Pandas
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An open-source data analysis and data manipulation library for Python.
It provides data structures like DataFrames and Series to efficiently handle and analyze data.
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Python Documentation
Refers to the official guides, tutorials,and references provided by the Python Software Foundation to help users understand and utilize Python's features and libraries.
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Python Library
A collection of modules and packages that provide reusable functions and tools to perform various tasks,such as data manipulation, web development, and machine learning.
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Python Object
An instance of a class that encapsulates data and functions.
Objects are the basic building blocks of Python programming, allowing for structured and modular code.
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Pandas DataFrame
A two-dimensional, size-mutable, and potentially heterogeneous tabular data structure with labeled axes (rows and columns)used for data manipulation and analysis.
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Series
A one-dimensional, labeled array capable of holding data of any type (integer, string, float, etc.). It is similar to a column in a DataFrame but can also function as a single array.
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Variables
Used to store data values. They are symbolic names that reference or point to an object or value, allowing for data manipulation and processing within the program.
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Panel Data
A multi-dimensional dataset involving measurements over time. In Pandas, it can be represented using multi-index DataFrames to handle complex data structures.
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Metadata
Refers to data that provides information about other data. In the context of Pandas, metadata can include information such as data types, column names, and descriptions of the dataset.
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Single-column Data
Refers to a dataset that consists of only one column of values. In Pandas, this is typically represented as a Series, which is a one-dimensional array-like structure.
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Multi-column Data
Refers to a dataset that consists of multiple columns of values. In Pandas, this is represented as a DataFrame, which is a two-dimensional array-like structure with labeled axes.
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version Method
Refers to checking the installed version of the Pandas library using pd.__version__. It helps ensure compatibility with other code and libraries.
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dtype: object
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Numpy
A fundamental Python library for numerical computations, providing support for arrays, matrices, and a wide range of mathematical functions to operate on these data structures.
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dtype: int32
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Attributes
Refer to the properties or metadata associated with data structures like DataFrames and Series. Common attributes include shape, dtype, and index.
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pd.dtype
Refers to the data type of the elements in a Series or DataFrame column. It provides information about the kind of data stored, such as integers, floats, or strings.
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pd.size
Refers to the total number of elements in a DataFrame or Series. It is calculated as the product of the DataFrame's shape dimensions (rows multiplied by columns).
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pd.name
Refers to the name of a Series. It is an attribute that can be set to provide a meaningful identifier for the Series, which can be useful for labeling and documentation purposes.
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int32
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5
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object
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4
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Product Categories
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Indexing
Refers to accessing and modifying data in Series or DataFrames using labels, integers, or boolean arrays. It allows for efficient data selection and manipulation.
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Label-based Indexing
Refers to accessing data using the labels or names of the rows and columns. It is achieved using the .loc accessor, enabling selection based on explicit index values.
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Position-based Indexing
Refers to accessing data using the integer positions of the rows and columns. It is achieved using the .iloc accessor, enabling selection based on numerical positions.
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RangeIndex
A default index for DataFrames and Series created using a range of integers. It is efficient and memory-saving, commonly used when explicit indexing is not necessary.
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<class 'dict'>
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Inedex(['Product A', 'Product B', 'Product C'], dtype='object')
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Indices
Refer to the labels or positions that uniquely identify rows and columns in a DataFrame or Series. They facilitate data alignment and access.
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22250
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15600
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20
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Methods
Refer to the functions that are associated with DataFrame and Series objects. These methods perform operations such as aggregation, transformation, and data manipulation.
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Functions
Rrefer to built-in methods that perform specific operations on data structures. They include aggregation, transformation, and manipulation functions like sum(), mean(), and groupby().
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pd.sum()
A function that returns the sum of the values over the requested axis in a DataFrame or Series. It can be used for quick aggregation of numerical data.
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pd.min()
A function that returns the minimum value over the requested axis in a DataFrame or Series. It is useful for finding the smallest value in a dataset.
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pd.max()
A function that returns the maximum value over the requested axis in a DataFrame or Series. It is used to identify the largest value in a dataset.
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pd.idmax()
A function that returns the index of the first occurrence of the maximum value over the requested axis in a DataFrame or Series. It helps locate the position of the highest value.
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pd.idmin()
A function that returns the index of the first occurrence of the minimum value over the requested axis in a DataFrame or Series. It helps locate the position of the smallest value.
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pd.head()
A function that returns the first n rows of a DataFrame or Series. By default, it returns the top 5 rows, providing a quick preview of the dataset.
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pd.tail()
A function that returns the last n rows of a DataFrame or Series. By default, it returns the bottom 5 rows, allowing a quick look at the end of the dataset.
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dropna()
A pandas function that removes missing values from a DataFrame. Rows or columns with missing values can be dropped using this function.
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fillna()
A pandas function used to replace NaN values with a specified value.
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merge()
A pandas function that combines DataFrames using database-style join operations based on common columns or indices.
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concat()
A pandas function used to concatenate DataFrames along a particular axis (row-wise or column-wise).
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drop_duplicates()
A pandas function used to remove duplicate rows from a DataFrame.
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groupby()
A pandas function that splits data into groups based on some criteria and applies a function to each group independently.
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interpolate()
A pandas function used to fill NaN values using various interpolation methods.
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isnull()
A pandas function that detects missing values in a DataFrame, returning a DataFrame of the same shape with boolean values.
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notnull()
A pandas function that detects non-missing values in a DataFrame, returning a DataFrame of the same shape with boolean values.
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6100
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100
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2000
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7/4/2014
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1/2/2015
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Parameters
Refer to the variables that are used in the function definition to accept input values. They define what kind of arguments the function can accept.
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Arguments
The actual values or data that are passed to a function when it is called. They correspond to the parameters defined in the function signature.
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pd.describe()
A function that generates descriptive statistics of a DataFrame or Series, including count, mean, standard deviation, minimum, and maximum values, and quartiles.
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pd.unique()
A function that returns the unique values in a Series or DataFrame column. It is useful for identifying distinct values within a dataset.
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pd.nunique()
A function that returns the number of unique values in a Series or DataFrame column. It helps to understand the variability within the data.
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pd.values()
An attribute that returns the underlying data of a DataFrame or Series as a NumPy array. It allows for efficient manipulation and computation.
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pd.array()
A function that creates an array object from a data structure. It is used to create new array-like structures, which can be useful for certain operations.
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pd.to_numpy()
A function that converts a DataFrame or Series to a NumPy array. This is useful for performing operations that require NumPy arrays.
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pd.sort_values()
A function that sorts the values in a DataFrame or Series by the specified axis. It is used for organizing data in ascending or descending order.
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Attribute Chaining
Refers to accessing multiple attributes in a single line of code. It allows for concise and readable data manipulation.
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Method Chaining
Refers to applying multiple methods in succession on a DataFrame or Series in a single line of code. It improves code readability and efficiency.
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None
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Series vs. DataFrame
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Series and DataFrames as Programming Objects
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Data Selection
Refers to accessing specific subsets of data within a DataFrame or Series using indexing, slicing, and boolean indexing techniques.
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pd.iloc[]
An indexer for position-based indexing. It is used to select data by row and column positions, specified as integer indices.
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pd.loc[]
An indexer for label-based indexing. It is used to select data by row and column labels, specified as strings or boolean arrays.
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Dos and Don'ts for .iloc[] and .loc[]
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Data Consistency
Refers to ensuring that the data in a dataset is accurate, reliable, and follows defined rules. It involves maintaining the integrity of the data throughout its lifecycle.
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Data Cleaning
Refers to the process of identifying and correcting errors or inconsistencies in a dataset. It includes handling missing values, duplicates, and incorrect data types.
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Data Preprocessing
Involves preparing raw data for analysis by transforming it into a clean and usable format. It includes tasks such as normalization, encoding, and scaling.
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Data Preparation
Refers to the steps taken to ready a dataset for analysis or modeling. It encompasses data cleaning, preprocessing, transformation, and feature engineering.