Python 熊猫：数数
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pandas: count things
提问by Mike Dewar
In the following, male_trips is a big pandas data frame and stations is a small pandas data frame. For each station id I'd like to know how many male trips took place. The following does the job, but takes a long time:
下面，male_trips 是大熊猫数据框，站是小熊猫数据框。对于每个车站 id，我想知道发生了多少男性旅行。以下完成了这项工作，但需要很长时间：
mc = [ sum( male_trips['start_station_id'] == id ) for id in stations['id'] ]
how should I go about this instead?
我应该怎么做呢？
Update! So there were two main approaches: groupby()followed by size(), and the simpler .value_counts(). I did a quick timeit, and the groupbyapproach wins by quite a large margin! Here is the code:
更新！因此有两种主要方法：groupby()其次是size()，以及更简单的.value_counts()。我做了一个快速的timeit，并且该groupby方法以相当大的优势获胜！这是代码：
from timeit import Timer
setup = "import pandas; male_trips=pandas.load('maletrips')"
a = "male_trips.start_station_id.value_counts()"
b = "male_trips.groupby('start_station_id').size()"
Timer(a,setup).timeit(100)
Timer(b,setup).timeit(100)
and here is the result:
结果如下：
In [4]: Timer(a,setup).timeit(100) # <- this is value_counts
Out[4]: 9.709594964981079
In [5]: Timer(b,setup).timeit(100) # <- this is groupby / size
Out[5]: 1.5574288368225098
Note that, at this speed, for exploring data typingvalue_counts is marginally quicker and less remembering!
请注意，以这种速度，探索数据类型value_counts 会稍微快一些，而且记忆力会降低！
采纳答案by Dani Arribas-Bel
I'd do like Vishal but instead of using sum() using size() to get a count of the number of rows allocated to each group of 'start_station_id'. So:
我确实喜欢 Vishal，但不是使用 sum() 使用 size() 来计算分配给每组“start_station_id”的行数。所以：
df = male_trips.groupby('start_station_id').size()
回答by Joran Beasley
回答by vgoklani
how long would this take:
这需要多长时间：
df = male_trips.groupby('start_station_id').sum()
回答by Arthur G
edit: after seeing in the answer above that isinand value_countsexist (and value_countseven comes with its own entry in pandas.core.algorithmand also isinisn't simply np.in1d) I updated the three methods below
编辑：在上面的答案中看到isin并value_counts存在后（value_counts甚至带有自己的条目pandas.core.algorithm，也isin不仅仅是简单的np.in1d）我更新了下面的三种方法
male_trips.start_station_id[male_trips.start_station_id.isin(station.id)].value_counts()
You could also do an inner join on stations.id:
pd.merge(male_trips, station, left_on='start_station_id', right_on='id')followed by value_counts.
Or:
您还可以对stations.id:pd.merge(male_trips, station, left_on='start_station_id', right_on='id')进行内部连接，
然后是value_counts. 或者：
male_trips.set_index('start_station_id, inplace=True)
station.set_index('id, inplace=True)
male_trips.ix[male_trips.index.intersection(station.index)].reset_index().start_station_id.value_counts()
If you have the time I'd be interested how this performs differently with a huge DataFrame.
如果您有时间，我会感兴趣如何使用巨大的 DataFrame 来执行不同的操作。
回答by ely
My answer below works in Pandas 0.7.3. Not sure about the new releases.
我下面的答案适用于 Pandas 0.7.3。不确定新版本。
This is what the pandas.Series.value_countsmethod is for:
这是该pandas.Series.value_counts方法的用途：
count_series = male_trips.start_station_id.value_counts()
It should be straight-forward to then inspect count_seriesbased on the values in stations['id']. However, if you insist on onlyconsidering those values, you could do the following:
然后count_series根据 中的值进行检查应该很简单stations['id']。但是，如果您坚持只考虑这些值，则可以执行以下操作：
count_series = (
male_trips[male_trips.start_station_id.isin(stations.id.values)]
.start_station_id
.value_counts()
)
and this will only give counts for station IDs actually found in stations.id.
并且这只会给出实际在stations.id.
