Pandas Tutorial

Pandas is an extension library for the Python language, used for data analysis.

The name Pandas is derived from the term "panel data" (panel data) and "Python data analysis" (Python Data Analysis).

Pandas is an open-source, BSD-licensed library that provides high-performance, easy-to-use data structures and data analysis tools.

Pandas is a powerful toolset for analyzing structured data, based onNumpy(provides high-performance matrix operations).

Before you start this tutorial, you need to know

Before starting the Pandas tutorial, you need to have a basic knowledge of Python. If you don't know Python yet, you can read our tutorials:

Pandas Applications

Pandas can import data from various file formats such as CSV, JSON, SQL, and Microsoft Excel.

Pandas can perform operations on various data, such as merging, reshaping, selection, as well as data cleaning and data processing features.

Pandas is widely used in various data analysis fields, including academia, finance, statistics, and more.

Pandas Features

Pandas is a powerful tool for data analysis. It not only provides efficient and flexible data structures, but also helps you complete complex data operation and analysis tasks at an extremely low cost.

Pandas provides a rich set of features, including:

  • Data cleaning: handle missing data, duplicate data, etc.
  • Data transformation: changing the shape, structure, or format of data.
  • Data analysis: perform statistical analysis, aggregation, grouping, etc.
  • Data visualization: by integrating libraries such as Matplotlib and Seaborn, data visualization can be achieved.

Data Structures

The main data structures of Pandas are Series (one-dimensional data) and DataFrame (two-dimensional data).

  • SeriesIt is an object similar to a one-dimensional array, consisting of a set of data (various Numpy data types) and a set of associated data labels (i.e., indexes).

  • DataFrameIt is a tabular data structure that contains an ordered set of columns, each of which can be a different value type (numeric, string, boolean). DataFrame has both row indexes and column indexes, and it can be viewed as a dictionary composed of Series (sharing one index).

Pandas is one of the indispensable tools in the Python data science field. Its flexibility and powerful features make data processing and analysis simpler and more efficient.

Your first pandas example

The following example creates a simple DataFrame:

Example

import pandas as pd

# Create a simple DataFrame
data = {'Name': ['Google', 'Example', 'Taobao'], 'Age': [25, 30, 35]}
df = pd.DataFrame(data)

# View the DataFrame
print(df)
The output of the above code is:
     Name  Age
0  Google   25
1  Example   30
2  Taobao   35

Related Links

Other Extensions