It includes two data structures: DataFrame and Series
The official address of the document:
pandas https://pandas.pydata.org/pandas-docs/stable/index.html
series https://pandas.pydata.org/pandas-docs/stable/reference/series.html
dataframe https://pandas.pydata.org/pandas-docs/stable/reference/frame.html
A, Pandas Profile
1, Python Data Analysis Library NumPy or pandas is a tool, the tool to solve data analysis tasks created based on. Pandas included a large library and some standard data model provides the tools needed to efficiently operate large data sets. pandas provides a number of functions and methods enable us to quickly and easily handle the data. You will soon find that it is one of the important factors that make Python become a powerful and efficient data analysis environment.
2, Pandas is a data python analysis package, initially in April 2008 developed by AQR Capital Management, and at the end of 2009 open out, are continuing to develop and maintain a focus on Python packets developed PyData development team, belonging to PyData project a part of. Pandas and was originally developed as a financial data analysis tools, therefore, pandas time series analysis provides a good support. Pandas name from python and data analysis (data analysis) data of the panel (panel data). panel data is a term regarding economics cube, also provided in the panel Pandas data types.
Transfer: https://blog.csdn.net/qq_26591517/article/details/80041296
3, Pandas Python is a large data processing module. Pandas using a two-dimensional data structure to represent DataFrame tabular data, compared to Numpy, Pandas hybrid data structure can be stored, while using NaN to represent the missing data, and the data do not like to be handled manually as Numpy missing, and using Pandas axis labels represent the row and column.
DataFrame categories:
DataFrame There are four important attributes:
index: the row index.
columns: column index.
values: a two-dimensional array of values.
name: the name.
Original link: https: //blog.csdn.net/qq_26591517/article/details/80041296
4, pandas and numpy
pandas is the most famous python environment statistics package, and DataFrame translated into data frame is a data organization, it says that you might not know it in the sense that, for example, you probably used Excel, but it is also a data organization and presentation of the way, it simply is a table, while the pandas with DataFrame organize data, if you do not print DataFrame, you can not see the data.
pandas and numpy difference:
(1) numpy is a numerical expansion pack, panadas do data processing.
(2) Introduction NumPy: N-dimensional array system is an open source container NumPy Python numerical calculation extension. This tool can be used to store and process large matrices, Python than their nested lists (nested list structure) is much more efficient structure (the structure may be used to represent a matrix (matrix)). It is said that Python NumPy will become a free equivalent of more powerful MatLab system.
Pandas Description: Form container pandas NumPy is based on a tool, the tool is created to solve data analysis tasks. Pandas included a large library and some standard data model provides the tools needed to efficiently operate large data sets. pandas provides a number of functions and a method for processing data quickly and easily. Python has become an important factor in the strong and efficient data analysis environment.
Reference: https://blog.csdn.net/yang9520/article/details/79847964
Two, Series and DataFrame
pandas higher based on data comprising data structures and tools constructed Numpy analysis package.
The core is similar to Numpy ndarray, pandas is around two core data structures Series and DataFrame expanded. Series and DataFrame structural sequence table correspond to the one-dimensional and two-dimensional. pandas convention introduced as follows:
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from pandas import Series,DataFrame
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import pandas as pd
Series:
Series object contains two main attributes: index and values, respectively, about the two embodiments.
Series object element will be strictly in accordance with the construction given index, which means: if there is a key parameter for the data, then only the key index are contained in used; and if the missing data in response to a key, even given NaN values, the key will be added.
DataFrame:
DataFrame is a tabular data structure, comprising an ordered set of columns (similar to index), each column may be a different value types (not only a ndarray dtype). DataFrame can be regarded as substantially share the same collection of index of Series.
Series DataFrame constructor and similar, but can accept a plurality of one-dimensional data source at the same time, every single one will be:
DataFrame.loc ([Name row], [column name])
DataFrame.iloc ([行 号] [列 号])
https://blog.csdn.net/llx1026/article/details/77722608
Reference: https://blog.csdn.net/qq_34941023/article/details/53317805
Third, the summary chart
Reference: https://www.jianshu.com/p/1b751406a7b6