From 7cd0ec140e3a036a02b65a366d8336c02a8c016d Mon Sep 17 00:00:00 2001 From: Lingamuneni Santhosh Siddhardha <103999924+Santhosh-Siddhardha@users.noreply.github.com> Date: Mon, 13 May 2024 15:58:31 +0530 Subject: [PATCH] Update Pandas_Series_Vs_NumPy_ndarray.md --- contrib/pandas/Pandas_Series_Vs_NumPy_ndarray.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/contrib/pandas/Pandas_Series_Vs_NumPy_ndarray.md b/contrib/pandas/Pandas_Series_Vs_NumPy_ndarray.md index d493136..e739766 100644 --- a/contrib/pandas/Pandas_Series_Vs_NumPy_ndarray.md +++ b/contrib/pandas/Pandas_Series_Vs_NumPy_ndarray.md @@ -6,7 +6,7 @@ Both NumPy ndarray and Pandas Series are essential tools for data manipulation i ## NumPy ndarray -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. +NumPy is short form for Numerical Python, provides a powerful array object called `ndarray`, It is very important for many scientific and mathematical Python libraries. ndarray is also called n-dimensional array. Indexing in ndarray is integer based indexing (like arr[0], arr[3], etc.). Features of NumPy `ndarray`: @@ -31,11 +31,11 @@ Output: - When you need to perform mathematical operations on numerical data. - When you’re working with multi-dimensional data. - When computational efficiency is important. -- When you need to store data of same data type. ` +- When you need to store data of same data type. ## Pandas Series -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. +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. Indexing in Pandas Series is Label-based. It effectively handles heterogeneous data. Features of Pandas `Series`: @@ -43,7 +43,7 @@ Features of Pandas `Series`: - **Heterogeneous Data**: Unlike NumPy arrays, Pandas Series can hold data of different types (integers, floats, strings, etc.) within the same object. -- **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. +- **Data Alignment**: One of the powerful features of Pandas Series is its ability to automatically align data based on label. Example of creating a Pandas Series: