在Python Pandas Dataframe中动态添加列的数据处理
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我有以下问题.
可以说这是我的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
..........
因此,我有可以按ID分组的行.
我想创建如下的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).在这种情况下,我抓了3行.
然后,我还将跳过一个或多个行(在这种情况下,仅跳过一个),以从同一id组的最后一行获取最后一列.由于某些原因,我想使用一个数据框.
经过3-4个小时的奋斗.我找到了下面给出的解决方案.
但是我的解决方案很慢.我大约有700,000行,可能有大约70,000组ID.在我的4GB 4核心Lenovo上,model = 3上的上述代码将花费近一个小时.我需要进入模型=可能是10或15.我仍然是Python的新手,并且我相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.
万分感谢.
型号：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)
解决方法:
Groupby是你的朋友.
这将很好地扩展；特征数量中只有很小的常数.大约为O(组数)
In [28]: features = ['f1','f2','f3']
创建一些测试数据,组大小为7-12,每组70k
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,在其中选择每个组的前3行和最后一行(请注意,这将处理大小小于4的组,但是您的最后一行可能与另一行重叠,您可能希望使用groupby.filter来解决此问题)
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
为了进行进一步的操作,通常应在此处停止并使用它(因为它以易于处理的很好的分组格式).
如果您想将其翻译成宽格式
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)
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