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 onNumpyNumpy (providing high-performance matrix operations).

What you need to know before learning this tutorial

Before starting the Pandas tutorial, you need to have a basic foundation in 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 manipulation features.

Pandas is widely used in various data analysis fields such as academia, finance, and statistics.

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 operations and analysis tasks at a very low cost.

Pandas provides a rich set of features, including:

  • Data cleaning: Handle missing data, duplicate data, etc.
  • Data transformation: Change 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 performed.

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 related data labels (i.e., indices).

  • DataFrameIt is a tabular data structure, containing an ordered set of columns, each column can be a different value type (numeric, string, boolean). DataFrame has both row indices and column indices, and it can be regarded as a dictionary composed of Series (sharing a common 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.

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 above code outputs the following:
     Name  Age
0  Google   25
1  Example   30
2  Taobao   35

Related Links

Other Extensions