[英]searching in a pandas df that contains ranges
我有一个熊猫df,其中包含2列“开始”和“结束”(均为整数)。 我想要一种有效的方法来搜索行,以便由行[start,end]表示的范围包含特定值。
另外两个注意事项:
例如:
start end
0 7216 7342
1 7343 7343
2 7344 7471
3 7472 8239
4 8240 8495
和查询
[7215,7217,7344]
将导致
{7217: 0, 7344: 2}
谢谢!
蛮力解决方案,尽管可以使用很多改进。
df = pd.DataFrame({'start': [7216, 7343, 7344, 7472, 8240],
'end': [7342, 7343, 7471, 8239, 8495]})
search = [7215, 7217, 7344]
res = {}
for i in search:
mask = (df.start <= i) & (df.end >= i)
idx = df[mask].index.values
if len(idx):
res[i] = idx[0]
print res
产量
{7344: 2, 7217: 0}
此新解决方案可能具有更好的性能。 但是有一个限制,只有在范围之间没有间隙(如提供的示例)中一样,它才有效。
# Test data
df = pd.DataFrame({'start': [7216, 7343, 7344, 7472, 8240],
'end': [7342, 7343, 7471, 8239, 8495]}, columns=['start','end'])
query = [7215,7217,7344]
# Reshaping the original DataFrame
df = df.reset_index()
df = pd.concat([df['start'], df['end']]).reset_index()
df = df.set_index(0).sort_index()
# Creating a DataFrame with a continuous index
max_range = max(df.index) + 1
min_range = min(df.index)
s = pd.DataFrame(index=range(min_range,max_range))
# Joining them
s = s.join(df)
# Filling the gaps
s = s.fillna(method='backfill')
# Then a simple selection gives the result
s.loc[query,:].dropna().to_dict()['index']
# Result
{7217: 0.0, 7344: 2.0}
# Test data
df = pd.DataFrame({'start': [7216, 7343, 7344, 7472, 8240],
'end': [7342, 7343, 7471, 8239, 8495]}, columns=['start','end'])
# Constructing a DataFrame containing the query numbers
query = [7215,7217,7344]
result = pd.DataFrame(np.tile(query, (len(df), 1)), columns=query)
# Merging the data and the query
df = pd.concat([df, result], axis=1)
# Making the test
df = df.apply(lambda x: (x >= x['start']) & (x <= x['end']), axis=1).loc[:,query]
# Keeping only values found
df = df[df==True]
df = df.dropna(how='all', axis=(0,1))
# Extracting to the output format
result = df.to_dict('split')
result = dict(zip(result['columns'], result['index']))
# The result
{7217: 0, 7344: 2}
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