Machine learning is a complex discipline. The implementation of machine learning models is now far much easier than it used to be, this is as a result of Machine learning frameworks such as pandas. Wait!! isn't panda an animal? As I recall panda is an animal, this was my reaction in a Data science class by the end of the class I had completely grasped the concept of pandas.

機器學習是一門復雜的學科。 機器學習模型的實現現在比以前容易得多，這是由于熊貓等機器學習框架的結果。 等待！！ 熊貓不是動物嗎？ 我記得熊貓是一種動物，這是我在數據科學課上的React，直到我完全掌握了熊貓的概念。

Pandas is an open-source library, free to use (under theBSD license) and it was originally written by Wes McKinney back in 2009. Today we look at Pandas Library an entirely different kind of panda that is not only powerful but also the most used Library when it comes to data munging/wrangling.

Pandas是一個開放源代碼庫，免費使用(已獲得BSD許可)，最初由Wes McKinney于2009年編寫。今天，我們將Pandas Library視為一種完全不同的熊貓，它不僅功能強大，而且使用最廣泛關于數據整理/整理的庫。

This article is purely for others like me who might be confused of the connection between the animal and the Data. Note: there is no connection between pandas the animal and the library.

本文僅適用于像我這樣的人，他們可能會對動物和數據之間的聯系感到困惑。 注意：熊貓與動物之間沒有任何聯系。

什么是熊貓 (What is Pandas.)

Pandas is a fast, powerful, flexible, and easy to use open-source data analysis and manipulation tool. It is the most common tool used by Data analyst Data scientists working with data and use the python platform.

Pandas是一種快速，強大，靈活且易于使用的開源數據分析和處理工具。 它是數據分析師，數據科學家使用數據并使用python平臺使用的最常用工具。

According to Wikipedia it is derived from the term ““panel data”, an econometrics term for data sets that include observations over multiple time periods for the same individuals. [Pandas] is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series.’’

根據維基百科 它源自術語“ 面板數據”，這是數據集的計量經濟學術語，其中包括同一個人在多個時間段內的觀察結果。 [Pandas] 是為Python編程語言編寫的用于數據處理和分析的軟件庫。 特別是，它提供了用于操縱數值表和時間序列的數據結構和操作。''

Before you work with pandas you have to install it in your system. Depending on the type of system the installation differs.The easiest way to install pandas is to install it as part of the Anaconda distribution, a cross-platform distribution for data analysis and scientific computing. It is the recommended installation method for most users. The anaconda distribution is the most used platform that is used when it comes to working with data it comes intergrated with a number of tools that are used in working with data.

在使用熊貓之前，必須將其安裝在系統中。 根據系統類型的不同，安裝熊貓的最簡單方法是將其作為Anaconda發行版的一部分進行安裝， Anaconda發行版是用于數據分析和科學計算的跨平臺發行版。 對于大多數用戶，這是推薦的安裝方法。 anaconda發行版是處理數據時最常用的平臺，它與許多用于處理數據的工具集成在一起。

為什么是熊貓？ (Why pandas?)

Have you ever tried working with data without the pandas’ library? If not, this will be a hard task you will have to perform when it comes to working with data unless you are using a language like R where the case is different. If you tried working without pandas then you understand the need for the library.

您是否曾經嘗試過在沒有熊貓庫的情況下使用數據？ 如果不是這樣，除非涉及不同的情況，否則在處理數據時這將是一項艱巨的任務，除非您使用R之類的語言。 如果您嘗試在沒有熊貓的情況下工作，那么您會了解對圖書館的需求。

The reason why pandas are the most used library is that when working with tabular data, exploration, cleaning, and processing of your data is the very first and most important steps. These steps ensure that you get to understand the structure of the data. In this case, identifying the missing values, the size of the data frame the type of data. With pandas, you get a general view of the kind of data that you are working with.

大熊貓是最常用的庫的原因是，在處理表格數據時，探索，清理和處理數據是最重要的第一步。 這些步驟可確保您了解數據的結構。 在這種情況下，識別丟失的值，數據幀的大小就是數據的類型。 使用熊貓，您可以大致了解正在使用的數據類型。

Pandas are suited for many different kinds of data:

熊貓適用于許多不同類型的數據：

-Arbitrary matrix data with row and column labels.-Ordered and unordered time-series data.- Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet, working with tabular data, such as data stored in spreadsheets or databases, pandas is the right tool for you

-具有行和列標簽的任意矩陣數據。-有序和無序時間序列數據。-具有異構類型列的表格數據，例如在SQL表或Excel電子表格中，使用表格數據，例如存儲在電子表格或數據庫中的數據，熊貓是您的正確工具

-Any other form of observational/statistical data sets.

-其他形式的觀察/統計數據集。

The fact that pandas support the integration with many file formats or data sources out of the box (CSV, Excel, SQL, JSON, parquet,. . . ) this is a bonus to pandas being the most popular library used in python. Pandas are commonly used for data analysis. The library allows various data manipulation operations such as merging, reshaping, selecting, as well as data cleaning, and data wrangling features.

大熊貓支持開箱即用的許多文件格式或數據源(CSV，Excel，SQL，JSON，parquet等)的集成，這是熊貓作為python中最受歡迎的庫的一大優勢。 熊貓通常用于數據分析。 該庫允許進行各種數據操作操作，例如合并，重塑，選擇以及數據清理和數據整理功能。

Pandas provide a platform to visualize the data this allows one to draw conclusions based on the relationships in the plots. Plots are a useful tool when it comes to understanding the relationship in the data. You are sure to use plots to get a conclusion based on the data. You also get the chance to choose the plot type (scatter, bar, boxplot,… ) corresponding to your data.

熊貓提供了一個可視化數據的平臺，這使人們可以根據地塊之間的關系得出結論。 當了解數據中的關系時，圖是一個有用的工具。 您一定要使用圖來根據數據得出結論。 您還可以選擇與數據相對應的繪圖類型(散點圖，條形圖，箱線圖等)。

摘要 (Summary)

Pandas is a package that provides a fast, flexible, and expressive library designed to make working with “relational” or “labeled” data both easy and intuitive. Its goal is to be a fundamental high-level building block for practicing, real-world data analysis in Python.

Pandas是一個軟件包，提供了快速，靈活和富于表現力的庫，旨在使使用“關系”或“標記”數據既簡單又直觀。 它的目標是成為在Python中進行實際數據分析的基本高級構建塊。

With Pandas you are offered the power to work with a variety of data including, Arbitrary matrix data with row and column labels, Ordered and unordered time-series data, Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet and any other form of observational/statistical data sets.

使用Pandas，您可以使用各種數據，包括帶有行和列標簽的任意矩陣數據，有序和無序時間序列數據，具有異構類型列的表格數據(如SQL表或Excel電子表格中的數據)以及任何其他形式的觀察/統計數據集。

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Africa Data School

非洲數據學校

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