Learn a little python knowledge in three minutes 5----------My understanding of pandas in python, I listed 4 commonly used examples of pandas to understand pandas in depth

1. What is Pandas?

1. Pandas is a very popular data processing and analysis library in Python, which can be used to read, clean, transform, analyze and visualize data.
2. The two most important data structures in Pandas are Series and DataFrame. A Series is a one-dimensional array, similar to a list or a one-dimensional array in Python, where each element has an index value. DataFrame is a two-dimensional table composed of multiple Series, similar to an Excel table or a table in SQL. Each Series corresponds to a column in the table.

2. Commonly used functions of Pandas:

2.1. Reading and writing data

Use the read_csv method to read CSV files, the read_excel method to read Excel files, the read_sql method to connect to the database to read data, the write_csv method to save data to a CSV file, and to_excel to save data to an Excel file.

Sample code:

import pandas as pd

# 读取CSV文件
data = pd.read_csv('data.csv')

# 读取Excel文件
data = pd.read_excel('data.xlsx')

# 连接数据库读取数据
data = pd.read_sql('SELECT * FROM my_table', conn)

# 将数据保存为CSV文件
data.to_csv('new_data.csv')

# 将数据保存为Excel文件
data.to_excel('new_data.xlsx')

2.2. Data cleaning and transformation

Pandas can easily clean and transform data, such as removing duplicate rows, replacing null values, changing data types, adding new columns, etc.

Sample code:

import pandas as pd

# 去除重复行
data.drop_duplicates(inplace=True)

# 替换空值
data.fillna(0, inplace=True)

# 更改数据类型
data['age'] = data['age'].astype(int)

# 添加新列
data['age_group'] = pd.cut(data['age'], bins=[0, 18, 30, 50, 100], labels=['<18', '18-29', '30-49', '50+'])

2.3. Data analysis and calculations

Pandas provides many commonly used data analysis and calculation functions, such as summation, statistical description, calculation by group, etc. At the same time, Pandas can also be easily integrated with other Python data analysis libraries, such as Numpy, Matplotlib and other libraries.

Sample code:

import pandas as pd

# 求和
total_sales = data['sales'].sum()

# 统计描述
describe = data.describe()

# 按组计算平均值
grouped_data = data.groupby('gender')['age'].mean()

2.4. Data visualization

Pandas provides many data visualization functions, such as drawing line charts, bar charts, scatter plots, etc. These functions are implemented based on the Matplotlib library, so you can use more advanced visualization functions provided by Matplotlib.

Sample code:

import pandas as pd
import matplotlib.pyplot as plt

# 绘制折线图
data.plot(kind='line', x='date', y='sales')

# 绘制柱状图
data.plot(kind='bar', x='product', y='sales')

# 绘制散点图
data.plot(kind='scatter', x='age', y='income')
plt.show()

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Summarize

The following are some tips and experiences I summarized when learning Pandas:

1. Familiar with the basic data structures of Pandas

The two most commonly used data structures in Pandas are Series and DataFrame. Before learning Pandas, you should first master their usage and features.

2. Learn how to read data from various data sources

Pandas can read data from a variety of data sources, including CSV, Excel, SQL databases, JSON, and more. When learning Pandas, you need to master how to read data from different data sources.

3. Learn common techniques for data cleaning and preprocessing

Data cleaning and preprocessing are important steps in data analysis. Pandas provides many data cleaning and preprocessing methods, such as processing missing values, duplicate values, outliers, text data, etc.

4. Familiar with methods and functions of data analysis and statistical calculations

Pandas can perform various data analysis and statistical calculations, such as sums, counts, averages, medians, standard deviations, etc. Mastering these methods and functions allows for better data analysis.

5. Learn how to visualize data

Pandas can be used for data visualization through the Matplotlib library. Learning how to use Matplotlib for data visualization can better display data analysis results.