Question

以下是我们要处理的数据示例：

df_size = 1000000
df_random = pd.DataFrame({'boat_id' : np.random.choice(range(300),df_size),
                       'X' :np.random.random_integers(0,1000,df_size),
                       'target_Y' :np.random.random_integers(0,10,df_size)})

    X   boat_id target_Y
0   482     275       6
1   705     245       4
2   328     102       6
3   631     227       6
4   234     236       8
...

我想获得这样的输出：

           X0      X1      X2      X3       X4     X5     X6    X7         X8      X9   target_Y    boat_id
40055   684.0   692.0   950.0   572.0   442.0   850.0   75.0    140.0   382.0   576.0   0.0             1
40056   178.0   949.0   490.0   777.0   335.0   559.0   397.0   729.0   701.0   44.0    4.0             1
40057   21.0    818.0   341.0   577.0   612.0   57.0    303.0   183.0   519.0   357.0   0.0             1
40058   501.0   1000.0  999.0   532.0   765.0   913.0   964.0   922.0   772.0   534.0   1.0             2
40059   305.0   906.0   724.0   996.0   237.0   197.0   414.0   171.0   369.0   299.0   8.0             2
40060   408.0   796.0   815.0   638.0   691.0   598.0   913.0   579.0   650.0   955.0   2.0             3
40061   298.0   512.0   247.0   824.0   764.0   414.0   71.0    440.0   135.0   707.0   9.0             4
40062   535.0   687.0   945.0   859.0   718.0   580.0   427.0   284.0   122.0   777.0   2.0             4
40063   352.0   115.0   228.0   69.0    497.0   387.0   552.0   473.0   574.0   759.0   3.0             4
40064   179.0   870.0   862.0   186.0   25.0    125.0   925.0   310.0   335.0   739.0   7.0             4
...

我做了下面的代码，但这是减慢速度的方法。分组，用枚举剪切，转置，然后将结果合并到一个熊猫数据框中。

start_time = time.time()

N = 10
col_names = map(lambda x: 'X'+str(x), range(N))
compil = pd.DataFrame(columns = col_names)
i = 0 

# I group by boat ID
for boat_id, df_boat in df_random.groupby('boat_id'):

    # then I cut every 50 line
    for (line_number, (index, row)) in enumerate(df_boat.iterrows()):
        if line_number%5 == 0:                                          
            compil_new_line_X = list(df_boat.iloc[line_number-N:line_number,:]["X"])

            # filter to avoid issues at the start and end of the columns
            if len (compil_new_line_X ) == N:

                compil.loc[i,col_names] = compil_new_line_X                                           
                compil.loc[i, 'target_Y'] = row['target_Y'] 
                compil.loc[i,'boat_id'] = row['boat_id']
                i += 1

print("Total  %s seconds" % (time.time() - start_time))

总232.947000027秒

我的问题是：

如何在每“ x行数”处执行操作？然后合并结果？是否存在将这种操作向量化的方法？

Answer 1

以下是将计算时间缩短35％的解决方案。它使用“ groupby”作为“ boat_ID”，然后使用“ groupby.apply”将组划分为小块。然后最后申请创建新行。我们可能仍然可以改善它。

df_size = 1000000
df_random = pd.DataFrame({'boat_id' : np.random.choice(range(300),df_size),
                       'X' :np.random.random_integers(0,1000,df_size),
                       'target_Y' :  np.random.random_integers(0,10,df_size)})

start_time = time.time()
len_of_chunks = 10
col_names = map(lambda x: 'X'+str(x), range(N))+['boat_id', 'target_Y']


def prepare_data(group):
    # this function create the new line we will put in 'compil'
    info_we_want_to_keep =['boat_id', 'target_Y']
    info_and_target = group.tail(1)[info_we_want_to_keep].values

    k = group["X"]
    return np.hstack([k.values, info_and_target[0]]) # this create the new line we will put in 'compil'


# we group by ID (boat)
# we divide in chunk of len "len_of_chunks"
# we apply prepare data from each chunk
groups =  df_random.groupby('boat_id').apply(lambda x: x.groupby(np.arange(len(x)) // len_of_chunks).apply(prepare_data))

# we reset index
# we take the '0' columns containing valuable info
# we put info in a new 'compil' dataframe
# we drop uncomplet line ( generated by chunk < len_of_chunks )
compil =  pd.DataFrame(groups.reset_index()[0].values.tolist(), columns= col_names).dropna()


print("Total  %s seconds" % (time.time() - start_time))

总153.781999826秒

如何使用矢量化对一组大熊猫数据框的结果进行分组，剪切，转置然后合并

1 个答案: