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python - For 使用 pandas 按索引循环遍历多行

问题描述

使用 train_data_sample 中的以下数据和以下代码，如何遍历每个索引纬度和经度？（请参阅下面的预期结果）

   latitude longitude price
0   55.6632 12.6288 2595000
1   55.6637 12.6291 2850000
2   55.6637 12.6291 2850000
3   55.6632 12.6290 3198000
4   55.6632 12.6290 2995000
5   55.6638 12.6294 2395000
6   55.6637 12.6291 2995000
7   55.6642 12.6285 4495000
8   55.6632 12.6285 3998000
9   55.6638 12.6294 3975000

from numpy import cos, sin, arcsin, sqrt
from math import radians

def haversine(row):
   
    for index in train_data_sample.index:
        lon1 = train_data_sample["longitude"].loc[train_data_sample.index==index]
        lat1 = train_data_sample["latitude"].loc[train_data_sample.index==index]
        lon2 = row['longitude']
        lat2 = row['latitude']
        lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])
        dlon = lon2 - lon1 
        dlat = lat2 - lat1 
        a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2
        c = 2 * arcsin(sqrt(a)) 
        km = 6367 * c
    return km

def insert_dist(df):
    df["distance"+str(index)] = df.apply(lambda row: haversine(row), axis=1)
    return df

print(insert_dist(train_data_sample))

这是索引 0 的结果。它查看索引 0 相对于其他每一行的坐标，并返回以米为单位的距离。因此索引 0 和 1 的坐标之间的距离约为 50 米。

latitude    longitude   price   distance0
0   55.6632 12.6288 2595000    0.000000
1   55.6637 12.6291 2850000    0.058658
2   55.6637 12.6291 2850000    0.058658
3   55.6632 12.6290 3198000    0.012536
4   55.6632 12.6290 2995000    0.012536
5   55.6638 12.6294 2395000    0.076550
6   55.6637 12.6291 2995000    0.058658
7   55.6642 12.6285 4495000    0.112705
8   55.6632 12.6285 3998000    0.018804
9   55.6638 12.6294 3975000    0.076550

最终结果不仅应该返回 distance0，还应该返回 distance1、distance2 等。

标签： pythonpandas

解决方案

看起来你让事情变得比必要的复杂一些。通过在另一个 for 循环中嵌套一个 for 循环，您可以以更直接的方式实现您想要的。

from numpy import cos, sin, arcsin, sqrt
from math import radians
import pandas as pd
import numpy as np


# recreate your dataframe
data = [[55.6632, 12.6288, 2595000],
        [55.6637, 12.6291, 2850000],
        [55.6637, 12.6291, 2850000], 
        [55.6632, 12.6290, 3198000]]

data = np.array(data)

train_data_sample = pd.DataFrame(data, columns = ["latitude", "longitude", "price"])


# copied  "distance calculating" code here
def GetDistance(lon1, lat1, lon2, lat2):
    lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])
    dlon = lon2 - lon1 
    dlat = lat2 - lat1 
    a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2
    c = 2 * arcsin(sqrt(a)) 
    km = 6367 * c
    return km

# loop over every row with iterrows
for index, row in train_data_sample.iterrows():
    
    distances = []
    
    lat1, lon1 = row[["longitude", "longitude"]]
    
    # loop again over every row with iterrows
    for index_2, row_2 in train_data_sample.iterrows():
        lat2, lon2 = row_2[["longitude", "longitude"]]
        # get the distance
        distances.append( GetDistance(lon1, lat1, lon2, lat2) )
        
    # add the column to the dataframe    
    train_data_sample["distance"+str(index)] = distances

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