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# Pandas
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Pandas is a comprehensive Python data analysis library that provides powerful, flexible, and expressive data structures designed for working with structured and time series data. It offers extensive functionality for data manipulation, cleaning, transformation, and analysis including data alignment, merging, reshaping, grouping, and statistical operations.
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## Package Information
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- **Package Name**: pandas
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- **Package Type**: library
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- **Language**: Python
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- **Installation**: `pip install pandas`
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## Core Imports
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```python
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import pandas as pd
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```
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Common imports for specific functionality:
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```python
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import pandas as pd
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from pandas import DataFrame, Series, Index
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```
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## Basic Usage
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```python
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import pandas as pd
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import numpy as np
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# Create a DataFrame from dictionary
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data = {
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    'name': ['Alice', 'Bob', 'Charlie', 'Diana'],
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    'age': [25, 30, 35, 28],
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    'city': ['New York', 'London', 'Tokyo', 'Paris'],
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    'salary': [50000, 60000, 70000, 55000]
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}
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df = pd.DataFrame(data)
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# Basic operations
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print(df.head())           # Display first 5 rows
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print(df.info())           # Display DataFrame info
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print(df.describe())       # Statistical summary
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# Data selection and filtering
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young_employees = df[df['age'] < 30]
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high_earners = df[df['salary'] > 55000]
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# Create a Series
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ages = pd.Series([25, 30, 35, 28], name='ages')
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print(ages.mean())         # Calculate mean age
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# Read data from files
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df_csv = pd.read_csv('data.csv')
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df_excel = pd.read_excel('data.xlsx')
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# Basic data manipulation
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df['bonus'] = df['salary'] * 0.1  # Add new column
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df_sorted = df.sort_values('salary')  # Sort by salary
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df_grouped = df.groupby('city')['salary'].mean()  # Group and aggregate
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```
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## Architecture
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Pandas is built around three fundamental data structures:
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- **Series**: One-dimensional labeled array capable of holding any data type
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- **DataFrame**: Two-dimensional labeled data structure with heterogeneous columns
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- **Index**: Immutable sequence used for indexing and alignment
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The library integrates seamlessly with NumPy, providing optimized performance through vectorized operations, and serves as the foundation for the Python data science ecosystem, including integration with Jupyter notebooks, matplotlib, scikit-learn, and hundreds of domain-specific analysis libraries.
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## Capabilities
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### Core Data Structures
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The fundamental data structures that form the foundation of pandas: DataFrame, Series, and various Index types. These structures provide the building blocks for all data manipulation operations.
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```python { .api }
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class DataFrame:
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    def __init__(self, data=None, index=None, columns=None, dtype=None, copy=None): ...
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class Series:
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    def __init__(self, data=None, index=None, dtype=None, name=None, copy=None, fastpath=False): ...
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class Index:
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    def __init__(self, data=None, dtype=None, copy=False, name=None, tupleize_cols=True): ...
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```
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[Core Data Structures](./core-data-structures.md)
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### Data Input/Output
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Comprehensive I/O capabilities for reading and writing data in various formats including CSV, Excel, JSON, SQL databases, HDF5, Parquet, and many statistical file formats.
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```python { .api }
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def read_csv(filepath_or_buffer, **kwargs): ...
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def read_excel(io, **kwargs): ...
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def read_json(path_or_buf, **kwargs): ...
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def read_sql(sql, con, **kwargs): ...
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def read_parquet(path, **kwargs): ...
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```
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[Data Input/Output](./data-io.md)
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### Data Manipulation and Reshaping
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Functions for combining, reshaping, and transforming data including merging, concatenation, pivoting, melting, and advanced data restructuring operations.
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```python { .api }
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def concat(objs, axis=0, join='outer', **kwargs): ...
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def merge(left, right, how='inner', on=None, **kwargs): ...
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def pivot_table(data, values=None, index=None, columns=None, **kwargs): ...
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def melt(data, id_vars=None, value_vars=None, **kwargs): ...
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```
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[Data Manipulation](./data-manipulation.md)
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### Time Series and Date Handling
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Comprehensive time series functionality including date/time parsing, time zone handling, frequency conversion, resampling, and specialized time-based operations.
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```python { .api }
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def date_range(start=None, end=None, periods=None, freq=None, **kwargs): ...
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def to_datetime(arg, **kwargs): ...
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class Timestamp:
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    def __init__(self, ts_input=None, freq=None, tz=None, **kwargs): ...
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```
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[Time Series](./time-series.md)
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### Data Types and Missing Data
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Extension data types, missing data handling, and type conversion utilities including nullable integer/boolean types, categorical data, and advanced missing value operations.
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```python { .api }
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def isna(obj): ...
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def notna(obj): ...
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class Categorical:
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    def __init__(self, values, categories=None, ordered=None, dtype=None, fastpath=False): ...
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```
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[Data Types](./data-types.md)
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### Statistical and Mathematical Operations
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Built-in statistical functions, mathematical operations, and data analysis utilities including descriptive statistics, correlation analysis, and numerical computations.
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```python { .api }
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def cut(x, bins, **kwargs): ...
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def qcut(x, q, **kwargs): ...
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def factorize(values, **kwargs): ...
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def value_counts(values, **kwargs): ...
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```
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[Statistics and Math](./statistics-math.md)
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### Configuration and Options
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Pandas configuration system for controlling display options, computational behavior, and library-wide settings.
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```python { .api }
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def get_option(pat): ...
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def set_option(pat, value): ...
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def reset_option(pat): ...
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def option_context(*args): ...
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```
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[Configuration](./configuration.md)
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### Plotting and Visualization
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Comprehensive plotting capabilities including basic plot types, statistical visualizations, and advanced multivariate analysis plots built on matplotlib.
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```python { .api }
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def scatter_matrix(frame, **kwargs): ...
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def parallel_coordinates(frame, class_column, **kwargs): ...
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def andrews_curves(frame, class_column, **kwargs): ...
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def radviz(frame, class_column, **kwargs): ...
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```
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[Plotting](./plotting.md)
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### API and Type Checking
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Type checking utilities and data type validation functions for working with pandas data structures and ensuring data quality.
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```python { .api }
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def is_numeric_dtype(arr_or_dtype): ...
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def is_datetime64_dtype(arr_or_dtype): ...
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def is_categorical_dtype(arr_or_dtype): ...
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def infer_dtype(value, **kwargs): ...
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```
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[API Types](./api-types.md)
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### Error Handling
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Exception and warning classes for proper error handling in pandas applications, including parsing errors, performance warnings, and data validation errors.
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```python { .api }
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class ParserError(ValueError): ...
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class PerformanceWarning(Warning): ...
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class SettingWithCopyWarning(Warning): ...
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class DtypeWarning(Warning): ...
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```
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[Errors](./errors.md)
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## Types
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```python { .api }
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# Core scalar types
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class Timestamp:
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    """Pandas timestamp object."""
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    pass
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class Timedelta:
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    """Pandas timedelta object.""" 
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    pass
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class Period:
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    """Pandas period object."""
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    pass
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class Interval:
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    """Pandas interval object."""
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    pass
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# Missing value sentinels
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NA: object  # Pandas missing value sentinel
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NaT: object  # Not-a-Time for datetime/timedelta
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# Common type aliases
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Scalar = Union[str, int, float, bool, Timestamp, Timedelta, Period, Interval]
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ArrayLike = Union[list, tuple, np.ndarray, Series, Index]
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Axes = Union[int, str, Sequence[Union[int, str]]]
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```