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Copy file name to clipboardExpand all lines: contrib/pandas/Pandas_Series_Vs_NumPy_ndarray.md
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NumPy ndarray and Pandas Series are two fundamental data structures in Python for handling and manipulating data. While they share some similarities, they also have distinct characteristics that make them suitable for different tasks.
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While both NumPy ndarray and Pandas Series are essential tools for data manipulation in Python, Choosing between them depends on the nature of your data and the specific tasks you need to perform.
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Both NumPy ndarray and Pandas Series are essential tools for data manipulation in Python, Choosing between them depends on the nature of your data and the specific tasks you need to perform.
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## NumPy ndarray (n-dimensional array)
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## NumPy ndarray
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NumPy is short form for Numerical Python, provides a powerful array object called `ndarray`, which is the backbone of many scientific and mathematical Python libraries.
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NumPy is short form for Numerical Python, provides a powerful array object called `ndarray`, which is the backbone of many scientific and mathematical Python libraries. ndarray is also called n-dimensional array. Indexing in ndarray is integer based indexing.
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Here are key points about NumPy `ndarray`:
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Features of NumPy `ndarray`:
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-**Homogeneous Data**: All elements in a NumPy array are of the same data type, which allows for efficient storage and computation.
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-**Efficient Computation**: NumPy arrays are designed for numerical operations and are highly efficient. They support vectorized operations, allowing you to perform operations on entire arrays rather than individual elements.
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-**Multi-dimensional**: NumPy arrays can be multi-dimensional, making them suitable for representing complex numerical data structures like matrices and tensors.
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-**Efficient Computation and Performance**: NumPy arrays are designed for numerical operations and are highly efficient. They support vectorized operations, allowing you to perform operations on entire arrays rather than individual elements.
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-**Multi-dimensional**: NumPy arrays can be multi-dimensional, making them suitable for representing complex numerical data structures like matrices and n-dimensional arrays.
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Example of creating a NumPy array:
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narr = np.array(['A', 'B', 'C', 'D', 'E'])
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print(narr)
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```
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### Use NumPy ndarray:
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Output:
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```python
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['A''B''C''D''E']
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```
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### Usage of NumPy ndarray:
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- When you need to perform mathematical operations on numerical data.
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- When you’re working with multi-dimensional data.
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- When computational efficiency is important.
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- When you need to store data of same data type. `
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## Pandas Series
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Pandas, built on top of NumPy, introduces the `Series` data structure, which is designed for handling labeled one-dimensional data efficiently.
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Pandas is a Python library used for data manipulation and analysis, introduces the `Series` data structure, which is designed for handling labeled one-dimensional data efficiently.
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Here are the key points about Pandas `Series`:
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Features of Pandas `Series`:
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-**Labeled Data**: Pandas Series associates a label (or index) with each element of the array, making it easier to work with heterogeneous or labeled data.
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-**Flexible Data Types**: Unlike NumPy arrays, Pandas Series can hold data of different types (integers, floats, strings, etc.) within the same object.
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-**Heterogeneous Data**: Unlike NumPy arrays, Pandas Series can hold data of different types (integers, floats, strings, etc.) within the same object.
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-**Data Alignment**: One of the powerful features of Pandas Series is its ability to automatically align data based on label. This makes handling and manipulating data much more intuitive and less error-prone.
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```python
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import pandas as pd
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series = pd.Series([1,3, 5, 7, 6, 8])
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series = pd.Series([1,'B', 5, 7, 6, 8], index= ['a','b','c','d','e','f'])
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print(series)
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```
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Output:
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```python
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a 1
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b B
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c 5
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d 7
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e 6
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f 8
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dtype: object
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```
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### Use Pandas Series:
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### Usage of Pandas Series:
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- When you need to manipulate and analyze labeled data.
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- When you’re dealing with heterogeneous data or missing values.
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- When you need more high-level, flexible data manipulation functions.
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