Explanation of the syntax `df[&#column&#] = expression` in pandas

Pandas df['column'] = expression Syntax Detailed Explanation: Used to create, modify or assign columns in Pandas DataFrame (df). Let’s break it down step by step, from basic to advanced.

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Basics

1. Create new column

• When a column does not exist in the DataFrame, assigning a value to df['column'] creates a new column.

• Example:

    import pandas as pd
    df = pd.DataFrame({'A': [1, 2, 3]})
    print(df)
    # 输出：
    #    A
    # 0  1
    # 1  2
    # 2  3
  
    # 创建一个新列 'B'，所有值都设置为 0
    df['B'] = 0
    print(df)
    # 输出：
    #    A  B
    # 0  1  0
    # 1  2  0
    # 2  3  0

2. Modify existing columns

• If the column already exists, assignment replaces its contents.

• Example:

    df['B'] = [4, 5, 6]  # 替换列 'B' 中的值
    print(df)
    # 输出：
    #    A  B
    # 0  1  4
    # 1  2  5
    # 2  3  6

---

Intermediate level

3. Expression-based assignment

• Can assign values ​​to columns based on calculations or transformations.

• Example:

    df['C'] = df['A'] + df['B']  # 创建列 'C' 为 'A' 和 'B' 的和
    print(df)
    # 输出：
    #    A  B   C
    # 0  1  4   5
    # 1  2  5   7
    # 2  3  6   9

4. Use conditional assignment

• You can use Pandas’ boolean indexing for conditional assignment.

• Example:

    df['D'] = df['A'].apply(lambda x: 'Even' if x % 2 == 0 else 'Odd')
    print(df)
    # 输出：
    #    A  B   C     D
    # 0  1  4   5   Odd
    # 1  2  5   7  Even
    # 2  3  6   9   Odd

5. Use multiple columns in expressions

• You can use multiple columns in one expression for more complex calculations.

• Example:

    df['E'] = (df['A'] + df['B']) * df['C']
    print(df)
    # 输出：
    #    A  B   C     D    E
    # 0  1  4   5   Odd   25
    # 1  2  5   7  Even   49
    # 2  3  6   9   Odd   81

---

Advanced Chapter

6. Vectorization operation

• Numerical assignments can use vectorization operations to improve performance.

• Example:

    df['F'] = df['A'] ** 2 + df['B'] ** 2  # 快速向量化计算
    print(df)
    # 输出：
    #    A  B   C     D    E   F
    # 0  1  4   5   Odd   25  17
    # 1  2  5   7  Even   49  29
    # 2  3  6   9   Odd   81  45

7. Use np.where for conditional logical assignment

• You can use NumPy for conditional assignment.

• Example:

    import numpy as np
    df['G'] = np.where(df['A'] > 2, 'High', 'Low')
    print(df)
    # 输出：
    #    A  B   C     D    E   F     G
    # 0  1  4   5   Odd   25  17   Low
    # 1  2  5   7  Even   49  29   Low
    # 2  3  6   9   Odd   81  45  High

8. Use external functions to assign values

• Assign values ​​to columns based on a custom function applied to the row or column.

• Example:

    def custom_function(row):
        return row['A'] * row['B']
  
    df['H'] = df.apply(custom_function, axis=1)
    print(df)
    # 输出：
    #    A  B   C     D    E   F     G   H
    # 0  1  4   5   Odd   25  17   Low   4
    # 1  2  5   7  Even   49  29   Low  10
    # 2  3  6   9   Odd   81  45  High  18

9. Chain operation

• Multiple operations can be chained together to make the code more concise.

• Example:

    df['I'] = df['A'].add(df['B']).mul(df['C'])
    print(df)
    # 输出：
    #    A  B   C     D    E   F     G   H    I
    # 0  1  4   5   Odd   25  17   Low   4   25
    # 1  2  5   7  Even   49  29   Low  10   49
    # 2  3  6   9   Odd   81  45  High  18   81

10. Assign multiple columns at one time

• Use assign() to create or modify multiple columns in one call.

• Example:

    df = df.assign(
        J=df['A'] + df['B'],
        K=lambda x: x['J'] * 2
    )
    print(df)
    # 输出：
    #    A  B   C     D    E   F     G   H    I   J   K
    # 0  1  4   5   Odd   25  17   Low   4   25   5  10
    # 1  2  5   7  Even   49  29   Low  10   49   7  14
    # 2  3  6   9   Odd   81  45  High  18   81   9  18

---

Experts

11. Dynamic column assignment

• Dynamically create column names based on external input.

• Example:

    columns_to_add = ['L', 'M']
    for col in columns_to_add:
        df[col] = df['A'] + df['B']
    print(df)

12. Use external data assignment

• Assign values ​​to columns based on an external DataFrame or dictionary.

• Example:

    mapping = {1: 'Low', 2: 'Medium', 3: 'High'}
    df['N'] = df['A'].map(mapping)
    print(df)
    # 输出：
    #    A  B   C     D    E   F     G   H    I   J   K      N
    # 0  1  4   5   Odd   25  17   Low   4   25   5  10    Low
    # 1  2  5   7  Even   49  29   Low  10   49   7  14  Medium
    # 2  3  6   9   Odd   81  45  High  18   81   9  18   High

13. Performance optimization:

• When assigning values, using Pandas’ built-in functions (apply, vectorized operations) has better performance than Python loops.

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Summary

df['column'] = expression Syntax is the core feature of Pandas and has a wide range of uses. It allows:

• Add, modify, and manipulate columns in a DataFrame.
• Perform complex calculations, including condition-based logic and multi-column transformations.
• Chain operations and dynamically generate new columns.

This makes Pandas a powerful data manipulation and analysis library.

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