


Installation guide for PythonPandas: easy to understand and operate
Simple and easy-to-understand Python Pandas installation guide
Python Pandas is a powerful data manipulation and analysis library that provides flexible and easy-to-use data structures and data Analysis tool is one of the important tools for Python data analysis. This article will provide you with a simple and easy-to-understand Python Pandas installation guide to help you quickly install Pandas, and attach specific code examples to make it easy for you to get started.
- Installing Python
Before installing Pandas, you need to install Python first. Python can be downloaded from the official website (https://www.python.org/downloads/). Select the installation package suitable for your operating system. After downloading, follow the installation wizard to install it.
- Install Pandas
After successfully installing Python, open a terminal (command prompt) and enter the following command to install Pandas:
pip install pandas
This command The Pandas library is automatically downloaded and installed from the Python Package Index (PyPI).
- Verify installation
After the installation is complete, you can enter the following code in the terminal to verify whether Pandas has been successfully installed:
import pandas as pd print(pd.__version__)
If the output is The version number of the Pandas library indicates that the installation was successful.
- Commonly used data structures of Pandas
Pandas provides two commonly used data structures, namely Series and DataFrame.
Series is a one-dimensional data structure in Pandas, which can be viewed as an array with labels. You can use the following code to create a Series:
import pandas as pd s = pd.Series([1, 3, 5, np.nan, 6, 8]) print(s)
DataFrame is a two-dimensional data structure in Pandas and can be viewed as a table. You can use the following code to create a DataFrame:
import pandas as pd import numpy as np data = {'name': ['Tom', 'John', 'Emily', 'Jane'], 'age': [20, 25, 30, 35], 'city': ['New York', 'Paris', 'London', 'Tokyo']} df = pd.DataFrame(data) print(df)
- Common data operations in Pandas
Pandas provides rich data operation and analysis functions, such as data filtering, sorting, and merging wait. The following are some commonly used data operation examples:
Filter data:
import pandas as pd df = pd.DataFrame({'name': ['Tom', 'John', 'Emily'], 'age': [20, 25, 30]}) filtered_df = df[df['age'] > 25] print(filtered_df)
Sort data:
import pandas as pd df = pd.DataFrame({'name': ['Tom', 'John', 'Emily'], 'age': [20, 25, 30]}) sorted_df = df.sort_values(by='age', ascending=False) print(sorted_df)
Merge data:
import pandas as pd data1 = {'name': ['Tom', 'John', 'Emily'], 'age': [20, 25, 30]} data2 = {'name': ['Peter', 'Jane'], 'age': [35, 40]} df1 = pd.DataFrame(data1) df2 = pd.DataFrame(data2) merged_df = pd.concat([df1, df2]) print(merged_df)
The above are some commonly used ones Pandas data operation examples, you can perform more data processing and analysis according to actual needs.
Summary:
Python Pandas is a powerful data manipulation and analysis library. This article provides you with a simple and easy-to-understand Python Pandas installation guide, with specific code examples, allowing you to Able to get started quickly. I hope this article is helpful to you, and I wish you go further and further on the road of data analysis!
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