如何在Python的DataFrame中按指定步长将指定列的部分值替换为NaN

Hey there! Let's work through this problem together. You want to keep every 3rd row's Lat value starting from the first one, and set the next two rows to NaN—here's a simple, efficient way to do this with pandas:

Step 1: Import Required Libraries #

First, make sure you have pandas and numpy ready:

import pandas as pd
import numpy as np

Step 2: Set Up Your Sample DataFrame #

Let's start with the dataset you provided:

data = {
    'N': [1, 2, 3, 4, 5, 6, 7, 8, 9],
    'Lat': [37.536866, 37.536867, 37.536868, 37.536868, 37.536869, 37.536869, 37.536870, 37.536871, 37.536871],
    'Long': [15.068850] * 9
}
df = pd.DataFrame(data)

Step 3: Apply the Replacement Logic #

We'll use pandas' where() method to retain Lat values only in rows where N modulo 3 equals 1 (since you want to keep rows N=1, 4, 7...). For all other rows, we'll replace Lat with NaN:

df['Lat'] = df['Lat'].where(df['N'] % 3 == 1, np.nan)

Step 4: Verify the Result #

If you print the DataFrame now, you'll get exactly the output you're looking for:

print(df)

Output:

N        Lat       Long
0  1  37.536866  15.068850
1  2        NaN  15.068850
2  3        NaN  15.068850
3  4  37.536868  15.068850
4  5        NaN  15.068850
5  6        NaN  15.068850
6  7  37.536870  15.068850
7  8        NaN  15.068850
8  9        NaN  15.068850

Quick Explanation #

• The where() method keeps the original value when the condition (df['N'] % 3 == 1) is true, and replaces it with np.nan when false.
• Tying the logic directly to your N column makes the code intuitive and easy to tweak if your step pattern changes later.

内容的提问来源于stack exchange，提问作者gcicceri