Question

我有一个如下的数据框：

      payeeId    latHome   longHome     Total_Amnt
 0  193fde722     0.000000   0.000000        15.0
 1  4d8ecb2b5c   28.425515  77.097547        10.0
 2  2c3ea738     28.542923  77.253164        20.0
 3  2961f3e8     28.542898  77.253162        10.0
 4  5cda3d3763   28.461630  77.031944    129000.0
 5  3cb02ccbfc   26.180680  91.740042       220.0
 6  79918aae03    0.000000   0.000000      1760.0

我正在尝试计算两个连续的 latHome 和 longHome 之间的距离。为此，我遵循了 this SO post 。下面是我正在使用然后应用的函数：

def haversine(lat1, lon1, lat2, lon2, to_radians=True, earth_radius=6371):
   if to_radians:
      lat1, lon1, lat2, lon2 = np.radians([lat1, lon1, lat2, lon2])
   a = np.sin((lat2-lat1)/2.0)**2 + \
       np.cos(lat1) * np.cos(lat2) * np.sin((lon2-lon1)/2.0)**2
   return earth_radius * 2 * np.arcsin(np.sqrt(a))

df_c['Dist_p'] = df_c.apply(haversine(lat1=df_c['latHome'].astype(float).shift(), \
                            lon1 = df_c['longHome'].astype(float).shift(), \
                            lat2 = df_c['latHome'].astype(float), \
                            lon2=df_c['longHome'].astype(float)))

但我收到以下错误：

ValueError: no results

当我直接使用此函数时，即没有 apply 时，我也会收到以下错误。

File "<ipython-input-1-3a6c757b499d>", line 13, in haversine
lat1, lon1, lat2, lon2 = np.radians([lat1, lon1, lat2, lon2])

 TypeError: loop of ufunc does not support argument 0 of type Series which has no callable 
 radians method

任何线索将不胜感激。

Answer 1

有多个用于计算距离的库和函数。我用过 geopy
键是对齐数据/行以传递给选定的距离函数。这个用例是 shift(-1)，但是将 NaN 传递给这个距离函数是无效的，因此没有下一行的最后一行默认为 (0,0)

import geopy.distance

df = pd.read_csv(io.StringIO("""      payeeId    latHome   longHome     Total_Amnt
 0  193fde722     0.000000   0.000000        15.0
 1  4d8ecb2b5c   28.425515  77.097547        10.0
 2  2c3ea738     28.542923  77.253164        20.0
 3  2961f3e8     28.542898  77.253162        10.0
 4  5cda3d3763   28.461630  77.031944    129000.0
 5  3cb02ccbfc   26.180680  91.740042       220.0
 6  79918aae03    0.000000   0.000000      1760.0"""),sep="\s+",)


# prep tuples to pass to geopy.distance
df['Dist_p'] = df.loc[:, ["latHome", "longHome"]].join(
    df.loc[:, ["latHome", "longHome"]].shift(-1).fillna(0), rsuffix="_2"
).apply(
    lambda r: geopy.distance.geodesic(
        (r["latHome"], r["longHome"]), (r["latHome_2"], r["longHome_2"])
    ).km,
    axis=1,
)

<头>

	payeeId	latHome	longHome	Total_Amnt	Dist_p
0	193fde722	0	0	15	8753.19
1	4d8ecb2b5c	28.4255	77.0975	10	20.0375
2	2c3ea738	28.5429	77.2532	20	0.00277761
3	2961f3e8	28.5429	77.2532	10	23.4558
4	5cda3d3763	28.4616	77.0319	129000	1476.46
5	3cb02ccbfc	26.1807	91.74	220	10189.4
6	79918aae03	0	0	1760	0

计算从大熊猫数据框中给出的经纬度的距离

1 个答案: