如何在Python中基于索引执行groupby并求和：按索引10分组

Got it, let's walk through how to group your Count column by the specified index ranges (0-10 and 11-20) and calculate the sums—super straightforward with pandas, which is the standard tool for this kind of data manipulation.

Step 1: Set up your sample data #

First, let's recreate your dataset so you can test the code directly:

import pandas as pd

# Build the Count data as described
count_values = {
    1: 5, 2: 10, 3: 15,
    **{i: 0 for i in range(4, 11)},  # Indexes 4-10: 0
    **{i: 0 for i in range(11, 15)}, # Indexes 11-14: 0
    15: 20
}

# Convert to a pandas Series (we can leave missing indexes as-is since their value is 0)
count_series = pd.Series(count_values)

Step 2: Group and sum using two simple methods #

Method 1: Lambda function for quick binary grouping

If you only have two simple ranges, a lambda function in groupby is the quickest way:

# Group by index range and sum
result = count_series.groupby(lambda idx: '0-10' if idx <= 10 else '11-20').sum()

print(result)

This will output exactly what you need:

0-10     30
11-20    20
dtype: int64

Method 2: pd.cut for more flexible intervals

If you ever need to add more index ranges later, pd.cut is more scalable. We'll define bins to map indexes to groups:

# Define bins (use -1 to ensure index 0 is included, since pd.cut is left-open by default)
bins = [-1, 10, 20]
# Names for our groups
group_labels = ['0-10', '11-20']

# Group and sum
result = count_series.groupby(pd.cut(count_series.index, bins=bins, labels=group_labels)).sum()

print(result)

This gives the same output as the first method, but it's easier to adjust if you need to split into more ranges (like 0-5, 6-10, 11-15, etc.) later.

Why this works #

• For the lambda method: We're checking each index directly—if it's ≤10, it goes to the '0-10' group, else to '11-20'. Missing indexes (like 0, 16-20) are treated as having a value of 0, so they don't affect the sum.
• For pd.cut: We create bins that capture the exact index ranges we want. The -1 in the first bin ensures index 0 is included (since pd.cut defaults to left-exclusive, right-inclusive intervals).

内容的提问来源于stack exchange，提问作者Anand