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我有两个数据框。

一个结构如下：

id,value

第二个：

id, neighbor_1, neighbor_2, neighbor_3, neighbor_4, neighbor_5, ...

现在我想id在neighborhood数据框中查找每个对应列的每个neighbor，并计算一个 id 的所有邻居，以找到邻域中的聚合总和总值最大的 id。sumvaluetotal sum

import pandas as pd
from h3 import h3
k=1

df = pd.DataFrame({'x': {0: 16,
  1: 17,
  2: 18,
  3: 19,
  4: 20},
 'y': {0: 48,
  1: 49,
  2: 50,
  3: 51,
  4: 52},
 'value': {0: 2.0, 1: 4.0, 2: 100.0, 3: 40.0, 4: 500.0},
 'id': {0: '891e15b706bffff',
  1: '891e15b738fffff',
  2: '891e15b714fffff',
  3: '891e15b44c3ffff',
  4: '891e15b448bffff'}})

display(df)

df_neighbors = df[['id']]
df_neighbors.index = df_neighbors['id']
df_neighbors = df_neighbors['id'].apply(lambda x: pd.Series(list(h3.k_ring(x,k))))
display(df_neighbors)

在 pandas 中计算此类问题（迭代连接和聚合）的有效方法是什么？

一个天真的解决方案：

import pandas as pd
from h3 import h3
import numpy as np
k=2

df = pd.DataFrame({'x': {0: 16,
  1: 17,
  2: 18,
  3: 19,
  4: 20},
 'y': {0: 48,
  1: 49,
  2: 50,
  3: 51,
  4: 52},
 'value': {0: 2.0, 1: 4.0, 2: 100.0, 3: 40.0, 4: 500.0},
 'id': {0: '891e15b706bffff',
  1: '891e15b738fffff',
  2: '891e15b714fffff',
  3: '891e15b44c3ffff',
  4: '891e15b448bffff'}})

display(df)

df_neighbors = df[['id']]
df_neighbors.index = df_neighbors['id']
df_neighbors = df_neighbors['id'].apply(lambda x: pd.Series(list(h3.k_ring(x,k))))
display(df_neighbors)

joined = df.merge(df_neighbors.reset_index(), left_on='id', right_on='id', how='left')#.drop(['id_neighbors'], axis=1)
# display(joined)

for c in joined[df_neighbors.columns].columns:
    joined[f'sum_of_{c}'] = joined.groupby([c]).value.transform(pd.Series.sum)

xx = [f'sum_of_{c}' for c in joined[df_neighbors.columns].columns]
joined['total_value_sum'] = joined[xx].sum(axis=1)
display(joined)

maximal_neighborhood = joined[df_neighbors.columns].iloc[joined.total_value_sum.argmax()]
display(maximal_neighborhood)

max_neighborhood_raw_elements = df[df['id'].isin(maximal_neighborhood)]
display(max_neighborhood_raw_elements)

avg_y_lat = np.average(max_neighborhood_raw_elements.y, weights=max_neighborhood_raw_elements.value)
avg_x_long = np.average(max_neighborhood_raw_elements.x, weights=max_neighborhood_raw_elements.value)

print(f'(x,y): ({avg_x_long},{avg_y_lat})')
4

1 回答 1

0

使用stackI 可以在没有迭代的情况下进行操作，只有两个groupby：

df_neighbors = df[['id']]
df_neighbors.index = df_neighbors['id']
df_neighbors = df_neighbors['id'].apply(lambda x: pd.Series(list(h3.k_ring(x,k)))).stack().to_frame('hexk').reset_index()#.reset_index(1, drop=True).reset_index()
#display(df_neighbors)

# df_neighbors.level_1.value_counts()

joined = df.merge(df_neighbors, left_on='id', right_on='id', how='left')#.drop(['id_neighbors'], axis=1)
#display(joined.head())

joined[f'sum_of_hexk'] = joined.groupby(['hexk']).value.transform(pd.Series.sum)
joined[f'total_value_sum'] = joined.groupby(['id']).sum_of_hexk.transform(pd.Series.sum)

#display(joined)
display(joined.total_value_sum.unique())

maximal_neighborhood = joined[joined.id == joined.iloc[joined.total_value_sum.argmax()].id].hexk
#display(maximal_neighborhood)
max_neighborhood_raw_elements = df[df['id'].isin(maximal_neighborhood)]
display(max_neighborhood_raw_elements)

avg_y_lat = np.average(max_neighborhood_raw_elements.y, weights=max_neighborhood_raw_elements.value)
avg_x_long = np.average(max_neighborhood_raw_elements.x, weights=max_neighborhood_raw_elements.value)

print(f'(x,y): ({avg_x_long},{avg_y_lat})')
于 2020-02-29T07:49:44.340 回答