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isin() 与 between() 筛选

使用 isin() 选择列值属于某个列表的行，或使用 between() 选择列值处于某个数值范围内的行。

isin() 与 between() 筛选 是 CoddyKit 上的免费 Pandas & NumPy Academy 课时。 这是第 3 节课，共 4 节。 你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。 这是 Pandas & NumPy Academy 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 Pandas & NumPy Academy 课程共包含 4 节课。

本课时的部分内容尚未翻译，以英文显示。

The isin() Method Overview

isin() checks whether each element of a Series is contained in a given list (or set) of values. It returns a boolean Series that you can use directly for row filtering. This is more concise and often faster than chaining multiple == comparisons with |.

For example, instead of (df['country'] == 'USA') | (df['country'] == 'UK'), you write df['country'].isin(['USA', 'UK']).

import pandas as pd

df = pd.DataFrame({
    'country': ['USA', 'Germany', 'UK', 'France', 'USA'],
    'sales': [400, 200, 150, 300, 600]
})

# Check membership
mask = df['country'].isin(['USA', 'UK'])
print(mask.tolist())  # [True, False, True, False, True]

result = df[mask]
print(result)
#   country  sales
# 0     USA    400
# 2      UK    150
# 4     USA    600

isin() with Sets for Speed

You can pass a Python set instead of a list to isin(). Membership look-up in a set is O(1) — it doesn't get slower as the list grows. This matters when your allowed values list has thousands of entries, making set-based isin() significantly faster than list-based isin().

import pandas as pd

df = pd.DataFrame({
    'sku': ['A001', 'B002', 'A003', 'C004', 'B005'],
    'qty': [10, 5, 3, 8, 12]
})

# Use a set for fast membership check
allowed_skus = {'A001', 'A003', 'B005'}
result = df[df['sku'].isin(allowed_skus)]
print(result)
#     sku  qty
# 0  A001   10
# 2  A003    3
# 4  B005   12

Negating isin() with ~

Combine isin() with the ~ operator to filter rows where a column does not contain the specified values. This is the cleanest way to exclude a list of specific values, far more readable than building a chain of != comparisons.

import pandas as pd

df = pd.DataFrame({
    'status': ['active', 'cancelled', 'shipped', 'refunded', 'active'],
    'amount': [100, 200, 300, 150, 500]
})

bad_statuses = ['cancelled', 'refunded']
# Keep all rows NOT in the excluded list
result = df[~df['status'].isin(bad_statuses)]
print(result)
#     status  amount
# 0   active     100
# 2  shipped     300
# 4   active     500

isin() with a DataFrame Column as the List

A powerful trick is to pass the values from another DataFrame's column to isin(). This is equivalent to a SQL semi-join: keep rows in df1 whose key appears in df2. Unlike a full merge, this does not duplicate rows or add extra columns — it just filters.

import pandas as pd

orders = pd.DataFrame({
    'order_id': [1, 2, 3, 4, 5],
    'revenue': [100, 200, 300, 400, 500]
})

vip_orders = pd.DataFrame({'order_id': [2, 4]})

# Keep orders whose ID appears in vip_orders
result = orders[orders['order_id'].isin(vip_orders['order_id'])]
print(result)
#    order_id  revenue
# 1         2      200
# 3         4      400

The between() Method Overview

between(left, right) returns a boolean Series that is True where the values fall within the closed range [left, right] (inclusive by default). It replaces two-sided conditions like (df['age'] >= 18) & (df['age'] <= 65) with a single, readable call.

The inclusive parameter controls boundary inclusion: 'both' (default), 'left', 'right', or 'neither'.

import pandas as pd

df = pd.DataFrame({
    'age': [15, 22, 35, 67, 45, 8]
})

# Select working-age population
result = df[df['age'].between(18, 65)]
print(result)
#    age
# 1   22
# 2   35
# 4   45

between() with inclusive Parameter

By default, both endpoints are included in the range. Setting inclusive='left' excludes the right boundary (useful for half-open intervals like time bins), inclusive='right' excludes the left, and inclusive='neither' excludes both endpoints for a strict open interval.

import pandas as pd

df = pd.DataFrame({'score': [0, 50, 100, 75, 50]})

# Include only scores strictly between 50 and 100
strict = df[df['score'].between(50, 100, inclusive='neither')]
print(strict)
#    score
# 3     75

# Include 50 but not 100
left_closed = df[df['score'].between(50, 100, inclusive='left')]
print(left_closed)
#    score
# 1     50
# 3     75
# 4     50

between() on Date Columns

between() works on datetime columns too. Pass date strings or Timestamp objects as the boundaries, and Pandas will perform the comparison on the underlying datetime values. This is a clean way to slice a time window without converting dates to integers.

import pandas as pd

df = pd.DataFrame({
    'date': pd.to_datetime(['2024-01-01', '2024-06-15', '2024-11-20', '2025-03-01']),
    'value': [10, 20, 30, 40]
})

# Filter rows in the year 2024
result = df[df['date'].between('2024-01-01', '2024-12-31')]
print(result)
#         date  value
# 0 2024-01-01     10
# 1 2024-06-15     20
# 2 2024-11-20     30

Combining isin() and between()

You can combine isin() and between() in the same boolean expression using & and |. This creates compact, readable filters that would otherwise require many nested conditions. Always enclose each call in parentheses when combining.

import pandas as pd

df = pd.DataFrame({
    'region': ['North', 'South', 'East', 'North', 'West'],
    'revenue': [100, 500, 200, 300, 400]
})

# Rows in North or South regions AND revenue between 200 and 400
result = df[
    df['region'].isin(['North', 'South']) &
    df['revenue'].between(200, 400)
]
print(result)
#    region  revenue
# 3   North      300
# 1   South      500  <- actually excluded (500 > 400)
# 3   North      300

isin() on Multiple Columns with Any

When you want to check if any of several columns contains a value from a list, you can call isin() on the entire DataFrame and use .any(axis=1) to collapse the result row-wise. This is useful for searching across multiple tag or category columns simultaneously.

import pandas as pd

df = pd.DataFrame({
    'tag1': ['python', 'java', 'sql'],
    'tag2': ['sql', 'python', 'go'],
    'topic': ['Database', 'Backend', 'Infra']
})

target_langs = ['python', 'sql']
# Check if either tag column matches
mask = df[['tag1', 'tag2']].isin(target_langs).any(axis=1)
result = df[mask]
print(result)
#      tag1    tag2     topic
# 0  python     sql  Database
# 1    java  python   Backend
# 2     sql      go     Infra

Performance Tip: isin() vs Multiple ==

For a small list of 2-3 values, the difference between isin() and chained == conditions is negligible. But for large lists (hundreds of values), isin() is dramatically faster because it converts the list to a hash set internally and performs O(1) look-ups per element rather than sequential comparison.

Always prefer isin() over a long chain of | conditions for cleaner code and better performance.

import pandas as pd
import numpy as np

# 1 million row DataFrame
df = pd.DataFrame({'id': np.random.randint(0, 10000, size=1_000_000)})

allowed = list(range(0, 500))  # 500 allowed IDs

# isin() is the right approach here — fast hash lookup
result = df[df['id'].isin(allowed)]
print(result.shape)  # around (50000, 1)

Real-World Filter: Product Catalogue

Here is a realistic example combining isin() for category filtering and between() for price range filtering — a common pattern in e-commerce analytics. The two filters together select exactly the product subset needed for a targeted promotion.

import pandas as pd

products = pd.DataFrame({
    'name': ['Laptop', 'Mouse', 'Monitor', 'Keyboard', 'Webcam'],
    'category': ['Computing', 'Accessories', 'Displays', 'Accessories', 'Peripherals'],
    'price': [1200, 25, 300, 80, 150]
})

# Products in Accessories or Peripherals, priced 50-200
eligible = products[
    products['category'].isin(['Accessories', 'Peripherals']) &
    products['price'].between(50, 200)
]
print(eligible)
#        name     category  price
# 3  Keyboard  Accessories     80
# 4    Webcam  Peripherals    150

Quick Check

Test your understanding of isin() and between() filters.

Lesson Recap

In this lesson you learned: isin() checks set membership and is faster than chaining multiple == conditions, ~isin() excludes a list of values, and between() filters a closed range with an optional inclusive parameter. Both methods work on numeric, string, and datetime columns. Next up we explore how to select DataFrame columns matching a name pattern.

常见问题解答

「isin() 与 between() 筛选」课时是免费的吗？

是的 — 「isin() 与 between() 筛选」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 Pandas & NumPy Academy 课程的其余内容，请升级到 CoddyKit PRO。 Pandas & NumPy Academy 课程共包含 4 节课。

「isin() 与 between() 筛选」这节课中我会学到什么？

使用 isin() 选择列值属于某个列表的行，或使用 between() 选择列值处于某个数值范围内的行。 你通过在浏览器中直接运行的动手代码来练习 Pandas & NumPy Academy，全天候 AI 导师会在你学习这节课的过程中回答你的问题。

学习 Pandas & NumPy Academy 需要有经验吗？

无需任何先前经验。CoddyKit 上的 Pandas & NumPy Academy 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。 这是第 3 节课，共 4 节。

「isin() 与 between() 筛选」课时需要多长时间？

大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。

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此课程中的所有课时

  1. DataFrames 上的布尔索引
  2. query() 方法
  3. isin() 与 between() 筛选
  4. 按模式选择列
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