Instance-oriented pandas data analysis method: practical combat of data loading and feature engineering

Pandas data analysis method practice: from data loading to feature engineering, specific code examples are required

Introduction:
Pandas is a widely used data analysis library in Python , providing a wealth of data processing and analysis tools. This article will introduce the specific method from data loading to feature engineering and provide relevant code examples.

1. Data loading
Data loading is the first step in data analysis. In Pandas, you can use a variety of methods to load data, including reading local files, reading network data, reading databases, etc.

1. Read local files
   Use Pandas’ read_csv() function to easily read local CSV files. The following is an example:

import pandas as pd

data = pd.read_csv("data.csv")

2. Reading network data
   Pandas also provides the function of reading network data. You can use the read_csv() function and pass in the network address as a parameter. The example is as follows:

import pandas as pd

url = "https://www.example.com/data.csv"
data = pd.read_csv(url)

3. Reading the database
   If the data is stored in the database, you can use Pandas to provide it The read_sql() function is used to read. First, you need to use Python's SQLAlchemy library to connect to the database, and then use Pandas' read_sql() function to read the data. The following is an example:

import pandas as pd
from sqlalchemy import create_engine

engine = create_engine('sqlite:///database.db')
data = pd.read_sql("SELECT * FROM table", engine)

2. Data Preview and Processing
After loading the data, you can use the methods provided by Pandas to preview and preliminary process the data.

1. Data Preview
   You can use the head() and tail() methods to preview the first and last few rows of data. For example:

data.head()  # 预览前5行
data.tail(10)  # 预览后10行

2. Data Cleaning
   Cleaning data is one of the important steps in data analysis. Pandas provides a series of methods to deal with missing values, duplicate values ​​and outliers.

• Handling missing values
  You can use the isnull() function to determine whether the data is a missing value, and then use the fillna() method to fill in the missing values. The following is an example:

data.isnull()  # 判断缺失值
data.fillna(0)  # 填充缺失值为0

• Handling duplicate values
  Use the duplicated() method to determine whether the data is a duplicate value, and then use the drop_duplicates() method to remove duplicate values. The sample code is as follows:

data.duplicated()  # 判断重复值
data.drop_duplicates()  # 去除重复值

• Handling abnormal values
  For abnormal values, you can use conditional judgment and index operations to process them. The following is an example:

data[data['column'] > 100] = 100  # 将大于100的值设为100

3. Feature Engineering
Feature engineering is a key step in data analysis. By transforming raw data into features more suitable for modeling, the performance of the model can be improved. Pandas provides multiple methods for feature engineering.

1. Feature selection
   You can use Pandas column operations and conditional judgments to select specific features. The following is an example:

selected_features = data[['feature1', 'feature2']]

2. Feature Encoding
   Before modeling, features need to be converted into a form that can be processed by machine learning algorithms. Pandas provides the get_dummies() method for one-hot encoding. The following is an example:

encoded_data = pd.get_dummies(data)

3. Feature Scaling
   For numerical features, you can use Pandas’ MinMaxScaler() or StandardScaler() method for feature scaling. The sample code is as follows:

from sklearn.preprocessing import MinMaxScaler

scaler = MinMaxScaler()
scaled_data = scaler.fit_transform(data)

4. Feature construction
   New features can be constructed by performing basic operations and combinations on original features. The sample code is as follows:

data['new_feature'] = data['feature1'] + data['feature2']

Conclusion:
This article introduces the method from data loading to feature engineering in Pandas data analysis, and demonstrates related operations through specific code examples. With the powerful data processing and analysis functions of Pandas, we can conduct data analysis and mining more efficiently. In practical applications, different operations and methods can be selected according to specific needs to improve the accuracy and effect of data analysis.

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