Data Processing and Cleaning Tips in Jupyter Notebook

# Chapter 1. Data Import and Overview

Data forms the bedrock of any data analysis endeavor. The first step in data processing is correctly importing and initially observing the data. This chapter will introduce how to perform data import and overview in Jupyter Notebook, including importing datasets, viewing dataset information, data preview, and preliminary observation.

### Importing Datasets
In the data processing journey, we often use the pandas library to handle data. Pandas provides a rich set of data structures and functions, facilitating the import of various formats of data files, such as CSV, Excel, SQL databases, and more. Below is an example code for importing datasets:

import pandas as pd

# Importing a dataset from a CSV file
df = pd.read_csv('data.csv')

# Importing a dataset from an Excel file
df = pd.read_excel('data.xlsx')

# Importing a dataset from an SQL database
import sqlite3
conn = sqlite3.connect('database.db')
df = pd.read_sql_query("SELECT * FROM table", conn)

### Viewing Dataset Information
After importing the dataset, we need to view the basic information of the dataset, including data dimensions, column names, data types, missing value situations, etc. The `info()` method can be used to quickly view the information of the dataset:

# ***
***()

### Data Preview and Preliminary Observation
In addition to viewing the information of the dataset, we can also use methods like `head()` and `tail()` to preview the first or last few rows of the dataset, allowing for a more intuitive understanding of the data structure:

# Viewing the first few rows of the dataset
df.head()

# Viewing the last few rows of the dataset
df.tail()

With these operations, we can have a preliminary understanding of the imported dataset, laying the groundwork for subsequent data cleaning and processing.

# Chapter 2. Data Cleaning and Processing

Data cleaning and processing are crucial in data analysis, as cleaning and processing data makes it more accurate and complete, thereby enhancing the accuracy and credibility of subsequent analysis. This chapter will introduce common data cleaning and processing techniques, including handling missing values, handling duplicate values, data type conversion, and outlier handling.

### Handling Missing ***
***mon methods include removing missing values and filling in missing values.

The table below shows a dataset with missing values, and we will demonstrate how to handle these missing values.

| Name | Age | Gender | Score |
|---------|-----|--------|-------|
| Xiao Ming | 25 | Male | 85 |
| Xiao Hong | 30 | Female | NaN |
| Xiao Hua | NaN | Male | 77 |
| Xiao Li | 28 | Male | 92 |

# Example code for handling missing values
import pandas as pd

data = {'Name': ['Xiao Ming', 'Xiao Hong', 'Xiao Hua', 'Xiao Li'],
        'Age': [25, 30, None, 28],
        'Gender': ['Male', 'Female', 'Male', 'Male'],
        'Score': [85, None, 77, 92]}

df = pd.DataFrame(data)
# Deleting rows with missing values
df.dropna(inplace=True)

The processed dataset will delete rows with missing values, retaining complete data.

### ***
***mon methods include deleting duplicate values and keeping unique values.

The following code demonstrates how to handle duplicate values:

# Example code for handling duplicate values
# Assuming df is a dataset with duplicate values
df.drop_duplicates(inplace=True)

With the above code, we can delete duplicate values in the dataset, ensuring uniqueness.

The above examples cover handling missing values and duplicate values. We will continue to introduce data type conversion and outlier handling later.

# Chapter 3. Data Filtering and Sorting

In the data processing process, data filtering and sorting are very common operations. We can select an interesting subset of data through filtering, and sorting can arrange data according to specific rules. In this chapter, we will introduce how to perform data filtering and sorting operations.

### Conditional Filtering

In a DataFrame, we often need to filter data rows based on certain conditions. The following example demonstrates how to perform conditional filtering using Pandas:

import pandas as pd

# Creating example data
data = {'A': [1, 2, 3, 4, 5],
        'B': ['a', 'b', 'c', 'd', 'e']}
df = pd.DataFrame(data)

# Filtering based on conditions
filtered_df = df[df['A'] > 2]
print(filtered_df)

With the above code, we can filter data rows where the value in column 'A' is greater than 2.

### Column Selection and Filtering

In addition to filtering rows, sometimes we need to select and filter columns as well. Pandas provides a simple way to achieve this:

# Selecting specific columns
selected_