如何在Pandas中为DataFrame新增列以选中指定行区间的行？

First, let's start with your sample DataFrame for reference:

import pandas as pd

data = {'currency': ['Euro', 'Euro', 'Euro', 'Dollar', 'Dollar', 'Yen', 'Yen', 'Yen', 'Pound', 'Pound', 'Pound', 'Pesos', 'Pesos'], 
        'cost': [34, 67, 32, 29, 48, 123, 23, 45, 78, 86, 23, 45, 67]}
df = pd.DataFrame(data, columns=['currency', 'cost'])

Option 1: Specify Rows by Index #

If you know the integer indices of the start and end rows, you can directly slice the DataFrame to mark the range.

Example: #

Suppose you want to mark rows between index 3 (Dollar, 29) and index 7 (Yen, 45) inclusive:

# Define your input parameters (start and end indices)
start_idx = 3
end_idx = 7

# Initialize the new column with False
df['in_range'] = False

# Set rows between start and end to True
# Use min and max to handle cases where start_idx > end_idx
df.loc[min(start_idx, end_idx):max(start_idx, end_idx), 'in_range'] = True

This will set in_range to True for rows 3 through 7.

Option 2: Specify Rows by Column Values #

If you don't know the indices, but can identify the start and end rows using column values (e.g., specific currency and cost combinations), first find their indices, then apply the same logic as above.

Example: #

Suppose you want to start at the row where currency == 'Dollar' and cost == 29, and end at currency == 'Yen' and cost == 45:

# Find start and end indices based on conditions
start_idx = df[(df['currency'] == 'Dollar') & (df['cost'] == 29)].index[0]
end_idx = df[(df['currency'] == 'Yen') & (df['cost'] == 45)].index[0]

# Create the in_range column
df['in_range'] = False
df.loc[min(start_idx, end_idx):max(start_idx, end_idx), 'in_range'] = True

Useful Notes: #

• The min and max ensure the code works even if you accidentally pass the start index as larger than the end index.
• If there are multiple rows matching your condition (e.g., multiple 'Dollar' rows with cost 29), index[0] picks the first match. Adjust this to index[-1] if you need the last matching row instead.
• To make this reusable, wrap it in a function:

def mark_row_range(df, start_condition, end_condition):
    start_idx = df[start_condition].index[0]
    end_idx = df[end_condition].index[0]
    df['in_range'] = False
    df.loc[min(start_idx, end_idx):max(start_idx, end_idx), 'in_range'] = True
    return df

# Usage example:
df = mark_row_range(df, 
                    start_condition=(df['currency'] == 'Dollar') & (df['cost'] == 29), 
                    end_condition=(df['currency'] == 'Yen') & (df['cost'] == 45))

内容的提问来源于stack exchange，提问作者Dsto4