<p>您可以使用:</p>
<pre><code>from math import radians, cos, sin, asin, sqrt
def haversine(lon1, lat1, lon2, lat2):
lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])
# haversine formula
dlon = lon2 - lon1
dlat = lat2 - lat1
a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2
c = 2 * asin(sqrt(a))
r = 6371 # Radius of earth in kilometers. Use 3956 for miles
return c * r
</code></pre>
<p>首先需要与<a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.merge.html" rel="nofollow noreferrer">^{<cd1>}</a>交叉联接,通过<a href="http://pandas.pydata.org/pandas-docs/stable/indexing.html#boolean-indexing" rel="nofollow noreferrer">^{<cd4>}</a>删除<code>city_x</code>和{<cd3>}中具有相同值的行:</p>
^{pr2}$
<p>然后应用<a href="https://stackoverflow.com/questions/4913349/haversine-formula-in-python-bearing-and-distance-between-two-gps-points">haversine</a>函数:</p>
<pre><code>df['dist'] = df.apply(lambda row: haversine(row['lng_x'],
row['lat_x'],
row['lng_y'],
row['lat_y']), axis=1)
</code></pre>
<p>过滤距离:</p>
<pre><code>df = df[df.dist < 500]
print (df)
city_x lat_x lng_x tmp city_y lat_y lng_y dist
1 Berlin 52.52437 13.41053 1 Potsdam 52.39886 13.06566 27.215704
2 Berlin 52.52437 13.41053 1 Hamburg 53.57532 10.01534 255.223782
3 Potsdam 52.39886 13.06566 1 Berlin 52.52437 13.41053 27.215704
5 Potsdam 52.39886 13.06566 1 Hamburg 53.57532 10.01534 242.464120
6 Hamburg 53.57532 10.01534 1 Berlin 52.52437 13.41053 255.223782
7 Hamburg 53.57532 10.01534 1 Potsdam 52.39886 13.06566 242.464120
</code></pre>
<p>最后创建<code>list</code>或使用<code>groupby</code>获得{<cd6>}:</p>
<pre><code>df1 = df.groupby('city_x')['city_y'].apply(list)
print (df1)
city_x
Berlin [Potsdam, Hamburg]
Hamburg [Berlin, Potsdam]
Potsdam [Berlin, Hamburg]
Name: city_y, dtype: object
df2 = df.groupby('city_x')['city_y'].size()
print (df2)
city_x
Berlin 2
Hamburg 2
Potsdam 2
dtype: int64
</code></pre>
<p>也可以使用<a href="https://stackoverflow.com/a/29546836/2901002">^{<cd8>}</a>:</p>
<pre><code>def haversine_np(lon1, lat1, lon2, lat2):
"""
Calculate the great circle distance between two points
on the earth (specified in decimal degrees)
All args must be of equal length.
"""
lon1, lat1, lon2, lat2 = map(np.radians, [lon1, lat1, lon2, lat2])
dlon = lon2 - lon1
dlat = lat2 - lat1
a = np.sin(dlat/2.0)**2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon/2.0)**2
c = 2 * np.arcsin(np.sqrt(a))
km = 6367 * c
return km
df['tmp'] = 1
df = pd.merge(df,df,on='tmp')
df = df[df.city_x != df.city_y]
#print (df)
df['dist'] = haversine_np(df['lng_x'],df['lat_x'],df['lng_y'],df['lat_y'])
city_x lat_x lng_x tmp city_y lat_y lng_y dist
1 Berlin 52.52437 13.41053 1 Potsdam 52.39886 13.06566 27.198616
2 Berlin 52.52437 13.41053 1 Hamburg 53.57532 10.01534 255.063541
3 Potsdam 52.39886 13.06566 1 Berlin 52.52437 13.41053 27.198616
5 Potsdam 52.39886 13.06566 1 Hamburg 53.57532 10.01534 242.311890
6 Hamburg 53.57532 10.01534 1 Berlin 52.52437 13.41053 255.063541
7 Hamburg 53.57532 10.01534 1 Potsdam 52.39886 13.06566 242.311890
</code></pre>