在 Python Pandas Dataframe 中动态添加列的数据处理

我有以下问题。 假设这是我的 CSV

id f1 f2 f3
1  4  5  5
1  3  1  0
1  7  4  4
1  4  3  1
1  1  4  6
2  2  6  0
..........

所以，我有可以按 id 分组的行。 我想创建一个如下所示的 csv 作为输出。

f1 f2 f3 f1_n f2_n f3_n f1_n_n f2_n_n f3_n_n f1_t f2_t f3_t
4  5  5   3   1    0    7      4      4      1   4     6  

因此，我希望能够选择要转换为列的行数（始终从 id 的第一行开始）。在本例中，我抓取了 3 行。 然后，我还将跳过一行或多行（在本例中仅跳过一次），以从同一 id 组的最后一行获取最终列。由于某些原因，我想使用数据框。

经过3-4个小时的奋战。我找到了如下所示的解决方案。 但我的解决方案非常慢。我有大约 700,000 行，可能有大约 70,000 组 id。上面的 model=3 代码在我的 4GB 4 核 Lenovo 上花费了将近一个小时。我需要转到 model = 也许是 10 或 15。我仍然是 Python 新手，我确信可以进行一些更改来加快速度。有人可以深入解释我如何改进代码吗？

万分感谢。

model : 要抓取的行数

# train data frame from reading the csv
train = pd.read_csv(filename)

# Get groups of rows with same id
csv_by_id = train.groupby('id')

modelTarget = { 'f1_t','f2_t','f3_t'}

# modelFeatures is a list of features I am interested in the csv. 
    # The csv actually has hundreds
modelFeatures = { 'f1, 'f2' , 'f3' }

coreFeatures = list(modelFeatures) # cloning 


selectedFeatures = list(modelFeatures) # cloning

newFeatures = list(selectedFeatures) # cloning

finalFeatures = list(selectedFeatures) # cloning

# Now create the column list depending on the number of rows I will grab from
for x in range(2,model+1):
    newFeatures = [s + '_n' for s in newFeatures]
    finalFeatures = finalFeatures + newFeatures

# This is the final column list for my one row in the final data frame
selectedFeatures = finalFeatures + list(modelTarget) 

# Empty dataframe which I want to populate
model_data = pd.DataFrame(columns=selectedFeatures)

for id_group in csv_by_id:
    #id_group is a tuple with first element as the id itself and second one a dataframe with the rows of a group
    group_data = id_group[1] 

    #hmm - can this be better? I am picking up the rows which I need from first row on wards
    df = group_data[coreFeatures][0:model] 

    # initialize a list
    tmp = [] 

    # now keep adding the column values into the list
    for index, row in df.iterrows(): 
        tmp = tmp + list(row)


    # Wow, this one below surely should have something better. 
    # So i am picking up the feature column values from the last row of the group of rows for a particular id 
    targetValues = group_data[list({'f1','f2','f3'})][len(group_data.index)-1:len(group_data.index)].values 

    # Think this can be done easier too ? . Basically adding the values to the tmp list again
    tmp = tmp + list(targetValues.flatten()) 

    # coverting the list to a dict.
    tmpDict = dict(zip(selectedFeatures,tmp))  

    # then the dict to a dataframe.
    tmpDf = pd.DataFrame(tmpDict,index={1}) 

    # I just could not find a better way of adding a dict or list directly into a dataframe. 
    # And I went through lots and lots of blogs on this topic, including some in StackOverflow.

    # finally I add the frame to my main frame
    model_data = model_data.append(tmpDf) 

# and write it
model_data.to_csv(wd+'model_data' + str(model) + '.csv',index=False) 

Groupby http://pandas.pydata.org/pandas-docs/stable/groupby.html是你的朋友。

这会很好地扩展；特征数量只有一个很小的常数。大约是 O(组数)

In [28]: features = ['f1','f2','f3']

创建一些测试数据，组大小为7-12，70k组

In [29]: def create_df(i):
   ....:     l = np.random.randint(7,12)
   ....:     df = DataFrame(dict([ (f,np.arange(l)) for f in features ]))
   ....:     df['A'] = i
   ....:     return df
   ....: 

In [30]: df = concat([ create_df(i) for i in xrange(70000) ])

In [39]: df.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 629885 entries, 0 to 9
Data columns (total 4 columns):
f1    629885 non-null int64
f2    629885 non-null int64
f3    629885 non-null int64
A     629885 non-null int64
dtypes: int64(4)

创建一个框架，在其中选择每组的前 3 行和最后一行（请注意，这将处理大小 groupby.filter来解决这个问题）

In [31]: groups = concat([df.groupby('A').head(3),df.groupby('A').tail(1)]).sort_index()

# This step is necesary in pandas < master/0.14 as the returned fields 
# will include the grouping field (the A), (is a bug/API issue)
In [33]: groups = groups[features]

In [34]: groups.head(20)
Out[34]: 
     f1  f2  f3
A              
0 0   0   0   0
  1   1   1   1
  2   2   2   2
  7   7   7   7
1 0   0   0   0
  1   1   1   1
  2   2   2   2
  9   9   9   9
2 0   0   0   0
  1   1   1   1
  2   2   2   2
  8   8   8   8
3 0   0   0   0
  1   1   1   1
  2   2   2   2
  8   8   8   8
4 0   0   0   0
  1   1   1   1
  2   2   2   2
  9   9   9   9

[20 rows x 3 columns]

In [38]: groups.info()
<class 'pandas.core.frame.DataFrame'>
MultiIndex: 280000 entries, (0, 0) to (69999, 9)
Data columns (total 3 columns):
f1    280000 non-null int64
f2    280000 non-null int64
f3    280000 non-null int64
dtypes: int64(3)

而且速度相当快

In [32]: %timeit concat([df.groupby('A').head(3),df.groupby('A').tail(1)]).sort_index()
1 loops, best of 3: 1.16 s per loop

为了进一步操作，您通常应该在这里停下来并使用它（因为它是一个很好的分组格式，很容易处理）。

如果您想将其转换为宽格式

In [35]: dfg = groups.groupby(level=0).apply(lambda x: Series(x.values.ravel()))

In [36]: %timeit groups.groupby(level=0).apply(lambda x: Series(x.values.ravel()))
dfg.head()
groups.info()
1 loops, best of 3: 14.5 s per loop
In [40]: dfg.columns = [ "{0}_{1}".format(f,i) for i in range(1,5) for f in features ]

In [41]: dfg.head()
Out[41]: 
   f1_1  f2_1  f3_1  f1_2  f2_2  f3_2  f1_3  f2_3  f3_3  f1_4  f2_4  f3_4
A                                                                        
0     0     0     0     1     1     1     2     2     2     7     7     7
1     0     0     0     1     1     1     2     2     2     9     9     9
2     0     0     0     1     1     1     2     2     2     8     8     8
3     0     0     0     1     1     1     2     2     2     8     8     8
4     0     0     0     1     1     1     2     2     2     9     9     9

[5 rows x 12 columns]

In [42]: dfg.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 70000 entries, 0 to 69999
Data columns (total 12 columns):
f1_1    70000 non-null int64
f2_1    70000 non-null int64
f3_1    70000 non-null int64
f1_2    70000 non-null int64
f2_2    70000 non-null int64
f3_2    70000 non-null int64
f1_3    70000 non-null int64
f2_3    70000 non-null int64
f3_3    70000 non-null int64
f1_4    70000 non-null int64
f2_4    70000 non-null int64
f3_4    70000 non-null int64
dtypes: int64(12)