
  • Other plots
  • Plotting with missing data
  • Plotting Tools
  • Plot Formatting
  • Plotting directly with matplotlib
  • Trellis plotting interface
• 计算工具
  • Statistical functions
  • Window Functions
  • Aggregation
  • Expanding windows
  • Exponentially weighted windows
• 组操作: 拆分-应用-组合
  • Splitting an object into groups
  • Iterating through groups
  • Selecting a group
  • Aggregation
  • Transformation
  • Filtration
  • Dispatching to instance methods
  • Flexible apply
  • Other useful features
  • Examples
• 时间序列/日期方法
  • Overview
  • Timestamps vs. Time Spans
  • Converting to timestamps
  • Generating ranges of timestamps
  • Timestamp limitations
  • Indexing
  • Time/date components
  • DateOffset objects
  • Time Series-Related Instance Methods
  • Resampling
  • Time span representation
  • Converting between representations
  • Representing out-of-bounds spans
  • Time zone handling
• 时间增量
  • Parsing
  • Operations
  • Reductions
  • Frequency conversion
  • Attributes
  • TimedeltaIndex
  • Resampling
• 样式
  • Building styles
  • Finer control: slicing
  • Finer Control: Display Values
  • Builtin styles
  • Sharing styles
  • Other Options
  • Fun stuff
  • Export to Excel
  • Extensibility
• 选项和设置
  • Overview
  • Getting and setting options
  • Setting startup options in Python/IPython environment
  • Frequently Used Options
  • Available options
  • Number formatting
  • Unicode formatting
  • Table schema display
• 提高性能
  • Cython (writing C extensions for pandas)
  • Using Numba
  • Expression evaluation via >eval()
• 稀疏数据结构
  • SparseArray
  • SparseDtype
  • Sparse accessor
  • Sparse calculation
  • Migrating
  • Interaction with scipy.sparse
  • Sparse subclasses
• 常见问题(FAQ)
  • DataFrame memory usage
  • Using if/truth statements with pandas
  • NaN, Integer NA values and NA type promotions
  • Differences with NumPy
  • Thread-safety
  • Byte-Ordering issues
• 烹饪指南
  • Idioms
  • Selection
  • MultiIndexing
  • Missing data
  • Grouping
  • Timeseries
  • Merge
  • Plotting
  • Data In/Out
  • Computation
  • Timedeltas
  • Aliasing axis names
  • Creating example data