pandas df.loc[z,x]=y 如何提高速度? [英] pandas df.loc[z,x]=y how to improve speed?
问题描述
我已经确定了一个 pandas 命令
timeseries.loc[z, x] = y负责迭代中花费的大部分时间.现在我正在寻找更好的方法来加速它.循环甚至不包括 50k 个元素(生产目标是 ~250k 或更多)，但已经需要一个悲伤的 20 秒.
这是我的代码(忽略上半部分，它只是计时助手)
def populateTimeseriesTable(df, observable, timeseries):"""遍历 df 的所有行和将 observable 放入时间序列在正确的行(符号)、列(tsMean)."""print "len(df.index)=", len(df.index) # 显示行数全球 bf, tbf = time.time() # 将之前"设置为现在t = dict([(i,0) for i in range(5)]) # 用零填充类别时间定义 T(i):"""计时助手:将经过的时间添加到类别i".然后将之前"设置为现在."""全球 bf, tt[i] = t[i] + (time.time()-bf)bf = time.time()for i in df.index: # 这是慢循环bf = time.time()sym = df["符号"][i]T(0)tsMean = df["tsMean"][i]T(1)tsMean = tsFormatter(tsMean)T(2)o = df[可观察的][i]T(3)timeseries.loc[sym, tsMean] = oT(4)从 pprint 导入 pprint打印每个命令所需的时间(总计 = %.1f 秒):" % sum(t.values())pprint (t)返回时间序列有(不重要，不慢)
def tsFormatter(ts):作为人类可读的字符串，最多只有整秒"return time.strftime("%Y-%m-%d %H:%M:%S", time.gmtime(ts))..
--> 待优化代码在for循环中.
(T, 和 t 只是辅助函数和字典，用于计时.)
我已经为每一步计时.绝大多数时间:
len(df.index)= 47160每个命令所需的时间(总计 = 20.2 秒):{0: 1.102,1:0.741，2:0.243，3:0.792，4:17.371}花费在最后一步
timeseries.loc[sym, tsMean] = o我已经下载并安装了 pypy - 但遗憾的是，它还不支持 pandas.
任何想法如何加快填充二维数组?
谢谢！
<小时>抱歉，没有提到 - 'timeseries' 也是一个数据框:
timeseries = pd.DataFrame({"name":titles}, index=index)更新: 从 Pandas 0.20.1 开始
时序数据作为 DF:
在[88]中:r出[88]:方法计时0 位置 502.01 iloc 394.02 在 66.83 美元 32.94 ix_label 64.85 ix_integer 503.0在 [89] 中:r.to_dict()出[89]:{'方法':{0:'loc'，1:'iloc'，2:'在'，3:'iat'，4: 'ix_label',5: 'ix_integer'},时间":{0:502.0，1:394.0，2: 66.799999999999997,3: 32.899999999999999,4: 64.799999999999997,5:503.0}}绘图
ax = sns.barplot(data=r, x='method', y='timing')ax.tick_params(labelsize=16)[ax.annotate(str(round(p.get_height(),2)), (p.get_x() + 0.2, p.get_height() + 5)) for p in ax.patches]ax.set_xlabel('索引方法', size=20)ax.set_ylabel('计时(微秒)', size=20)I have identified one pandas command
timeseries.loc[z, x] = y
to be responsible for most of the time spent in an iteration. And now I am looking for better approaches to accelerate it. The loop covers not even 50k elements (and production goal is ~250k or more), but already needs a sad 20 seconds.
Here is my code (ignore the top half, it is just the timing helper)
def populateTimeseriesTable(df, observable, timeseries):
"""
Go through all rows of df and
put the observable into the timeseries
at correct row (symbol), column (tsMean).
"""
print "len(df.index)=", len(df.index) # show number of rows
global bf, t
bf = time.time() # set 'before' to now
t = dict([(i,0) for i in range(5)]) # fill category timing with zeros
def T(i):
"""
timing helper: Add passed time to category 'i'. Then set 'before' to now.
"""
global bf, t
t[i] = t[i] + (time.time()-bf)
bf = time.time()
for i in df.index: # this is the slow loop
bf = time.time()
sym = df["symbol"][i]
T(0)
tsMean = df["tsMean"][i]
T(1)
tsMean = tsFormatter(tsMean)
T(2)
o = df[observable][i]
T(3)
timeseries.loc[sym, tsMean] = o
T(4)
from pprint import pprint
print "times needed (total = %.1f seconds) for each command:" % sum(t.values())
pprint (t)
return timeseries
With (not important, not slow)
def tsFormatter(ts):
"as human readable string, only up to whole seconds"
return time.strftime("%Y-%m-%d %H:%M:%S", time.gmtime(ts))
. .
--> The to-be-optimized code is in the for-loop.
(T, and t are just helper function & dict, for the timing.)
I have timed every step. The vast majority of time:
len(df.index)= 47160
times needed (total = 20.2 seconds) for each command:
{0: 1.102,
1: 0.741,
2: 0.243,
3: 0.792,
4: 17.371}
is spent in the last step
timeseries.loc[sym, tsMean] = o
I have already downloaded and install pypy - but sadly, that doesn't support pandas yet.
Any ideas how to speed up populating a 2D array?
Thanks!
Edit: Sorry, hadn't mentioned - 'timeseries' is a dataframe too:
timeseries = pd.DataFrame({"name": titles}, index=index)
UPDATE: starting from Pandas 0.20.1 the .ix indexer is deprecated, in favor of the more strict .iloc and .loc indexers.
=====================================================================
@jezrael has provided an interesting comparison and i decided to repeat it using more indexing methods and against 10M rows DF (actually the size doesn't matter in this particular case):
setup:
In [15]: df = pd.DataFrame(np.random.rand(10**7, 5), columns=list('abcde'))
In [16]: df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10000000 entries, 0 to 9999999
Data columns (total 5 columns):
a float64
b float64
c float64
d float64
e float64
dtypes: float64(5)
memory usage: 381.5 MB
In [17]: df.shape
Out[17]: (10000000, 5)
Timing:
In [37]: %timeit df.loc[random.randint(0, 10**7), 'b']
1000 loops, best of 3: 502 µs per loop
In [38]: %timeit df.iloc[random.randint(0, 10**7), 1]
1000 loops, best of 3: 394 µs per loop
In [39]: %timeit df.at[random.randint(0, 10**7), 'b']
10000 loops, best of 3: 66.8 µs per loop
In [41]: %timeit df.iat[random.randint(0, 10**7), 1]
10000 loops, best of 3: 32.9 µs per loop
In [42]: %timeit df.ix[random.randint(0, 10**7), 'b']
10000 loops, best of 3: 64.8 µs per loop
In [43]: %timeit df.ix[random.randint(0, 10**7), 1]
1000 loops, best of 3: 503 µs per loop
Results as a bar plot:
Timing data as DF:
In [88]: r
Out[88]:
method timing
0 loc 502.0
1 iloc 394.0
2 at 66.8
3 iat 32.9
4 ix_label 64.8
5 ix_integer 503.0
In [89]: r.to_dict()
Out[89]:
{'method': {0: 'loc',
1: 'iloc',
2: 'at',
3: 'iat',
4: 'ix_label',
5: 'ix_integer'},
'timing': {0: 502.0,
1: 394.0,
2: 66.799999999999997,
3: 32.899999999999999,
4: 64.799999999999997,
5: 503.0}}
Plotting
ax = sns.barplot(data=r, x='method', y='timing')
ax.tick_params(labelsize=16)
[ax.annotate(str(round(p.get_height(),2)), (p.get_x() + 0.2, p.get_height() + 5)) for p in ax.patches]
ax.set_xlabel('indexing method', size=20)
ax.set_ylabel('timing (microseconds)', size=20)
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