经过几周的完善之后,由于SO上的精湛技术使我有了以下代码,我可以根据需要生成数据帧,但是我不确定如何将程序中的数据帧合并为一个用于最终数据帧对象变量的代码。我只是将concat语句分配给变量,然后最终只得到了最后一个数据帧。
{
"zipcode":"08989",
"current" {"canwc":null,"cig":4900,"class":"observation","clds":"OVC","day_ind":"D","dewpt":19,"expireTimeGMT":1385486700,"feels_like":34,"gust":null,"hi":37,"humidex":null,"icon_code":26,"icon_extd":2600,"max_temp":37,"wxMan":"wx1111"},
"triggers":[53,31,9,21,48,7,40,178,55,179,176,26,103,175,33,51,20,57,112,30,50,113]
}
{
"zipcode":"08990",
"current":{"canwc":null,"cig":4900,"class":"observation","clds":"OVC","day_ind":"D","dewpt":19,"expireTimeGMT":1385486700,"feels_like":34,"gust":null,"hi":37,"humidex":null,"icon_code":26,"icon_extd":2600,"max_temp":37, "wxMan":"wx1111"},
"triggers":[53,31,9,21,48,7,40,178,55,179,176,26,103,175,33,51,20,57,112,30,50,113]
}
def lines_per_n(f, n):
for line in f:
yield ''.join(chain([line], itertools.islice(f, n - 1)))
def series_chunk(chunk):
try:
jfile = json.loads(chunk)
zipcode = jfile['zipcode']
datetime = jfile['current']['proc_time']
triggers = jfile['triggers']
return pd.Series([jfile['zipcode'], jfile['current']['proc_time'],\
jfile['triggers']])
except ValueError, e:
pass
else:
pass
for fin in glob.glob('*.txt'):
with open(fin) as f:
print pd.concat([series_chunk(chunk) for chunk in lines_per_n(f, 5)], axis=1).T
我需要将上面的程序输出为一个数据帧:
0 1 2
0 08988 20131126102946 []
1 08989 20131126102946 [53, 31, 9, 21, 48, 7, 40, 178, 55, 179, 176, ...
0 1 2
0 08988 20131126102946 []
1 08989 20131126102946 [53, 31, 9, 21, 48, 7, 40, 178, 55, 179, 176, ...
最终摔跤到提交。这是完成我需要的最终代码:
dfs = []
for fin in glob.glob('*.txt'):
with open(fin) as f:
df = pd.concat([series_chunk(chunk) for\
chunk in lines_per_n(f, 7)], axis=1).T
dfs.append(df)
df = pd.concat(dfs, ignore_index=True)
最佳答案
很高兴您能解决这个问题。 IMO以一种更简洁的方式将其作为列表理解
def dataframe_from_file(fin):
with open(fin) as f:
return pd.concat([series_chunk(chunk) for chunk in lines_per_n(f, 7)],
axis=1).T
df = pd.concat([dataframe_from_file(fin) for fin in glob.glob('*.txt')],
ignore_index=True)
注意:在最终连拍中使用axis = 1可能意味着您可以更早地避免进行T-ing。
关于python - 将程序中的Python Pandas Dataframe输出合并为一个Dataframe,我们在Stack Overflow上找到一个类似的问题:https://stackoverflow.com/questions/20683491/