我有一个数据框架,从中删除一些行。结果,我得到了一个索引类似于[1,5,6,10,11]的数据框架,我想把它重置为[0,1,2,3,4]。我该怎么做呢?
以下方法似乎有效:
df = df.reset_index()
del df['index']
以下选项无效:
df = df.reindex()
我有一个数据框架,从中删除一些行。结果,我得到了一个索引类似于[1,5,6,10,11]的数据框架,我想把它重置为[0,1,2,3,4]。我该怎么做呢?
以下方法似乎有效:
df = df.reset_index()
del df['index']
以下选项无效:
df = df.reindex()
当前回答
DataFrame。Reset_index就是你要找的。如果你不希望它保存为一个列,那么执行:
df = df.reset_index(drop=True)
如果你不想重新分配:
df.reset_index(drop=True, inplace=True)
其他回答
data1.reset_index(inplace=True)
另一个解决方案是分配RangeIndex或range:
df.index = pd.RangeIndex(len(df.index))
df.index = range(len(df.index))
它更快:
df = pd.DataFrame({'a':[8,7], 'c':[2,4]}, index=[7,8])
df = pd.concat([df]*10000)
print (df.head())
In [298]: %timeit df1 = df.reset_index(drop=True)
The slowest run took 7.26 times longer than the fastest. This could mean that an intermediate result is being cached.
10000 loops, best of 3: 105 µs per loop
In [299]: %timeit df.index = pd.RangeIndex(len(df.index))
The slowest run took 15.05 times longer than the fastest. This could mean that an intermediate result is being cached.
100000 loops, best of 3: 7.84 µs per loop
In [300]: %timeit df.index = range(len(df.index))
The slowest run took 7.10 times longer than the fastest. This could mean that an intermediate result is being cached.
100000 loops, best of 3: 14.2 µs per loop
DataFrame。Reset_index就是你要找的。如果你不希望它保存为一个列,那么执行:
df = df.reset_index(drop=True)
如果你不想重新分配:
df.reset_index(drop=True, inplace=True)