Python Pandas Introduction And Installation

Pandas is an open-source third-party Python library built from NumPy and Matplotlib. It has become a necessary advanced tool for Python data analysis. This article will tell you what is python pandas and how to download and install the python pandas.

1. Python Pandas Introduction.

1.1 Pandas’ Advantages & Main Features.

  1. Pandas’ DataFrame and Series build a storage structure suitable for data analysis.
  2. Pandas’ concise API allows you to focus on the core level of your code.
  3. Pandas integrate with other libraries, such as SciPy, scikit-learn, and Matplotlib.
  4. Pandas official website provides perfect data support and a good community environment
  5. It provides a simple, efficient DataFrame object with default tags (or custom tags).
  6. It can quickly load data from files in different formats (such as Excel, CSV, and SQL files), and then convert them into processable objects.
  7. It can be grouped by row and column labels of data, and aggregate and convert the grouped objects.
  8. It can easily process data normalization and missing value processing.
  9. It can easily add, modify or delete data columns of DataFrame.
  10. It can handle data sets in different formats, such as matrix data, heterogeneous data tables, time series, etc.
  11. Provides a variety of ways to process data sets, such as building subsets, slicing, filtering, grouping, and reordering.

1.2 Pandas Built-in Data Structure.

  1. We know that building and processing two-dimensional and multi-dimensional arrays are a cumbersome task.
  2. To solve this problem, pandas constructed two different data structures based on the Ndarray (array in NumPy), which are Series (one-dimensional data structure) and DataFrame (two-dimensional data structure).
1.2.1 Pandas Series.
  1. Series is a one-dimensional array with labels. The labels here can be understood as indexes, but this index is not limited to integers. It can also be character types, such as ‘python’, ‘java’, ‘javascript’, etc.
  2. This structure can store various data types, such as characters, integer, floating-point number, python object, etc. Series is a one-dimensional data structure, so its dimension cannot be changed.
1.2.2 Pandas DataFrame.
  1. DataFrame is a tabular data structure with both row and column labels.
  2. DataFrame is a two-dimensional tabular data structure with both row and column indexes. The row index name is index and the column index name is columns. When you create the structure, you can specify the corresponding index values.

2. How To Install Python Pandas.

2.1 Install Pandas On macOS.

  1. To install Pandas on macOS, you can run the below command in a terminal.
    pip install pandas

2.2 Install Pandas on Linux.

  1. For different versions of Linux systems, you can use their respective package managers to install pandas.
  2. For Ubuntu, Pandas usually needs to be used with other software packages, so you can use the following command to install all packages at one time.
    sudo apt-get install numpy scipy matplotlib pandas
  3. For Fedora users, you can use the following command to install pandas.
    sudo yum install numpy scipy matplotlib pandas

2.3 Install Pandas on Windows.

  1. Installing pandas using the PIP package manager is the simplest installation method. Execute the following command from the CMD command prompt interface line.
    pip install pandas

2.4 Install Pandas With Third-Party Python Distribution.

  1. The official Python standard distribution does not have its own Pandas library, so it needs to be installed separately.
  2. In addition to the standard release, there are free Python releases released by some third-party organizations. They are developed on the basis of the official version and have targeted Python modules installed in advance to meet the needs of some specific fields.
  3. For third-party distributions, they already have their own pandas library, so there is no need to install them separately, so we recommend that you use third-party distributions.
  4. Below are some common free distributions that have integrated the Python Pandas library.
  5. Anaconda (download from the official website: )is an open-source Python distribution that contains more than 180 science packages and their dependencies. In addition to supporting Windows systems, it also supports Linux and MAC systems.
  6. WinPython (download address: ) is a free Python distribution that includes commonly used scientific computing packages and Spyder IDE but only supports Windows systems.
  7. Python (x, y) (download address: ) is a software developed based on python, QT (graphical user interface), and Spyder (interactive development environment). It is mainly used for engineering projects such as numerical calculation, data analysis, and data visualization. At present, it only supports Python 2 version.

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