Pandas 密钥错误日期
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Pandas Key Error date
提问by eWizardII
df['ts'] = pd.to_datetime(df['_created_at'])
df = df.set_index('ts')
def f(x):
x = x.reindex(df.index)
x = x.sort_values('battery')
x['ts'] = x['ts'].fillna(method='ffill')
x['battery'] = x['battery'].combine_first(df['battery'])
x['model'] = x['model'].combine_first(df['model'])
x['user'] = x['user'].combine_first(df['user'])
x['version'] = x['version'].combine_first(df['version'])
return x
I have the above code and it seems I run into an error when I get to the x['ts'] = x['ts'].fillna(method='ffill')line. This occurs when I run the following command:
我有上面的代码，当我到达x['ts'] = x['ts'].fillna(method='ffill')线路时似乎遇到了错误。当我运行以下命令时会发生这种情况：
df = df.groupby(level=0, sort=False).apply(f).reset_index(level=0, drop=True).reset_index()
df = df.groupby(level=0, sort=False).apply(f).reset_index(level=0, drop=True).reset_index()
My tsvalues look like : 2013-03-04 13:56:29.662and are datetime64; I don't understand what I am doing wrong that is causing this key error on tsas I thought seeing them as to_datetimewould put the index in a format pandas understands. Ideas on how to fix this?
我的ts值看起来像：2013-03-04 13:56:29.662并且是 datetime64; 我不明白我做错了什么导致了这个关键错误，ts因为我认为看到它们to_datetime会将索引置于Pandas理解的格式中。关于如何解决这个问题的想法？
回答by jezrael
I think you have to omit this problematic row like, because column tsis set to indexand is filled values by x.reindex(df.index). I think you need delete column _created_atby drop:
我认为你必须省略这个有问题的行，因为列ts被设置为index并由x.reindex(df.index). 我认为您需要_created_at通过drop以下方式删除列：
print df
_created_at user battery model version
0 2013-03-04 13:56:29.662 R 3 A 1
1 2013-03-05 13:56:29.662 S 5 B 3
2 2013-03-06 13:56:29.662 J 6 C 2
df['ts'] = pd.to_datetime(df['_created_at'])
df = df.drop('_created_at', axis=1)
df = df.set_index(['ts'])
def f(x):
#print x
x = x.reindex(df.index)
x = x.sort_values('battery')
#x['ts'] = x['ts'].fillna(method='ffill')
x['battery'] = x['battery'].combine_first(df['battery'])
x['model'] = x['model'].combine_first(df['model'])
x['user'] = x['user'].combine_first(df['user'])
x['version'] = x['version'].combine_first(df['version'])
return x
df = df.groupby(level=0, sort=False).apply(f).reset_index(level=0, drop=True).reset_index()
print df
ts user battery model version
0 2013-03-04 13:56:29.662 R 3 A 1
1 2013-03-05 13:56:29.662 S 5 B 3
2 2013-03-06 13:56:29.662 J 6 C 2
3 2013-03-05 13:56:29.662 S 5 B 3
4 2013-03-04 13:56:29.662 R 3 A 1
5 2013-03-06 13:56:29.662 J 6 C 2
6 2013-03-06 13:56:29.662 J 6 C 2
7 2013-03-04 13:56:29.662 R 3 A 1
8 2013-03-05 13:56:29.662 S 5 B 3
But maybe you need fillnafor other column e.g. user:
但也许您需要fillna其他列，例如user：
df['ts'] = pd.to_datetime(df['_created_at'])
df = df.drop('_created_at', axis=1)
df = df.set_index(['ts'])
def f(x):
#print x
x = x.reindex(df.index)
x = x.sort_values('battery')
#x['ts'] = x['ts'].fillna(method='ffill')
x['battery'] = x['battery'].combine_first(df['battery'])
x['model'] = x['model'].combine_first(df['model'])
x['user'] = x['user'].fillna(method='ffill')
x['version'] = x['version'].combine_first(df['version'])
return x
df = df.groupby(level=0, sort=False).apply(f).reset_index(level=0, drop=True).reset_index()
print df
ts user battery model version
0 2013-03-04 13:56:29.662 R 3 A 1
1 2013-03-05 13:56:29.662 R 5 B 3
2 2013-03-06 13:56:29.662 R 6 C 2
3 2013-03-05 13:56:29.662 S 5 B 3
4 2013-03-04 13:56:29.662 S 3 A 1
5 2013-03-06 13:56:29.662 S 6 C 2
6 2013-03-06 13:56:29.662 J 6 C 2
7 2013-03-04 13:56:29.662 J 3 A 1
8 2013-03-05 13:56:29.662 J 5 B 3
